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Merge branch 'dev' into commandertvis/hermitian-conjugate

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# Conflicts:
#	kmath-core/src/commonMain/kotlin/kscience/kmath/linear/VirtualMatrix.kt
#	kmath-core/src/commonMain/kotlin/kscience/kmath/nd/Structure2D.kt
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Iaroslav Postovalov 2021-01-29 16:54:42 +07:00
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4
CHANGELOG.md
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@ -32,9 +32,11 @@
- Use `Point<Double>` 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`
- Matrix LUP `inverse` renamed to `inverseWithLup`
- `NumericAlgebra` moved outside of regular algebra chain (`Ring` no longer implements it).
- Features moved to NDStructure and became transparent.
- Capitalization of LUP in many names changed to Lup.
- Refactored `NDStructure` algebra to be more simple, preferring under-the-hood conversion to explicit NDStructure types
### Deprecated

2
README.md
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@ -114,7 +114,7 @@ submit a feature request if you want something to be implemented first.
>
> **Features:**
> - [algebras](kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Algebraic structures: contexts and elements
> - [nd](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures
> - [nd](kmath-core/src/commonMain/kotlin/kscience/kmath/nd/NDStructure.kt) : Many-dimensional structures
> - [buffers](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
> - [expressions](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions) : Functional Expressions
> - [domains](kmath-core/src/commonMain/kotlin/kscience/kmath/domains) : Domains

2
build.gradle.kts
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@ -4,7 +4,7 @@ plugins {
id("ru.mipt.npm.project")
}
internal val kmathVersion: String by extra("0.2.0-dev-5")
internal val kmathVersion: String by extra("0.2.0-dev-6")
internal val bintrayRepo: String by extra("kscience")
internal val githubProject: String by extra("kmath")

3
examples/build.gradle.kts
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@ -68,11 +68,12 @@ benchmark {
targets.register("benchmarks")
// This one matches sourceSet name above
configurations.register("fast") {
configurations.register("dot") {
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("DotBenchmark")
}
}

19
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -3,14 +3,12 @@ package kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kscience.kmath.commons.linear.CMMatrixContext
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.linear.BufferMatrixContext
import kscience.kmath.linear.Matrix
import kscience.kmath.linear.RealMatrixContext
import kscience.kmath.linear.real
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Matrix
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlin.random.Random
@ -33,38 +31,35 @@ class DotBenchmark {
}
@Benchmark
fun commonsMathMultiplication() {
fun cmDot() {
CMMatrixContext {
cmMatrix1 dot cmMatrix2
}
}
@Benchmark
fun ejmlMultiplication() {
fun ejmlDot() {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@Benchmark
fun ejmlMultiplicationwithConversion() {
fun ejmlDotWithConversion() {
EjmlMatrixContext {
val ejmlMatrix1 = matrix1.toEjml()
val ejmlMatrix2 = matrix2.toEjml()
ejmlMatrix1 dot ejmlMatrix2
matrix1 dot matrix2
}
}
@Benchmark
fun bufferedMultiplication() {
fun bufferedDot() {
BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
matrix1 dot matrix2
}
}
@Benchmark
fun realMultiplication() {
fun realDot() {
RealMatrixContext {
matrix1 dot matrix2
}

4
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
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@ -26,8 +26,8 @@ class LinearAlgebraBenchmark {
}
@Benchmark
fun kmathLUPInversion() {
MatrixContext.real.inverseWithLUP(matrix)
fun kmathLupInversion() {
MatrixContext.real.inverseWithLup(matrix)
}
@Benchmark

25
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -1,8 +1,9 @@
package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.*
import kscience.kmath.structures.Buffer
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
@ -11,22 +12,16 @@ import org.openjdk.jmh.annotations.State
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd() {
bufferedField {
var res: NDBuffer<Double> = one
autoField {
var res: NDStructure<Double> = one
repeat(n) { res += one }
}
}
@Benchmark
fun autoElementAdd() {
var res = genericField.one
repeat(n) { res += 1.0 }
}
@Benchmark
fun specializedFieldAdd() {
specializedField {
var res: NDBuffer<Double> = one
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@ -35,16 +30,16 @@ internal class NDFieldBenchmark {
@Benchmark
fun boxingFieldAdd() {
genericField {
var res: NDBuffer<Double> = one
repeat(n) { res += one }
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
companion object {
const val dim: Int = 1000
const val n: Int = 100
val bufferedField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
val specializedField: RealNDField = NDField.real(dim, dim)
val genericField: BoxingNDField<Double, RealField> = NDField.boxing(RealField, dim, dim)
val autoField = NDAlgebra.auto(RealField, dim, dim)
val specializedField: RealNDField = NDAlgebra.real(dim, dim)
val genericField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
}
}

42
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -1,10 +1,8 @@
package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.BufferedNDField
import kscience.kmath.structures.NDField
import kscience.kmath.structures.RealNDField
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
@ -17,15 +15,23 @@ internal class ViktorBenchmark {
final val n: Int = 100
// automatically build context most suited for given type.
final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
final val realField: RealNDField = NDField.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
final val realField: RealNDField = NDAlgebra.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(dim, dim)
@Benchmark
fun automaticFieldAddition() {
autoField {
var res = one
repeat(n) { res += one }
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun realFieldAddition() {
realField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@ -33,7 +39,7 @@ internal class ViktorBenchmark {
fun viktorFieldAddition() {
viktorField {
var res = one
repeat(n) { res += one }
repeat(n) { res += 1.0 }
}
}
@ -43,22 +49,4 @@ internal class ViktorBenchmark {
var res = one
repeat(n) { res = res + one }
}
@Benchmark
fun realFieldLog() {
realField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun rawViktorLog() {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
var res: F64Array
repeat(n) {
res = fortyTwo.log()
}
}
}

49
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/ViktorLogBenchmark.kt Normal file
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@ -0,0 +1,49 @@
package kscience.kmath.benchmarks
import kscience.kmath.nd.*
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
@State(Scope.Benchmark)
internal class ViktorLogBenchmark {
final val dim: Int = 1000
final val n: Int = 100
// automatically build context most suited for given type.
final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
final val realField: RealNDField = NDAlgebra.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
@Benchmark
fun realFieldLog() {
realField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun viktorFieldLog() {
viktorField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun rawViktorLog() {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
var res: F64Array
repeat(n) {
res = fortyTwo.log()
}
}
}

9
examples/src/main/kotlin/kscience/kmath/operations/ComplexDemo.kt
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@ -1,18 +1,17 @@
package kscience.kmath.operations
import kscience.kmath.structures.NDElement
import kscience.kmath.structures.NDField
import kscience.kmath.structures.complex
import kscience.kmath.nd.NDAlgebra
import kscience.kmath.nd.complex
fun main() {
// 2d element
val element = NDElement.complex(2, 2) { (i,j) ->
val element = NDAlgebra.complex(2, 2).produce { (i,j) ->
Complex(i.toDouble() - j.toDouble(), i.toDouble() + j.toDouble())
}
println(element)
// 1d element operation
val result = with(NDField.complex(8)) {
val result = with(NDAlgebra.complex(8)) {
val a = produce { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)

15
examples/src/main/kotlin/kscience/kmath/structures/ComplexND.kt
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@ -1,6 +1,9 @@
@file:Suppress("unused")
package kscience.kmath.structures
import kscience.kmath.linear.transpose
import kscience.kmath.nd.*
import kscience.kmath.operations.Complex
import kscience.kmath.operations.ComplexField
import kscience.kmath.operations.invoke
@ -10,12 +13,12 @@ fun main() {
val dim = 1000
val n = 1000
val realField = NDField.real(dim, dim)
val complexField: ComplexNDField = NDField.complex(dim, dim)
val realField = NDAlgebra.real(dim, dim)
val complexField: ComplexNDField = NDAlgebra.complex(dim, dim)
val realTime = measureTimeMillis {
realField {
var res: NDBuffer<Double> = one
var res: NDStructure<Double> = one
repeat(n) {
res += 1.0
}
@ -26,8 +29,10 @@ fun main() {
val complexTime = measureTimeMillis {
complexField {
var res: NDBuffer<Complex> = one
repeat(n) { res += 1.0 }
var res: NDStructure<Complex> = one
repeat(n) {
res += 1.0
}
}
}

62
examples/src/main/kotlin/kscience/kmath/structures/NDField.kt
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@ -1,9 +1,11 @@
package kscience.kmath.structures
import kotlinx.coroutines.GlobalScope
import kscience.kmath.nd.*
import kscience.kmath.nd4j.Nd4jArrayField
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.viktor.ViktorNDField
import org.nd4j.linalg.factory.Nd4j
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -22,42 +24,62 @@ fun main() {
val n = 1000
// automatically build context most suited for given type.
val autoField = NDField.auto(RealField, dim, dim)
val autoField = NDAlgebra.auto(RealField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
val specializedField = NDField.real(dim, dim)
val realField = NDAlgebra.real(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val genericField = NDField.boxing(RealField, dim, dim)
val boxingField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
// Nd4j specialized field.
val nd4jField = Nd4jArrayField.real(dim, dim)
//viktor field
val viktorField = ViktorNDField(dim,dim)
//parallel processing based on Java Streams
val parallelField = NDAlgebra.realWithStream(dim,dim)
measureAndPrint("Automatic field addition") {
autoField {
var res: NDBuffer<Double> = one
measureAndPrint("Boxing addition") {
boxingField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Element addition") {
var res = genericField.one
repeat(n) { res += 1.0 }
}
measureAndPrint("Specialized addition") {
specializedField {
var res: NDBuffer<Double> = one
realField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Nd4j specialized addition") {
nd4jField {
var res = one
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Viktor addition") {
viktorField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Parallel stream addition") {
parallelField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Lazy addition") {
val res = specializedField.one.mapAsync(GlobalScope) {
val res = realField.one.mapAsync(GlobalScope) {
var c = 0.0
repeat(n) {
c += 1.0
@ -67,14 +89,4 @@ fun main() {
res.elements().forEach { it.second }
}
measureAndPrint("Generic addition") {
//genericField.run(action)
genericField {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0 // couldn't avoid using `one` due to resolution ambiguity }
}
}
}
}

103
examples/src/main/kotlin/kscience/kmath/structures/ParallelRealNDField.kt Normal file
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@ -0,0 +1,103 @@
package kscience.kmath.structures
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.*
import kscience.kmath.operations.ExtendedField
import kscience.kmath.operations.RealField
import kscience.kmath.operations.RingWithNumbers
import java.util.*
import java.util.stream.IntStream
/**
* A demonstration implementation of NDField over Real using Java [DoubleStream] for parallel execution
*/
@OptIn(UnstableKMathAPI::class)
class StreamRealNDField(
override val shape: IntArray,
) : NDField<Double, RealField>,
RingWithNumbers<NDStructure<Double>>,
ExtendedField<NDStructure<Double>> {
private val strides = DefaultStrides(shape)
override val elementContext: RealField get() = RealField
override val zero: NDBuffer<Double> by lazy { produce { zero } }
override val one: NDBuffer<Double> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
private val NDStructure<Double>.buffer: RealBuffer
get() = when {
!shape.contentEquals(this@StreamRealNDField.shape) -> throw ShapeMismatchException(
this@StreamRealNDField.shape,
shape
)
this is NDBuffer && this.strides == this@StreamRealNDField.strides -> this.buffer as RealBuffer
else -> RealBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun produce(initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun NDStructure<Double>.map(
transform: RealField.(Double) -> Double,
): NDBuffer<Double> {
val array = Arrays.stream(buffer.array).parallel().map { RealField.transform(it) }.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun NDStructure<Double>.mapIndexed(
transform: RealField.(index: IntArray, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(
strides.index(offset),
buffer.array[offset]
)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun combine(
a: NDStructure<Double>,
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(a.buffer.array[offset], b.buffer.array[offset])
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun power(arg: NDStructure<Double>, pow: Number): NDBuffer<Double> = arg.map() { power(it, pow) }
override fun exp(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { exp(it) }
override fun ln(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { ln(it) }
override fun sin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { sin(it) }
override fun cos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { cos(it) }
override fun tan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { tan(it) }
override fun asin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { asin(it) }
override fun acos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { acos(it) }
override fun atan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { atan(it) }
override fun sinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { sinh(it) }
override fun cosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { cosh(it) }
override fun tanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { tanh(it) }
override fun asinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { asinh(it) }
override fun acosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { acosh(it) }
override fun atanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { atanh(it) }
}
fun NDAlgebra.Companion.realWithStream(vararg shape: Int): StreamRealNDField = StreamRealNDField(shape)

4
examples/src/main/kotlin/kscience/kmath/structures/StructureReadBenchmark.kt
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@ -1,5 +1,7 @@
package kscience.kmath.structures
import kscience.kmath.nd.DefaultStrides
import kscience.kmath.nd.NDBuffer
import kotlin.system.measureTimeMillis
fun main() {
@ -7,7 +9,7 @@ fun main() {
val array = DoubleArray(n * n) { 1.0 }
val buffer = RealBuffer(array)
val strides = DefaultStrides(intArrayOf(n, n))
val structure = BufferNDStructure(strides, buffer)
val structure = NDBuffer(strides, buffer)
measureTimeMillis {
var res = 0.0

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

@ -1,5 +1,7 @@
package kscience.kmath.structures
import kscience.kmath.nd.NDStructure
import kscience.kmath.nd.mapToBuffer
import kotlin.system.measureTimeMillis
fun main() {

41
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
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@ -1,11 +1,8 @@
package kscience.kmath.commons.linear
import kscience.kmath.linear.DiagonalFeature
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.linear.origin
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
import org.apache.commons.math3.linear.*
import kotlin.reflect.KClass
import kotlin.reflect.cast
@ -17,8 +14,40 @@ public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
DeterminantFeature::class, LupDecompositionFeature::class -> object :
DeterminantFeature<Double>,
LupDecompositionFeature<Double> {
private val lup by lazy { LUDecomposition(origin) }
override val determinant: Double by lazy { lup.determinant }
override val l: Matrix<Double> by lazy { CMMatrix(lup.l) + LFeature }
override val u: Matrix<Double> by lazy { CMMatrix(lup.u) + UFeature }
override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
override val l: Matrix<Double> by lazy {
val cholesky = CholeskyDecomposition(origin)
CMMatrix(cholesky.l) + LFeature
}
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy { QRDecomposition(origin) }
override val q: Matrix<Double> by lazy { CMMatrix(qr.q) + OrthogonalFeature }
override val r: Matrix<Double> by lazy { CMMatrix(qr.r) + UFeature }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
private val sv by lazy { SingularValueDecomposition(origin) }
override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
}
else -> null
}?.let { type.cast(it) }
}?.let(type::cast)
public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
}

2
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMSolver.kt
View File

@ -1,7 +1,7 @@
package kscience.kmath.commons.linear
import kscience.kmath.linear.Matrix
import kscience.kmath.linear.Point
import kscience.kmath.structures.Matrix
import org.apache.commons.math3.linear.*
public enum class CMDecomposition {

2
kmath-core/README.md
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@ -3,7 +3,7 @@
The core features of KMath:
- [algebras](src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Algebraic structures: contexts and elements
- [nd](src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures
- [nd](src/commonMain/kotlin/kscience/kmath/nd/NDStructure.kt) : Many-dimensional structures
- [buffers](src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
- [expressions](src/commonMain/kotlin/kscience/kmath/expressions) : Functional Expressions
- [domains](src/commonMain/kotlin/kscience/kmath/domains) : Domains

2
kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SymbolIndexer.kt
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@ -1,8 +1,8 @@
package kscience.kmath.expressions
import kscience.kmath.linear.Point
import kscience.kmath.nd.Structure2D
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.Structure2D
/**
* An environment to easy transform indexed variables to symbols and back.

