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Eigenvalue decomposition API

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Cosmetic change Double -> Float64
This commit is contained in:
Alexander Nozik 2024-08-17 21:11:13 +03:00
parent 91513a1629
commit b818a8981f
142 changed files with 1094 additions and 871 deletions
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2
CHANGELOG.md
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@ -201,7 +201,7 @@
- Full autodiff refactoring based on `Symbol`
- `kmath-prob` renamed to `kmath-stat`
- Grid generators moved to `kmath-for-real`
- Use `Point<Double>` instead of specialized type in `kmath-for-real`
- Use `Point<Float64>` instead of specialized type in `kmath-for-real`
- Optimized dot product for buffer matrices moved to `kmath-for-real`
- EjmlMatrix context is an object
- Matrix LUP `inverse` renamed to `inverseWithLup`

5
benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -14,6 +14,7 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float64
import kotlin.math.sin
import kotlin.random.Random
import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
@ -67,7 +68,7 @@ class ExpressionsInterpretersBenchmark {
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
val m = HashMap<Symbol, Double>()
@ -99,7 +100,7 @@ class ExpressionsInterpretersBenchmark {
private val wasm = node.wasmCompileToExpression(Float64Field)
private val estree = node.estreeCompileToExpression(Float64Field)
private val raw = Expression<Double> { args ->
private val raw = Expression<Float64> { args ->
val x = args.getValue(x)
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}

7
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -15,6 +15,7 @@ import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float64
import kotlin.math.sin
import kotlin.random.Random
@ -83,7 +84,7 @@ internal class ExpressionsInterpretersBenchmark {
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
val m = HashMap<Symbol, Double>()
@ -114,9 +115,9 @@ internal class ExpressionsInterpretersBenchmark {
private val asmPrimitive = node.compileToExpression(Float64Field)
private val xIdx = asmPrimitive.indexer.indexOf(x)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Double>)
private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Float64>)
private val raw = Expression<Double> { args ->
private val raw = Expression<Float64> { args ->
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}

11
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -17,6 +17,7 @@ import space.kscience.kmath.UnsafeKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -37,28 +38,28 @@ internal class NDFieldBenchmark {
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@ -87,7 +88,7 @@ internal class NDFieldBenchmark {
// @Benchmark
// fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
// var res: StructureND<Double> = one(dim, dim)
// var res: StructureND<Float64> = one(dim, dim)
// repeat(n) { res += 1.0 }
// blackhole.consume(res)
// }

3
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -15,6 +15,7 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
@ -23,7 +24,7 @@ internal class ViktorBenchmark {
@Benchmark
fun doubleFieldAddition(blackhole: Blackhole) {
with(doubleField) {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}

4
docs/nd-structure.md
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@ -31,7 +31,7 @@ The code to run this looks like:
```kotlin
specializedField.run {
var res: NDBuffer<Double> = one
var res: NDBuffer<Float64> = one
repeat(n) {
res += 1.0
}
@ -103,7 +103,7 @@ The boxing field produced by
```kotlin
genericField.run {
var res: NDBuffer<Double> = one
var res: NDBuffer<Float64> = one
repeat(n) {
res += 1.0
}

3
examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt
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@ -14,11 +14,12 @@ import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.math.pow
fun main() {
//Define a function
val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
val function: Function1D<Float64> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = Float64Field.gaussIntegrator.integrate(0.0..10.0, function = function)

3
examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt
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@ -8,6 +8,7 @@ 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.Float64
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -23,7 +24,7 @@ fun main() {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val polynomial: PiecewisePolynomial<Float64> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val function = polynomial.asFunction(Float64Field, 0.0)

5
examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt
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@ -10,6 +10,7 @@ import space.kscience.kmath.interpolation.splineInterpolator
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.kmath.structures.Float64
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -18,7 +19,7 @@ import space.kscience.plotly.scatter
@OptIn(UnstablePlotlyAPI::class)
fun main() {
val function: Function1D<Double> = { x ->
val function: Function1D<Float64> = { x ->
if (x in 30.0..50.0) {
1.0
} else {
@ -28,7 +29,7 @@ fun main() {
val xs = 0.0..100.0 step 0.5
val ys = xs.map(function)
val polynomial: PiecewisePolynomial<Double> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polynomial: PiecewisePolynomial<Float64> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(Float64Field, 0.0)

3
examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt
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@ -12,6 +12,7 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.structureND
import space.kscience.kmath.nd.withNdAlgebra
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.math.pow
fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
@ -22,7 +23,7 @@ fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
}
//Define a function in a nd space
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
val function: (Double) -> StructureND<Float64> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = gaussIntegrator.integrate(0.0..10.0, function = function)

5
examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt
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@ -6,18 +6,19 @@
package space.kscience.kmath.linear
import space.kscience.kmath.real.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
fun main() {
val x0 = DoubleVector(0.0, 0.0, 0.0)
val sigma = DoubleVector(1.0, 1.0, 1.0)
val gaussian: (Point<Double>) -> Double = { x ->
val gaussian: (Point<Float64>) -> Double = { x ->
require(x.size == x0.size)
kotlin.math.exp(-((x - x0) / sigma).square().sum())
}
fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
fun ((Point<Float64>) -> Double).grad(x: Point<Float64>): Point<Float64> {
require(x.size == x0.size)
return Float64Buffer(x.size) { i ->
val h = sigma[i] / 5

3
examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt
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@ -11,6 +11,7 @@ import space.kscience.kmath.nd.Float64BufferND
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.mutableStructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.viktor.viktorAlgebra
import kotlin.collections.component1
import kotlin.collections.component2
@ -20,7 +21,7 @@ fun main() {
if (i == j) 2.0 else 0.0
}
val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val cmMatrix: Structure2D<Float64> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
val res: Float64BufferND = Float64Field.ndAlgebra {
exp(viktorStructure) + 2.0 * cmMatrix

7
examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt
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@ -14,6 +14,7 @@ import space.kscience.kmath.operations.toList
import space.kscience.kmath.stat.KMComparisonResult
import space.kscience.kmath.stat.ksComparisonStatistic
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.slice
import space.kscience.plotly.*
import kotlin.math.PI
@ -24,7 +25,7 @@ fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebr
fun main() = with(Double.seriesAlgebra()) {
fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {
fun Plot.plotSeries(name: String, buffer: Buffer<Float64>) {
scatter {
this.name = name
x.numbers = buffer.labels
@ -37,10 +38,10 @@ fun main() = with(Double.seriesAlgebra()) {
val s2 = s1.slice(20..50).moveTo(40)
val s3: Buffer<Double> = s1.zip(s2) { l, r -> l + r } //s1 + s2
val s3: Buffer<Float64> = s1.zip(s2) { l, r -> l + r } //s1 + s2
val s4 = s3.map { ln(it) }
val kmTest: KMComparisonResult<Double> = ksComparisonStatistic(s1, s2)
val kmTest: KMComparisonResult<Float64> = ksComparisonStatistic(s1, s2)
Plotly.page {
h1 { +"This is my plot" }

9
examples/src/main/kotlin/space/kscience/kmath/series/seriesBuilder.kt
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@ -6,10 +6,7 @@
package space.kscience.kmath.series
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.toDoubleArray
import space.kscience.kmath.structures.*
import space.kscience.plotly.*
import space.kscience.plotly.models.Scatter
import space.kscience.plotly.models.ScatterMode
@ -22,7 +19,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
val arrayOfRandoms = DoubleArray(20) { random.nextDouble() }
val series1: Float64Buffer = arrayOfRandoms.asBuffer()
val series2: Series<Double> = series1.moveBy(3)
val series2: Series<Float64> = series1.moveBy(3)
val res = series2 - series1
@ -30,7 +27,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
println(res)
fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
fun Plot.series(name: String, buffer: Buffer<Float64>, block: Scatter.() -> Unit = {}) {
scatter {
this.name = name
x.numbers = buffer.offsetIndices

2
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
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@ -37,7 +37,7 @@ private suspend fun runKMathChained(): Duration {
}
private fun runCMDirect(): Duration {
val rng = RandomSource.create(RandomSource.MT, 123L)
val rng = RandomSource.MT.create(123L)
val sampler = CMGaussianSampler.of(
BoxMullerNormalizedGaussianSampler.of(rng),

3
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
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@ -10,13 +10,14 @@ import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.combineWithState
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.structures.Float64
private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
/**
* Averaging.
*/
private fun Chain<Double>.mean(): Chain<Double> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
private fun Chain<Float64>.mean(): Chain<Float64> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
val next = chain.next()
num++
value += next

2
examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
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@ -26,7 +26,7 @@ fun main() {
val realTime = measureTimeMillis {
realField {
var res: StructureND<Double> = one
var res: StructureND<Float64> = one
repeat(n) {
res += 1.0
}

10
examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
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@ -45,35 +45,35 @@ fun main() {
measureAndPrint("Boxing addition") {
genericField {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
doubleField {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Nd4j specialized addition") {
nd4jField {
var res: StructureND<Double> = one(shape)
var res: StructureND<Float64> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Viktor addition") {
viktorField {
var res: StructureND<Double> = one
var res: StructureND<Float64> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Parallel stream addition") {
parallelField {
var res: StructureND<Double> = one
var res: StructureND<Float64> = one
repeat(n) { res += 1.0 }
}
}

64
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
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@ -19,21 +19,21 @@ import java.util.stream.IntStream
* execution.
*/
class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
NumbersAddOps<StructureND<Float64>>,
ExtendedField<StructureND<Float64>> {
private val strides = ColumnStrides(shape)
override val elementAlgebra: Float64Field get() = Float64Field
override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
override val one: BufferND<Double> by lazy { structureND(shape) { one } }
override val zero: BufferND<Float64> by lazy { structureND(shape) { zero } }
override val one: BufferND<Float64> by lazy { structureND(shape) { one } }
override fun number(value: Number): BufferND<Double> {
override fun number(value: Number): BufferND<Float64> {
val d = value.toDouble() // minimize conversions
return structureND(shape) { d }
}
@OptIn(PerformancePitfall::class)
private val StructureND<Double>.buffer: Float64Buffer
private val StructureND<Float64>.buffer: Float64Buffer
get() = when {
shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
this@StreamDoubleFieldND.shape,
@ -44,7 +44,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
else -> Float64Buffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Double> {
override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Float64> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
Float64Field.initializer(index)
@ -56,7 +56,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
override fun mutableStructureND(
shape: ShapeND,
initializer: DoubleField.(IntArray) -> Double,
): MutableBufferND<Double> {
): MutableBufferND<Float64> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
@ -66,17 +66,17 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.map(
override fun StructureND<Float64>.map(
transform: Float64Field.(Double) -> Double,
): BufferND<Double> {
): BufferND<Float64> {
val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.mapIndexed(
override fun StructureND<Float64>.mapIndexed(
transform: Float64Field.(index: IntArray, Double) -> Double,
): BufferND<Double> {
): BufferND<Float64> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(
strides.index(offset),
@ -89,39 +89,39 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,
left: StructureND<Float64>,
right: StructureND<Float64>,
transform: Float64Field.(Double, Double) -> Double,
): BufferND<Double> {
): BufferND<Float64> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Double>.unaryMinus(): StructureND<Double> = map { -it }
override fun StructureND<Float64>.unaryMinus(): StructureND<Float64> = map { -it }
override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
override fun scale(a: StructureND<Float64>, value: Double): StructureND<Float64> = a.map { it * value }
override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
override fun power(arg: StructureND<Float64>, pow: Number): BufferND<Float64> = arg.map { power(it, pow) }
override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
override fun exp(arg: StructureND<Float64>): BufferND<Float64> = arg.map { exp(it) }
override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
override fun ln(arg: StructureND<Float64>): BufferND<Float64> = arg.map { ln(it) }
override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
override fun sin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sin(it) }
override fun cos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cos(it) }
override fun tan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tan(it) }
override fun asin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asin(it) }
override fun acos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acos(it) }
override fun atan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atan(it) }
override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
override fun sinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atanh(it) }
}
fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

2
examples/src/main/kotlin/space/kscience/kmath/structures/mutableNd.kt
View File

@ -11,7 +11,7 @@ import space.kscience.kmath.operations.algebra
@OptIn(PerformancePitfall::class)
fun main(): Unit = with(Double.algebra.ndAlgebra) {
val structure: MutableStructure2D<Double> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
val structure: MutableStructure2D<Float64> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
i.toDouble() + j.toDouble()
}.as2D()

7
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt
View File

@ -12,6 +12,7 @@ import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -20,9 +21,9 @@ import space.kscience.kmath.tensors.core.levenbergMarquardt
import kotlin.random.Random
fun streamLm(
lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
lm_func: (MutableStructure2D<Float64>, MutableStructure2D<Float64>, Int) -> (MutableStructure2D<Float64>),
startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int,
): Flow<MutableStructure2D<Double>> = flow {
): Flow<MutableStructure2D<Float64>> = flow {
var example_number = startData.example_number
var p_init = startData.p_init
@ -64,7 +65,7 @@ fun streamLm(
}
}
fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double> {
fun generateNewYDat(y_dat: MutableStructure2D<Float64>, delta: Double): MutableStructure2D<Float64> {
val n = y_dat.shape.component1()
val y_dat_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
for (i in 0 until n) {

35
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt
View File

@ -9,6 +9,7 @@ import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -19,24 +20,24 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
import space.kscience.kmath.tensors.core.asDoubleTensor
public data class StartDataLm(
var lm_matx_y_dat: MutableStructure2D<Double>,
var lm_matx_y_dat: MutableStructure2D<Float64>,
var example_number: Int,
var p_init: MutableStructure2D<Double>,
var t: MutableStructure2D<Double>,
var y_dat: MutableStructure2D<Double>,
var p_init: MutableStructure2D<Float64>,
var t: MutableStructure2D<Float64>,
var y_dat: MutableStructure2D<Float64>,
var weight: Double,
var dp: MutableStructure2D<Double>,
var p_min: MutableStructure2D<Double>,
var p_max: MutableStructure2D<Double>,
var consts: MutableStructure2D<Double>,
var dp: MutableStructure2D<Float64>,
var p_min: MutableStructure2D<Float64>,
var p_max: MutableStructure2D<Float64>,
var consts: MutableStructure2D<Float64>,
var opts: DoubleArray,
)
fun funcEasyForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
exampleNumber: Int,
): MutableStructure2D<Double> {
): MutableStructure2D<Float64> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
@ -59,10 +60,10 @@ fun funcEasyForLm(
}
fun funcMiddleForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
exampleNumber: Int,
): MutableStructure2D<Double> {
): MutableStructure2D<Float64> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
@ -79,10 +80,10 @@ fun funcMiddleForLm(
}
fun funcDifficultForLm(
t: MutableStructure2D<Double>,
p: MutableStructure2D<Double>,
t: MutableStructure2D<Float64>,
p: MutableStructure2D<Float64>,
exampleNumber: Int,
): MutableStructure2D<Double> {
): MutableStructure2D<Float64> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))

3
examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt
View File

@ -10,12 +10,13 @@ import org.jetbrains.kotlinx.multik.api.ndarray
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.multik.MultikDoubleAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.structures.Float64
val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())
fun main(): Unit = with(multikAlgebra) {
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Double>().wrap()
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Float64>().wrap()
val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
one(a.shape) - a + b * 3.0
}

3
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt
View File

@ -9,6 +9,7 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.operations.Int8Ring
import space.kscience.kmath.operations.pi
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.fail
@ -41,7 +42,7 @@ internal class TestFolding {
@Test
fun foldSymbol() = assertEquals(
Float64Field.pi,
("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Double> ?: fail()).value,
("pi".parseMath().evaluateConstants(Float64Field) as? TypedMst.Constant<Float64> ?: fail()).value,
)
@Test

3
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -10,12 +10,13 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
internal interface CompilerTestContext {
fun MST.compileToExpression(algebra: Int32Ring): Expression<Int>
fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int
fun MST.compile(algebra: Int32Ring, vararg arguments: Pair<Symbol, Int>): Int = compile(algebra, mapOf(*arguments))
fun MST.compileToExpression(algebra: Float64Field): Expression<Double>
fun MST.compileToExpression(algebra: Float64Field): Expression<Float64>
fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double
fun MST.compile(algebra: Float64Field, vararg arguments: Pair<Symbol, Double>): Double =

9
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt
View File

@ -11,6 +11,7 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.internal.binaryen.*
import space.kscience.kmath.internal.webassembly.Instance
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.internal.binaryen.Module as BinaryenModule
import space.kscience.kmath.internal.webassembly.Module as WasmModule
@ -85,13 +86,13 @@ internal sealed class WasmBuilder<T : Number, out E : Expression<T>>(
}
@UnstableKMathAPI
internal class DoubleWasmBuilder(target: TypedMst<Double>) :
internal class DoubleWasmBuilder(target: TypedMst<Float64>) :
WasmBuilder<Double, DoubleExpression>(f64, Float64Field, target) {
override val instance by lazy {
object : DoubleExpression {
override val indexer = SimpleSymbolIndexer(keys)
override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast<Double>()
override fun invoke(arguments: DoubleArray) = spreader(executable, arguments).unsafeCast<Float64>()
}
}
@ -99,7 +100,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Double>) :
override fun visitNumber(number: Number) = ctx.f64.const(number.toDouble())
override fun visitUnary(node: TypedMst.Unary<Double>): ExpressionRef = when (node.operation) {
override fun visitUnary(node: TypedMst.Unary<Float64>): ExpressionRef = when (node.operation) {
GroupOps.MINUS_OPERATION -> ctx.f64.neg(visit(node.value))
GroupOps.PLUS_OPERATION -> visit(node.value)
PowerOperations.SQRT_OPERATION -> ctx.f64.sqrt(visit(node.value))
@ -120,7 +121,7 @@ internal class DoubleWasmBuilder(target: TypedMst<Double>) :
else -> super.visitUnary(node)
}
override fun visitBinary(mst: TypedMst.Binary<Double>): ExpressionRef = when (mst.operation) {
override fun visitBinary(mst: TypedMst.Binary<Float64>): ExpressionRef = when (mst.operation) {
GroupOps.PLUS_OPERATION -> ctx.f64.add(visit(mst.left), visit(mst.right))
GroupOps.MINUS_OPERATION -> ctx.f64.sub(visit(mst.left), visit(mst.right))
RingOps.TIMES_OPERATION -> ctx.f64.mul(visit(mst.left), visit(mst.right))

3
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/wasm.kt
View File

@ -13,6 +13,7 @@ import space.kscience.kmath.ast.evaluateConstants
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.wasm.internal.DoubleWasmBuilder
import space.kscience.kmath.wasm.internal.IntWasmBuilder
@ -58,7 +59,7 @@ public fun MST.compile(algebra: Int32Ring, vararg arguments: Pair<Symbol, Int>):
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public fun MST.compileToExpression(algebra: Float64Field): Expression<Double> {
public fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> {
val typed = evaluateConstants(algebra)
return if (typed is TypedMst.Constant) object : DoubleExpression {

5
kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -13,6 +13,7 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import space.kscience.kmath.estree.compile as estreeCompile
@ -24,7 +25,7 @@ import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression
private object WasmCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = wasmCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = wasmCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = wasmCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = wasmCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
wasmCompile(algebra, arguments)
@ -33,7 +34,7 @@ private object WasmCompilerTestContext : CompilerTestContext {
private object ESTreeCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = estreeCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = estreeCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = estreeCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = estreeCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
estreeCompile(algebra, arguments)

7
kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/PrimitiveAsmBuilder.kt
View File

@ -15,6 +15,7 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.ast.TypedMst
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import java.lang.invoke.MethodHandles
import java.lang.invoke.MethodType
import java.nio.file.Paths
@ -381,7 +382,7 @@ internal sealed class PrimitiveAsmBuilder<T : Number, out E : Expression<T>>(
}
@UnstableKMathAPI
internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
internal class DoubleAsmBuilder(target: TypedMst<Float64>) : PrimitiveAsmBuilder<Double, DoubleExpression>(
Float64Field,
java.lang.Double::class.java,
java.lang.Double.TYPE,
@ -410,7 +411,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<
false,
)
override fun visitUnary(node: TypedMst.Unary<Double>) {
override fun visitUnary(node: TypedMst.Unary<Float64>) {
super.visitUnary(node)
when (node.operation) {
@ -435,7 +436,7 @@ internal class DoubleAsmBuilder(target: TypedMst<Double>) : PrimitiveAsmBuilder<
}
}
override fun visitBinary(node: TypedMst.Binary<Double>) {
override fun visitBinary(node: TypedMst.Binary<Float64>) {
super.visitBinary(node)
when (node.operation) {

9
kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -12,6 +12,7 @@ import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.asm.compile as asmCompile
import space.kscience.kmath.asm.compileToExpression as asmCompileToExpression
@ -22,18 +23,18 @@ private object GenericAsmCompilerTestContext : CompilerTestContext {
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int =
asmCompile(algebra as Algebra<Int>, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> =
asmCompileToExpression(algebra as Algebra<Double>)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> =
asmCompileToExpression(algebra as Algebra<Float64>)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
asmCompile(algebra as Algebra<Double>, arguments)
asmCompile(algebra as Algebra<Float64>, arguments)
}
@OptIn(UnstableKMathAPI::class)
private object PrimitiveAsmCompilerTestContext : CompilerTestContext {
override fun MST.compileToExpression(algebra: Int32Ring): Expression<Int> = asmCompileToExpression(algebra)
override fun MST.compile(algebra: Int32Ring, arguments: Map<Symbol, Int>): Int = asmCompile(algebra, arguments)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Double> = asmCompileToExpression(algebra)
override fun MST.compileToExpression(algebra: Float64Field): Expression<Float64> = asmCompileToExpression(algebra)
override fun MST.compile(algebra: Float64Field, arguments: Map<Symbol, Double>): Double =
asmCompile(algebra, arguments)

7
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/expressions/CmDsExpression.kt
View File

@ -14,6 +14,7 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.NumbersAddOps
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
/**
@ -132,9 +133,9 @@ public object CmDsProcessor : AutoDiffProcessor<Double, DerivativeStructure, CmD
@Deprecated("Use generic DSAlgebra from the core")
public class CmDsExpression(
public val function: CmDsField.() -> DerivativeStructure,
) : DifferentiableExpression<Double> {
) : DifferentiableExpression<Float64> {
override val type: SafeType<Double> get() = DoubleField.type
override val type: SafeType<Float64> get() = DoubleField.type
override operator fun invoke(arguments: Map<Symbol, Double>): Double =
CmDsField(0, arguments).function().value
@ -142,7 +143,7 @@ public class CmDsExpression(
/**
* Get the derivative expression with given orders
*/
override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression(type) { arguments ->
override fun derivativeOrNull(symbols: List<Symbol>): Expression<Float64> = Expression(type) { arguments ->
with(CmDsField(symbols.size, arguments)) { function().derivative(symbols) }
}
}

9
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMGaussRuleIntegrator.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.commons.integration
import org.apache.commons.math3.analysis.integration.gauss.GaussIntegrator
import org.apache.commons.math3.analysis.integration.gauss.GaussIntegratorFactory
import space.kscience.kmath.integration.*
import space.kscience.kmath.structures.Float64
/**
* A simple one-pass integrator based on Gauss rule
@ -14,9 +15,9 @@ import space.kscience.kmath.integration.*
public class CMGaussRuleIntegrator(
private val numpoints: Int,
private var type: GaussRule = GaussRule.LEGENDRE,
) : UnivariateIntegrator<Double> {
) : UnivariateIntegrator<Float64> {
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
val range = integrand[IntegrationRange]
?: error("Integration range is not provided")
val integrator: GaussIntegrator = getIntegrator(range)
@ -28,7 +29,7 @@ public class CMGaussRuleIntegrator(
}
}
private fun getIntegrator(range: ClosedRange<Double>): GaussIntegrator {
private fun getIntegrator(range: ClosedRange<Float64>): GaussIntegrator {
return when (type) {
GaussRule.LEGENDRE -> factory.legendre(
numpoints,
@ -77,7 +78,7 @@ public class CMGaussRuleIntegrator(
private val factory: GaussIntegratorFactory = GaussIntegratorFactory()
public fun integrate(
range: ClosedRange<Double>,
range: ClosedRange<Float64>,
numPoints: Int = 100,
type: GaussRule = GaussRule.LEGENDRE,
function: (Double) -> Double,

7
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/integration/CMIntegrator.kt
View File

@ -10,6 +10,7 @@ import org.apache.commons.math3.analysis.integration.SimpsonIntegrator
import space.kscience.attributes.Attributes
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.integration.*
import space.kscience.kmath.structures.Float64
import org.apache.commons.math3.analysis.integration.UnivariateIntegrator as CMUnivariateIntegrator
/**
@ -17,10 +18,10 @@ import org.apache.commons.math3.analysis.integration.UnivariateIntegrator as CMU
*/
public class CMIntegrator(
private val defaultMaxCalls: Int = 200,
public val integratorBuilder: (Integrand<Double>) -> CMUnivariateIntegrator,
) : UnivariateIntegrator<Double> {
public val integratorBuilder: (Integrand<Float64>) -> CMUnivariateIntegrator,
) : UnivariateIntegrator<Float64> {
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
val integrator = integratorBuilder(integrand)
val maxCalls = integrand[IntegrandMaxCalls] ?: defaultMaxCalls
val remainingCalls = maxCalls - integrand.calls

46
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
View File

@ -12,6 +12,7 @@ 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.EigenDecomposition
import space.kscience.kmath.linear.QRDecomposition
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureAttribute
@ -22,7 +23,7 @@ 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> {
public class CMMatrix(public val origin: RealMatrix) : Matrix<Float64> {
override val rowNum: Int get() = origin.rowDimension
override val colNum: Int get() = origin.columnDimension
@ -31,12 +32,12 @@ public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
}
@JvmInline
public value class CMVector(public val origin: RealVector) : Point<Double> {
public value class CMVector(public val origin: RealVector) : Point<Float64> {
override val size: Int get() = origin.dimension
override operator fun get(index: Int): Double = origin.getEntry(index)
override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
override operator fun iterator(): Iterator<Float64> = origin.toArray().iterator()
override fun toString(): String = Buffer.toString(this)
}
@ -46,7 +47,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMLinearSpace : LinearSpace<Double, Float64Field> {
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Double> get() = DoubleField.type
override val type: SafeType<Float64> get() = DoubleField.type
override fun buildMatrix(
rows: Int,
@ -58,7 +59,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(UnstableKMathAPI::class)
public fun Matrix<Double>.toCM(): CMMatrix = when (val matrix = origin) {
public fun Matrix<Float64>.toCM(): CMMatrix = when (val matrix = origin) {
is CMMatrix -> matrix
else -> {
//TODO add feature analysis
@ -67,7 +68,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
}
public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
public fun Point<Float64>.toCM(): CMVector = if (this is CMVector) this else {
val array = DoubleArray(size) { this[it] }
ArrayRealVector(array).wrap()
}
@ -75,40 +76,40 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
internal fun RealMatrix.wrap(): CMMatrix = CMMatrix(this)
internal fun RealVector.wrap(): CMVector = CMVector(this)
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Point<Double> =
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Point<Float64> =
ArrayRealVector(DoubleArray(size) { Float64Field.initializer(it) }).wrap()
override fun Matrix<Double>.plus(other: Matrix<Double>): CMMatrix =
override fun Matrix<Float64>.plus(other: Matrix<Float64>): CMMatrix =
toCM().origin.add(other.toCM().origin).wrap()
override fun Point<Double>.plus(other: Point<Double>): CMVector =
override fun Point<Float64>.plus(other: Point<Float64>): CMVector =
toCM().origin.add(other.toCM().origin).wrap()
override fun Point<Double>.minus(other: Point<Double>): CMVector =
override fun Point<Float64>.minus(other: Point<Float64>): CMVector =
toCM().origin.subtract(other.toCM().origin).wrap()
override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
override fun Matrix<Float64>.dot(other: Matrix<Float64>): CMMatrix =
toCM().origin.multiply(other.toCM().origin).wrap()
override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
override fun Matrix<Float64>.dot(vector: Point<Float64>): CMVector =
toCM().origin.preMultiply(vector.toCM().origin).wrap()
override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): CMMatrix =
toCM().origin.subtract(other.toCM().origin).wrap()
override operator fun Matrix<Double>.times(value: Double): CMMatrix =
override operator fun Matrix<Float64>.times(value: Double): CMMatrix =
toCM().origin.scalarMultiply(value).wrap()
override fun Double.times(m: Matrix<Double>): CMMatrix =
override fun Double.times(m: Matrix<Float64>): CMMatrix =
m * this
override fun Point<Double>.times(value: Double): CMVector =
override fun Point<Float64>.times(value: Double): CMVector =
toCM().origin.mapMultiply(value).wrap()
override fun Double.times(v: Point<Double>): CMVector =
override fun Double.times(v: Point<Float64>): CMVector =
v * this
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
val origin = structure.toCM().origin
@ -125,7 +126,7 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
Cholesky -> object : CholeskyDecomposition<Float64> {
val cmCholesky by lazy { org.apache.commons.math3.linear.CholeskyDecomposition(origin) }
override val l: Matrix<Double> get() = cmCholesky.l.wrap()
override val l: Matrix<Float64> get() = cmCholesky.l.wrap()
}
QR -> object : QRDecomposition<Float64> {
@ -144,6 +145,13 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
}
EIG -> object : EigenDecomposition<Float64> {
val cmEigen by lazy { org.apache.commons.math3.linear.EigenDecomposition(origin) }
override val v: Matrix<Float64> get() = cmEigen.v.wrap()
override val d: Matrix<Float64> get() = cmEigen.d.wrap()
}
else -> null
}
@Suppress("UNCHECKED_CAST")

23
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt
View File

@ -9,6 +9,7 @@ import org.apache.commons.math3.linear.*
import space.kscience.kmath.linear.LinearSolver
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
public enum class CMDecomposition {
LUP,
@ -19,7 +20,7 @@ public enum class CMDecomposition {
}
private fun CMLinearSpace.solver(
a: Matrix<Double>,
a: Matrix<Float64>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): DecompositionSolver = when (decomposition) {
CMDecomposition.LUP -> LUDecomposition(a.toCM().origin).solver
@ -30,31 +31,31 @@ private fun CMLinearSpace.solver(
}
public fun CMLinearSpace.solve(
a: Matrix<Double>,
b: Matrix<Double>,
a: Matrix<Float64>,
b: Matrix<Float64>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMMatrix = solver(a, decomposition).solve(b.toCM().origin).wrap()
public fun CMLinearSpace.solve(
a: Matrix<Double>,
b: Point<Double>,
a: Matrix<Float64>,
b: Point<Float64>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMVector = solver(a, decomposition).solve(b.toCM().origin).toPoint()
public fun CMLinearSpace.inverse(
a: Matrix<Double>,
a: Matrix<Float64>,
decomposition: CMDecomposition = CMDecomposition.LUP,
): CMMatrix = solver(a, decomposition).inverse.wrap()
public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver<Double> = object : LinearSolver<Double> {
override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> =
public fun CMLinearSpace.solver(decomposition: CMDecomposition): LinearSolver<Float64> = object : LinearSolver<Float64> {
override fun solve(a: Matrix<Float64>, b: Matrix<Float64>): Matrix<Float64> =
solver(a, decomposition).solve(b.toCM().origin).wrap()
override fun solve(a: Matrix<Double>, b: Point<Double>): Point<Double> =
override fun solve(a: Matrix<Float64>, b: Point<Float64>): Point<Float64> =
solver(a, decomposition).solve(b.toCM().origin).toPoint()
override fun inverse(matrix: Matrix<Double>): Matrix<Double> = solver(matrix, decomposition).inverse.wrap()
override fun inverse(matrix: Matrix<Float64>): Matrix<Float64> = solver(matrix, decomposition).inverse.wrap()
}
public fun CMLinearSpace.lupSolver(): LinearSolver<Double> = solver((CMDecomposition.LUP))
public fun CMLinearSpace.lupSolver(): LinearSolver<Float64> = solver((CMDecomposition.LUP))