70
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/BufferMatrix.kt
View File

@ -1,7 +1,19 @@
package kscience.kmath.linear
import kscience.kmath.nd.NDStructure
import kscience.kmath.nd.Structure2D
import kscience.kmath.operations.Ring
import kscience.kmath.structures.*
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.asSequence
/**
* Alias for [Structure2D] with more familiar name.
*
* @param T the type of items.
*/
public typealias Matrix<T> = Structure2D<T>
/**
* Basic implementation of Matrix space based on [NDStructure]
@ -17,6 +29,62 @@ public class BufferMatrixContext<T : Any, R : Ring<T>>(
public override fun point(size: Int, initializer: (Int) -> T): Point<T> = bufferFactory(size, initializer)
private fun Matrix<T>.toBufferMatrix(): BufferMatrix<T> = if (this is BufferMatrix) this else {
produce(rowNum, colNum) { i, j -> get(i, j) }
}
public fun one(rows: Int, columns: Int): Matrix<Double> = VirtualMatrix(rows, columns) { i, j ->
if (i == j) 1.0 else 0.0
} + DiagonalFeature
public override infix fun Matrix<T>.dot(other: Matrix<T>): BufferMatrix<T> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val bufferMatrix = toBufferMatrix()
val otherBufferMatrix = other.toBufferMatrix()
return elementContext {
produce(rowNum, other.colNum) { i, j ->
var res = one
for (l in 0 until colNum) {
res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
}
res
}
}
}
public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val bufferMatrix = toBufferMatrix()
return elementContext {
bufferFactory(rowNum) { i ->
var res = one
for (j in 0 until colNum) {
res += bufferMatrix[i, j] * vector[j]
}
res
}
}
}
override fun add(a: Matrix<T>, b: Matrix<T>): BufferMatrix<T> {
require(a.rowNum == b.rowNum) { "Row number mismatch in matrix addition. Left side: ${a.rowNum}, right side: ${b.rowNum}" }
require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
val aBufferMatrix = a.toBufferMatrix()
val bBufferMatrix = b.toBufferMatrix()
return elementContext {
produce(a.rowNum, a.colNum) { i, j ->
aBufferMatrix[i, j] + bBufferMatrix[i, j]
}
}
}
override fun multiply(a: Matrix<T>, k: Number): BufferMatrix<T> {
val aBufferMatrix = a.toBufferMatrix()
return elementContext {
produce(a.rowNum, a.colNum) { i, j -> aBufferMatrix[i, j] * k.toDouble() }
}
}
public companion object
}

1
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LinearAlgebra.kt
View File

@ -1,7 +1,6 @@
package kscience.kmath.linear
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.VirtualBuffer
public typealias Point<T> = Buffer<T>

25
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt
View File

@ -1,6 +1,7 @@
package kscience.kmath.linear
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.getFeature
import kscience.kmath.operations.*
import kscience.kmath.structures.*
@ -151,7 +152,7 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
public fun MatrixContext<Double, Matrix<Double>>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
public fun <T : Any> LupDecomposition<T>.solveWithLUP(
public fun <T : Any> LupDecomposition<T>.solveWithLup(
factory: MutableBufferFactory<T>,
matrix: Matrix<T>,
): Matrix<T> {
@ -199,14 +200,14 @@ public fun <T : Any> LupDecomposition<T>.solveWithLUP(
}
}
public inline fun <reified T : Any> LupDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
solveWithLUP(MutableBuffer.Companion::auto, matrix)
public inline fun <reified T : Any> LupDecomposition<T>.solveWithLup(matrix: Matrix<T>): Matrix<T> =
solveWithLup(MutableBuffer.Companion::auto, matrix)
/**
* Solve a linear equation **a*x = b** using LUP decomposition
* Solves a system of linear equations *ax = b** using LUP decomposition.
*/
@OptIn(UnstableKMathAPI::class)
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLUP(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLup(
a: Matrix<T>,
b: Matrix<T>,
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
@ -214,26 +215,26 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
): Matrix<T> {
// Use existing decomposition if it is provided by matrix
val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
return decomposition.solveWithLUP(bufferFactory, b)
return decomposition.solveWithLup(bufferFactory, b)
}
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLUP(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLup(
matrix: Matrix<T>,
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
): Matrix<T> = solveWithLup(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
@OptIn(UnstableKMathAPI::class)
public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
public fun RealMatrixContext.solveWithLup(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
// Use existing decomposition if it is provided by matrix
val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
val decomposition: LupDecomposition<Double> = a.getFeature() ?: lup(bufferFactory, RealField, a) { it < 1e-11 }
return decomposition.solveWithLUP(bufferFactory, b)
return decomposition.solveWithLup(bufferFactory, b)
}
/**
* Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
*/
public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): Matrix<Double> =
solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))
public fun RealMatrixContext.inverseWithLup(matrix: Matrix<Double>): Matrix<Double> =
solveWithLup(matrix, one(matrix.rowNum, matrix.colNum))

9
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixBuilder.kt
View File

@ -1,6 +1,9 @@
package kscience.kmath.linear
import kscience.kmath.structures.*
import kscience.kmath.nd.Structure2D
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.asBuffer
public class MatrixBuilder(public val rows: Int, public val columns: Int) {
public operator fun <T : Any> invoke(vararg elements: T): Matrix<T> {
@ -22,7 +25,7 @@ public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
public inline fun <reified T : Any> Structure2D.Companion.row(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
noinline builder: (Int) -> T,
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(1, size, buffer)
@ -36,7 +39,7 @@ public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
public inline fun <reified T : Any> Structure2D.Companion.column(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
noinline builder: (Int) -> T,
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(size, 1, buffer)

1
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt
View File

@ -6,7 +6,6 @@ import kscience.kmath.operations.invoke
import kscience.kmath.operations.sum
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.asSequence
/**

8
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixFeatures.kt
View File

@ -1,7 +1,5 @@
package kscience.kmath.linear
import kscience.kmath.structures.Matrix
/**
* A marker interface representing some properties of matrices or additional transformations of them. Features are used
* to optimize matrix operations performance in some cases or retrieve the APIs.
@ -11,8 +9,8 @@ public interface MatrixFeature
/**
* Matrices with this feature are considered to have only diagonal non-null elements.
*/
public interface DiagonalFeature : MatrixFeature{
public companion object: DiagonalFeature
public interface DiagonalFeature : MatrixFeature {
public companion object : DiagonalFeature
}
/**
@ -39,6 +37,8 @@ public interface InverseMatrixFeature<T : Any> : MatrixFeature {
/**
* Matrices with this feature can compute their determinant.
*
* @param T the type of matrices' items.
*/
public interface DeterminantFeature<T : Any> : MatrixFeature {
/**

14
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixWrapper.kt
View File

@ -1,11 +1,10 @@
package kscience.kmath.linear
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.Structure2D
import kscience.kmath.nd.getFeature
import kscience.kmath.operations.Ring
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
import kscience.kmath.structures.asBuffer
import kscience.kmath.structures.getFeature
import kotlin.math.sqrt
import kotlin.reflect.KClass
import kotlin.reflect.safeCast
@ -39,7 +38,8 @@ public class MatrixWrapper<T : Any> internal constructor(
* Origin does not necessary store all features.
*/
@UnstableKMathAPI
public val <T : Any> Matrix<T>.origin: Matrix<T> get() = (this as? MatrixWrapper)?.origin ?: this
public val <T : Any> Matrix<T>.origin: Matrix<T>
get() = (this as? MatrixWrapper)?.origin ?: this
/**
* Add a single feature to a [Matrix]
@ -60,12 +60,6 @@ public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeatu
MatrixWrapper(this, newFeatures.toSet())
}
public inline fun Structure2D.Companion.real(
rows: Int,
columns: Int,
initializer: (Int, Int) -> Double,
): BufferMatrix<Double> = MatrixContext.real.produce(rows, columns, initializer)
/**
* Build a square matrix from given elements.
*/

29
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/RealMatrixContext.kt
View File

@ -1,12 +1,10 @@
package kscience.kmath.linear
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
@Suppress("OVERRIDE_BY_INLINE")
public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
public override inline fun produce(
public override fun produce(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
@ -15,7 +13,7 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
return BufferMatrix(rows, columns, buffer)
}
private fun Matrix<Double>.wrap(): BufferMatrix<Double> = if (this is BufferMatrix) this else {
public fun Matrix<Double>.toBufferMatrix(): BufferMatrix<Double> = if (this is BufferMatrix) this else {
produce(rowNum, colNum) { i, j -> get(i, j) }
}
@ -25,10 +23,12 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
public override infix fun Matrix<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val bufferMatrix = toBufferMatrix()
val otherBufferMatrix = other.toBufferMatrix()
return produce(rowNum, other.colNum) { i, j ->
var res = 0.0
for (l in 0 until colNum) {
res += get(i, l) * other.get(l, j)
res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
}
res
}
@ -36,10 +36,11 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
public override infix fun Matrix<Double>.dot(vector: Point<Double>): Point<Double> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val bufferMatrix = toBufferMatrix()
return RealBuffer(rowNum) { i ->
var res = 0.0
for (j in 0 until colNum) {
res += get(i, j) * vector[j]
res += bufferMatrix[i, j] * vector[j]
}
res
}
@ -48,17 +49,23 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
override fun add(a: Matrix<Double>, b: Matrix<Double>): BufferMatrix<Double> {
require(a.rowNum == b.rowNum) { "Row number mismatch in matrix addition. Left side: ${a.rowNum}, right side: ${b.rowNum}" }
require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
val aBufferMatrix = a.toBufferMatrix()
val bBufferMatrix = b.toBufferMatrix()
return produce(a.rowNum, a.colNum) { i, j ->
a[i, j] + b[i, j]
aBufferMatrix[i, j] + bBufferMatrix[i, j]
}
}
override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> =
produce(rowNum, colNum) { i, j -> get(i, j) * value }
override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> {
val bufferMatrix = toBufferMatrix()
return produce(rowNum, colNum) { i, j -> bufferMatrix[i, j] * value }
}
override fun multiply(a: Matrix<Double>, k: Number): BufferMatrix<Double> =
produce(a.rowNum, a.colNum) { i, j -> a[i, j] * k.toDouble() }
override fun multiply(a: Matrix<Double>, k: Number): BufferMatrix<Double> {
val aBufferMatrix = a.toBufferMatrix()
return produce(a.rowNum, a.colNum) { i, j -> aBufferMatrix[i, j] * k.toDouble() }
}
}

2
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/VirtualMatrix.kt
View File

@ -1,7 +1,5 @@
package kscience.kmath.linear
import kscience.kmath.structures.Matrix
/**
* The matrix where each element is evaluated each time when is being accessed.
*

2
kmath-core/src/commonMain/kotlin/kscience/kmath/misc/annotations.kt
View File

@ -1,4 +1,4 @@
package kscience.kmath.misc
@RequiresOptIn("This API is unstable and could change in future", RequiresOptIn.Level.WARNING)
public annotation class UnstableKMathAPI
public annotation class UnstableKMathAPI

134
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/BufferNDAlgebra.kt Normal file
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@ -0,0 +1,134 @@
package kscience.kmath.nd
import kscience.kmath.nd.*
import kscience.kmath.operations.*
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
public interface BufferNDAlgebra<T, C> : NDAlgebra<T, C> {
public val strides: Strides
public val bufferFactory: BufferFactory<T>
override fun produce(initializer: C.(IntArray) -> T): NDBuffer<T> = NDBuffer(
strides,
bufferFactory(strides.linearSize) { offset ->
elementContext.initializer(strides.index(offset))
}
)
public val NDStructure<T>.buffer: Buffer<T>
get() = when {
!shape.contentEquals(this@BufferNDAlgebra.shape) -> throw ShapeMismatchException(
this@BufferNDAlgebra.shape,
shape
)
this is NDBuffer && this.strides == this@BufferNDAlgebra.strides -> this.buffer
else -> bufferFactory(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun NDStructure<T>.map(transform: C.(T) -> T): NDBuffer<T> {
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(buffer[offset])
}
return NDBuffer(strides, buffer)
}
override fun NDStructure<T>.mapIndexed(transform: C.(index: IntArray, T) -> T): NDBuffer<T> {
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(
strides.index(offset),
buffer[offset]
)
}
return NDBuffer(strides, buffer)
}
override fun combine(a: NDStructure<T>, b: NDStructure<T>, transform: C.(T, T) -> T): NDBuffer<T> {
val buffer = bufferFactory(strides.linearSize) { offset ->
elementContext.transform(a.buffer[offset], b.buffer[offset])
}
return NDBuffer(strides, buffer)
}
}
public open class BufferedNDSpace<T, R : Space<T>>(
final override val shape: IntArray,
final override val elementContext: R,
final override val bufferFactory: BufferFactory<T>,
) : NDSpace<T, R>, BufferNDAlgebra<T, R> {
override val strides: Strides = DefaultStrides(shape)
override val zero: NDBuffer<T> by lazy { produce { zero } }
}
public open class BufferedNDRing<T, R : Ring<T>>(
shape: IntArray,
elementContext: R,
bufferFactory: BufferFactory<T>,
) : BufferedNDSpace<T, R>(shape, elementContext, bufferFactory), NDRing<T, R> {
override val one: NDBuffer<T> by lazy { produce { one } }
}
public open class BufferedNDField<T, R : Field<T>>(
shape: IntArray,
elementContext: R,
bufferFactory: BufferFactory<T>,
) : BufferedNDRing<T, R>(shape, elementContext, bufferFactory), NDField<T, R>
// space factories
public fun <T, A : Space<T>> NDAlgebra.Companion.space(
space: A,
bufferFactory: BufferFactory<T>,
vararg shape: Int,
): BufferedNDSpace<T, A> = BufferedNDSpace(shape, space, bufferFactory)
public inline fun <T, A : Space<T>, R> A.ndSpace(
noinline bufferFactory: BufferFactory<T>,
vararg shape: Int,
action: BufferedNDSpace<T, A>.() -> R,
): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return NDAlgebra.space(this, bufferFactory, *shape).run(action)
}
//ring factories
public fun <T, A : Ring<T>> NDAlgebra.Companion.ring(
ring: A,
bufferFactory: BufferFactory<T>,
vararg shape: Int,
): BufferedNDRing<T, A> = BufferedNDRing(shape, ring, bufferFactory)
public inline fun <T, A : Ring<T>, R> A.ndRing(
noinline bufferFactory: BufferFactory<T>,
vararg shape: Int,
action: BufferedNDRing<T, A>.() -> R,
): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return NDAlgebra.ring(this, bufferFactory, *shape).run(action)
}
//field factories
public fun <T, A : Field<T>> NDAlgebra.Companion.field(
field: A,
bufferFactory: BufferFactory<T>,
vararg shape: Int,
): BufferedNDField<T, A> = BufferedNDField(shape, field, bufferFactory)
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any, A : Field<T>> NDAlgebra.Companion.auto(
field: A,
vararg shape: Int,
): NDField<T, A> = when (field) {
RealField -> RealNDField(shape) as NDField<T, A>
else -> BufferedNDField(shape, field, Buffer.Companion::auto)
}
public inline fun <T, A : Field<T>, R> A.ndField(
noinline bufferFactory: BufferFactory<T>,
vararg shape: Int,
action: BufferedNDField<T, A>.() -> R,
): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return NDAlgebra.field(this, bufferFactory, *shape).run(action)
}

113
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/ComplexNDField.kt Normal file
View File

@ -0,0 +1,113 @@
package kscience.kmath.nd
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kscience.kmath.structures.Buffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* An optimized nd-field for complex numbers
*/
@OptIn(UnstableKMathAPI::class)
public class ComplexNDField(
shape: IntArray,
) : BufferedNDField<Complex, ComplexField>(shape, ComplexField, Buffer.Companion::complex),
RingWithNumbers<NDStructure<Complex>>,
ExtendedField<NDStructure<Complex>> {
override val zero: NDBuffer<Complex> by lazy { produce { zero } }
override val one: NDBuffer<Complex> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Complex> {
val d = value.toComplex() // minimize conversions
return produce { d }
}
//
// @Suppress("OVERRIDE_BY_INLINE")
// override inline fun map(
// arg: AbstractNDBuffer<Double>,
// transform: RealField.(Double) -> Double,
// ): RealNDElement {
// check(arg)
// val array = RealBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
// return BufferedNDFieldElement(this, array)
// }
//
// @Suppress("OVERRIDE_BY_INLINE")
// override inline fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
// val array = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
// return BufferedNDFieldElement(this, array)
// }
//
// @Suppress("OVERRIDE_BY_INLINE")
// override inline fun mapIndexed(
// arg: AbstractNDBuffer<Double>,
// transform: RealField.(index: IntArray, Double) -> Double,
// ): RealNDElement {
// check(arg)
// return BufferedNDFieldElement(
// this,
// RealBuffer(arg.strides.linearSize) { offset ->
// elementContext.transform(
// arg.strides.index(offset),
// arg.buffer[offset]
// )
// })
// }
//
// @Suppress("OVERRIDE_BY_INLINE")
// override inline fun combine(
// a: AbstractNDBuffer<Double>,
// b: AbstractNDBuffer<Double>,
// transform: RealField.(Double, Double) -> Double,
// ): RealNDElement {
// check(a, b)
// val buffer = RealBuffer(strides.linearSize) { offset ->
// elementContext.transform(a.buffer[offset], b.buffer[offset])
// }
// return BufferedNDFieldElement(this, buffer)
// }
override fun power(arg: NDStructure<Complex>, pow: Number): NDBuffer<Complex> = arg.map() { power(it, pow) }
override fun exp(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { exp(it) }
override fun ln(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { ln(it) }
override fun sin(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { sin(it) }
override fun cos(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { cos(it) }
override fun tan(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { tan(it) }
override fun asin(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { asin(it) }
override fun acos(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { acos(it) }
override fun atan(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { atan(it) }
override fun sinh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { sinh(it) }
override fun cosh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { cosh(it) }
override fun tanh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { tanh(it) }
override fun asinh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { asinh(it) }
override fun acosh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { acosh(it) }
override fun atanh(arg: NDStructure<Complex>): NDBuffer<Complex> = arg.map() { atanh(it) }
}
/**
* Fast element production using function inlining
*/
public inline fun BufferedNDField<Complex, ComplexField>.produceInline(initializer: ComplexField.(Int) -> Complex): NDBuffer<Complex> {
contract { callsInPlace(initializer, InvocationKind.EXACTLY_ONCE) }
val buffer = Buffer.complex(strides.linearSize) { offset -> ComplexField.initializer(offset) }
return NDBuffer(strides, buffer)
}
public fun NDAlgebra.Companion.complex(vararg shape: Int): ComplexNDField = ComplexNDField(shape)
/**
* Produce a context for n-dimensional operations inside this real field
*/
public inline fun <R> ComplexField.nd(vararg shape: Int, action: ComplexNDField.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return ComplexNDField(shape).action()
}

262
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/NDAlgebra.kt Normal file
View File

@ -0,0 +1,262 @@
package kscience.kmath.nd
import kscience.kmath.operations.Field
import kscience.kmath.operations.Ring
import kscience.kmath.operations.Space
import kscience.kmath.structures.*
/**
* An exception is thrown when the expected ans actual shape of NDArray differs.
*
* @property expected the expected shape.
* @property actual the actual shape.
*/
public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) :
RuntimeException("Shape ${actual.contentToString()} doesn't fit in expected shape ${expected.contentToString()}.")
/**
* The base interface for all ND-algebra implementations.
*
* @param T the type of ND-structure element.
* @param C the type of the element context.
* @param N the type of the structure.
*/
public interface NDAlgebra<T, C> {
/**
* The shape of ND-structures this algebra operates on.
*/
public val shape: IntArray
/**
* The algebra over elements of ND structure.
*/
public val elementContext: C
/**
* Produces a new [N] structure using given initializer function.
*/
public fun produce(initializer: C.(IntArray) -> T): NDStructure<T>
/**
* Maps elements from one structure to another one by applying [transform] to them.
*/
public fun NDStructure<T>.map(transform: C.(T) -> T): NDStructure<T>
/**
* Maps elements from one structure to another one by applying [transform] to them alongside with their indices.
*/
public fun NDStructure<T>.mapIndexed(transform: C.(index: IntArray, T) -> T): NDStructure<T>
/**
* Combines two structures into one.
*/
public fun combine(a: NDStructure<T>, b: NDStructure<T>, transform: C.(T, T) -> T): NDStructure<T>
/**
* Element-wise invocation of function working on [T] on a [NDStructure].
*/
public operator fun Function1<T, T>.invoke(structure: NDStructure<T>): NDStructure<T> =
structure.map() { value -> this@invoke(value) }
public companion object
}
/**
* Checks if given elements are consistent with this context.
*
* @param structures the structures to check.
* @return the array of valid structures.
*/
internal fun <T, C> NDAlgebra<T, C>.checkShape(vararg structures: NDStructure<T>): Array<out NDStructure<T>> = structures
.map(NDStructure<T>::shape)
.singleOrNull { !shape.contentEquals(it) }
?.let<IntArray, Array<out NDStructure<T>>> { throw ShapeMismatchException(shape, it) }
?: structures
/**
* Checks if given element is consistent with this context.
*
* @param element the structure to check.
* @return the valid structure.
*/
internal fun <T, C> NDAlgebra<T, C>.checkShape(element: NDStructure<T>): NDStructure<T> {
if (!element.shape.contentEquals(shape)) throw ShapeMismatchException(shape, element.shape)
return element
}
/**
* Space of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param S the type of space of structure elements.
*/
public interface NDSpace<T, S : Space<T>> : Space<NDStructure<T>>, NDAlgebra<T, S> {
/**
* Element-wise addition.
*
* @param a the addend.
* @param b the augend.
* @return the sum.
*/
public override fun add(a: NDStructure<T>, b: NDStructure<T>): NDStructure<T> =
combine(a, b) { aValue, bValue -> add(aValue, bValue) }
/**
* Element-wise multiplication by scalar.
*
* @param a the multiplicand.
* @param k the multiplier.
* @return the product.
*/
public override fun multiply(a: NDStructure<T>, k: Number): NDStructure<T> = a.map() { multiply(it, k) }
// TODO move to extensions after KEEP-176
/**
* Adds an ND structure to an element of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun NDStructure<T>.plus(arg: T): NDStructure<T> = this.map() { value -> add(arg, value) }
/**
* Subtracts an element from ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun NDStructure<T>.minus(arg: T): NDStructure<T> = this.map() { value -> add(arg, -value) }
/**
* Adds an element to ND structure of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun T.plus(arg: NDStructure<T>): NDStructure<T> = arg.map() { value -> add(this@plus, value) }
/**
* Subtracts an ND structure from an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.minus(arg: NDStructure<T>): NDStructure<T> = arg.map() { value -> add(-this@minus, value) }
public companion object
}
/**
* Ring of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param R the type of ring of structure elements.
*/
public interface NDRing<T, R : Ring<T>> : Ring<NDStructure<T>>, NDSpace<T, R> {
/**
* Element-wise multiplication.
*
* @param a the multiplicand.
* @param b the multiplier.
* @return the product.
*/
public override fun multiply(a: NDStructure<T>, b: NDStructure<T>): NDStructure<T> =
combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
//TODO move to extensions after KEEP-176
/**
* Multiplies an ND structure by an element of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun NDStructure<T>.times(arg: T): NDStructure<T> = this.map() { value -> multiply(arg, value) }
/**
* Multiplies an element by a ND structure of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun T.times(arg: NDStructure<T>): NDStructure<T> = arg.map() { value -> multiply(this@times, value) }
public companion object
}
/**
* Field of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param F the type field of structure elements.
*/
public interface NDField<T, F : Field<T>> : Field<NDStructure<T>>, NDRing<T, F> {
/**
* Element-wise division.
*
* @param a the dividend.
* @param b the divisor.
* @return the quotient.
*/
public override fun divide(a: NDStructure<T>, b: NDStructure<T>): NDStructure<T> =
combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
//TODO move to extensions after KEEP-176
/**
* Divides an ND structure by an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun NDStructure<T>.div(arg: T): NDStructure<T> = this.map() { value -> divide(arg, value) }
/**
* Divides an element by an ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.div(arg: NDStructure<T>): NDStructure<T> = arg.map() { divide(it, this@div) }
// @ThreadLocal
// public companion object {
// private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf()
//
// /**
// * Create a nd-field for [Double] values or pull it from cache if it was created previously.
// */
// public fun real(vararg shape: Int): RealNDField = realNDFieldCache.getOrPut(shape) { RealNDField(shape) }
//
// /**
// * Create an ND field with boxing generic buffer.
// */
// public fun <T : Any, F : Field<T>> boxing(
// field: F,
// vararg shape: Int,
// bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
// ): BufferedNDField<T, F> = BufferedNDField(shape, field, bufferFactory)
//
// /**
// * Create a most suitable implementation for nd-field using reified class.
// */
// @Suppress("UNCHECKED_CAST")
// public inline fun <reified T : Any, F : Field<T>> auto(field: F, vararg shape: Int): NDField<T, F> =
// when {
// T::class == Double::class -> real(*shape) as NDField<T, F>
// T::class == Complex::class -> complex(*shape) as BufferedNDField<T, F>
// else -> BoxingNDField(shape, field, Buffer.Companion::auto)
// }
// }
}

98
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt → kmath-core/src/commonMain/kotlin/kscience/kmath/nd/NDStructure.kt
View File

@ -1,6 +1,10 @@
package kscience.kmath.structures
package kscience.kmath.nd
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.MutableBuffer
import kscience.kmath.structures.asSequence
import kotlin.jvm.JvmName
import kotlin.native.concurrent.ThreadLocal
import kotlin.reflect.KClass
@ -74,8 +78,8 @@ public interface NDStructure<T> {
strides: Strides,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
initializer: (IntArray) -> T,
): BufferNDStructure<T> =
BufferNDStructure(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> =
NDBuffer(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
/**
* Inline create NDStructure with non-boxing buffer implementation if it is possible
@ -83,40 +87,40 @@ public interface NDStructure<T> {
public inline fun <reified T : Any> auto(
strides: Strides,
crossinline initializer: (IntArray) -> T,
): BufferNDStructure<T> =
BufferNDStructure(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> =
NDBuffer(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
public inline fun <T : Any> auto(
type: KClass<T>,
strides: Strides,
crossinline initializer: (IntArray) -> T,
): BufferNDStructure<T> =
BufferNDStructure(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
): NDBuffer<T> =
NDBuffer(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> build(
shape: IntArray,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
initializer: (IntArray) -> T,
): BufferNDStructure<T> = build(DefaultStrides(shape), bufferFactory, initializer)
): NDBuffer<T> = build(DefaultStrides(shape), bufferFactory, initializer)
public inline fun <reified T : Any> auto(
shape: IntArray,
crossinline initializer: (IntArray) -> T,
): BufferNDStructure<T> =
): NDBuffer<T> =
auto(DefaultStrides(shape), initializer)
@JvmName("autoVarArg")
public inline fun <reified T : Any> auto(
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): BufferNDStructure<T> =
): NDBuffer<T> =
auto(DefaultStrides(shape), initializer)
public inline fun <T : Any> auto(
type: KClass<T>,
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): BufferNDStructure<T> =
): NDBuffer<T> =
auto(type, DefaultStrides(shape), initializer)
}
}
@ -156,7 +160,7 @@ public inline fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) ->
*/
public interface Strides {
/**
* Shape of NDstructure
* Shape of NDStructure
*/
public val shape: IntArray
@ -185,7 +189,9 @@ public interface Strides {
/**
* Iterate over ND indices in a natural order
*/
public fun indices(): Sequence<IntArray> = (0 until linearSize).asSequence().map { index(it) }
public fun indices(): Sequence<IntArray> = (0 until linearSize).asSequence().map {
index(it)
}
}
/**
@ -211,9 +217,7 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
}
override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
if (value < 0 || value >= this.shape[i])
throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
if (value < 0 || value >= shape[i]) throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
value * strides[i]
}.sum()
@ -256,23 +260,29 @@ public class DefaultStrides private constructor(override val shape: IntArray) :
* Represents [NDStructure] over [Buffer].
*
* @param T the type of items.
* @param strides The strides to access elements of [Buffer] by linear indices.
* @param buffer The underlying buffer.
*/
public abstract class NDBuffer<T> : NDStructure<T> {
/**
* The underlying buffer.
*/
public abstract val buffer: Buffer<T>
public open class NDBuffer<T>(
public val strides: Strides,
buffer: Buffer<T>,
) : NDStructure<T> {
/**
* The strides to access elements of [Buffer] by linear indices.
*/
public abstract val strides: Strides
init {
if (strides.linearSize != buffer.size) {
error("Expected buffer side of ${strides.linearSize}, but found ${buffer.size}")
}
}
public open val buffer: Buffer<T> = buffer
override operator fun get(index: IntArray): T = buffer[strides.offset(index)]
override val shape: IntArray get() = strides.shape
override fun elements(): Sequence<Pair<IntArray, T>> = strides.indices().map { it to this[it] }
override fun elements(): Sequence<Pair<IntArray, T>> = strides.indices().map {
it to this[it]
}
override fun equals(other: Any?): Boolean {
return NDStructure.contentEquals(this, other as? NDStructure<*> ?: return false)
@ -297,46 +307,30 @@ public abstract class NDBuffer<T> : NDStructure<T> {
}
return "NDBuffer(shape=${shape.contentToString()}, buffer=$bufferRepr)"
}
}
/**
* Boxing generic [NDStructure]
*/
public class BufferNDStructure<T>(
override val strides: Strides,
override val buffer: Buffer<T>,
) : NDBuffer<T>() {
init {
if (strides.linearSize != buffer.size) {
error("Expected buffer side of ${strides.linearSize}, but found ${buffer.size}")
}
}
}
/**
* Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferNDStructure]
* Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [NDBuffer]
*/
public inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
factory: BufferFactory<R> = Buffer.Companion::auto,
crossinline transform: (T) -> R,
): BufferNDStructure<R> {
return if (this is BufferNDStructure<T>)
BufferNDStructure(this.strides, factory.invoke(strides.linearSize) { transform(buffer[it]) })
): NDBuffer<R> {
return if (this is NDBuffer<T>)
NDBuffer(this.strides, factory.invoke(strides.linearSize) { transform(buffer[it]) })
else {
val strides = DefaultStrides(shape)
BufferNDStructure(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
NDBuffer(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
}
}
/**
* Mutable ND buffer based on linear [MutableBuffer].
*/
public class MutableBufferNDStructure<T>(
override val strides: Strides,
override val buffer: MutableBuffer<T>,
) : NDBuffer<T>(), MutableNDStructure<T> {
public class MutableNDBuffer<T>(
strides: Strides,
buffer: MutableBuffer<T>,
) : NDBuffer<T>(strides, buffer), MutableNDStructure<T> {
init {
require(strides.linearSize == buffer.size) {
@ -344,6 +338,8 @@ public class MutableBufferNDStructure<T>(
}
}
override val buffer: MutableBuffer<T> = super.buffer as MutableBuffer<T>
override operator fun set(index: IntArray, value: T): Unit = buffer.set(strides.offset(index), value)
}