15
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt
View File

@ -22,6 +22,7 @@ import space.kscience.kmath.expressions.SymbolIndexer
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.withSymbols
import space.kscience.kmath.optimization.*
import space.kscience.kmath.structures.Float64
import kotlin.collections.set
import kotlin.reflect.KClass
@ -33,7 +34,7 @@ public object CMOptimizerEngine : OptimizationAttribute<() -> MultivariateOptimi
/**
* Specify a Commons-maths optimization engine
*/
public fun AttributesBuilder<FunctionOptimization<Double>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
public fun AttributesBuilder<FunctionOptimization<Float64>>.cmEngine(optimizerBuilder: () -> MultivariateOptimizer) {
set(CMOptimizerEngine, optimizerBuilder)
}
@ -42,18 +43,18 @@ public object CMOptimizerData : SetAttribute<SymbolIndexer.() -> OptimizationDat
/**
* Specify Commons-maths optimization data.
*/
public fun AttributesBuilder<FunctionOptimization<Double>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
public fun AttributesBuilder<FunctionOptimization<Float64>>.cmOptimizationData(data: SymbolIndexer.() -> OptimizationData) {
CMOptimizerData add data
}
public fun AttributesBuilder<FunctionOptimization<Double>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
public fun AttributesBuilder<FunctionOptimization<Float64>>.simplexSteps(vararg steps: Pair<Symbol, Double>) {
//TODO use convergence checker from features
cmEngine { SimplexOptimizer(CMOptimizer.defaultConvergenceChecker) }
cmOptimizationData { NelderMeadSimplex(mapOf(*steps).toDoubleArray()) }
}
@OptIn(UnstableKMathAPI::class)
public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
public object CMOptimizer : Optimizer<Double, FunctionOptimization<Float64>> {
public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
@ -67,12 +68,12 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
override suspend fun optimize(
problem: FunctionOptimization<Double>,
): FunctionOptimization<Double> {
problem: FunctionOptimization<Float64>,
): FunctionOptimization<Float64> {
val startPoint = problem.startPoint
val parameters = problem.attributes[OptimizationParameters]
?: problem.attributes[OptimizationStartPoint<Double>()]?.keys
?: problem.attributes[OptimizationStartPoint<Float64>()]?.keys
?: startPoint.keys

6
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/transform/Transformations.kt
View File

@ -77,7 +77,7 @@ public fun Flow<Buffer<Complex>>.fft(
}
@JvmName("realFFT")
public fun Flow<Buffer<Double>>.fft(
public fun Flow<Buffer<Float64>>.fft(
normalization: DftNormalization = DftNormalization.STANDARD,
direction: TransformType = TransformType.FORWARD,
): Flow<Buffer<Complex>> {
@ -89,7 +89,7 @@ public fun Flow<Buffer<Double>>.fft(
* Process a continuous flow of real numbers in FFT splitting it in chunks of [bufferSize].
*/
@JvmName("realFFT")
public fun Flow<Double>.fft(
public fun Flow<Float64>.fft(
bufferSize: Int = Int.MAX_VALUE,
normalization: DftNormalization = DftNormalization.STANDARD,
direction: TransformType = TransformType.FORWARD,
@ -99,4 +99,4 @@ public fun Flow<Double>.fft(
* Map a complex flow into real flow by taking real part of each number
*/
@FlowPreview
public fun Flow<Complex>.real(): Flow<Double> = map { it.re }
public fun Flow<Complex>.real(): Flow<Float64> = map { it.re }

3
kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt
View File

@ -18,6 +18,7 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.optimization.*
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.stat.chiSquaredExpression
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import kotlin.test.Test
@ -70,7 +71,7 @@ internal class OptimizeTest {
bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault
}
val result: FunctionOptimization<Double> = chi2.optimizeWith(
val result: FunctionOptimization<Float64> = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
) {

5
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt
View File

@ -11,6 +11,7 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.memory.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.*
@ -28,7 +29,7 @@ public class Quaternion(
public val x: Double,
public val y: Double,
public val z: Double,
) : Buffer<Double> {
) : Buffer<Float64> {
init {
require(!w.isNaN()) { "w-component of quaternion is not-a-number" }
require(!x.isNaN()) { "x-component of quaternion is not-a-number" }
@ -51,7 +52,7 @@ public class Quaternion(
else -> error("Index $index out of bounds [0,3]")
}
override fun iterator(): Iterator<Double> = listOf(w, x, y, z).iterator()
override fun iterator(): Iterator<Float64> = listOf(w, x, y, z).iterator()
override fun equals(other: Any?): Boolean {
if (this === other) return true

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain1D.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
@UnstableKMathAPI
public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<T>) : Domain<T> {
@ -22,8 +23,8 @@ public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<
@UnstableKMathAPI
public class DoubleDomain1D(
@Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Double>,
) : Domain1D<Double>(doubleRange), Float64Domain {
@Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Float64>,
) : Domain1D<Float64>(doubleRange), Float64Domain {
override fun getLowerBound(num: Int): Double {
require(num == 0)
return range.start
@ -55,5 +56,5 @@ public class DoubleDomain1D(
}
@UnstableKMathAPI
public val Domain1D<Double>.center: Double
public val Domain1D<Float64>.center: Double
get() = (range.endInclusive + range.start) / 2

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Float64Domain.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Float64
/**
* n-dimensional volume
@ -12,7 +13,7 @@ import space.kscience.kmath.UnstableKMathAPI
* @author Alexander Nozik
*/
@UnstableKMathAPI
public interface Float64Domain : Domain<Double> {
public interface Float64Domain : Domain<Float64> {
/**
* Global lower edge
* @param num axis number

5
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.indices
@ -15,7 +16,7 @@ import space.kscience.kmath.structures.indices
* and a [Buffer] of upper boundaries. Upper should be greater or equals than lower.
*/
@UnstableKMathAPI
public class HyperSquareDomain(public val lower: Buffer<Double>, public val upper: Buffer<Double>) : Float64Domain {
public class HyperSquareDomain(public val lower: Buffer<Float64>, public val upper: Buffer<Float64>) : Float64Domain {
init {
require(lower.size == upper.size) {
"Domain borders size mismatch. Lower borders size is ${lower.size}, but upper borders size is ${upper.size}."
@ -29,7 +30,7 @@ public class HyperSquareDomain(public val lower: Buffer<Double>, public val uppe
public val center: Float64Buffer get() = Float64Buffer(dimension) { (lower[it] + upper[it]) / 2.0 }
override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
override operator fun contains(point: Point<Float64>): Boolean = point.indices.all { i ->
point[i] in lower[i]..upper[i]
}

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt
View File

@ -6,10 +6,11 @@ package space.kscience.kmath.domains
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Float64
@UnstableKMathAPI
public class UnconstrainedDomain(override val dimension: Int) : Float64Domain {
override operator fun contains(point: Point<Double>): Boolean = true
override operator fun contains(point: Point<Float64>): Boolean = true
override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY

5
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt
View File

@ -13,6 +13,7 @@ import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.LongRing
import space.kscience.kmath.structures.Float64
import kotlin.jvm.JvmName
import kotlin.properties.ReadOnlyProperty
@ -47,9 +48,9 @@ public inline fun <reified T> Expression(noinline block: (Map<Symbol, T>) -> T):
* Specialization of [Expression] for [Double] allowing better performance because of using array.
*/
@UnstableKMathAPI
public interface DoubleExpression : Expression<Double> {
public interface DoubleExpression : Expression<Float64> {
override val type: SafeType<Double> get() = DoubleField.type
override val type: SafeType<Float64> get() = DoubleField.type
/**
* The indexer of this expression's arguments that should be used to build array for [invoke].

5
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.expressions
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -197,5 +198,5 @@ public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
): Expression<T> = FunctionalExpressionExtendedField(this).block()
public inline fun Float64Field.expression(
block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Double>,
): Expression<Double> = FunctionalExpressionExtendedField(this).block()
block: FunctionalExpressionExtendedField<Double, Float64Field>.() -> Expression<Float64>,
): Expression<Float64> = FunctionalExpressionExtendedField(this).block()

28
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/EigenDecomposition.kt Normal file
View File

@ -0,0 +1,28 @@
/*
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.linear
import space.kscience.attributes.PolymorphicAttribute
import space.kscience.attributes.safeTypeOf
public interface EigenDecomposition<T> {
/**
* Eigenvector matrix.
*/
public val v: Matrix<T>
/**
* A diagonal matrix of eigenvalues. Must have [IsDiagonal]
*/
public val d: Matrix<T>
}
public class EigenDecompositionAttribute<T> :
PolymorphicAttribute<EigenDecomposition<T>>(safeTypeOf()),
MatrixAttribute<EigenDecomposition<T>>
public val <T> MatrixScope<T>.EIG: EigenDecompositionAttribute<T>
get() = EigenDecompositionAttribute()

35
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/Float64LinearSpace.kt
View File

@ -6,12 +6,19 @@
package space.kscience.kmath.linear
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd.Floa64FieldOpsND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.operations.Float64BufferOps
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.collections.component1
import kotlin.collections.component2
import kotlin.collections.map
public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
@ -21,36 +28,36 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
rows: Int,
columns: Int,
initializer: Float64Field.(i: Int, j: Int) -> Double,
): Matrix<Double> = Floa64FieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
): Matrix<Float64> = Floa64FieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
Float64Field.initializer(i, j)
}.as2D()
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
Float64Buffer(size) { Float64Field.initializer(it) }
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
asND().map { -it }.as2D()
}
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
asND().plus(other.asND()).as2D()
}
override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
asND().minus(other.asND()).as2D()
}
// Create a continuous in-memory representation of this vector for better memory layout handling
private fun Buffer<Double>.linearize() = if (this is Float64Buffer) {
private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
this.array
} else {
DoubleArray(size) { get(it) }
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val rows = this@dot.rows.map { it.linearize() }
val columns = other.columns.map { it.linearize() }
@ -67,7 +74,7 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
override fun Matrix<Float64>.dot(vector: Point<Float64>): Float64Buffer {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val rows = this@dot.rows.map { it.linearize() }
val indices = 0 until this.colNum
@ -82,27 +89,27 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
}
override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
asND().map { it * value }.as2D()
}
public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
this@plus + other
}
public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
this@minus - other
}
public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@times, value)
}
public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@div, 1.0 / value)
}
public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
}

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt
View File

@ -163,9 +163,9 @@ public fun <T : Comparable<T>> Field<T>.lup(
public fun Field<Float64>.lup(
matrix: Matrix<Double>,
matrix: Matrix<Float64>,
singularityThreshold: Double = 1e-11,
): GenericLupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
): GenericLupDecomposition<Float64> = lup(matrix) { it < singularityThreshold }
private fun <T> Field<T>.solve(
lup: LupDecomposition<T>,
@ -235,5 +235,5 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lupSolver(
override fun inverse(matrix: Matrix<T>): Matrix<T> = solve(matrix, one(matrix.rowNum, matrix.colNum))
}
public fun LinearSpace<Double, Float64Field>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Double> =
public fun LinearSpace<Double, Float64Field>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Float64> =
lupSolver { it < singularityThreshold }

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt
View File

@ -8,6 +8,7 @@ package space.kscience.kmath.misc
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import kotlin.jvm.JvmName
/**
@ -47,7 +48,7 @@ public fun <T> Iterable<T>.cumulativeSum(ring: Ring<T>): Iterable<T> =
ring { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun Iterable<Double>.cumulativeSum(): Iterable<Double> = cumulative(0.0) { element, sum -> sum + element }
public fun Iterable<Float64>.cumulativeSum(): Iterable<Float64> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun Iterable<Int>.cumulativeSum(): Iterable<Int> = cumulative(0) { element, sum -> sum + element }
@ -59,7 +60,7 @@ public fun <T> Sequence<T>.cumulativeSum(ring: Ring<T>): Sequence<T> =
ring { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun Sequence<Double>.cumulativeSum(): Sequence<Double> = cumulative(0.0) { element, sum -> sum + element }
public fun Sequence<Float64>.cumulativeSum(): Sequence<Float64> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun Sequence<Int>.cumulativeSum(): Sequence<Int> = cumulative(0) { element, sum -> sum + element }
@ -71,7 +72,7 @@ public fun <T> List<T>.cumulativeSum(group: Ring<T>): List<T> =
group { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
public fun List<Double>.cumulativeSum(): List<Double> = cumulative(0.0) { element, sum -> sum + element }
public fun List<Float64>.cumulativeSum(): List<Float64> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
public fun List<Int>.cumulativeSum(): List<Int> = cumulative(0) { element, sum -> sum + element }

109
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Float64FieldND.kt
View File