107
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/RealNDField.kt Normal file
View File

@ -0,0 +1,107 @@
package kscience.kmath.nd
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.ExtendedField
import kscience.kmath.operations.RealField
import kscience.kmath.operations.RingWithNumbers
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.RealBuffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@OptIn(UnstableKMathAPI::class)
public class RealNDField(
shape: IntArray,
) : BufferedNDField<Double, RealField>(shape, RealField, Buffer.Companion::real),
RingWithNumbers<NDStructure<Double>>,
ExtendedField<NDStructure<Double>> {
override val zero: NDBuffer<Double> by lazy { produce { zero } }
override val one: NDBuffer<Double> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
override val NDStructure<Double>.buffer: RealBuffer
get() = when {
!shape.contentEquals(this@RealNDField.shape) -> throw ShapeMismatchException(
this@RealNDField.shape,
shape
)
this is NDBuffer && this.strides == this@RealNDField.strides -> this.buffer as RealBuffer
else -> RealBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun NDStructure<Double>.map(
transform: RealField.(Double) -> Double,
): NDBuffer<Double> {
val buffer = RealBuffer(strides.linearSize) { offset -> RealField.transform(buffer.array[offset]) }
return NDBuffer(strides, buffer)
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun produce(initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
val array = DoubleArray(strides.linearSize) { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}
return NDBuffer(strides, RealBuffer(array))
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun NDStructure<Double>.mapIndexed(
transform: RealField.(index: IntArray, Double) -> Double,
): NDBuffer<Double> = NDBuffer(
strides,
buffer = RealBuffer(strides.linearSize) { offset ->
RealField.transform(
strides.index(offset),
buffer.array[offset]
)
})
@Suppress("OVERRIDE_BY_INLINE")
override inline fun combine(
a: NDStructure<Double>,
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): NDBuffer<Double> {
val buffer = RealBuffer(strides.linearSize) { offset ->
RealField.transform(a.buffer.array[offset], b.buffer.array[offset])
}
return NDBuffer(strides, buffer)
}
override fun power(arg: NDStructure<Double>, pow: Number): NDBuffer<Double> = arg.map { power(it, pow) }
override fun exp(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { exp(it) }
override fun ln(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { ln(it) }
override fun sin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { sin(it) }
override fun cos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { cos(it) }
override fun tan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { tan(it) }
override fun asin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { asin(it) }
override fun acos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { acos(it) }
override fun atan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { atan(it) }
override fun sinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { sinh(it) }
override fun cosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { cosh(it) }
override fun tanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { tanh(it) }
override fun asinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { asinh(it) }
override fun acosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { acosh(it) }
override fun atanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map { atanh(it) }
}
public fun NDAlgebra.Companion.real(vararg shape: Int): RealNDField = RealNDField(shape)
/**
* Produce a context for n-dimensional operations inside this real field
*/
public inline fun <R> RealField.nd(vararg shape: Int, action: RealNDField.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return RealNDField(shape).run(action)
}

36
kmath-core/src/commonMain/kotlin/kscience/kmath/nd/ShortNDRing.kt Normal file
View File

@ -0,0 +1,36 @@
package kscience.kmath.nd
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.RingWithNumbers
import kscience.kmath.operations.ShortRing
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.ShortBuffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@OptIn(UnstableKMathAPI::class)
public class ShortNDRing(
shape: IntArray,
) : BufferedNDRing<Short, ShortRing>(shape, ShortRing, Buffer.Companion::auto),
RingWithNumbers<NDStructure<Short>> {
override val zero: NDBuffer<Short> by lazy { produce { zero } }
override val one: NDBuffer<Short> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Short> {
val d = value.toShort() // minimize conversions
return produce { d }
}
}
/**
* Fast element production using function inlining.
*/
public inline fun BufferedNDRing<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): NDBuffer<Short> {
return NDBuffer(strides, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
}
public inline fun <R> ShortRing.nd(vararg shape: Int, action: ShortNDRing.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return ShortNDRing(shape).run(action)
}

7
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure1D.kt → kmath-core/src/commonMain/kotlin/kscience/kmath/nd/Structure1D.kt
View File

@ -1,4 +1,7 @@
package kscience.kmath.structures
package kscience.kmath.nd
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.asSequence
/**
* A structure that is guaranteed to be one-dimensional
@ -34,7 +37,7 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
override val size: Int get() = buffer.size
override fun elements(): Sequence<Pair<IntArray, T>> =
asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
buffer.asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
override operator fun get(index: Int): T = buffer[index]
}

17
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure2D.kt → kmath-core/src/commonMain/kotlin/kscience/kmath/nd/Structure2D.kt
View File

@ -1,4 +1,9 @@
package kscience.kmath.structures
package kscience.kmath.nd
import kscience.kmath.linear.BufferMatrix
import kscience.kmath.linear.RealMatrixContext
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.VirtualBuffer
/**
* A structure that is guaranteed to be two-dimensional.
@ -49,7 +54,15 @@ public interface Structure2D<T> : NDStructure<T> {
for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
}
public companion object
public companion object {
public inline fun real(
rows: Int,
columns: Int,
crossinline init: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> = RealMatrixContext.produce(rows,columns) { i, j ->
init(i, j)
}
}
}
/**

14
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/BigInt.kt
View File

@ -1,9 +1,12 @@
package kscience.kmath.operations
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.BufferedNDRing
import kscience.kmath.nd.NDAlgebra
import kscience.kmath.operations.BigInt.Companion.BASE
import kscience.kmath.operations.BigInt.Companion.BASE_SIZE
import kscience.kmath.structures.*
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.MutableBuffer
import kotlin.math.log2
import kotlin.math.max
import kotlin.math.min
@ -462,10 +465,5 @@ public inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigIn
public inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> =
boxing(size, initializer)
public fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInt, BigIntField> =
BoxingNDRing(shape, BigIntField, Buffer.Companion::bigInt)
public fun NDElement.Companion.bigInt(
vararg shape: Int,
initializer: BigIntField.(IntArray) -> BigInt
): BufferedNDRingElement<BigInt, BigIntField> = NDAlgebra.bigInt(*shape).produce(initializer)
public fun NDAlgebra.Companion.bigInt(vararg shape: Int): BufferedNDRing<BigInt, BigIntField> =
BufferedNDRing(shape, BigIntField, Buffer.Companion::bigInt)

81
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BoxingNDField.kt
View File

@ -1,81 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.Field
import kscience.kmath.operations.FieldElement
public class BoxingNDField<T, F : Field<T>>(
public override val shape: IntArray,
public override val elementContext: F,
public val bufferFactory: BufferFactory<T>
) : BufferedNDField<T, F> {
public override val zero: BufferedNDFieldElement<T, F> by lazy { produce { zero } }
public override val one: BufferedNDFieldElement<T, F> by lazy { produce { one } }
public override val strides: Strides = DefaultStrides(shape)
public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
bufferFactory(size, initializer)
public override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
require(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
return elements
}
public override fun produce(initializer: F.(IntArray) -> T): BufferedNDFieldElement<T, F> =
BufferedNDFieldElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) })
public override fun map(arg: NDBuffer<T>, transform: F.(T) -> T): BufferedNDFieldElement<T, F> {
check(arg)
return BufferedNDFieldElement(
this,
buildBuffer(arg.strides.linearSize) { offset -> elementContext.transform(arg.buffer[offset]) })
// val buffer = arg.buffer.transform { _, value -> elementContext.transform(value) }
// return BufferedNDFieldElement(this, buffer)
}
public override fun mapIndexed(
arg: NDBuffer<T>,
transform: F.(index: IntArray, T) -> T
): BufferedNDFieldElement<T, F> {
check(arg)
return BufferedNDFieldElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementContext.transform(
arg.strides.index(offset),
arg.buffer[offset]
)
})
// val buffer =
// arg.buffer.transform { offset, value -> elementContext.transform(arg.strides.index(offset), value) }
// return BufferedNDFieldElement(this, buffer)
}
public override fun combine(
a: NDBuffer<T>,
b: NDBuffer<T>,
transform: F.(T, T) -> T
): BufferedNDFieldElement<T, F> {
check(a, b)
return BufferedNDFieldElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
}
public override fun NDBuffer<T>.toElement(): FieldElement<NDBuffer<T>, *, out BufferedNDField<T, F>> =
BufferedNDFieldElement(this@BoxingNDField, buffer)
}
public inline fun <T : Any, F : Field<T>, R> F.nd(
noinline bufferFactory: BufferFactory<T>,
vararg shape: Int,
action: NDField<T, F, *>.() -> R
): R {
val ndfield = NDField.boxing(this, *shape, bufferFactory = bufferFactory)
return ndfield.action()
}

71
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BoxingNDRing.kt
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@ -1,71 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.Ring
import kscience.kmath.operations.RingElement
public class BoxingNDRing<T, R : Ring<T>>(
override val shape: IntArray,
override val elementContext: R,
public val bufferFactory: BufferFactory<T>
) : BufferedNDRing<T, R> {
override val strides: Strides = DefaultStrides(shape)
override val zero: BufferedNDRingElement<T, R> by lazy { produce { zero } }
override val one: BufferedNDRingElement<T, R> by lazy { produce { one } }
public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = bufferFactory(size, initializer)
override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
return elements
}
override fun produce(initializer: R.(IntArray) -> T): BufferedNDRingElement<T, R> =
BufferedNDRingElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) })
override fun map(arg: NDBuffer<T>, transform: R.(T) -> T): BufferedNDRingElement<T, R> {
check(arg)
return BufferedNDRingElement(
this,
buildBuffer(arg.strides.linearSize) { offset -> elementContext.transform(arg.buffer[offset]) })
// val buffer = arg.buffer.transform { _, value -> elementContext.transform(value) }
// return BufferedNDFieldElement(this, buffer)
}
override fun mapIndexed(
arg: NDBuffer<T>,
transform: R.(index: IntArray, T) -> T
): BufferedNDRingElement<T, R> {
check(arg)
return BufferedNDRingElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementContext.transform(
arg.strides.index(offset),
arg.buffer[offset]
)
})
// val buffer =
// arg.buffer.transform { offset, value -> elementContext.transform(arg.strides.index(offset), value) }
// return BufferedNDFieldElement(this, buffer)
}
override fun combine(
a: NDBuffer<T>,
b: NDBuffer<T>,
transform: R.(T, T) -> T
): BufferedNDRingElement<T, R> {
check(a, b)
return BufferedNDRingElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
}
override fun NDBuffer<T>.toElement(): RingElement<NDBuffer<T>, *, out BufferedNDRing<T, R>> =
BufferedNDRingElement(this@BoxingNDRing, buffer)
}

5
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BufferAccessor2D.kt
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@ -1,5 +1,10 @@
package kscience.kmath.structures
import kscience.kmath.nd.DefaultStrides
import kscience.kmath.nd.NDStructure
import kscience.kmath.nd.Structure2D
import kscience.kmath.nd.as2D
/**
* A context that allows to operate on a [MutableBuffer] as on 2d array
*/

43
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BufferedNDAlgebra.kt
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@ -1,43 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.*
public interface BufferedNDAlgebra<T, C> : NDAlgebra<T, C, NDBuffer<T>> {
public val strides: Strides
public override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
require(elements.all { it.strides == strides }) { "Strides mismatch" }
return elements
}
/**
* Convert any [NDStructure] to buffered structure using strides from this context.
* If the structure is already [NDBuffer], conversion is free. If not, it could be expensive because iteration over
* indices.
*
* If the argument is [NDBuffer] with different strides structure, the new element will be produced.
*/
public fun NDStructure<T>.toBuffer(): NDBuffer<T> =
if (this is NDBuffer<T> && this.strides == this@BufferedNDAlgebra.strides)
this
else
produce { index -> this@toBuffer[index] }
/**
* Convert a buffer to element of this algebra
*/
public fun NDBuffer<T>.toElement(): MathElement<out BufferedNDAlgebra<T, C>>
}
public interface BufferedNDSpace<T, S : Space<T>> : NDSpace<T, S, NDBuffer<T>>, BufferedNDAlgebra<T, S> {
public override fun NDBuffer<T>.toElement(): SpaceElement<NDBuffer<T>, *, out BufferedNDSpace<T, S>>
}
public interface BufferedNDRing<T, R : Ring<T>> : NDRing<T, R, NDBuffer<T>>, BufferedNDSpace<T, R> {
override fun NDBuffer<T>.toElement(): RingElement<NDBuffer<T>, *, out BufferedNDRing<T, R>>
}
public interface BufferedNDField<T, F : Field<T>> : NDField<T, F, NDBuffer<T>>, BufferedNDRing<T, F> {
override fun NDBuffer<T>.toElement(): FieldElement<NDBuffer<T>, *, out BufferedNDField<T, F>>
}