@ -8,6 +8,7 @@ package space.kscience.kmath.nd
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 +21,7 @@ import kotlin.math.pow as kpow
public class Float64BufferND(
indexes: ShapeIndexer,
override val buffer: Float64Buffer,
) : MutableBufferND<Double>(indexes, buffer), MutableStructureNDOfDouble {
) : MutableBufferND<Float64>(indexes, buffer), MutableStructureNDOfDouble {
override fun getDouble(index: IntArray): Double = buffer[indices.offset(index)]
@ -30,11 +31,11 @@ public class Float64BufferND(
}
public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Double, Float64Field>(Float64Field.bufferAlgebra),
ScaleOperations<StructureND<Double>>, ExtendedFieldOps<StructureND<Double>> {
public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Float64, Float64Field>(Float64Field.bufferAlgebra),
ScaleOperations<StructureND<Float64>>, ExtendedFieldOps<StructureND<Float64>> {
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.toBufferND(): Float64BufferND = when (this) {
override fun StructureND<Float64>.toBufferND(): Float64BufferND = when (this) {
is Float64BufferND -> this
else -> {
val indexer = indexerBuilder(shape)
@ -64,16 +65,16 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Double, Float64Field>(
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.map(transform: Float64Field.(Double) -> Double): BufferND<Double> =
override fun StructureND<Float64>.map(transform: Float64Field.(Double) -> Double): BufferND<Float64> =
mapInline(toBufferND()) { Float64Field.transform(it) }
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,
left: StructureND<Float64>,
right: StructureND<Float64>,
transform: Float64Field.(Double, Double) -> Double,
): BufferND<Double> = zipInline(left.toBufferND(), right.toBufferND()) { l, r -> Float64Field.transform(l, r) }
): BufferND<Float64> = zipInline(left.toBufferND(), right.toBufferND()) { l, r -> Float64Field.transform(l, r) }
override fun mutableStructureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): Float64BufferND {
val indexer = indexerBuilder(shape)
@ -85,102 +86,102 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Double, Float64Field>(
)
}
override fun add(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
override fun add(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l + r }
override fun multiply(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
override fun multiply(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l, r -> l * r }
override fun StructureND<Double>.unaryMinus(): Float64BufferND = mapInline(toBufferND()) { -it }
override fun StructureND<Float64>.unaryMinus(): Float64BufferND = mapInline(toBufferND()) { -it }
override fun StructureND<Double>.div(arg: StructureND<Double>): Float64BufferND =
override fun StructureND<Float64>.div(arg: StructureND<Float64>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l, r -> l / r }
override fun divide(left: StructureND<Double>, right: StructureND<Double>): Float64BufferND =
override fun divide(left: StructureND<Float64>, right: StructureND<Float64>): Float64BufferND =
zipInline(left.toBufferND(), right.toBufferND()) { l: Double, r: Double -> l / r }
override fun StructureND<Double>.div(arg: Double): Float64BufferND =
override fun StructureND<Float64>.div(arg: Double): Float64BufferND =
mapInline(toBufferND()) { it / arg }
override fun Double.div(arg: StructureND<Double>): Float64BufferND =
override fun Double.div(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { this / it }
override fun StructureND<Double>.unaryPlus(): Float64BufferND = toBufferND()
override fun StructureND<Float64>.unaryPlus(): Float64BufferND = toBufferND()
override fun StructureND<Double>.plus(arg: StructureND<Double>): Float64BufferND =
override fun StructureND<Float64>.plus(arg: StructureND<Float64>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l + r }
override fun StructureND<Double>.minus(arg: StructureND<Double>): Float64BufferND =
override fun StructureND<Float64>.minus(arg: StructureND<Float64>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l - r }
override fun StructureND<Double>.times(arg: StructureND<Double>): Float64BufferND =
override fun StructureND<Float64>.times(arg: StructureND<Float64>): Float64BufferND =
zipInline(toBufferND(), arg.toBufferND()) { l: Double, r: Double -> l * r }
override fun StructureND<Double>.times(k: Number): Float64BufferND =
override fun StructureND<Float64>.times(k: Number): Float64BufferND =
mapInline(toBufferND()) { it * k.toDouble() }
override fun StructureND<Double>.div(k: Number): Float64BufferND =
override fun StructureND<Float64>.div(k: Number): Float64BufferND =
mapInline(toBufferND()) { it / k.toDouble() }
override fun Number.times(arg: StructureND<Double>): Float64BufferND = arg * this
override fun Number.times(arg: StructureND<Float64>): Float64BufferND = arg * this
override fun StructureND<Double>.plus(arg: Double): Float64BufferND = mapInline(toBufferND()) { it + arg }
override fun StructureND<Float64>.plus(arg: Double): Float64BufferND = mapInline(toBufferND()) { it + arg }
override fun StructureND<Double>.minus(arg: Double): StructureND<Double> = mapInline(toBufferND()) { it - arg }
override fun StructureND<Float64>.minus(arg: Double): StructureND<Float64> = mapInline(toBufferND()) { it - arg }
override fun Double.plus(arg: StructureND<Double>): StructureND<Double> = arg + this
override fun Double.plus(arg: StructureND<Float64>): StructureND<Float64> = arg + this
override fun Double.minus(arg: StructureND<Double>): StructureND<Double> = mapInline(arg.toBufferND()) { this - it }
override fun Double.minus(arg: StructureND<Float64>): StructureND<Float64> = mapInline(arg.toBufferND()) { this - it }
override fun scale(a: StructureND<Double>, value: Double): Float64BufferND =
override fun scale(a: StructureND<Float64>, value: Double): Float64BufferND =
mapInline(a.toBufferND()) { it * value }
override fun exp(arg: StructureND<Double>): Float64BufferND =
override fun exp(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.exp(it) }
override fun ln(arg: StructureND<Double>): Float64BufferND =
override fun ln(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.ln(it) }
override fun sin(arg: StructureND<Double>): Float64BufferND =
override fun sin(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.sin(it) }
override fun cos(arg: StructureND<Double>): Float64BufferND =
override fun cos(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.cos(it) }
override fun tan(arg: StructureND<Double>): Float64BufferND =
override fun tan(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.tan(it) }
override fun asin(arg: StructureND<Double>): Float64BufferND =
override fun asin(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.asin(it) }
override fun acos(arg: StructureND<Double>): Float64BufferND =
override fun acos(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.acos(it) }
override fun atan(arg: StructureND<Double>): Float64BufferND =
override fun atan(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.atan(it) }
override fun sinh(arg: StructureND<Double>): Float64BufferND =
override fun sinh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.sinh(it) }
override fun cosh(arg: StructureND<Double>): Float64BufferND =
override fun cosh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.cosh(it) }
override fun tanh(arg: StructureND<Double>): Float64BufferND =
override fun tanh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.tanh(it) }
override fun asinh(arg: StructureND<Double>): Float64BufferND =
override fun asinh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.asinh(it) }
override fun acosh(arg: StructureND<Double>): Float64BufferND =
override fun acosh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.acosh(it) }
override fun atanh(arg: StructureND<Double>): Float64BufferND =
override fun atanh(arg: StructureND<Float64>): Float64BufferND =
mapInline(arg.toBufferND()) { kotlin.math.atanh(it) }
override fun power(
arg: StructureND<Double>,
arg: StructureND<Float64>,
pow: Number,
): StructureND<Double> = if (pow is Int) {
): StructureND<Float64> = if (pow is Int) {
mapInline(arg.toBufferND()) { it.pow(pow) }
} else {
mapInline(arg.toBufferND()) { it.pow(pow.toDouble()) }
@ -191,18 +192,18 @@ public sealed class Floa64FieldOpsND : BufferedFieldOpsND<Double, Float64Field>(
@OptIn(UnstableKMathAPI::class)
public class Float64FieldND(override val shape: ShapeND) :
Floa64FieldOpsND(), FieldND<Double, Float64Field>, NumbersAddOps<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
Floa64FieldOpsND(), FieldND<Double, Float64Field>, NumbersAddOps<StructureND<Float64>>,
ExtendedField<StructureND<Float64>> {
override fun power(arg: StructureND<Double>, pow: UInt): Float64BufferND = mapInline(arg.toBufferND()) {
override fun power(arg: StructureND<Float64>, pow: UInt): Float64BufferND = mapInline(arg.toBufferND()) {
it.kpow(pow.toInt())
}
override fun power(arg: StructureND<Double>, pow: Int): Float64BufferND = mapInline(arg.toBufferND()) {
override fun power(arg: StructureND<Float64>, pow: Int): Float64BufferND = mapInline(arg.toBufferND()) {
it.kpow(pow)
}
override fun power(arg: StructureND<Double>, pow: Number): Float64BufferND = if (pow.isInteger()) {
override fun power(arg: StructureND<Float64>, pow: Number): Float64BufferND = if (pow.isInteger()) {
power(arg, pow.toInt())
} else {
val dpow = pow.toDouble()
@ -212,17 +213,17 @@ public class Float64FieldND(override val shape: ShapeND) :
}
}
override fun sinh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.sinh(arg)
override fun sinh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.sinh(arg)
override fun cosh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.cosh(arg)
override fun cosh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.cosh(arg)
override fun tanh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.tan(arg)
override fun tanh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.tan(arg)
override fun asinh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.asinh(arg)
override fun asinh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.asinh(arg)
override fun acosh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.acosh(arg)
override fun acosh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.acosh(arg)
override fun atanh(arg: StructureND<Double>): Float64BufferND = super<Floa64FieldOpsND>.atanh(arg)
override fun atanh(arg: StructureND<Float64>): Float64BufferND = super<Floa64FieldOpsND>.atanh(arg)
override fun number(value: Number): Float64BufferND {
val d = value.toDouble() // minimize conversions

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
View File

@ -14,6 +14,7 @@ import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import kotlin.math.abs
public interface StructureAttribute<T> : Attribute<T>
@ -76,8 +77,8 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
@PerformancePitfall
public fun contentEquals(
st1: StructureND<Double>,
st2: StructureND<Double>,
st1: StructureND<Float64>,
st2: StructureND<Float64>,
tolerance: Double = 1e-11,
): Boolean {
if (st1 === st2) return true
@ -210,7 +211,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.contentEquals(
@PerformancePitfall
public operator fun <T> StructureND<T>.get(vararg index: Int): T = get(index)
public operator fun StructureND<Double>.get(vararg index: Int): Double = getDouble(index)
public operator fun StructureND<Float64>.get(vararg index: Int): Double = getDouble(index)
public operator fun StructureND<Int>.get(vararg index: Int): Int = getInt(index)

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/VirtualStructureND.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.nd
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Float64
public open class VirtualStructureND<T>(
override val shape: ShapeND,
@ -24,7 +25,7 @@ public open class VirtualStructureND<T>(
public class VirtualDoubleStructureND(
shape: ShapeND,
producer: (IntArray) -> Double,
) : VirtualStructureND<Double>(shape, producer)
) : VirtualStructureND<Float64>(shape, producer)
@UnstableKMathAPI
public class VirtualIntStructureND(

9
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/primitiveStructureND.kt
View File

@ -6,8 +6,9 @@
package space.kscience.kmath.nd
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.structures.Float64
public interface StructureNDOfDouble : StructureND<Double> {
public interface StructureNDOfDouble : StructureND<Float64> {
/**
* Guaranteed non-blocking access to content
@ -19,10 +20,10 @@ public interface StructureNDOfDouble : StructureND<Double> {
* Optimized method to access primitive without boxing if possible
*/
@OptIn(PerformancePitfall::class)
public fun StructureND<Double>.getDouble(index: IntArray): Double =
public fun StructureND<Float64>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Double> {
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Float64> {
/**
* Guaranteed non-blocking access to content
@ -31,7 +32,7 @@ public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStruct
}
@OptIn(PerformancePitfall::class)
public fun MutableStructureND<Double>.getDouble(index: IntArray): Double =
public fun MutableStructureND<Float64>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
@ -182,7 +183,7 @@ public fun <T, A : Field<T>> BufferFieldOps<T, A>.withSize(size: Int): BufferFie
//Double buffer specialization
public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Double> {
public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Float64> {
require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
return elementBufferFactory(size) { elements[it].toDouble() }
}

23
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Float64BufferField.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
@ -13,23 +14,23 @@ import space.kscience.kmath.structures.Float64Buffer
*
* @property size the size of buffers to operate on.
*/
public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Double>>, Float64BufferOps() {
override val zero: Buffer<Double> by lazy { Float64Buffer(size) { 0.0 } }
override val one: Buffer<Double> by lazy { Float64Buffer(size) { 1.0 } }
public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Float64>>, Float64BufferOps() {
override val zero: Buffer<Float64> by lazy { Float64Buffer(size) { 0.0 } }
override val one: Buffer<Float64> by lazy { Float64Buffer(size) { 1.0 } }
override fun sinh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.sinh(arg)
override fun sinh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.sinh(arg)
override fun cosh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.cosh(arg)
override fun cosh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.cosh(arg)
override fun tanh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.tanh(arg)
override fun tanh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.tanh(arg)
override fun asinh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.asinh(arg)
override fun asinh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.asinh(arg)
override fun acosh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.acosh(arg)
override fun acosh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.acosh(arg)
override fun atanh(arg: Buffer<Double>): Float64Buffer = super<Float64BufferOps>.atanh(arg)
override fun atanh(arg: Buffer<Float64>): Float64Buffer = super<Float64BufferOps>.atanh(arg)
override fun power(arg: Buffer<Double>, pow: Number): Float64Buffer = if (pow.isInteger()) {
override fun power(arg: Buffer<Float64>, pow: Number): Float64Buffer = if (pow.isInteger()) {
arg.map { it.pow(pow.toInt()) }
} else {
arg.map {
@ -38,6 +39,6 @@ public class Float64BufferField(public val size: Int) : ExtendedField<Buffer<Dou
}
}
override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
override fun unaryOperationFunction(operation: String): (arg: Buffer<Float64>) -> Buffer<Float64> =
super<ExtendedField>.unaryOperationFunction(operation)
}

82
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Float64BufferOps.kt
View File

@ -14,43 +14,43 @@ import kotlin.math.sqrt
/**
* [ExtendedFieldOps] over [Float64Buffer].
*/
public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Double>>,
Norm<Buffer<Double>, Double> {
public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, ExtendedFieldOps<Buffer<Float64>>,
Norm<Buffer<Float64>, Double> {
override val elementAlgebra: Float64Field get() = Float64Field
override val elementBufferFactory: MutableBufferFactory<Double> get() = elementAlgebra.bufferFactory
override val elementBufferFactory: MutableBufferFactory<Float64> get() = elementAlgebra.bufferFactory
@Suppress("OVERRIDE_BY_INLINE")
@OptIn(UnstableKMathAPI::class)
final override inline fun Buffer<Double>.map(block: Float64Field.(Double) -> Double): Float64Buffer =
final override inline fun Buffer<Float64>.map(block: Float64Field.(Double) -> Double): Float64Buffer =
DoubleArray(size) { Float64Field.block(getDouble(it)) }.asBuffer()
@OptIn(UnstableKMathAPI::class)
@Suppress("OVERRIDE_BY_INLINE")
final override inline fun Buffer<Double>.mapIndexed(block: Float64Field.(index: Int, arg: Double) -> Double): Float64Buffer =
final override inline fun Buffer<Float64>.mapIndexed(block: Float64Field.(index: Int, arg: Double) -> Double): Float64Buffer =
Float64Buffer(size) { Float64Field.block(it, getDouble(it)) }
@OptIn(UnstableKMathAPI::class)
@Suppress("OVERRIDE_BY_INLINE")
final override inline fun Buffer<Double>.zip(
other: Buffer<Double>,
final override inline fun Buffer<Float64>.zip(
other: Buffer<Float64>,
block: Float64Field.(left: Double, right: Double) -> Double,
): Float64Buffer {
require(size == other.size) { "Incompatible buffer sizes. left: ${size}, right: ${other.size}" }
return Float64Buffer(size) { Float64Field.block(getDouble(it), other.getDouble(it)) }
}
override fun unaryOperationFunction(operation: String): (arg: Buffer<Double>) -> Buffer<Double> =
override fun unaryOperationFunction(operation: String): (arg: Buffer<Float64>) -> Buffer<Float64> =
super<ExtendedFieldOps>.unaryOperationFunction(operation)
override fun binaryOperationFunction(operation: String): (left: Buffer<Double>, right: Buffer<Double>) -> Buffer<Double> =
override fun binaryOperationFunction(operation: String): (left: Buffer<Float64>, right: Buffer<Float64>) -> Buffer<Float64> =
super<ExtendedFieldOps>.binaryOperationFunction(operation)
override fun Buffer<Double>.unaryMinus(): Float64Buffer = map { -it }
override fun Buffer<Float64>.unaryMinus(): Float64Buffer = map { -it }
override fun add(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
override fun add(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -62,9 +62,9 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] + right[it] })
}
override fun Buffer<Double>.plus(arg: Buffer<Double>): Float64Buffer = add(this, arg)
override fun Buffer<Float64>.plus(arg: Buffer<Float64>): Float64Buffer = add(this, arg)
override fun Buffer<Double>.minus(arg: Buffer<Double>): Float64Buffer {
override fun Buffer<Float64>.minus(arg: Buffer<Float64>): Float64Buffer {
require(arg.size == this.size) {
"The size of the first buffer ${this.size} should be the same as for second one: ${arg.size} "
}
@ -77,7 +77,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
}
//
// override fun multiply(a: Buffer<Double>, k: Number): RealBuffer {
// override fun multiply(a: Buffer<Float64>, k: Number): RealBuffer {
// val kValue = k.toDouble()
//
// return if (a is RealBuffer) {
@ -86,7 +86,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
// } else RealBuffer(DoubleArray(a.size) { a[it] * kValue })
// }
//
// override fun divide(a: Buffer<Double>, k: Number): RealBuffer {
// override fun divide(a: Buffer<Float64>, k: Number): RealBuffer {
// val kValue = k.toDouble()
//
// return if (a is RealBuffer) {
@ -96,7 +96,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
// }
@UnstableKMathAPI
override fun multiply(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
override fun multiply(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -108,7 +108,7 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] * right[it] })
}
override fun divide(left: Buffer<Double>, right: Buffer<Double>): Float64Buffer {
override fun divide(left: Buffer<Float64>, right: Buffer<Float64>): Float64Buffer {
require(right.size == left.size) {
"The size of the first buffer ${left.size} should be the same as for second one: ${right.size} "
}
@ -120,39 +120,39 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
} else Float64Buffer(DoubleArray(left.size) { left[it] / right[it] })
}
override fun sin(arg: Buffer<Double>): Float64Buffer = arg.map { sin(it) }
override fun sin(arg: Buffer<Float64>): Float64Buffer = arg.map { sin(it) }
override fun cos(arg: Buffer<Double>): Float64Buffer = arg.map { cos(it) }
override fun cos(arg: Buffer<Float64>): Float64Buffer = arg.map { cos(it) }
override fun tan(arg: Buffer<Double>): Float64Buffer = arg.map { tan(it) }
override fun tan(arg: Buffer<Float64>): Float64Buffer = arg.map { tan(it) }
override fun asin(arg: Buffer<Double>): Float64Buffer = arg.map { asin(it) }
override fun asin(arg: Buffer<Float64>): Float64Buffer = arg.map { asin(it) }
override fun acos(arg: Buffer<Double>): Float64Buffer = arg.map { acos(it) }
override fun acos(arg: Buffer<Float64>): Float64Buffer = arg.map { acos(it) }
override fun atan(arg: Buffer<Double>): Float64Buffer = arg.map { atan(it) }
override fun atan(arg: Buffer<Float64>): Float64Buffer = arg.map { atan(it) }
override fun sinh(arg: Buffer<Double>): Float64Buffer = arg.map { sinh(it) }
override fun sinh(arg: Buffer<Float64>): Float64Buffer = arg.map { sinh(it) }
override fun cosh(arg: Buffer<Double>): Float64Buffer = arg.map { cosh(it) }
override fun cosh(arg: Buffer<Float64>): Float64Buffer = arg.map { cosh(it) }
override fun tanh(arg: Buffer<Double>): Float64Buffer = arg.map { tanh(it) }
override fun tanh(arg: Buffer<Float64>): Float64Buffer = arg.map { tanh(it) }
override fun asinh(arg: Buffer<Double>): Float64Buffer = arg.map { asinh(it) }
override fun asinh(arg: Buffer<Float64>): Float64Buffer = arg.map { asinh(it) }
override fun acosh(arg: Buffer<Double>): Float64Buffer = arg.map { acosh(it) }
override fun acosh(arg: Buffer<Float64>): Float64Buffer = arg.map { acosh(it) }
override fun atanh(arg: Buffer<Double>): Float64Buffer = arg.map { atanh(it) }
override fun atanh(arg: Buffer<Float64>): Float64Buffer = arg.map { atanh(it) }
override fun exp(arg: Buffer<Double>): Float64Buffer = arg.map { exp(it) }
override fun exp(arg: Buffer<Float64>): Float64Buffer = arg.map { exp(it) }
override fun ln(arg: Buffer<Double>): Float64Buffer = arg.map { ln(it) }
override fun ln(arg: Buffer<Float64>): Float64Buffer = arg.map { ln(it) }
override fun norm(arg: Buffer<Double>): Double = Float64L2Norm.norm(arg)
override fun norm(arg: Buffer<Float64>): Double = Float64L2Norm.norm(arg)
override fun scale(a: Buffer<Double>, value: Double): Float64Buffer = a.map { it * value }
override fun scale(a: Buffer<Float64>, value: Double): Float64Buffer = a.map { it * value }
override fun power(arg: Buffer<Double>, pow: Number): Buffer<Double> = if (pow is Int) {
override fun power(arg: Buffer<Float64>, pow: Number): Buffer<Float64> = if (pow is Int) {
arg.map { it.pow(pow) }
} else {
arg.map { it.pow(pow.toDouble()) }
@ -161,11 +161,11 @@ public abstract class Float64BufferOps : BufferAlgebra<Double, Float64Field>, Ex
public companion object : Float64BufferOps()
}
public object Float64L2Norm : Norm<Point<Double>, Double> {
override fun norm(arg: Point<Double>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
public object Float64L2Norm : Norm<Point<Float64>, Double> {
override fun norm(arg: Point<Float64>): Double = sqrt(arg.fold(0.0) { acc: Double, d: Double -> acc + d.pow(2) })
}
public fun Float64BufferOps.sum(buffer: Buffer<Double>): Double = buffer.reduce(Double::plus)
public fun Float64BufferOps.sum(buffer: Buffer<Float64>): Double = buffer.reduce(Double::plus)
/**
* Sum of elements using given [conversion]
@ -173,7 +173,7 @@ public fun Float64BufferOps.sum(buffer: Buffer<Double>): Double = buffer.reduce(
public inline fun <T> Float64BufferOps.sumOf(buffer: Buffer<T>, conversion: (T) -> Double): Double =
buffer.fold(0.0) { acc, value -> acc + conversion(value) }
public fun Float64BufferOps.average(buffer: Buffer<Double>): Double = sum(buffer) / buffer.size
public fun Float64BufferOps.average(buffer: Buffer<Float64>): Double = sum(buffer) / buffer.size
/**
* Average of elements using given [conversion]
@ -181,14 +181,14 @@ public fun Float64BufferOps.average(buffer: Buffer<Double>): Double = sum(buffer
public inline fun <T> Float64BufferOps.averageOf(buffer: Buffer<T>, conversion: (T) -> Double): Double =
sumOf(buffer, conversion) / buffer.size
public fun Float64BufferOps.dispersion(buffer: Buffer<Double>): Double {
public fun Float64BufferOps.dispersion(buffer: Buffer<Float64>): Double {
val av = average(buffer)
return buffer.fold(0.0) { acc, value -> acc + (value - av).pow(2) } / buffer.size
}
public fun Float64BufferOps.std(buffer: Buffer<Double>): Double = sqrt(dispersion(buffer))
public fun Float64BufferOps.std(buffer: Buffer<Float64>): Double = sqrt(dispersion(buffer))
public fun Float64BufferOps.covariance(x: Buffer<Double>, y: Buffer<Double>): Double {
public fun Float64BufferOps.covariance(x: Buffer<Float64>, y: Buffer<Float64>): Double {
require(x.size == y.size) { "Expected buffers of the same size, but x.size == ${x.size} and y.size == ${y.size}" }
val xMean = average(x)
val yMean = average(y)

5
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt
View File

@ -4,6 +4,7 @@
*/
package space.kscience.kmath.operations
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow as kpow
@ -66,8 +67,8 @@ public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, NumericAlgebr
* A field for [Double] without boxing. Does not produce an appropriate field element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER")
public object Float64Field : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
override val bufferFactory: MutableBufferFactory<Double> = MutableBufferFactory()
public object Float64Field : ExtendedField<Float64>, Norm<Double, Double>, ScaleOperations<Float64> {
override val bufferFactory: MutableBufferFactory<Float64> = MutableBufferFactory()
override val zero: Double get() = 0.0
override val one: Double get() = 1.0

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
View File

@ -115,7 +115,7 @@ public fun <T> Buffer(
size: Int,
initializer: (Int) -> T,
): Buffer<T> = when (type.kType) {
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Short>() -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
typeOf<Int>() -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
typeOf<Long>() -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>
@ -134,7 +134,7 @@ public inline fun <reified T> Buffer(size: Int, initializer: (Int) -> T): Buffer
//code duplication here because we want to inline initializers
val type = safeTypeOf<T>()
return when (type.kType) {
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Short>() -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
typeOf<Int>() -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
typeOf<Long>() -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>

8
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Float64Buffer.kt
View File

@ -14,7 +14,7 @@ import kotlin.jvm.JvmInline
* @property array the underlying array.
*/
@JvmInline
public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Double> {
public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffer<Float64> {
override val size: Int get() = array.size
@ -54,7 +54,7 @@ public fun Float64Buffer(vararg doubles: Double): Float64Buffer = Float64Buffer(
/**
* Returns a new [DoubleArray] containing all the elements of this [Buffer].
*/
public fun Buffer<Double>.toDoubleArray(): DoubleArray = when (this) {
public fun Buffer<Float64>.toDoubleArray(): DoubleArray = when (this) {
is Float64Buffer -> array
else -> DoubleArray(size, ::get)
}
@ -62,7 +62,7 @@ public fun Buffer<Double>.toDoubleArray(): DoubleArray = when (this) {
/**
* Represent this buffer as [Float64Buffer]. Does not guarantee that changes in the original buffer are reflected on this buffer.
*/
public fun Buffer<Double>.toFloat64Buffer(): Float64Buffer = when (this) {
public fun Buffer<Float64>.toFloat64Buffer(): Float64Buffer = when (this) {
is Float64Buffer -> this
else -> DoubleArray(size, ::get).asBuffer()
}
@ -79,5 +79,5 @@ public fun DoubleArray.asBuffer(): Float64Buffer = Float64Buffer(this)
public fun interface Float64BufferTransform : BufferTransform<Double, Double> {
public fun transform(arg: Float64Buffer): Float64Buffer
override fun transform(arg: Buffer<Double>): Float64Buffer = arg.toFloat64Buffer()
override fun transform(arg: Buffer<Float64>): Float64Buffer = arg.toFloat64Buffer()
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt
View File

@ -99,7 +99,7 @@ public inline fun <T> MutableBuffer(
typeOf<Int32>() -> MutableBuffer.int(size) { initializer(it) as Int32 } as MutableBuffer<T>
typeOf<Int64>() -> MutableBuffer.long(size) { initializer(it) as Int64 } as MutableBuffer<T>
typeOf<Float>() -> MutableBuffer.float(size) { initializer(it) as Float } as MutableBuffer<T>
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
typeOf<Float64>() -> MutableBuffer.double(size) { initializer(it) as Double } as MutableBuffer<T>
//TODO add unsigned types
else -> MutableListBuffer(MutableList(size, initializer))
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/bufferPrimitiveAccess.kt
View File

@ -11,7 +11,7 @@ import space.kscience.kmath.UnstableKMathAPI
* Non-boxing access to primitive [Double]
*/
@UnstableKMathAPI
public fun Buffer<Double>.getDouble(index: Int): Double = if (this is BufferView) {
public fun Buffer<Float64>.getDouble(index: Int): Double = if (this is BufferView) {
val originIndex = originIndex(index)
if (originIndex >= 0) {
origin.getDouble(originIndex)

3
kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/ExpressionFieldTest.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.expressions
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertFails
@ -37,7 +38,7 @@ class ExpressionFieldTest {
@Test
fun valueExpression() {
val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Double> = {
val expressionBuilder: FunctionalExpressionField<Double, *>.() -> Expression<Float64> = {
val x by binding
x * x + 2 * x + one
}

11
kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/SimpleAutoDiffTest.kt
View File

@ -8,6 +8,7 @@ package space.kscience.kmath.expressions
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.asBuffer
import kotlin.math.E
import kotlin.math.PI
@ -21,18 +22,18 @@ internal class SimpleAutoDiffTest {
fun dx(
xBinding: Pair<Symbol, Double>,
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding) { body(bindSymbol(xBinding.first)) }
fun dxy(
xBinding: Pair<Symbol, Double>,
yBinding: Pair<Symbol, Double>,
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Double>, y: AutoDiffValue<Double>) -> AutoDiffValue<Double>,
): DerivationResult<Double> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
body: SimpleAutoDiffField<Double, Float64Field>.(x: AutoDiffValue<Float64>, y: AutoDiffValue<Float64>) -> AutoDiffValue<Float64>,
): DerivationResult<Float64> = Float64Field.simpleAutoDiff(xBinding, yBinding) {
body(bindSymbol(xBinding.first), bindSymbol(yBinding.first))
}
fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Double>): SimpleAutoDiffExpression<Double, Float64Field> {
fun diff(block: SimpleAutoDiffField<Double, Float64Field>.() -> AutoDiffValue<Float64>): SimpleAutoDiffExpression<Double, Float64Field> {
return SimpleAutoDiffExpression(Float64Field, block)
}

5
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt
View File

@ -10,6 +10,7 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -38,7 +39,7 @@ class MatrixTest {
@Test
fun testMatrixExtension() = Double.algebra.linearSpace.run {
val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
when {
col == 0 -> .50
row + 1 == col -> .50
@ -47,7 +48,7 @@ class MatrixTest {
}
}
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
var res = this
repeat(power - 1) {
res = res dot this@pow

27
kmath-core/src/jvmMain/kotlin/space/kscience/kmath/linear/Float64ParallelLinearSpace.kt
View File

@ -11,6 +11,7 @@ import space.kscience.kmath.operations.Float64BufferOps
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import java.util.stream.IntStream
@ -25,7 +26,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
rows: Int,
columns: Int,
initializer: Float64Field.(i: Int, j: Int) -> Double,
): Matrix<Double> {
): Matrix<Float64> {
val shape = ShapeND(rows, columns)
val indexer = BufferAlgebraND.defaultIndexerBuilder(shape)
@ -43,29 +44,29 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
IntStream.range(0, size).parallel().mapToDouble { Float64Field.initializer(it) }.toArray().asBuffer()
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = Floa64FieldOpsND {
asND().map { -it }.as2D()
}
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.plus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
asND().plus(other.asND()).as2D()
}
override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.minus(other: Matrix<Float64>): Matrix<Float64> = Floa64FieldOpsND {
require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
asND().minus(other.asND()).as2D()
}
// Create a continuous in-memory representation of this vector for better memory layout handling
private fun Buffer<Double>.linearize() = if (this is Float64Buffer) {
private fun Buffer<Float64>.linearize() = if (this is Float64Buffer) {
this.array
} else {
DoubleArray(size) { get(it) }
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
override fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val rows = this@dot.rows.map { it.linearize() }
val columns = other.columns.map { it.linearize() }
@ -82,7 +83,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
override fun Matrix<Float64>.dot(vector: Point<Float64>): Float64Buffer {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val rows = this@dot.rows.map { it.linearize() }
val indices = 0 until this.colNum
@ -97,27 +98,27 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
}
override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
override fun Matrix<Float64>.times(value: Double): Matrix<Float64> = Floa64FieldOpsND {
asND().map { it * value }.as2D()
}
public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.plus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
this@plus + other
}
public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.minus(other: Point<Float64>): Float64Buffer = Float64BufferOps.run {
this@minus - other
}
public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
public override fun Point<Float64>.times(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@times, value)
}
public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
public operator fun Point<Float64>.div(value: Double): Float64Buffer = Float64BufferOps.run {
scale(this@div, 1.0 / value)
}
public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
public override fun Double.times(v: Point<Float64>): Float64Buffer = v * this
}

2
kmath-core/src/jvmMain/kotlin/space/kscience/kmath/structures/parallelMutableBuffer.kt
View File

@ -30,7 +30,7 @@ public fun <T> MutableBuffer.Companion.parallel(
.asBuffer() as MutableBuffer<T>
typeOf<Float>() -> Float32Buffer(size) { initializer(it) as Float } as MutableBuffer<T>
typeOf<Double>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
typeOf<Float64>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
.asBuffer() as MutableBuffer<T>
//TODO add unsigned types
else -> IntStream.range(0, size).parallel().mapToObj { initializer(it) }.collect(Collectors.toList<T>())

5
kmath-core/src/jvmTest/kotlin/space/kscience/kmath/linear/ParallelMatrixTest.kt
View File

@ -9,6 +9,7 @@ import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -37,7 +38,7 @@ class ParallelMatrixTest {
@Test
fun testMatrixExtension() = Float64Field.linearSpace.parallel {
val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
val transitionMatrix: Matrix<Float64> = VirtualMatrix(6, 6) { row, col ->
when {
col == 0 -> .50
row + 1 == col -> .50
@ -46,7 +47,7 @@ class ParallelMatrixTest {
}
}
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
infix fun Matrix<Float64>.pow(power: Int): Matrix<Float64> {
var res = this
repeat(power - 1) {
res = res dot this@pow

3
kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt
View File

@ -5,12 +5,13 @@
package space.kscience.kmath.chains
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
* Chunked, specialized chain for double values, which supports blocking [nextBlocking] operation
*/
public interface BlockingDoubleChain : BlockingBufferChain<Double> {
public interface BlockingDoubleChain : BlockingBufferChain<Float64> {
/**
* Returns an [DoubleArray] chunk of [size] values of [next].