86
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BufferedNDElement.kt
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@ -1,86 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.*
/**
* Base class for an element with context, containing strides
*/
public abstract class BufferedNDElement<T, C> : NDBuffer<T>(), NDElement<T, C, NDBuffer<T>> {
abstract override val context: BufferedNDAlgebra<T, C>
override val strides: Strides get() = context.strides
override val shape: IntArray get() = context.shape
}
public class BufferedNDSpaceElement<T, S : Space<T>>(
override val context: BufferedNDSpace<T, S>,
override val buffer: Buffer<T>
) : BufferedNDElement<T, S>(), SpaceElement<NDBuffer<T>, BufferedNDSpaceElement<T, S>, BufferedNDSpace<T, S>> {
override fun unwrap(): NDBuffer<T> = this
override fun NDBuffer<T>.wrap(): BufferedNDSpaceElement<T, S> {
context.check(this)
return BufferedNDSpaceElement(context, buffer)
}
}
public class BufferedNDRingElement<T, R : Ring<T>>(
override val context: BufferedNDRing<T, R>,
override val buffer: Buffer<T>
) : BufferedNDElement<T, R>(), RingElement<NDBuffer<T>, BufferedNDRingElement<T, R>, BufferedNDRing<T, R>> {
override fun unwrap(): NDBuffer<T> = this
override fun NDBuffer<T>.wrap(): BufferedNDRingElement<T, R> {
context.check(this)
return BufferedNDRingElement(context, buffer)
}
}
public class BufferedNDFieldElement<T, F : Field<T>>(
override val context: BufferedNDField<T, F>,
override val buffer: Buffer<T>
) : BufferedNDElement<T, F>(), FieldElement<NDBuffer<T>, BufferedNDFieldElement<T, F>, BufferedNDField<T, F>> {
override fun unwrap(): NDBuffer<T> = this
override fun NDBuffer<T>.wrap(): BufferedNDFieldElement<T, F> {
context.check(this)
return BufferedNDFieldElement(context, buffer)
}
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy.
*/
public operator fun <T : Any, F : Field<T>> Function1<T, T>.invoke(ndElement: BufferedNDElement<T, F>): MathElement<out BufferedNDAlgebra<T, F>> =
ndElement.context.run { map(ndElement) { invoke(it) }.toElement() }
/* plus and minus */
/**
* Summation operation for [BufferedNDElement] and single element
*/
public operator fun <T : Any, F : Space<T>> BufferedNDElement<T, F>.plus(arg: T): NDElement<T, F, NDBuffer<T>> =
context.map(this) { it + arg }.wrap()
/**
* Subtraction operation between [BufferedNDElement] and single element
*/
public operator fun <T : Any, F : Space<T>> BufferedNDElement<T, F>.minus(arg: T): NDElement<T, F, NDBuffer<T>> =
context.map(this) { it - arg }.wrap()
/* prod and div */
/**
* Product operation for [BufferedNDElement] and single element
*/
public operator fun <T : Any, F : Ring<T>> BufferedNDElement<T, F>.times(arg: T): NDElement<T, F, NDBuffer<T>> =
context.map(this) { it * arg }.wrap()
/**
* Division operation between [BufferedNDElement] and single element
*/
public operator fun <T : Any, F : Field<T>> BufferedNDElement<T, F>.div(arg: T): NDElement<T, F, NDBuffer<T>> =
context.map(this) { it / arg }.wrap()

158
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/ComplexNDField.kt
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@ -1,158 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
public typealias ComplexNDElement = BufferedNDFieldElement<Complex, ComplexField>
/**
* An optimized nd-field for complex numbers
*/
@OptIn(UnstableKMathAPI::class)
public class ComplexNDField(override val shape: IntArray) :
BufferedNDField<Complex, ComplexField>,
ExtendedNDField<Complex, ComplexField, NDBuffer<Complex>>,
RingWithNumbers<NDBuffer<Complex>>{
override val strides: Strides = DefaultStrides(shape)
override val elementContext: ComplexField get() = ComplexField
override val zero: ComplexNDElement by lazy { produce { zero } }
override val one: ComplexNDElement by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Complex> {
val c = value.toComplex()
return produce { c }
}
public inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Complex): Buffer<Complex> =
Buffer.complex(size) { initializer(it) }
/**
* Inline transform an NDStructure to another structure
*/
override fun map(
arg: NDBuffer<Complex>,
transform: ComplexField.(Complex) -> Complex,
): ComplexNDElement {
check(arg)
val array = buildBuffer(arg.strides.linearSize) { offset -> ComplexField.transform(arg.buffer[offset]) }
return BufferedNDFieldElement(this, array)
}
override fun produce(initializer: ComplexField.(IntArray) -> Complex): ComplexNDElement {
val array = buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
return BufferedNDFieldElement(this, array)
}
override fun mapIndexed(
arg: NDBuffer<Complex>,
transform: ComplexField.(index: IntArray, Complex) -> Complex,
): ComplexNDElement {
check(arg)
return BufferedNDFieldElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementContext.transform(
arg.strides.index(offset),
arg.buffer[offset]
)
})
}
override fun combine(
a: NDBuffer<Complex>,
b: NDBuffer<Complex>,
transform: ComplexField.(Complex, Complex) -> Complex,
): ComplexNDElement {
check(a, b)
return BufferedNDFieldElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
}
override fun NDBuffer<Complex>.toElement(): FieldElement<NDBuffer<Complex>, *, out BufferedNDField<Complex, ComplexField>> =
BufferedNDFieldElement(this@ComplexNDField, buffer)
override fun power(arg: NDBuffer<Complex>, pow: Number): ComplexNDElement =
map(arg) { power(it, pow) }
override fun exp(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { exp(it) }
override fun ln(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { ln(it) }
override fun sin(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { sin(it) }
override fun cos(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { cos(it) }
override fun tan(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { tan(it) }
override fun asin(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { asin(it) }
override fun acos(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { acos(it) }
override fun atan(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { atan(it) }
override fun sinh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { sinh(it) }
override fun cosh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { cosh(it) }
override fun tanh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { tanh(it) }
override fun asinh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { asinh(it) }
override fun acosh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { acosh(it) }
override fun atanh(arg: NDBuffer<Complex>): ComplexNDElement = map(arg) { atanh(it) }
}
/**
* Fast element production using function inlining
*/
public inline fun BufferedNDField<Complex, ComplexField>.produceInline(initializer: ComplexField.(Int) -> Complex): ComplexNDElement {
val buffer = Buffer.complex(strides.linearSize) { offset -> ComplexField.initializer(offset) }
return BufferedNDFieldElement(this, buffer)
}
/**
* Map one [ComplexNDElement] using function with indices.
*/
public inline fun ComplexNDElement.mapIndexed(transform: ComplexField.(index: IntArray, Complex) -> Complex): ComplexNDElement =
context.produceInline { offset -> transform(strides.index(offset), buffer[offset]) }
/**
* Map one [ComplexNDElement] using function without indices.
*/
public inline fun ComplexNDElement.map(transform: ComplexField.(Complex) -> Complex): ComplexNDElement {
val buffer = Buffer.complex(strides.linearSize) { offset -> ComplexField.transform(buffer[offset]) }
return BufferedNDFieldElement(context, buffer)
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy
*/
public operator fun Function1<Complex, Complex>.invoke(ndElement: ComplexNDElement): ComplexNDElement =
ndElement.map { this@invoke(it) }
/* plus and minus */
/**
* Summation operation for [BufferedNDElement] and single element
*/
public operator fun ComplexNDElement.plus(arg: Complex): ComplexNDElement = map { it + arg }
/**
* Subtraction operation between [BufferedNDElement] and single element
*/
public operator fun ComplexNDElement.minus(arg: Complex): ComplexNDElement = map { it - arg }
public operator fun ComplexNDElement.plus(arg: Double): ComplexNDElement = map { it + arg }
public operator fun ComplexNDElement.minus(arg: Double): ComplexNDElement = map { it - arg }
public fun NDField.Companion.complex(vararg shape: Int): ComplexNDField = ComplexNDField(shape)
public fun NDElement.Companion.complex(
vararg shape: Int,
initializer: ComplexField.(IntArray) -> Complex,
): ComplexNDElement = NDField.complex(*shape).produce(initializer)
/**
* Produce a context for n-dimensional operations inside this real field
*/
public inline fun <R> ComplexField.nd(vararg shape: Int, action: ComplexNDField.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return NDField.complex(*shape).action()
}

44
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/ExtendedNDField.kt
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@ -1,44 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.ExtendedField
/**
* [ExtendedField] over [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param F the extended field of structure elements.
*/
public interface ExtendedNDField<T : Any, F : ExtendedField<T>, N : NDStructure<T>> : NDField<T, F, N>, ExtendedField<N>
///**
// * NDField that supports [ExtendedField] operations on its elements
// */
//class ExtendedNDFieldWrapper<T : Any, F : ExtendedField<T>, N : NDStructure<T>>(private val ndField: NDField<T, F, N>) :
// ExtendedNDField<T, F, N>, NDField<T, F, N> by ndField {
//
// override val shape: IntArray get() = ndField.shape
// override val elementContext: F get() = ndField.elementContext
//
// override fun produce(initializer: F.(IntArray) -> T) = ndField.produce(initializer)
//
// override fun power(arg: N, pow: Double): N {
// return produce { with(elementContext) { power(arg[it], pow) } }
// }
//
// override fun exp(arg: N): N {
// return produce { with(elementContext) { exp(arg[it]) } }
// }
//
// override fun ln(arg: N): N {
// return produce { with(elementContext) { ln(arg[it]) } }
// }
//
// override fun sin(arg: N): N {
// return produce { with(elementContext) { sin(arg[it]) } }
// }
//
// override fun cos(arg: N): N {
// return produce { with(elementContext) { cos(arg[it]) } }
// }
//}

259
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDAlgebra.kt
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@ -1,259 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.Complex
import kscience.kmath.operations.Field
import kscience.kmath.operations.Ring
import kscience.kmath.operations.Space
import kotlin.native.concurrent.ThreadLocal
/**
* An exception is thrown when the expected ans actual shape of NDArray differs.
*
* @property expected the expected shape.
* @property actual the actual shape.
*/
public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) :
RuntimeException("Shape ${actual.contentToString()} doesn't fit in expected shape ${expected.contentToString()}.")
/**
* The base interface for all ND-algebra implementations.
*
* @param T the type of ND-structure element.
* @param C the type of the element context.
* @param N the type of the structure.
*/
public interface NDAlgebra<T, C, N : NDStructure<T>> {
/**
* The shape of ND-structures this algebra operates on.
*/
public val shape: IntArray
/**
* The algebra over elements of ND structure.
*/
public val elementContext: C
/**
* Produces a new [N] structure using given initializer function.
*/
public fun produce(initializer: C.(IntArray) -> T): N
/**
* Maps elements from one structure to another one by applying [transform] to them.
*/
public fun map(arg: N, transform: C.(T) -> T): N
/**
* Maps elements from one structure to another one by applying [transform] to them alongside with their indices.
*/
public fun mapIndexed(arg: N, transform: C.(index: IntArray, T) -> T): N
/**
* Combines two structures into one.
*/
public fun combine(a: N, b: N, transform: C.(T, T) -> T): N
/**
* Checks if given element is consistent with this context.
*
* @param element the structure to check.
* @return the valid structure.
*/
public fun check(element: N): N {
if (!element.shape.contentEquals(shape)) throw ShapeMismatchException(shape, element.shape)
return element
}
/**
* Checks if given elements are consistent with this context.
*
* @param elements the structures to check.
* @return the array of valid structures.
*/
public fun check(vararg elements: N): Array<out N> = elements
.map(NDStructure<T>::shape)
.singleOrNull { !shape.contentEquals(it) }
?.let<IntArray, Array<out N>> { throw ShapeMismatchException(shape, it) }
?: elements
/**
* Element-wise invocation of function working on [T] on a [NDStructure].
*/
public operator fun Function1<T, T>.invoke(structure: N): N = map(structure) { value -> this@invoke(value) }
public companion object
}
/**
* Space of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param S the type of space of structure elements.
*/
public interface NDSpace<T, S : Space<T>, N : NDStructure<T>> : Space<N>, NDAlgebra<T, S, N> {
/**
* Element-wise addition.
*
* @param a the addend.
* @param b the augend.
* @return the sum.
*/
public override fun add(a: N, b: N): N = combine(a, b) { aValue, bValue -> add(aValue, bValue) }
/**
* Element-wise multiplication by scalar.
*
* @param a the multiplicand.
* @param k the multiplier.
* @return the product.
*/
public override fun multiply(a: N, k: Number): N = map(a) { multiply(it, k) }
// TODO move to extensions after KEEP-176
/**
* Adds an ND structure to an element of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun N.plus(arg: T): N = map(this) { value -> add(arg, value) }
/**
* Subtracts an element from ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun N.minus(arg: T): N = map(this) { value -> add(arg, -value) }
/**
* Adds an element to ND structure of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun T.plus(arg: N): N = map(arg) { value -> add(this@plus, value) }
/**
* Subtracts an ND structure from an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.minus(arg: N): N = map(arg) { value -> add(-this@minus, value) }
public companion object
}
/**
* Ring of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param R the type of ring of structure elements.
*/
public interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T, R, N> {
/**
* Element-wise multiplication.
*
* @param a the multiplicand.
* @param b the multiplier.
* @return the product.
*/
public override fun multiply(a: N, b: N): N = combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
//TODO move to extensions after KEEP-176
/**
* Multiplies an ND structure by an element of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun N.times(arg: T): N = map(this) { value -> multiply(arg, value) }
/**
* Multiplies an element by a ND structure of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun T.times(arg: N): N = map(arg) { value -> multiply(this@times, value) }
public companion object
}
/**
* Field of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param F the type field of structure elements.
*/
public interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F, N> {
/**
* Element-wise division.
*
* @param a the dividend.
* @param b the divisor.
* @return the quotient.
*/
public override fun divide(a: N, b: N): N = combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
//TODO move to extensions after KEEP-176
/**
* Divides an ND structure by an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun N.div(arg: T): N = map(this) { value -> divide(arg, value) }
/**
* Divides an element by an ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.div(arg: N): N = map(arg) { divide(it, this@div) }
@ThreadLocal
public companion object {
private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf()
/**
* Create a nd-field for [Double] values or pull it from cache if it was created previously.
*/
public fun real(vararg shape: Int): RealNDField = realNDFieldCache.getOrPut(shape) { RealNDField(shape) }
/**
* Create an ND field with boxing generic buffer.
*/
public fun <T : Any, F : Field<T>> boxing(
field: F,
vararg shape: Int,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
): BoxingNDField<T, F> = BoxingNDField(shape, field, bufferFactory)
/**
* Create a most suitable implementation for nd-field using reified class.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any, F : Field<T>> auto(field: F, vararg shape: Int): BufferedNDField<T, F> =
when {
T::class == Double::class -> real(*shape) as BufferedNDField<T, F>
T::class == Complex::class -> complex(*shape) as BufferedNDField<T, F>
else -> BoxingNDField(shape, field, Buffer.Companion::auto)
}
}
}