3
kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt
View File

@ -17,6 +17,7 @@ import space.kscience.kmath.chains.BlockingDoubleChain
import space.kscience.kmath.operations.Group
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
@ -56,7 +57,7 @@ public fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>)
/**
* Specialized flow chunker for real buffer
*/
public fun Flow<Double>.chunked(bufferSize: Int): Flow<Float64Buffer> = flow {
public fun Flow<Float64>.chunked(bufferSize: Int): Flow<Float64Buffer> = flow {
require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
if (this@chunked is BlockingDoubleChain) {

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

@ -5,8 +5,9 @@ plugins {
val ejmlVerision = "0.43.1"
dependencies {
api("org.ejml:ejml-all:$ejmlVerision")
api(projects.kmathCore)
api(projects.kmathComplex)
api("org.ejml:ejml-all:$ejmlVerision")
}
readme {

12
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt
View File

@ -6,9 +6,13 @@
package space.kscience.kmath.ejml
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.*
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.linear.Inverted
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.structures.Float64
/**
* [LinearSpace] implementation specialized for a certain EJML type.
@ -38,6 +42,6 @@ public abstract class EjmlLinearSpace<T : Any, out A : Ring<T>, out M : org.ejml
public abstract override fun buildVector(size: Int, initializer: A.(Int) -> T): EjmlVector<T, M>
@UnstableKMathAPI
public fun EjmlMatrix<T, *>.inverted(): Matrix<Double> =
computeAttribute(this, Float64Field.linearSpace.Inverted)!!
public fun Structure2D<T>.inverted(): Matrix<Float64> =
computeAttribute(this, Inverted()) ?: error("Can't invert matrix")
}

185
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/implementations.kt
View File

@ -12,6 +12,8 @@ import org.ejml.dense.row.CommonOps_DDRM
import org.ejml.dense.row.CommonOps_FDRM
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.dense.row.factory.DecompositionFactory_FDRM
import org.ejml.interfaces.decomposition.EigenDecomposition_F32
import org.ejml.interfaces.decomposition.EigenDecomposition_F64
import org.ejml.sparse.FillReducing
import org.ejml.sparse.csc.CommonOps_DSCC
import org.ejml.sparse.csc.CommonOps_FSCC
@ -19,6 +21,7 @@ import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
import space.kscience.attributes.SafeType
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.nd.Structure2D
@ -27,9 +30,15 @@ import space.kscience.kmath.operations.Float32Field
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float32
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.asBuffer
/**
* Copy EJML [Complex_F64] into KMath [Complex]
*/
public fun Complex_F64.toKMathComplex(): Complex = Complex(real, imaginary)
/**
* [EjmlVector] specialization for [Double].
*/
@ -79,16 +88,16 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
*/
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Double> get() = safeTypeOf()
override val type: SafeType<Float64> get() = safeTypeOf()
@Suppress("UNCHECKED_CAST")
override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
override fun Matrix<Float64>.toEjml(): EjmlDoubleMatrix<DMatrixRMaj> = when {
this is EjmlDoubleMatrix<*> && origin is DMatrixRMaj -> this as EjmlDoubleMatrix<DMatrixRMaj>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
override fun Point<Double>.toEjml(): EjmlDoubleVector<DMatrixRMaj> = when {
override fun Point<Float64>.toEjml(): EjmlDoubleVector<DMatrixRMaj> = when {
this is EjmlDoubleVector<*> && origin is DMatrixRMaj -> this as EjmlDoubleVector<DMatrixRMaj>
else -> EjmlDoubleVector(DMatrixRMaj(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
@ -115,21 +124,21 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
private fun <T : DMatrix> T.wrapMatrix() = EjmlDoubleMatrix(this)
private fun <T : DMatrix> T.wrapVector() = EjmlDoubleVector(this)
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -143,19 +152,19 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.changeSign(toEjml().origin, res)
return res.wrapVector()
}
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -169,7 +178,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -183,7 +192,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
@ -197,18 +206,18 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> = m * this
override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixRMaj> {
override fun Point<Float64>.times(value: Double): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.scale(value, toEjml().origin, res)
return res.wrapVector()
}
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixRMaj> = v * this
override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> = v * this
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toEjml().origin
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
val origin: DMatrixRMaj = structure.toEjml().origin
val raw: Any? = when (attribute) {
Inverted -> {
@ -218,28 +227,28 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
}
Determinant -> CommonOps_DDRM.det(origin)
SVD -> object : SingularValueDecomposition<Double> {
SVD -> object : SingularValueDecomposition<Float64> {
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 u: Matrix<Float64> 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 s: Matrix<Float64> get() = ejmlSvd.getW(null).wrapMatrix()
override val v: Matrix<Float64> get() = ejmlSvd.getV(null, false).wrapMatrix()
override val singularValues: Point<Float64> get() = ejmlSvd.singularValues.asBuffer()
}
QR -> object : QRDecomposition<Double> {
QR -> object : QRDecomposition<Float64> {
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()
override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
Cholesky -> object : CholeskyDecomposition<Float64> {
override val l: Matrix<Float64> by lazy {
val cholesky =
DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
@ -247,20 +256,49 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
}
}
LUP -> object : LupDecomposition<Double> {
LUP -> object : LupDecomposition<Float64> {
private val lup by lazy {
DecompositionFactory_DDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
}
override val l: Matrix<Double>
override val l: Matrix<Float64>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Double>
override val u: Matrix<Float64>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
EIG -> {
check(origin.numCols == origin.numRows) { "Eigenvalue decomposition requires symmetric matrix" }
object : EigenDecomposition<Float64> {
val cmEigen: EigenDecomposition_F64<DMatrixRMaj> by lazy {
DecompositionFactory_DDRM.eig(origin.numRows, true).apply { decompose(origin) }
}
override val v: Matrix<Float64> by lazy {
val eigenvectors = List(origin.numRows) { cmEigen.getEigenVector(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
eigenvectors[row][column]
}
}
override val d: Matrix<Float64> by lazy {
val eigenvalues = List(origin.numRows) { cmEigen.getEigenvalue(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
when (row) {
column -> eigenvalues[row].real
column - 1 -> eigenvalues[row].imaginary
column + 1 -> -eigenvalues[row].imaginary
else -> 0.0
}
}
}
}
}
else -> null
}
@ -275,7 +313,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p matrix.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
return res.wrapMatrix()
@ -288,7 +326,7 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p vector.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixRMaj> {
public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixRMaj> {
val res = DMatrixRMaj(1, 1)
CommonOps_DDRM.solve(DMatrixRMaj(a.toEjml().origin), DMatrixRMaj(b.toEjml().origin), res)
return EjmlDoubleVector(res)
@ -488,6 +526,35 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
EIG -> {
check(origin.numCols == origin.numRows) { "Eigenvalue decomposition requires symmetric matrix" }
object : EigenDecomposition<Float32> {
val cmEigen: EigenDecomposition_F32<FMatrixRMaj> by lazy {
DecompositionFactory_FDRM.eig(origin.numRows, true).apply { decompose(origin) }
}
override val v by lazy {
val eigenvectors: List<FMatrixRMaj> = List(origin.numRows) { cmEigen.getEigenVector(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
eigenvectors[row][column]
}
}
override val d: Matrix<Float32> by lazy {
val eigenvalues = List(origin.numRows) { cmEigen.getEigenvalue(it) }
buildMatrix(origin.numRows, origin.numCols) { row, column ->
when (row) {
column -> eigenvalues[row].real
column - 1 -> eigenvalues[row].imaginary
column + 1 -> -eigenvalues[row].imaginary
else -> 0f
}
}
}
}
}
else -> null
}
@ -533,16 +600,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
*/
override val elementAlgebra: Float64Field get() = Float64Field
override val type: SafeType<Double> get() = safeTypeOf()
override val type: SafeType<Float64> get() = safeTypeOf()
@Suppress("UNCHECKED_CAST")
override fun Matrix<Double>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
override fun Matrix<Float64>.toEjml(): EjmlDoubleMatrix<DMatrixSparseCSC> = when {
this is EjmlDoubleMatrix<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleMatrix<DMatrixSparseCSC>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
override fun Point<Double>.toEjml(): EjmlDoubleVector<DMatrixSparseCSC> = when {
override fun Point<Float64>.toEjml(): EjmlDoubleVector<DMatrixSparseCSC> = when {
this is EjmlDoubleVector<*> && origin is DMatrixSparseCSC -> this as EjmlDoubleVector<DMatrixSparseCSC>
else -> EjmlDoubleVector(DMatrixSparseCSC(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
@ -569,21 +636,21 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
private fun <T : DMatrix> T.wrapMatrix() = EjmlDoubleMatrix(this)
private fun <T : DMatrix> T.wrapVector() = EjmlDoubleVector(this)
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * elementAlgebra { -one }
override fun Matrix<Float64>.unaryMinus(): Matrix<Float64> = this * elementAlgebra { -one }
override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override fun Matrix<Float64>.dot(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
override fun Matrix<Double>.dot(vector: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Matrix<Float64>.dot(vector: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
override operator fun Matrix<Double>.minus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override operator fun Matrix<Float64>.minus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -599,19 +666,19 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override operator fun Matrix<Double>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
override operator fun Matrix<Float64>.times(value: Double): EjmlDoubleMatrix<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
override fun Point<Double>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Float64>.unaryMinus(): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.changeSign(toEjml().origin, res)
return res.wrapVector()
}
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
override fun Matrix<Float64>.plus(other: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -627,7 +694,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapMatrix()
}
override fun Point<Double>.plus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Float64>.plus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -643,7 +710,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Point<Double>.minus(other: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Float64>.minus(other: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
@ -659,17 +726,17 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
return out.wrapVector()
}
override fun Double.times(m: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
override fun Double.times(m: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> = m * this
override fun Point<Double>.times(value: Double): EjmlDoubleVector<DMatrixSparseCSC> {
override fun Point<Float64>.times(value: Double): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.scale(value, toEjml().origin, res)
return res.wrapVector()
}
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
override fun Double.times(v: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float64>, attribute: A): V? {
val origin = structure.toEjml().origin
val raw: Any? = when (attribute) {
@ -681,16 +748,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
Determinant -> CommonOps_DSCC.det(origin)
QR -> object : QRDecomposition<Double> {
QR -> object : QRDecomposition<Float64> {
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()
override val q: Matrix<Float64> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float64> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
Cholesky -> object : CholeskyDecomposition<Float64> {
override val l: Matrix<Float64> by lazy {
val cholesky =
DecompositionFactory_DSCC.cholesky().apply { decompose(origin.copy()) }
@ -698,16 +765,16 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
}
}
LUP -> object : LupDecomposition<Double> {
LUP -> object : LupDecomposition<Float64> {
private val lup by lazy {
DecompositionFactory_DSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val l: Matrix<Double>
override val l: Matrix<Float64>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Double>
override val u: Matrix<Float64>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
@ -726,7 +793,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p matrix.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Double>, b: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
public fun solve(a: Matrix<Float64>, b: Matrix<Float64>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
return res.wrapMatrix()
@ -739,7 +806,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
* @param b n by p vector.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<Double>, b: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> {
public fun solve(a: Matrix<Float64>, b: Point<Float64>): EjmlDoubleVector<DMatrixSparseCSC> {
val res = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.solve(DMatrixSparseCSC(a.toEjml().origin), DMatrixSparseCSC(b.toEjml().origin), res)
return EjmlDoubleVector(res)

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

@ -17,6 +17,7 @@ import space.kscience.kmath.linear.*
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.toArray
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
import kotlin.random.Random
import kotlin.random.asJavaRandom
import kotlin.test.*
@ -63,7 +64,7 @@ internal class EjmlMatrixTest {
val w = EjmlDoubleMatrix(m)
val det: Double = w.getOrComputeAttribute(Determinant) ?: fail()
assertEquals(CommonOps_DDRM.det(m), det)
val lup: LupDecomposition<Double> = w.getOrComputeAttribute(LUP) ?: fail()
val lup: LupDecomposition<Float64> = w.getOrComputeAttribute(LUP) ?: fail()
val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols)
.also { it.decompose(m.copy()) }
@ -104,4 +105,11 @@ internal class EjmlMatrixTest {
assertTrue { StructureND.contentEquals(one(dim, dim), res, 1e-3) }
}
@Test
fun eigenValueDecomposition() = EjmlLinearSpaceDDRM {
val matrix = EjmlDoubleMatrix(randomMatrix)
val eigen = matrix.getOrComputeAttribute(EIG) ?: fail()
assertMatrixEquals(matrix, eigen.v dot eigen.d dot eigen.v.transposed())
}
}

5
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt
View File

@ -9,12 +9,13 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.Point
import space.kscience.kmath.operations.Float64L2Norm
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBuffer.Companion.double
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.indices
import kotlin.math.pow
public typealias DoubleVector = Point<Double>
public typealias DoubleVector = Point<Float64>
public fun DoubleVector(vararg doubles: Double): DoubleVector = doubles.asBuffer()
@ -41,7 +42,7 @@ public operator fun DoubleVector.plus(number: Number): DoubleVector = map { it +
public operator fun Number.plus(vector: DoubleVector): DoubleVector = vector + this
public operator fun DoubleVector.unaryMinus(): Buffer<Double> = map { -it }
public operator fun DoubleVector.unaryMinus(): Buffer<Float64> = map { -it }
public operator fun DoubleVector.minus(other: DoubleVector): DoubleVector {
require(size == other.size) { "Vector size $size expected but ${other.size} found" }

5
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt
View File

@ -15,6 +15,7 @@ import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.math.pow
@ -30,7 +31,7 @@ import kotlin.math.pow
* Functions that help create a real (Double) matrix
*/
public typealias RealMatrix = Matrix<Double>
public typealias RealMatrix = Matrix<Float64>
public fun realMatrix(rowNum: Int, colNum: Int, initializer: Float64Field.(i: Int, j: Int) -> Double): RealMatrix =
Double.algebra.linearSpace.buildMatrix(rowNum, colNum, initializer)
@ -114,7 +115,7 @@ public operator fun RealMatrix.minus(other: RealMatrix): RealMatrix =
* Operations on columns
*/
public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): RealMatrix =
public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Float64>) -> Double): RealMatrix =
Double.algebra.linearSpace.buildMatrix(rowNum, colNum + 1) { row, col ->
if (col < colNum)
get(row, col)

3
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/dot.kt
View File

@ -8,11 +8,12 @@ package space.kscience.kmath.real
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64
/**
* Optimized dot product for real matrices
*/
public infix fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> = Double.algebra.linearSpace.run {
public infix fun Matrix<Float64>.dot(other: Matrix<Float64>): Matrix<Float64> = Double.algebra.linearSpace.run {
this@dot dot other
}

9
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/grids.kt
View File

@ -7,16 +7,17 @@ package space.kscience.kmath.real
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.math.floor
public val ClosedFloatingPointRange<Double>.length: Double get() = endInclusive - start
public val ClosedFloatingPointRange<Float64>.length: Double get() = endInclusive - start
/**
* Create a Buffer-based grid with equally distributed [numberOfPoints] points. The range could be increasing or decreasing.
* If range has a zero size, then the buffer consisting of [numberOfPoints] equal values is returned.
*/
public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, numberOfPoints: Int): Float64Buffer {
public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Float64>, numberOfPoints: Int): Float64Buffer {
require(numberOfPoints >= 2) { "Number of points in grid must be more than 1" }
val normalizedRange = when {
range.endInclusive > range.start -> range
@ -31,7 +32,7 @@ public fun Buffer.Companion.fromRange(range: ClosedFloatingPointRange<Double>, n
* Create a Buffer-based grid with equally distributed points with a fixed [step]. The range could be increasing or decreasing.
* If the step is larger than the range size, single point is returned.
*/
public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Double>, step: Double): Float64Buffer {
public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Float64>, step: Double): Float64Buffer {
require(step > 0) { "The grid step must be positive" }
val normalizedRange = when {
range.endInclusive > range.start -> range
@ -51,4 +52,4 @@ public fun Buffer.Companion.withFixedStep(range: ClosedFloatingPointRange<Double
* If step is negative, the same goes from upper boundary downwards
*/
@UnstableKMathAPI
public infix fun ClosedFloatingPointRange<Double>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)
public infix fun ClosedFloatingPointRange<Float64>.step(step: Double): Float64Buffer = Buffer.withFixedStep(this, step)

9
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/realND.kt
View File

@ -7,12 +7,13 @@ package space.kscience.kmath.real
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
/**
* Map one [BufferND] using function without indices.
*/
public inline fun BufferND<Double>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Double> {
public inline fun BufferND<Float64>.mapInline(crossinline transform: Float64Field.(Double) -> Double): BufferND<Float64> {
val array = DoubleArray(indices.linearSize) { offset -> Float64Field.transform(buffer[offset]) }
return BufferND(indices, Float64Buffer(array))
}
@ -20,7 +21,7 @@ public inline fun BufferND<Double>.mapInline(crossinline transform: Float64Field
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy.
*/
public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Double>): BufferND<Double> =
public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Float64>): BufferND<Float64> =
elementND.mapInline { this@invoke(it) }
/* plus and minus */
@ -28,9 +29,9 @@ public operator fun Function1<Double, Double>.invoke(elementND: BufferND<Double>
/**
* Summation operation for [BufferND] and single element
*/
public operator fun BufferND<Double>.plus(arg: Double): BufferND<Double> = mapInline { it + arg }
public operator fun BufferND<Float64>.plus(arg: Double): BufferND<Float64> = mapInline { it + arg }
/**
* Subtraction operation between [BufferND] and single element
*/
public operator fun BufferND<Double>.minus(arg: Double): BufferND<Double> = mapInline { it - arg }
public operator fun BufferND<Float64>.minus(arg: Double): BufferND<Float64> = mapInline { it - arg }

2
kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt
View File

@ -170,7 +170,7 @@ internal class DoubleMatrixTest {
assertEquals(matrix1.average(), 1.375)
}
// fun printMatrix(m: Matrix<Double>) {
// fun printMatrix(m: Matrix<Float64>) {
// for (row in 0 until m.shape[0]) {
// for (col in 0 until m.shape[1]) {
// print(m[row, col])

3
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/polynomialUtil.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.functions
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.math.max
@ -31,7 +32,7 @@ public inline fun <C, A, R> A.polynomialSpace(block: PolynomialSpace<C, A>.() ->
/**
* Evaluates value of [this] Double polynomial on provided Double argument.
*/
public fun Polynomial<Double>.value(arg: Double): Double =
public fun Polynomial<Float64>.value(arg: Double): Double =
coefficients.reduceIndexedOrNull { index, acc, c ->
acc + c * arg.pow(index)
} ?: .0

5
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt
View File

@ -8,6 +8,7 @@ import space.kscience.attributes.AttributesBuilder
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Field
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.indices
@ -27,7 +28,7 @@ public class GaussIntegrator<T : Any>(
public val algebra: Field<T>,
) : UnivariateIntegrator<T> {
private fun buildRule(integrand: UnivariateIntegrand<T>): Pair<Buffer<Double>, Buffer<Double>> {
private fun buildRule(integrand: UnivariateIntegrand<T>): Pair<Buffer<Float64>, Buffer<Float64>> {
val factory = integrand[GaussIntegratorRuleFactory] ?: GaussLegendreRuleFactory
val predefinedRanges = integrand[UnivariateIntegrandRanges]
if (predefinedRanges == null || predefinedRanges.ranges.isEmpty()) {
@ -89,7 +90,7 @@ public val <T : Any> Field<T>.gaussIntegrator: GaussIntegrator<T> get() = GaussI
*/
@UnstableKMathAPI
public inline fun <reified T : Any> GaussIntegrator<T>.integrate(
range: ClosedRange<Double>,
range: ClosedRange<Float64>,
order: Int = 10,
intervals: Int = 10,
noinline attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},

21
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegratorRuleFactory.kt
View File

@ -8,16 +8,17 @@ package space.kscience.kmath.integration
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.mapToBuffer
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.asBuffer
import kotlin.math.ulp
import kotlin.native.concurrent.ThreadLocal
public interface GaussIntegratorRuleFactory {
public fun build(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>>
public fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>>
public companion object : IntegrandAttribute<GaussIntegratorRuleFactory> {
public fun double(numPoints: Int, range: ClosedRange<Double>): Pair<Buffer<Double>, Buffer<Double>> =
public fun double(numPoints: Int, range: ClosedRange<Float64>): Pair<Buffer<Float64>, Buffer<Float64>> =
GaussLegendreRuleFactory.build(numPoints, range)
}
}
@ -28,9 +29,9 @@ public interface GaussIntegratorRuleFactory {
*/
public fun GaussIntegratorRuleFactory.build(
numPoints: Int,
range: ClosedRange<Double>,
): Pair<Buffer<Double>, Buffer<Double>> {
val normalized: Pair<Buffer<Double>, Buffer<Double>> = build(numPoints)
range: ClosedRange<Float64>,
): Pair<Buffer<Float64>, Buffer<Float64>> {
val normalized: Pair<Buffer<Float64>, Buffer<Float64>> = build(numPoints)
val length = range.endInclusive - range.start
val points = normalized.first.mapToBuffer(Float64Field.bufferFactory) {
@ -55,13 +56,13 @@ public fun GaussIntegratorRuleFactory.build(
@ThreadLocal
public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
private val cache = HashMap<Int, Pair<Buffer<Double>, Buffer<Double>>>()
private val cache = HashMap<Int, Pair<Buffer<Float64>, Buffer<Float64>>>()
private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> =
private fun getOrBuildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> =
cache.getOrPut(numPoints) { buildRule(numPoints) }
private fun buildRule(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> {
private fun buildRule(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> {
if (numPoints == 1) {
// Break recursion.
return Pair(
@ -73,7 +74,7 @@ public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
// Get previous rule.
// If it has not been computed, yet it will trigger a recursive call
// to this method.
val previousPoints: Buffer<Double> = getOrBuildRule(numPoints - 1).first
val previousPoints: Buffer<Float64> = getOrBuildRule(numPoints - 1).first
// Compute next rule.
val points = DoubleArray(numPoints)
@ -162,7 +163,7 @@ public object GaussLegendreRuleFactory : GaussIntegratorRuleFactory {
return Pair(points.asBuffer(), weights.asBuffer())
}
override fun build(numPoints: Int): Pair<Buffer<Double>, Buffer<Double>> = getOrBuildRule(numPoints)
override fun build(numPoints: Int): Pair<Buffer<Float64>, Buffer<Float64>> = getOrBuildRule(numPoints)
override fun toString(): String = "GaussLegendreRule"
}

5
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/Integrand.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.integration
import space.kscience.attributes.*
import space.kscience.kmath.structures.Float64
public interface IntegrandAttribute<T> : Attribute<T>
@ -44,9 +45,9 @@ public inline val <reified T : Any> Integrand<T>.valueOrNull: T? get() = attribu
*/
public inline val <reified T : Any> Integrand<T>.value: T get() = valueOrNull ?: error("No value in the integrand")
public object IntegrandRelativeAccuracy : IntegrandAttribute<Double>
public object IntegrandRelativeAccuracy : IntegrandAttribute<Float64>
public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Double>
public object IntegrandAbsoluteAccuracy : IntegrandAttribute<Float64>
public object IntegrandCallsPerformed : IntegrandAttribute<Int>

9
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SimpsonIntegrator.kt
View File

@ -10,6 +10,7 @@ import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.operations.sum
import space.kscience.kmath.structures.Float64
/**
* Use double pass Simpson rule integration with a fixed number of points.
@ -24,7 +25,7 @@ public class SimpsonIntegrator<T : Any>(
) : UnivariateIntegrator<T> {
private fun integrateRange(
integrand: UnivariateIntegrand<T>, range: ClosedRange<Double>, numPoints: Int,
integrand: UnivariateIntegrand<T>, range: ClosedRange<Float64>, numPoints: Int,
): T = algebra {
val h: Double = (range.endInclusive - range.start) / (numPoints - 1)
val values: List<T> = List(numPoints) { i ->
@ -75,9 +76,9 @@ public val <T : Any> Field<T>.simpsonIntegrator: SimpsonIntegrator<T> get() = Si
* * [IntegrandMaxCalls]&mdash;the maximum number of function calls during integration. For non-iterative rules, always uses
* the maximum number of points. By default, uses 10 points.
*/
public object DoubleSimpsonIntegrator : UnivariateIntegrator<Double> {
public object DoubleSimpsonIntegrator : UnivariateIntegrator<Float64> {
private fun integrateRange(
integrand: UnivariateIntegrand<Double>, range: ClosedRange<Double>, numPoints: Int,
integrand: UnivariateIntegrand<Float64>, range: ClosedRange<Float64>, numPoints: Int,
): Double {
val h: Double = (range.endInclusive - range.start) / (numPoints - 1)
val values = DoubleArray(numPoints) { i ->
@ -96,7 +97,7 @@ public object DoubleSimpsonIntegrator : UnivariateIntegrator<Double> {
return res
}
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
val ranges = integrand[UnivariateIntegrandRanges]
return if (ranges != null) {
val res = ranges.ranges.sumOf { integrateRange(integrand, it.first, it.second) }

13
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/SplineIntegrator.kt
View File

@ -14,6 +14,7 @@ import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.kmath.structures.MutableBufferFactory
@ -59,7 +60,7 @@ public class SplineIntegrator<T : Comparable<T>>(
val interpolator: PolynomialInterpolator<T> = SplineInterpolator(algebra, bufferFactory)
val nodes: Buffer<Double> = integrand[UnivariateIntegrationNodes] ?: run {
val nodes: Buffer<Float64> = integrand[UnivariateIntegrationNodes] ?: run {
val numPoints = integrand[IntegrandMaxCalls] ?: 100
val step = (range.endInclusive - range.start) / (numPoints - 1)
Float64Buffer(numPoints) { i -> range.start + i * step }
@ -85,12 +86,12 @@ public class SplineIntegrator<T : Comparable<T>>(
* uses the maximum number of points. By default, uses 10 points.
*/
@UnstableKMathAPI
public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
override fun integrate(integrand: UnivariateIntegrand<Double>): UnivariateIntegrand<Double> {
public object DoubleSplineIntegrator : UnivariateIntegrator<Float64> {
override fun integrate(integrand: UnivariateIntegrand<Float64>): UnivariateIntegrand<Float64> {
val range = integrand[IntegrationRange] ?: 0.0..1.0
val interpolator: PolynomialInterpolator<Double> = SplineInterpolator(Float64Field, Float64Field.bufferFactory)
val interpolator: PolynomialInterpolator<Float64> = SplineInterpolator(Float64Field, Float64Field.bufferFactory)
val nodes: Buffer<Double> = integrand[UnivariateIntegrationNodes] ?: run {
val nodes: Buffer<Float64> = integrand[UnivariateIntegrationNodes] ?: run {
val numPoints = integrand[IntegrandMaxCalls] ?: 100
val step = (range.endInclusive - range.start) / (numPoints - 1)
Float64Buffer(numPoints) { i -> range.start + i * step }
@ -108,5 +109,5 @@ public object DoubleSplineIntegrator : UnivariateIntegrator<Double> {
@Suppress("unused")
@UnstableKMathAPI
public inline val Float64Field.splineIntegrator: UnivariateIntegrator<Double>
public inline val Float64Field.splineIntegrator: UnivariateIntegrator<Float64>
get() = DoubleSplineIntegrator

11
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/UnivariateIntegrand.kt
View File

@ -8,6 +8,7 @@ package space.kscience.kmath.integration
import space.kscience.attributes.*
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
public class UnivariateIntegrand<T>(
@ -36,15 +37,15 @@ public inline fun <reified T : Any> UnivariateIntegrand(
public typealias UnivariateIntegrator<T> = Integrator<T, UnivariateIntegrand<T>>
public object IntegrationRange : IntegrandAttribute<ClosedRange<Double>>
public object IntegrationRange : IntegrandAttribute<ClosedRange<Float64>>
/**
* Set of univariate integration ranges. First components correspond to the ranges themselves, second components to
* the number of integration nodes per range.
*/
public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<Double>, Int>>) {
public constructor(vararg pairs: Pair<ClosedRange<Double>, Int>) : this(pairs.toList())
public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<Float64>, Int>>) {
public constructor(vararg pairs: Pair<ClosedRange<Float64>, Int>) : this(pairs.toList())
override fun toString(): String {
val rangesString = ranges.joinToString(separator = ",") { (range, points) ->
@ -56,7 +57,7 @@ public class UnivariateIntegrandRanges(public val ranges: List<Pair<ClosedRange<
public companion object : IntegrandAttribute<UnivariateIntegrandRanges>
}
public object UnivariateIntegrationNodes : IntegrandAttribute<Buffer<Double>>
public object UnivariateIntegrationNodes : IntegrandAttribute<Buffer<Float64>>
public fun AttributesBuilder<UnivariateIntegrand<*>>.integrationNodes(vararg nodes: Double) {
UnivariateIntegrationNodes(Float64Buffer(nodes))
@ -78,7 +79,7 @@ public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
*/
@UnstableKMathAPI
public inline fun <reified T : Any> UnivariateIntegrator<T>.integrate(
range: ClosedRange<Double>,
range: ClosedRange<Float64>,
noinline attributeBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},
noinline function: (Double) -> T,
): UnivariateIntegrand<T> {

3
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt
View File

@ -12,6 +12,7 @@ import space.kscience.kmath.functions.Polynomial
import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
/**
@ -78,5 +79,5 @@ public fun <T : Comparable<T>> Field<T>.splineInterpolator(
bufferFactory: MutableBufferFactory<T>,
): SplineInterpolator<T> = SplineInterpolator(this, bufferFactory)
public val Float64Field.splineInterpolator: SplineInterpolator<Double>
public val Float64Field.splineInterpolator: SplineInterpolator<Float64>
get() = SplineInterpolator(this, bufferFactory)

2
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/LinearInterpolatorTest.kt
View File