134
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDElement.kt
View File

@ -1,134 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.Field
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.operations.Space
/**
* The root for all [NDStructure] based algebra elements. Does not implement algebra element root because of problems with recursive self-types
* @param T the type of the element of the structure
* @param C the type of the context for the element
* @param N the type of the underlying [NDStructure]
*/
public interface NDElement<T, C, N : NDStructure<T>> : NDStructure<T> {
public val context: NDAlgebra<T, C, N>
public fun unwrap(): N
public fun N.wrap(): NDElement<T, C, N>
public companion object {
/**
* Create a optimized NDArray of doubles
*/
public fun real(shape: IntArray, initializer: RealField.(IntArray) -> Double = { 0.0 }): RealNDElement =
NDField.real(*shape).produce(initializer)
public inline fun real1D(dim: Int, crossinline initializer: (Int) -> Double = { _ -> 0.0 }): RealNDElement =
real(intArrayOf(dim)) { initializer(it[0]) }
public inline fun real2D(
dim1: Int,
dim2: Int,
crossinline initializer: (Int, Int) -> Double = { _, _ -> 0.0 }
): RealNDElement = real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
public inline fun real3D(
dim1: Int,
dim2: Int,
dim3: Int,
crossinline initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }
): RealNDElement = real(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
/**
* Simple boxing NDArray
*/
public fun <T : Any, F : Field<T>> boxing(
shape: IntArray,
field: F,
initializer: F.(IntArray) -> T
): BufferedNDElement<T, F> {
val ndField = BoxingNDField(shape, field, Buffer.Companion::boxing)
return ndField.produce(initializer)
}
public inline fun <reified T : Any, F : Field<T>> auto(
shape: IntArray,
field: F,
noinline initializer: F.(IntArray) -> T
): BufferedNDFieldElement<T, F> {
val ndField = NDField.auto(field, *shape)
return BufferedNDFieldElement(ndField, ndField.produce(initializer).buffer)
}
}
}
public fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.mapIndexed(transform: C.(index: IntArray, T) -> T): NDElement<T, C, N> =
context.mapIndexed(unwrap(), transform).wrap()
public fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.map(transform: C.(T) -> T): NDElement<T, C, N> =
context.map(unwrap(), transform).wrap()
/**
* Element by element application of any operation on elements to the whole [NDElement]
*/
public operator fun <T, C, N : NDStructure<T>> Function1<T, T>.invoke(ndElement: NDElement<T, C, N>): NDElement<T, C, N> =
ndElement.map { value -> this@invoke(value) }
/* plus and minus */
/**
* Summation operation for [NDElement] and single element
*/
public operator fun <T, S : Space<T>, N : NDStructure<T>> NDElement<T, S, N>.plus(arg: T): NDElement<T, S, N> =
map { value -> arg + value }
/**
* Subtraction operation between [NDElement] and single element
*/
public operator fun <T, S : Space<T>, N : NDStructure<T>> NDElement<T, S, N>.minus(arg: T): NDElement<T, S, N> =
map { value -> arg - value }
/* prod and div */
/**
* Product operation for [NDElement] and single element
*/
public operator fun <T, R : Ring<T>, N : NDStructure<T>> NDElement<T, R, N>.times(arg: T): NDElement<T, R, N> =
map { value -> arg * value }
/**
* Division operation between [NDElement] and single element
*/
public operator fun <T, F : Field<T>, N : NDStructure<T>> NDElement<T, F, N>.div(arg: T): NDElement<T, F, N> =
map { value -> arg / value }
// /**
// * Reverse sum operation
// */
// operator fun T.plus(arg: NDStructure<T>): NDElement<T, F> = produce { index ->
// field.run { this@plus + arg[index] }
// }
//
// /**
// * Reverse minus operation
// */
// operator fun T.minus(arg: NDStructure<T>): NDElement<T, F> = produce { index ->
// field.run { this@minus - arg[index] }
// }
//
// /**
// * Reverse product operation
// */
// operator fun T.times(arg: NDStructure<T>): NDElement<T, F> = produce { index ->
// field.run { this@times * arg[index] }
// }
//
// /**
// * Reverse division operation
// */
// operator fun T.div(arg: NDStructure<T>): NDElement<T, F> = produce { index ->
// field.run { this@div / arg[index] }
// }

140
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt
View File

@ -1,140 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.FieldElement
import kscience.kmath.operations.RealField
import kscience.kmath.operations.RingWithNumbers
public typealias RealNDElement = BufferedNDFieldElement<Double, RealField>
@OptIn(UnstableKMathAPI::class)
public class RealNDField(override val shape: IntArray) :
BufferedNDField<Double, RealField>,
ExtendedNDField<Double, RealField, NDBuffer<Double>>,
RingWithNumbers<NDBuffer<Double>> {
override val strides: Strides = DefaultStrides(shape)
override val elementContext: RealField get() = RealField
override val zero: RealNDElement by lazy { produce { zero } }
override val one: RealNDElement by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble()
return produce { d }
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun map(
arg: NDBuffer<Double>,
transform: RealField.(Double) -> Double,
): RealNDElement {
check(arg)
val array = RealBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
return BufferedNDFieldElement(this, array)
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
val array = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
return BufferedNDFieldElement(this, array)
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun mapIndexed(
arg: NDBuffer<Double>,
transform: RealField.(index: IntArray, Double) -> Double,
): RealNDElement {
check(arg)
return BufferedNDFieldElement(
this,
RealBuffer(arg.strides.linearSize) { offset ->
elementContext.transform(
arg.strides.index(offset),
arg.buffer[offset]
)
})
}
@Suppress("OVERRIDE_BY_INLINE")
override inline fun combine(
a: NDBuffer<Double>,
b: NDBuffer<Double>,
transform: RealField.(Double, Double) -> Double,
): RealNDElement {
check(a, b)
val buffer = RealBuffer(strides.linearSize) { offset ->
elementContext.transform(a.buffer[offset], b.buffer[offset])
}
return BufferedNDFieldElement(this, buffer)
}
override fun NDBuffer<Double>.toElement(): FieldElement<NDBuffer<Double>, *, out BufferedNDField<Double, RealField>> =
BufferedNDFieldElement(this@RealNDField, buffer)
override fun power(arg: NDBuffer<Double>, pow: Number): RealNDElement = map(arg) { power(it, pow) }
override fun exp(arg: NDBuffer<Double>): RealNDElement = map(arg) { exp(it) }
override fun ln(arg: NDBuffer<Double>): RealNDElement = map(arg) { ln(it) }
override fun sin(arg: NDBuffer<Double>): RealNDElement = map(arg) { sin(it) }
override fun cos(arg: NDBuffer<Double>): RealNDElement = map(arg) { cos(it) }
override fun tan(arg: NDBuffer<Double>): RealNDElement = map(arg) { tan(it) }
override fun asin(arg: NDBuffer<Double>): RealNDElement = map(arg) { asin(it) }
override fun acos(arg: NDBuffer<Double>): RealNDElement = map(arg) { acos(it) }
override fun atan(arg: NDBuffer<Double>): RealNDElement = map(arg) { atan(it) }
override fun sinh(arg: NDBuffer<Double>): RealNDElement = map(arg) { sinh(it) }
override fun cosh(arg: NDBuffer<Double>): RealNDElement = map(arg) { cosh(it) }
override fun tanh(arg: NDBuffer<Double>): RealNDElement = map(arg) { tanh(it) }
override fun asinh(arg: NDBuffer<Double>): RealNDElement = map(arg) { asinh(it) }
override fun acosh(arg: NDBuffer<Double>): RealNDElement = map(arg) { acosh(it) }
override fun atanh(arg: NDBuffer<Double>): RealNDElement = map(arg) { atanh(it) }
}
/**
* Fast element production using function inlining
*/
public inline fun BufferedNDField<Double, RealField>.produceInline(crossinline initializer: RealField.(Int) -> Double): RealNDElement {
val array = DoubleArray(strides.linearSize) { offset -> RealField.initializer(offset) }
return BufferedNDFieldElement(this, RealBuffer(array))
}
/**
* Map one [RealNDElement] using function with indices.
*/
public inline fun RealNDElement.mapIndexed(crossinline transform: RealField.(index: IntArray, Double) -> Double): RealNDElement =
context.produceInline { offset -> transform(strides.index(offset), buffer[offset]) }
/**
* Map one [RealNDElement] using function without indices.
*/
public inline fun RealNDElement.map(crossinline transform: RealField.(Double) -> Double): RealNDElement {
val array = DoubleArray(strides.linearSize) { offset -> RealField.transform(buffer[offset]) }
return BufferedNDFieldElement(context, RealBuffer(array))
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy.
*/
public operator fun Function1<Double, Double>.invoke(ndElement: RealNDElement): RealNDElement =
ndElement.map { this@invoke(it) }
/* plus and minus */
/**
* Summation operation for [BufferedNDElement] and single element
*/
public operator fun RealNDElement.plus(arg: Double): RealNDElement = map { it + arg }
/**
* Subtraction operation between [BufferedNDElement] and single element
*/
public operator fun RealNDElement.minus(arg: Double): RealNDElement = map { it - arg }
/**
* Produce a context for n-dimensional operations inside this real field
*/
public inline fun <R> RealField.nd(vararg shape: Int, action: RealNDField.() -> R): R = NDField.real(*shape).run(action)

93
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/ShortNDRing.kt
View File

@ -1,93 +0,0 @@
package kscience.kmath.structures
import kscience.kmath.operations.RingElement
import kscience.kmath.operations.ShortRing
public typealias ShortNDElement = BufferedNDRingElement<Short, ShortRing>
public class ShortNDRing(override val shape: IntArray) :
BufferedNDRing<Short, ShortRing> {
override val strides: Strides = DefaultStrides(shape)
override val elementContext: ShortRing get() = ShortRing
override val zero: ShortNDElement by lazy { produce { zero } }
override val one: ShortNDElement by lazy { produce { one } }
public inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Short): Buffer<Short> =
ShortBuffer(ShortArray(size) { initializer(it) })
/**
* Inline transform an NDStructure to
*/
override fun map(
arg: NDBuffer<Short>,
transform: ShortRing.(Short) -> Short
): ShortNDElement {
check(arg)
val array = buildBuffer(arg.strides.linearSize) { offset -> ShortRing.transform(arg.buffer[offset]) }
return BufferedNDRingElement(this, array)
}
override fun produce(initializer: ShortRing.(IntArray) -> Short): ShortNDElement {
val array = buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
return BufferedNDRingElement(this, array)
}
override fun mapIndexed(
arg: NDBuffer<Short>,
transform: ShortRing.(index: IntArray, Short) -> Short
): ShortNDElement {
check(arg)
return BufferedNDRingElement(
this,
buildBuffer(arg.strides.linearSize) { offset ->
elementContext.transform(
arg.strides.index(offset),
arg.buffer[offset]
)
})
}
override fun combine(
a: NDBuffer<Short>,
b: NDBuffer<Short>,
transform: ShortRing.(Short, Short) -> Short
): ShortNDElement {
check(a, b)
return BufferedNDRingElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
}
override fun NDBuffer<Short>.toElement(): RingElement<NDBuffer<Short>, *, out BufferedNDRing<Short, ShortRing>> =
BufferedNDRingElement(this@ShortNDRing, buffer)
}
/**
* Fast element production using function inlining.
*/
public inline fun BufferedNDRing<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): ShortNDElement =
BufferedNDRingElement(this, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
/**
* Element by element application of any operation on elements to the whole array.
*/
public operator fun Function1<Short, Short>.invoke(ndElement: ShortNDElement): ShortNDElement =
ndElement.context.produceInline { i -> invoke(ndElement.buffer[i]) }
/* plus and minus */
/**
* Summation operation for [ShortNDElement] and single element.
*/
public operator fun ShortNDElement.plus(arg: Short): ShortNDElement =
context.produceInline { i -> (buffer[i] + arg).toShort() }
/**
* Subtraction operation between [ShortNDElement] and single element.
*/
public operator fun ShortNDElement.minus(arg: Short): ShortNDElement =
context.produceInline { i -> (buffer[i] - arg).toShort() }

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

@ -1,9 +1,8 @@
package kscience.kmath.linear
import kscience.kmath.nd.NDStructure
import kscience.kmath.nd.as2D
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.NDStructure
import kscience.kmath.structures.as2D
import kotlin.test.Test
import kotlin.test.assertEquals

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

@ -1,6 +1,5 @@
package kscience.kmath.linear
import kscience.kmath.structures.Matrix
import kotlin.test.Test
import kotlin.test.assertEquals
@ -9,7 +8,7 @@ class RealLUSolverTest {
@Test
fun testInvertOne() {
val matrix = MatrixContext.real.one(2, 2)
val inverted = MatrixContext.real.inverseWithLUP(matrix)
val inverted = MatrixContext.real.inverseWithLup(matrix)
assertEquals(matrix, inverted)
}
@ -37,7 +36,7 @@ class RealLUSolverTest {
1.0, 3.0
)
val inverted = MatrixContext.real.inverseWithLUP(matrix)
val inverted = MatrixContext.real.inverseWithLup(matrix)
val expected = Matrix.square(
0.375, -0.125,

7
kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NDFieldTest.kt
View File

@ -1,5 +1,8 @@
package kscience.kmath.structures
import kscience.kmath.nd.NDAlgebra
import kscience.kmath.nd.get
import kscience.kmath.nd.real
import kscience.kmath.operations.internal.FieldVerifier
import kotlin.test.Test
import kotlin.test.assertEquals
@ -7,12 +10,12 @@ import kotlin.test.assertEquals
internal class NDFieldTest {
@Test
fun verify() {
NDField.real(12, 32).run { FieldVerifier(this, one + 3, one - 23, one * 12, 6.66) }
NDAlgebra.real(12, 32).run { FieldVerifier(this, one + 3, one - 23, one * 12, 6.66) }
}
@Test
fun testStrides() {
val ndArray = NDElement.real(intArrayOf(10, 10)) { (it[0] + it[1]).toDouble() }
val ndArray = NDAlgebra.real(10, 10).produce { (it[0] + it[1]).toDouble() }
assertEquals(ndArray[5, 5], 10.0)
}
}

41
kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NumberNDFieldTest.kt
View File

@ -1,8 +1,8 @@
package kscience.kmath.structures
import kscience.kmath.nd.*
import kscience.kmath.operations.Norm
import kscience.kmath.operations.invoke
import kscience.kmath.structures.NDElement.Companion.real2D
import kotlin.math.abs
import kotlin.math.pow
import kotlin.test.Test
@ -10,25 +10,30 @@ import kotlin.test.assertEquals
@Suppress("UNUSED_VARIABLE")
class NumberNDFieldTest {
val array1: RealNDElement = real2D(3, 3) { i, j -> (i + j).toDouble() }
val array2: RealNDElement = real2D(3, 3) { i, j -> (i - j).toDouble() }
val algebra = NDAlgebra.real(3,3)
val array1 = algebra.produce { (i, j) -> (i + j).toDouble() }
val array2 = algebra.produce { (i, j) -> (i - j).toDouble() }
@Test
fun testSum() {
val sum = array1 + array2
assertEquals(4.0, sum[2, 2])
algebra {
val sum = array1 + array2
assertEquals(4.0, sum[2, 2])
}
}
@Test
fun testProduct() {
val product = array1 * array2
assertEquals(0.0, product[2, 2])
algebra {
val product = array1 * array2
assertEquals(0.0, product[2, 2])
}
}
@Test
fun testGeneration() {
val array = real2D(3, 3) { i, j -> (i * 10 + j).toDouble() }
val array = Structure2D.real(3, 3) { i, j -> (i * 10 + j).toDouble() }
for (i in 0..2) {
for (j in 0..2) {
@ -40,16 +45,20 @@ class NumberNDFieldTest {
@Test
fun testExternalFunction() {
val function: (Double) -> Double = { x -> x.pow(2) + 2 * x + 1 }
val result = function(array1) + 1.0
assertEquals(10.0, result[1, 1])
algebra {
val function: (Double) -> Double = { x -> x.pow(2) + 2 * x + 1 }
val result = function(array1) + 1.0
assertEquals(10.0, result[1, 1])
}
}
@Test
fun testLibraryFunction() {
val abs: (Double) -> Double = ::abs
val result = abs(array2)
assertEquals(2.0, result[0, 2])
algebra {
val abs: (Double) -> Double = ::abs
val result = abs(array2)
assertEquals(2.0, result[0, 2])
}
}
@Test
@ -64,6 +73,8 @@ class NumberNDFieldTest {
@Test
fun testInternalContext() {
(NDField.real(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
algebra {
(NDAlgebra.real(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
}
}
}