@ -19,7 +19,7 @@ internal class LinearInterpolatorTest {
3.0 to 4.0
)
//val polynomial: PiecewisePolynomial<Double> = DoubleField.linearInterpolator.interpolatePolynomials(data)
//val polynomial: PiecewisePolynomial<Float64> = DoubleField.linearInterpolator.interpolatePolynomials(data)
val function = Float64Field.linearInterpolator.interpolate(data)
assertEquals(null, function(-1.0))
assertEquals(0.5, function(0.5))

2
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/interpolation/SplineInterpolatorTest.kt
View File

@ -19,7 +19,7 @@ internal class SplineInterpolatorTest {
x to sin(x)
}
//val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(data)
//val polynomial: PiecewisePolynomial<Float64> = DoubleField.splineInterpolator.interpolatePolynomials(data)
val function = Float64Field.splineInterpolator.interpolate(data, Double.NaN)

2
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Circle2D.kt
View File

@ -25,7 +25,7 @@ public val Circle2D<*>.circumference: Double get() = radius * 2 * PI
public data class Float64Circle2D(
@Serializable(Float64Space2D.VectorSerializer::class) override val center: Float64Vector2D,
override val radius: Float64,
) : Circle2D<Double>
) : Circle2D<Float64>
public fun Circle2D(center: Vector2D<Float64>, radius: Double): Float64Circle2D = Float64Circle2D(
center as? Float64Vector2D ?: Float64Vector2D(center.x, center.y),

2
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/Float64Space3D.kt
View File

@ -113,7 +113,7 @@ public object Float64Space3D : GeometrySpace<Vector3D<Float64>, Double> {
/**
* Vector product with the right basis
*/
public infix fun Vector3D<Float64>.cross(other: Vector3D<Float64>): Vector3D<Double> = vectorProduct(this, other)
public infix fun Vector3D<Float64>.cross(other: Vector3D<Float64>): Vector3D<Float64> = vectorProduct(this, other)
public val xAxis: Float64Vector3D = vector(1.0, 0.0, 0.0)
public val yAxis: Float64Vector3D = vector(0.0, 1.0, 0.0)

8
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/rotations3D.kt
View File

@ -90,7 +90,7 @@ public fun Float64Space3D.rotate(
/**
* Rotate a [Float64] vector in 3D space with a rotation matrix
*/
public fun Float64Space3D.rotate(vector: Float64Vector3D, matrix: Matrix<Double>): Vector3D<Float64> {
public fun Float64Space3D.rotate(vector: Float64Vector3D, matrix: Matrix<Float64>): Vector3D<Float64> {
require(matrix.colNum == 3 && matrix.rowNum == 3) { "Square 3x3 rotation matrix is required" }
return with(linearSpace) { (matrix dot vector).asVector3D() }
}
@ -101,7 +101,7 @@ public fun Float64Space3D.rotate(vector: Float64Vector3D, matrix: Matrix<Double>
@OptIn(UnstableKMathAPI::class)
public fun Quaternion.toRotationMatrix(
linearSpace: LinearSpace<Double, *> = Float64Field.linearSpace,
): Matrix<Double> {
): Matrix<Float64> {
val s = QuaternionAlgebra.norm(this).pow(-2)
return linearSpace.matrix(3, 3)(
1.0 - 2 * s * (y * y + z * z), 2 * s * (x * y - z * w), 2 * s * (x * z + y * w),
@ -115,7 +115,7 @@ public fun Quaternion.toRotationMatrix(
*
* taken from https://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/
*/
public fun Quaternion.Companion.fromRotationMatrix(matrix: Matrix<Double>): Quaternion {
public fun Quaternion.Companion.fromRotationMatrix(matrix: Matrix<Float64>): Quaternion {
require(matrix.colNum == 3 && matrix.rowNum == 3) { "Rotation matrix should be 3x3 but is ${matrix.rowNum}x${matrix.colNum}" }
val trace = matrix[0, 0] + matrix[1, 1] + matrix[2, 2]
@ -256,7 +256,7 @@ public fun Quaternion.Companion.fromEuler(
* Based on https://github.com/mrdoob/three.js/blob/master/src/math/Euler.js
*/
public fun AngleVector.Companion.fromRotationMatrix(
matrix: Matrix<Double>,
matrix: Matrix<Float64>,
rotationOrder: RotationOrder,
gimbaldLockThreshold: Double = 0.9999999,
): AngleVector = when (rotationOrder) {

6
kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/testUtils.kt
View File

@ -9,15 +9,15 @@ import space.kscience.kmath.structures.Float64
import kotlin.math.abs
import kotlin.test.assertEquals
fun ClosedRange<Double>.generateList(step: Double): List<Double> = generateSequence(start) { previous ->
fun ClosedRange<Float64>.generateList(step: Double): List<Float64> = generateSequence(start) { previous ->
if (previous == Double.POSITIVE_INFINITY) return@generateSequence null
val next = previous + step
if (next > endInclusive) null else next
}.toList()
fun grid(
xRange: ClosedRange<Double>,
yRange: ClosedRange<Double>,
xRange: ClosedRange<Float64>,
yRange: ClosedRange<Float64>,
step: Double,
): List<Pair<Double, Double>> {
val xs = xRange.generateList(step)

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

@ -9,6 +9,7 @@ import kotlinx.atomicfu.atomic
import kotlinx.atomicfu.getAndUpdate
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Group
import space.kscience.kmath.structures.Float64
/**
* Common representation for atomic counters
@ -18,7 +19,7 @@ public interface Counter<T : Any> {
public val value: T
public companion object {
public fun ofDouble(): ObjectCounter<Double> = ObjectCounter(Float64Field)
public fun ofDouble(): ObjectCounter<Float64> = ObjectCounter(Float64Field)
public fun <T : Any> of(group: Group<T>): ObjectCounter<T> = ObjectCounter(group)
}
}

3
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram1D.kt
View File

@ -11,6 +11,7 @@ import space.kscience.kmath.domains.center
import space.kscience.kmath.linear.Point
import space.kscience.kmath.operations.asSequence
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64
/**
@ -49,7 +50,7 @@ public interface Histogram1DBuilder<in T : Any, V : Any> : HistogramBuilder<T, V
}
@UnstableKMathAPI
public fun Histogram1DBuilder<Double, *>.fill(items: Iterable<Double>): Unit =
public fun Histogram1DBuilder<Double, *>.fill(items: Iterable<Float64>): Unit =
items.forEach(this::putValue)
@UnstableKMathAPI

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.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.floor
@ -137,7 +138,7 @@ public fun <V : Any, A> Histogram.Companion.uniform1D(
@UnstableKMathAPI
public fun <V : Any> UniformHistogram1DGroup<V, *>.produce(
buffer: Buffer<Double>,
buffer: Buffer<Float64>,
): UniformHistogram1D<V> = produce { fill(buffer) }
/**

30
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt
View File

@ -24,8 +24,8 @@ public typealias HyperSquareBin<V> = DomainBin<Double, HyperSquareDomain, V>
*/
public class UniformHistogramGroupND<V : Any, A : Field<V>>(
override val valueAlgebraND: FieldOpsND<V, A>,
private val lower: Buffer<Double>,
private val upper: Buffer<Double>,
private val lower: Buffer<Float64>,
private val upper: Buffer<Float64>,
private val binNums: IntArray = IntArray(lower.size) { 20 },
private val valueBufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
) : HistogramGroupND<Double, HyperSquareDomain, V> {
@ -54,7 +54,7 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
else -> floor((value - lower[axis]) / binSize[axis]).toInt()
}
override fun getIndexOrNull(point: Buffer<Double>): IntArray = IntArray(dimension) {
override fun getIndexOrNull(point: Buffer<Float64>): IntArray = IntArray(dimension) {
getIndex(it, point[it])
}
@ -93,7 +93,7 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
val hBuilder = object : HistogramBuilder<Double, V> {
override val defaultValue: V get() = valueAlgebraND.elementAlgebra.one
override fun putValue(point: Point<Double>, value: V) = with(valueAlgebraND.elementAlgebra) {
override fun putValue(point: Point<Float64>, value: V) = with(valueAlgebraND.elementAlgebra) {
val index = getIndexOrNull(point)
ndCounter[index].add(value)
}
@ -120,17 +120,17 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
*/
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
valueAlgebraND: FieldOpsND<V, A>,
vararg ranges: ClosedFloatingPointRange<Double>,
vararg ranges: ClosedFloatingPointRange<Float64>,
bufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
valueAlgebraND,
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer(),
ranges.map(ClosedFloatingPointRange<Float64>::start).asBuffer(),
ranges.map(ClosedFloatingPointRange<Float64>::endInclusive).asBuffer(),
valueBufferFactory = bufferFactory
)
public fun Histogram.Companion.uniformDoubleNDFromRanges(
vararg ranges: ClosedFloatingPointRange<Double>,
vararg ranges: ClosedFloatingPointRange<Float64>,
): UniformHistogramGroupND<Double, Float64Field> = uniformNDFromRanges(Floa64FieldOpsND, *ranges)
@ -145,22 +145,22 @@ public fun Histogram.Companion.uniformDoubleNDFromRanges(
*/
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
valueAlgebraND: FieldOpsND<V, A>,
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
vararg ranges: Pair<ClosedFloatingPointRange<Float64>, Int>,
bufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
valueAlgebraND,
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::start)
.map(Pair<ClosedFloatingPointRange<Float64>, Int>::first)
.map(ClosedFloatingPointRange<Float64>::start)
.asBuffer(),
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::endInclusive)
.map(Pair<ClosedFloatingPointRange<Float64>, Int>::first)
.map(ClosedFloatingPointRange<Float64>::endInclusive)
.asBuffer(),
ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
ranges.map(Pair<ClosedFloatingPointRange<Float64>, Int>::second).toIntArray(),
valueBufferFactory = bufferFactory
)
public fun Histogram.Companion.uniformDoubleNDFromRanges(
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
vararg ranges: Pair<ClosedFloatingPointRange<Float64>, Int>,
): UniformHistogramGroupND<Double, Float64Field> = uniformNDFromRanges(Floa64FieldOpsND, *ranges)

6
kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramGroup.kt
View File

@ -17,7 +17,7 @@ import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.*
import java.util.*
private fun <B : ClosedRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
private fun <B : ClosedRange<Float64>> TreeMap<Double, B>.getBin(value: Double): B? {
// check ceiling entry and return it if it is what needed
val ceil = ceilingEntry(value)?.value
if (ceil != null && value in ceil) return ceil
@ -53,7 +53,7 @@ public class TreeHistogramGroup<V : Any, A>(
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
ClosedRange<Float64> by domain.range
@PublishedApi
internal inner class TreeHistogramBuilder : Histogram1DBuilder<Double, V> {
@ -153,7 +153,7 @@ public class TreeHistogramGroup<V : Any, A>(
*/
public fun <V : Any, A> Histogram.Companion.custom1D(
valueAlgebra: A,
borders: Buffer<Double>,
borders: Buffer<Float64>,
): TreeHistogramGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> {
val sorted = borders.sorted()

9
kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/KMathJafama.kt
View File

@ -8,15 +8,16 @@ package space.kscience.kmath.jafama
import net.jafama.FastMath
import net.jafama.StrictFastMath
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
/**
* A field for [Double] (using FastMath) without boxing. Does not produce appropriate field element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object JafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
public object JafamaDoubleField : ExtendedField<Float64>, Norm<Double, Double>, ScaleOperations<Float64> {
override val bufferFactory: MutableBufferFactory<Double> get() = DoubleField.bufferFactory
override val bufferFactory: MutableBufferFactory<Float64> get() = DoubleField.bufferFactory
override inline val zero: Double get() = 0.0
override inline val one: Double get() = 1.0
@ -68,9 +69,9 @@ public object JafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, S
* A field for [Double] (using StrictMath) without boxing. Does not produce appropriate field element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object StrictJafamaDoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
public object StrictJafamaDoubleField : ExtendedField<Float64>, Norm<Double, Double>, ScaleOperations<Float64> {
override val bufferFactory: MutableBufferFactory<Double> get() = DoubleField.bufferFactory
override val bufferFactory: MutableBufferFactory<Float64> get() = DoubleField.bufferFactory
override inline val zero: Double get() = 0.0
override inline val one: Double get() = 1.0

33
kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt
View File

@ -14,56 +14,57 @@ import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.ExponentialOperations
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.TrigonometricOperations
import space.kscience.kmath.structures.Float64
public class MultikDoubleAlgebra(
multikEngine: Engine,
) : MultikDivisionTensorAlgebra<Double, Float64Field>(multikEngine),
TrigonometricOperations<StructureND<Double>>, ExponentialOperations<StructureND<Double>> {
TrigonometricOperations<StructureND<Float64>>, ExponentialOperations<StructureND<Float64>> {
override val elementAlgebra: Float64Field get() = Float64Field
override val dataType: DataType get() = DataType.DoubleDataType
override fun sin(arg: StructureND<Double>): MultikTensor<Double> =
override fun sin(arg: StructureND<Float64>): MultikTensor<Float64> =
multikMath.mathEx.sin(arg.asMultik().array).wrap()
override fun cos(arg: StructureND<Double>): MultikTensor<Double> =
override fun cos(arg: StructureND<Float64>): MultikTensor<Float64> =
multikMath.mathEx.cos(arg.asMultik().array).wrap()
override fun tan(arg: StructureND<Double>): MultikTensor<Double> = sin(arg) / cos(arg)
override fun tan(arg: StructureND<Float64>): MultikTensor<Float64> = sin(arg) / cos(arg)
@PerformancePitfall
override fun asin(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asin(it) }
override fun asin(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { asin(it) }
@PerformancePitfall
override fun acos(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acos(it) }
override fun acos(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { acos(it) }
@PerformancePitfall
override fun atan(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atan(it) }
override fun atan(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { atan(it) }
override fun exp(arg: StructureND<Double>): MultikTensor<Double> =
override fun exp(arg: StructureND<Float64>): MultikTensor<Float64> =
multikMath.mathEx.exp(arg.asMultik().array).wrap()
override fun ln(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.log(arg.asMultik().array).wrap()
override fun ln(arg: StructureND<Float64>): MultikTensor<Float64> = multikMath.mathEx.log(arg.asMultik().array).wrap()
override fun sinh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) - exp(-arg)) / 2.0
override fun sinh(arg: StructureND<Float64>): MultikTensor<Float64> = (exp(arg) - exp(-arg)) / 2.0
override fun cosh(arg: StructureND<Double>): MultikTensor<Double> = (exp(arg) + exp(-arg)) / 2.0
override fun cosh(arg: StructureND<Float64>): MultikTensor<Float64> = (exp(arg) + exp(-arg)) / 2.0
override fun tanh(arg: StructureND<Double>): MultikTensor<Double> {
override fun tanh(arg: StructureND<Float64>): MultikTensor<Float64> {
val expPlus = exp(arg)
val expMinus = exp(-arg)
return (expPlus - expMinus) / (expPlus + expMinus)
}
@PerformancePitfall
override fun asinh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asinh(it) }
override fun asinh(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { asinh(it) }
@PerformancePitfall
override fun acosh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acosh(it) }
override fun acosh(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { acosh(it) }
@PerformancePitfall
override fun atanh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atanh(it) }
override fun atanh(arg: StructureND<Float64>): MultikTensor<Float64> = arg.map { atanh(it) }
override fun scalar(value: Double): MultikTensor<Double> = Multik.ndarrayOf(value).wrap()
override fun scalar(value: Double): MultikTensor<Float64> = Multik.ndarrayOf(value).wrap()
}
//public val Double.Companion.multikAlgebra: MultikTensorAlgebra<Double, DoubleField> get() = MultikDoubleAlgebra

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