2
kmath-coroutines/src/jvmMain/kotlin/kscience/kmath/structures/LazyNDStructure.kt
View File

@ -2,6 +2,8 @@ package kscience.kmath.structures
import kotlinx.coroutines.*
import kscience.kmath.coroutines.Math
import kscience.kmath.nd.DefaultStrides
import kscience.kmath.nd.NDStructure
public class LazyNDStructure<T>(
public val scope: CoroutineScope,

3
kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Wrappers.kt
View File

@ -1,9 +1,8 @@
package kscience.kmath.dimensions
import kscience.kmath.linear.*
import kscience.kmath.nd.Structure2D
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
/**
* A matrix with compile-time controlled dimension

25
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt
View File

@ -2,8 +2,7 @@ package kscience.kmath.ejml
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.NDStructure
import kscience.kmath.nd.NDStructure
import kscience.kmath.structures.RealBuffer
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.simple.SimpleMatrix
@ -16,21 +15,20 @@ import kotlin.reflect.cast
* @property origin the underlying [SimpleMatrix].
* @author Iaroslav Postovalov
*/
public class EjmlMatrix(
public val origin: SimpleMatrix,
) : Matrix<Double> {
public class EjmlMatrix(public val origin: SimpleMatrix) : Matrix<Double> {
public override val rowNum: Int get() = origin.numRows()
public override val colNum: Int get() = origin.numCols()
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
public override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
override val inverse: Matrix<Double> by lazy { EjmlMatrix(origin.invert()) }
}
DeterminantFeature::class -> object : DeterminantFeature<Double> {
override val determinant: Double by lazy(origin::determinant)
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
private val svd by lazy {
DecompositionFactory_DDRM.svd(origin.numRows(), origin.numCols(), true, true, false)
@ -42,14 +40,19 @@ public class EjmlMatrix(
override val v: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
override val singularValues: Point<Double> by lazy { RealBuffer(svd.singularValues) }
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy {
DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
}
override val q: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
override val q: Matrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) + OrthogonalFeature
}
override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) + UFeature }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
@ -58,6 +61,7 @@ public class EjmlMatrix(
EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
}
}
LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
private val lup by lazy {
DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols()).apply { decompose(origin.ddrm.copy()) }
@ -73,8 +77,9 @@ public class EjmlMatrix(
override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
}
else -> null
}?.let { type.cast(it) }
}?.let(type::cast)
public override operator fun get(i: Int, j: Int): Double = origin[i, j]

8
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt
View File

@ -1,12 +1,8 @@
package kscience.kmath.ejml
import kscience.kmath.linear.InverseMatrixFeature
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.linear.origin
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.getFeature
import kscience.kmath.nd.getFeature
import org.ejml.simple.SimpleMatrix
/**

2
kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlMatrixTest.kt
View File

@ -5,7 +5,7 @@ import kscience.kmath.linear.LupDecompositionFeature
import kscience.kmath.linear.MatrixFeature
import kscience.kmath.linear.plus
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.getFeature
import kscience.kmath.nd.getFeature
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.simple.SimpleMatrix
import kotlin.random.Random

10
kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt
View File

@ -1,12 +1,8 @@
package kscience.kmath.real
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.VirtualMatrix
import kscience.kmath.linear.inverseWithLUP
import kscience.kmath.linear.real
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
import kscience.kmath.structures.asIterable
import kotlin.math.pow
@ -144,7 +140,7 @@ public fun RealMatrix.min(): Double? = elements().map { (_, value) -> value }.mi
public fun RealMatrix.max(): Double? = elements().map { (_, value) -> value }.maxOrNull()
public fun RealMatrix.average(): Double = elements().map { (_, value) -> value }.average()
public inline fun RealMatrix.map(transform: (Double) -> Double): RealMatrix =
public inline fun RealMatrix.map(crossinline transform: (Double) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { i, j ->
transform(get(i, j))
}
@ -152,7 +148,7 @@ public inline fun RealMatrix.map(transform: (Double) -> Double): RealMatrix =
/**
* Inverse a square real matrix using LUP decomposition
*/
public fun RealMatrix.inverseWithLUP(): RealMatrix = MatrixContext.real.inverseWithLUP(this)
public fun RealMatrix.inverseWithLup(): RealMatrix = MatrixContext.real.inverseWithLup(this)
//extended operations

31
kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/realND.kt Normal file
View File

@ -0,0 +1,31 @@
package kscience.kmath.real
import kscience.kmath.nd.NDBuffer
import kscience.kmath.operations.RealField
import kscience.kmath.structures.RealBuffer
/**
* Map one [NDBuffer] using function without indices.
*/
public inline fun NDBuffer<Double>.mapInline(crossinline transform: RealField.(Double) -> Double): NDBuffer<Double> {
val array = DoubleArray(strides.linearSize) { offset -> RealField.transform(buffer[offset]) }
return NDBuffer(strides, RealBuffer(array))
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy.
*/
public operator fun Function1<Double, Double>.invoke(ndElement: NDBuffer<Double>): NDBuffer<Double> =
ndElement.mapInline { this@invoke(it) }
/* plus and minus */
/**
* Summation operation for [NDBuffer] and single element
*/
public operator fun NDBuffer<Double>.plus(arg: Double): NDBuffer<Double> = mapInline { it + arg }
/**
* Subtraction operation between [NDBuffer] and single element
*/
public operator fun NDBuffer<Double>.minus(arg: Double): NDBuffer<Double> = mapInline { it - arg }

2
kmath-for-real/src/commonTest/kotlin/kaceince/kmath/real/RealMatrixTest.kt
View File

@ -1,8 +1,8 @@
package kaceince.kmath.real
import kscience.kmath.linear.Matrix
import kscience.kmath.linear.build
import kscience.kmath.real.*
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.contentEquals
import kotlin.test.Test
import kotlin.test.assertEquals

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

@ -1,7 +1,7 @@
package kscience.kmath.interpolation
import kscience.kmath.nd.Structure2D
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Structure2D
public interface XYPointSet<X, Y> {
public val size: Int

18
kmath-histograms/build.gradle.kts
View File

@ -1,8 +1,18 @@
plugins { id("ru.mipt.npm.mpp") }
kotlin.sourceSets.commonMain {
dependencies {
api(project(":kmath-core"))
api(project(":kmath-for-real"))
kotlin.sourceSets {
commonMain {
dependencies {
api(project(":kmath-core"))
}
}
commonTest{
dependencies{
implementation(project(":kmath-for-real"))
}
}
}
readme {
this.maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE
}

2
kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/RealHistogram.kt
View File

@ -1,6 +1,8 @@
package kscience.kmath.histogram
import kscience.kmath.linear.Point
import kscience.kmath.nd.DefaultStrides
import kscience.kmath.nd.NDStructure
import kscience.kmath.operations.SpaceOperations
import kscience.kmath.operations.invoke
import kscience.kmath.structures.*

1
kmath-histograms/src/commonTest/kotlin/scietifik/kmath/histogram/MultivariateHistogramTest.kt
View File

@ -5,7 +5,6 @@ import kscience.kmath.histogram.fill
import kscience.kmath.histogram.put
import kscience.kmath.real.RealVector
import kscience.kmath.real.invoke
import kscience.kmath.structures.Buffer
import kotlin.random.Random
import kotlin.test.*

11
kmath-histograms/src/jvmMain/kotlin/kscience/kmath/histogram/UnivariateHistogram.kt
View File

@ -1,6 +1,6 @@
package kscience.kmath.histogram
import kscience.kmath.real.RealVector
import kscience.kmath.linear.Point
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.asBuffer
import java.util.*
@ -11,12 +11,12 @@ import kotlin.math.floor
public class UnivariateBin(
public val position: Double,
public val size: Double,
public val counter: LongCounter = LongCounter()
public val counter: LongCounter = LongCounter(),
) : Bin<Double> {
//TODO add weighting
public override val value: Number get() = counter.sum()
public override val center: RealVector get() = doubleArrayOf(position).asBuffer()
public override val center: Point<Double> get() = doubleArrayOf(position).asBuffer()
public override val dimension: Int get() = 1
public operator fun contains(value: Double): Boolean = value in (position - size / 2)..(position + size / 2)
@ -27,8 +27,9 @@ public class UnivariateBin(
/**
* Univariate histogram with log(n) bin search speed
*/
public class UnivariateHistogram private constructor(private val factory: (Double) -> UnivariateBin) :
MutableHistogram<Double, UnivariateBin> {
public class UnivariateHistogram private constructor(
private val factory: (Double) -> UnivariateBin,
) : MutableHistogram<Double, UnivariateBin> {
private val bins: TreeMap<Double, UnivariateBin> = TreeMap()

151
kmath-nd4j/src/main/kotlin/kscience.kmath.nd4j/Nd4jArrayAlgebra.kt
View File

@ -1,55 +1,68 @@
package kscience.kmath.nd4j
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.*
import kscience.kmath.operations.*
import kscience.kmath.structures.NDAlgebra
import kscience.kmath.structures.NDField
import kscience.kmath.structures.NDRing
import kscience.kmath.structures.NDSpace
import kscience.kmath.structures.*
import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.factory.Nd4j
internal fun NDAlgebra<*, *>.checkShape(array: INDArray): INDArray {
val arrayShape = array.shape().toIntArray()
if (!shape.contentEquals(arrayShape)) throw ShapeMismatchException(shape, arrayShape)
return array
}
/**
* Represents [NDAlgebra] over [Nd4jArrayAlgebra].
*
* @param T the type of ND-structure element.
* @param C the type of the element context.
*/
public interface Nd4jArrayAlgebra<T, C> : NDAlgebra<T, C, Nd4jArrayStructure<T>> {
public interface Nd4jArrayAlgebra<T, C> : NDAlgebra<T, C> {
/**
* Wraps [INDArray] to [N].
*/
public fun INDArray.wrap(): Nd4jArrayStructure<T>
public val NDStructure<T>.ndArray: INDArray
get() = when {
!shape.contentEquals(this@Nd4jArrayAlgebra.shape) -> throw ShapeMismatchException(
this@Nd4jArrayAlgebra.shape,
shape
)
this is Nd4jArrayStructure -> ndArray //TODO check strides
else -> {
TODO()
}
}
public override fun produce(initializer: C.(IntArray) -> T): Nd4jArrayStructure<T> {
val struct = Nd4j.create(*shape)!!.wrap()
struct.indicesIterator().forEach { struct[it] = elementContext.initializer(it) }
return struct
}
public override fun map(arg: Nd4jArrayStructure<T>, transform: C.(T) -> T): Nd4jArrayStructure<T> {
check(arg)
val newStruct = arg.ndArray.dup().wrap()
public override fun NDStructure<T>.map(transform: C.(T) -> T): Nd4jArrayStructure<T> {
val newStruct = ndArray.dup().wrap()
newStruct.elements().forEach { (idx, value) -> newStruct[idx] = elementContext.transform(value) }
return newStruct
}
public override fun mapIndexed(
arg: Nd4jArrayStructure<T>,
public override fun NDStructure<T>.mapIndexed(
transform: C.(index: IntArray, T) -> T,
): Nd4jArrayStructure<T> {
check(arg)
val new = Nd4j.create(*shape).wrap()
new.indicesIterator().forEach { idx -> new[idx] = elementContext.transform(idx, arg[idx]) }
val new = Nd4j.create(*this@Nd4jArrayAlgebra.shape).wrap()
new.indicesIterator().forEach { idx -> new[idx] = elementContext.transform(idx, this[idx]) }
return new
}
public override fun combine(
a: Nd4jArrayStructure<T>,
b: Nd4jArrayStructure<T>,
a: NDStructure<T>,
b: NDStructure<T>,
transform: C.(T, T) -> T,
): Nd4jArrayStructure<T> {
check(a, b)
val new = Nd4j.create(*shape).wrap()
new.indicesIterator().forEach { idx -> new[idx] = elementContext.transform(a[idx], b[idx]) }
return new
@ -62,38 +75,32 @@ public interface Nd4jArrayAlgebra<T, C> : NDAlgebra<T, C, Nd4jArrayStructure<T>>
* @param T the type of the element contained in ND structure.
* @param S the type of space of structure elements.
*/
public interface Nd4jArraySpace<T, S : Space<T>> : NDSpace<T, S, Nd4jArrayStructure<T>>, Nd4jArrayAlgebra<T, S> {
public interface Nd4jArraySpace<T, S : Space<T>> : NDSpace<T, S>, Nd4jArrayAlgebra<T, S> {
public override val zero: Nd4jArrayStructure<T>
get() = Nd4j.zeros(*shape).wrap()
public override fun add(a: Nd4jArrayStructure<T>, b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(a, b)
public override fun add(a: NDStructure<T>, b: NDStructure<T>): Nd4jArrayStructure<T> {
return a.ndArray.add(b.ndArray).wrap()
}
public override operator fun Nd4jArrayStructure<T>.minus(b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(this, b)
public override operator fun NDStructure<T>.minus(b: NDStructure<T>): Nd4jArrayStructure<T> {
return ndArray.sub(b.ndArray).wrap()
}
public override operator fun Nd4jArrayStructure<T>.unaryMinus(): Nd4jArrayStructure<T> {
check(this)
public override operator fun NDStructure<T>.unaryMinus(): Nd4jArrayStructure<T> {
return ndArray.neg().wrap()
}
public override fun multiply(a: Nd4jArrayStructure<T>, k: Number): Nd4jArrayStructure<T> {
check(a)
public override fun multiply(a: NDStructure<T>, k: Number): Nd4jArrayStructure<T> {
return a.ndArray.mul(k).wrap()
}
public override operator fun Nd4jArrayStructure<T>.div(k: Number): Nd4jArrayStructure<T> {
check(this)
public override operator fun NDStructure<T>.div(k: Number): Nd4jArrayStructure<T> {
return ndArray.div(k).wrap()
}
public override operator fun Nd4jArrayStructure<T>.times(k: Number): Nd4jArrayStructure<T> {
check(this)
public override operator fun NDStructure<T>.times(k: Number): Nd4jArrayStructure<T> {
return ndArray.mul(k).wrap()
}
}
@ -105,13 +112,12 @@ public interface Nd4jArraySpace<T, S : Space<T>> : NDSpace<T, S, Nd4jArrayStruct
* @param R the type of ring of structure elements.
*/
@OptIn(UnstableKMathAPI::class)
public interface Nd4jArrayRing<T, R : Ring<T>> : NDRing<T, R, Nd4jArrayStructure<T>>, Nd4jArraySpace<T, R> {
public interface Nd4jArrayRing<T, R : Ring<T>> : NDRing<T, R>, Nd4jArraySpace<T, R> {
public override val one: Nd4jArrayStructure<T>
get() = Nd4j.ones(*shape).wrap()
public override fun multiply(a: Nd4jArrayStructure<T>, b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(a, b)
public override fun multiply(a: NDStructure<T>, b: NDStructure<T>): Nd4jArrayStructure<T> {
return a.ndArray.mul(b.ndArray).wrap()
}
//
@ -168,17 +174,12 @@ public interface Nd4jArrayRing<T, R : Ring<T>> : NDRing<T, R, Nd4jArrayStructure
* @param N the type of ND structure.
* @param F the type field of structure elements.
*/
public interface Nd4jArrayField<T, F : Field<T>> : NDField<T, F, Nd4jArrayStructure<T>>, Nd4jArrayRing<T, F> {
public interface Nd4jArrayField<T, F : Field<T>> : NDField<T, F>, Nd4jArrayRing<T, F> {
public override fun divide(a: Nd4jArrayStructure<T>, b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(a, b)
return a.ndArray.div(b.ndArray).wrap()
}
public override fun divide(a: NDStructure<T>, b: NDStructure<T>): Nd4jArrayStructure<T> =
a.ndArray.div(b.ndArray).wrap()
public override operator fun Number.div(b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(b)
return b.ndArray.rdiv(this).wrap()
}
public override operator fun Number.div(b: NDStructure<T>): Nd4jArrayStructure<T> = b.ndArray.rdiv(this).wrap()
public companion object {
@ -219,35 +220,29 @@ public class RealNd4jArrayField(public override val shape: IntArray) : Nd4jArray
public override val elementContext: RealField
get() = RealField
public override fun INDArray.wrap(): Nd4jArrayStructure<Double> = check(asRealStructure())
public override fun INDArray.wrap(): Nd4jArrayStructure<Double> = checkShape(this).asRealStructure()
public override operator fun Nd4jArrayStructure<Double>.div(arg: Double): Nd4jArrayStructure<Double> {
check(this)
public override operator fun NDStructure<Double>.div(arg: Double): Nd4jArrayStructure<Double> {
return ndArray.div(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Double>.plus(arg: Double): Nd4jArrayStructure<Double> {
check(this)
public override operator fun NDStructure<Double>.plus(arg: Double): Nd4jArrayStructure<Double> {
return ndArray.add(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Double>.minus(arg: Double): Nd4jArrayStructure<Double> {
check(this)
public override operator fun NDStructure<Double>.minus(arg: Double): Nd4jArrayStructure<Double> {
return ndArray.sub(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Double>.times(arg: Double): Nd4jArrayStructure<Double> {
check(this)
public override operator fun NDStructure<Double>.times(arg: Double): Nd4jArrayStructure<Double> {
return ndArray.mul(arg).wrap()
}
public override operator fun Double.div(arg: Nd4jArrayStructure<Double>): Nd4jArrayStructure<Double> {
check(arg)
public override operator fun Double.div(arg: NDStructure<Double>): Nd4jArrayStructure<Double> {
return arg.ndArray.rdiv(this).wrap()
}
public override operator fun Double.minus(arg: Nd4jArrayStructure<Double>): Nd4jArrayStructure<Double> {
check(arg)
public override operator fun Double.minus(arg: NDStructure<Double>): Nd4jArrayStructure<Double> {
return arg.ndArray.rsub(this).wrap()
}
}
@ -259,35 +254,29 @@ public class FloatNd4jArrayField(public override val shape: IntArray) : Nd4jArra
public override val elementContext: FloatField
get() = FloatField
public override fun INDArray.wrap(): Nd4jArrayStructure<Float> = check(asFloatStructure())
public override fun INDArray.wrap(): Nd4jArrayStructure<Float> = checkShape(this).asFloatStructure()
public override operator fun Nd4jArrayStructure<Float>.div(arg: Float): Nd4jArrayStructure<Float> {
check(this)
public override operator fun NDStructure<Float>.div(arg: Float): Nd4jArrayStructure<Float> {
return ndArray.div(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Float>.plus(arg: Float): Nd4jArrayStructure<Float> {
check(this)
public override operator fun NDStructure<Float>.plus(arg: Float): Nd4jArrayStructure<Float> {
return ndArray.add(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Float>.minus(arg: Float): Nd4jArrayStructure<Float> {
check(this)
public override operator fun NDStructure<Float>.minus(arg: Float): Nd4jArrayStructure<Float> {
return ndArray.sub(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Float>.times(arg: Float): Nd4jArrayStructure<Float> {
check(this)
public override operator fun NDStructure<Float>.times(arg: Float): Nd4jArrayStructure<Float> {
return ndArray.mul(arg).wrap()
}
public override operator fun Float.div(arg: Nd4jArrayStructure<Float>): Nd4jArrayStructure<Float> {
check(arg)
public override operator fun Float.div(arg: NDStructure<Float>): Nd4jArrayStructure<Float> {
return arg.ndArray.rdiv(this).wrap()
}
public override operator fun Float.minus(arg: Nd4jArrayStructure<Float>): Nd4jArrayStructure<Float> {
check(arg)
public override operator fun Float.minus(arg: NDStructure<Float>): Nd4jArrayStructure<Float> {
return arg.ndArray.rsub(this).wrap()
}
}
@ -299,25 +288,21 @@ public class IntNd4jArrayRing(public override val shape: IntArray) : Nd4jArrayRi
public override val elementContext: IntRing
get() = IntRing
public override fun INDArray.wrap(): Nd4jArrayStructure<Int> = check(asIntStructure())
public override fun INDArray.wrap(): Nd4jArrayStructure<Int> = checkShape(this).asIntStructure()
public override operator fun Nd4jArrayStructure<Int>.plus(arg: Int): Nd4jArrayStructure<Int> {
check(this)
public override operator fun NDStructure<Int>.plus(arg: Int): Nd4jArrayStructure<Int> {
return ndArray.add(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Int>.minus(arg: Int): Nd4jArrayStructure<Int> {
check(this)
public override operator fun NDStructure<Int>.minus(arg: Int): Nd4jArrayStructure<Int> {
return ndArray.sub(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Int>.times(arg: Int): Nd4jArrayStructure<Int> {
check(this)
public override operator fun NDStructure<Int>.times(arg: Int): Nd4jArrayStructure<Int> {
return ndArray.mul(arg).wrap()
}
public override operator fun Int.minus(arg: Nd4jArrayStructure<Int>): Nd4jArrayStructure<Int> {
check(arg)
public override operator fun Int.minus(arg: NDStructure<Int>): Nd4jArrayStructure<Int> {
return arg.ndArray.rsub(this).wrap()
}
}
@ -329,25 +314,21 @@ public class LongNd4jArrayRing(public override val shape: IntArray) : Nd4jArrayR
public override val elementContext: LongRing
get() = LongRing
public override fun INDArray.wrap(): Nd4jArrayStructure<Long> = check(asLongStructure())
public override fun INDArray.wrap(): Nd4jArrayStructure<Long> = checkShape(this).asLongStructure()
public override operator fun Nd4jArrayStructure<Long>.plus(arg: Long): Nd4jArrayStructure<Long> {
check(this)
public override operator fun NDStructure<Long>.plus(arg: Long): Nd4jArrayStructure<Long> {
return ndArray.add(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Long>.minus(arg: Long): Nd4jArrayStructure<Long> {
check(this)
public override operator fun NDStructure<Long>.minus(arg: Long): Nd4jArrayStructure<Long> {
return ndArray.sub(arg).wrap()
}
public override operator fun Nd4jArrayStructure<Long>.times(arg: Long): Nd4jArrayStructure<Long> {
check(this)
public override operator fun NDStructure<Long>.times(arg: Long): Nd4jArrayStructure<Long> {
return ndArray.mul(arg).wrap()
}
public override operator fun Long.minus(arg: Nd4jArrayStructure<Long>): Nd4jArrayStructure<Long> {
check(arg)
public override operator fun Long.minus(arg: NDStructure<Long>): Nd4jArrayStructure<Long> {
return arg.ndArray.rsub(this).wrap()
}
}

4
kmath-nd4j/src/main/kotlin/kscience.kmath.nd4j/Nd4jArrayStructure.kt
View File

@ -1,7 +1,7 @@
package kscience.kmath.nd4j
import kscience.kmath.structures.MutableNDStructure
import kscience.kmath.structures.NDStructure
import kscience.kmath.nd.MutableNDStructure
import kscience.kmath.nd.NDStructure
import org.nd4j.linalg.api.ndarray.INDArray
/**

4
kmath-nd4j/src/test/kotlin/kscience/kmath/nd4j/Nd4jArrayAlgebraTest.kt
View File

@ -1,7 +1,7 @@
package kscience.kmath.nd4j
import org.nd4j.linalg.factory.Nd4j
import kscience.kmath.operations.invoke
import org.nd4j.linalg.factory.Nd4j
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.fail
@ -20,7 +20,7 @@ internal class Nd4jArrayAlgebraTest {
@Test
fun testMap() {
val res = (IntNd4jArrayRing(intArrayOf(2, 2))) { map(one) { it + it * 2 } }
val res = (IntNd4jArrayRing(intArrayOf(2, 2))) { one.map() { it + it * 2 } }
val expected = (Nd4j.create(2, 2) ?: fail()).asIntStructure()
expected[intArrayOf(0, 0)] = 3
expected[intArrayOf(0, 1)] = 3

2
kmath-nd4j/src/test/kotlin/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt
View File

@ -1,6 +1,6 @@
package kscience.kmath.nd4j
import kscience.kmath.structures.get
import kscience.kmath.nd.get
import org.nd4j.linalg.factory.Nd4j
import kotlin.test.Test
import kotlin.test.assertEquals

81
kmath-viktor/src/main/kotlin/kscience/kmath/viktor/ViktorNDStructure.kt
View File

@ -1,10 +1,10 @@
package kscience.kmath.viktor
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.nd.*
import kscience.kmath.operations.ExtendedField
import kscience.kmath.operations.RealField
import kscience.kmath.structures.DefaultStrides
import kscience.kmath.structures.MutableNDStructure
import kscience.kmath.structures.NDField
import kscience.kmath.structures.Strides
import kscience.kmath.operations.RingWithNumbers
import org.jetbrains.bio.viktor.F64Array
@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
@ -23,15 +23,28 @@ public inline class ViktorNDStructure(public val f64Buffer: F64Array) : MutableN
public fun F64Array.asStructure(): ViktorNDStructure = ViktorNDStructure(this)
@OptIn(UnstableKMathAPI::class)
@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public class ViktorNDField(public override val shape: IntArray) : NDField<Double, RealField, ViktorNDStructure> {
public class ViktorNDField(public override val shape: IntArray) : NDField<Double, RealField>,
RingWithNumbers<NDStructure<Double>>, ExtendedField<NDStructure<Double>> {
public val NDStructure<Double>.f64Buffer: F64Array
get() = when {
!shape.contentEquals(this@ViktorNDField.shape) -> throw ShapeMismatchException(
this@ViktorNDField.shape,
shape
)
this is ViktorNDStructure && this.f64Buffer.shape.contentEquals(this@ViktorNDField.shape) -> this.f64Buffer
else -> produce { this@f64Buffer[it] }.f64Buffer
}
public override val zero: ViktorNDStructure
get() = F64Array.full(init = 0.0, shape = shape).asStructure()
public override val one: ViktorNDStructure
get() = F64Array.full(init = 1.0, shape = shape).asStructure()
public val strides: Strides = DefaultStrides(shape)
private val strides: Strides = DefaultStrides(shape)
public override val elementContext: RealField get() = RealField
@ -42,47 +55,67 @@ public class ViktorNDField(public override val shape: IntArray) : NDField<Double
}
}.asStructure()
public override fun map(arg: ViktorNDStructure, transform: RealField.(Double) -> Double): ViktorNDStructure =
F64Array(*shape).apply {
public override fun NDStructure<Double>.map(transform: RealField.(Double) -> Double): ViktorNDStructure =
F64Array(*this@ViktorNDField.shape).apply {
this@ViktorNDField.strides.indices().forEach { index ->
set(value = RealField.transform(arg[index]), indices = index)
set(value = RealField.transform(this@map[index]), indices = index)
}
}.asStructure()
public override fun mapIndexed(
arg: ViktorNDStructure,
transform: RealField.(index: IntArray, Double) -> Double
): ViktorNDStructure = F64Array(*shape).apply {
public override fun NDStructure<Double>.mapIndexed(
transform: RealField.(index: IntArray, Double) -> Double,
): ViktorNDStructure = F64Array(*this@ViktorNDField.shape).apply {
this@ViktorNDField.strides.indices().forEach { index ->
set(value = RealField.transform(index, arg[index]), indices = index)
set(value = RealField.transform(index, this@mapIndexed[index]), indices = index)
}
}.asStructure()
public override fun combine(
a: ViktorNDStructure,
b: ViktorNDStructure,
transform: RealField.(Double, Double) -> Double
a: NDStructure<Double>,
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): ViktorNDStructure = F64Array(*shape).apply {
this@ViktorNDField.strides.indices().forEach { index ->
set(value = RealField.transform(a[index], b[index]), indices = index)
}
}.asStructure()
public override fun add(a: ViktorNDStructure, b: ViktorNDStructure): ViktorNDStructure =
public override fun add(a: NDStructure<Double>, b: NDStructure<Double>): ViktorNDStructure =
(a.f64Buffer + b.f64Buffer).asStructure()
public override fun multiply(a: ViktorNDStructure, k: Number): ViktorNDStructure =
public override fun multiply(a: NDStructure<Double>, k: Number): ViktorNDStructure =
(a.f64Buffer * k.toDouble()).asStructure()
public override inline fun ViktorNDStructure.plus(b: ViktorNDStructure): ViktorNDStructure =
public override inline fun NDStructure<Double>.plus(b: NDStructure<Double>): ViktorNDStructure =
(f64Buffer + b.f64Buffer).asStructure()
public override inline fun ViktorNDStructure.minus(b: ViktorNDStructure): ViktorNDStructure =
public override inline fun NDStructure<Double>.minus(b: NDStructure<Double>): ViktorNDStructure =
(f64Buffer - b.f64Buffer).asStructure()
public override inline fun ViktorNDStructure.times(k: Number): ViktorNDStructure =
public override inline fun NDStructure<Double>.times(k: Number): ViktorNDStructure =
(f64Buffer * k.toDouble()).asStructure()
public override inline fun ViktorNDStructure.plus(arg: Double): ViktorNDStructure =
public override inline fun NDStructure<Double>.plus(arg: Double): ViktorNDStructure =
(f64Buffer.plus(arg)).asStructure()
}
override fun number(value: Number): ViktorNDStructure =
F64Array.full(init = value.toDouble(), shape = shape).asStructure()
override fun sin(arg: NDStructure<Double>): ViktorNDStructure = arg.map { sin(it) }
override fun cos(arg: NDStructure<Double>): ViktorNDStructure = arg.map { cos(it) }
override fun asin(arg: NDStructure<Double>): ViktorNDStructure = arg.map { asin(it) }
override fun acos(arg: NDStructure<Double>): ViktorNDStructure = arg.map { acos(it) }
override fun atan(arg: NDStructure<Double>): ViktorNDStructure = arg.map { atan(it) }
override fun power(arg: NDStructure<Double>, pow: Number): ViktorNDStructure = arg.map { it.pow(pow) }
override fun exp(arg: NDStructure<Double>): ViktorNDStructure = arg.f64Buffer.exp().asStructure()
override fun ln(arg: NDStructure<Double>): ViktorNDStructure = arg.f64Buffer.log().asStructure()
}
public fun ViktorNDField(vararg shape: Int): ViktorNDField = ViktorNDField(shape)