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Merge branch 'dev' into feature/quaternion

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# Conflicts:
#	CHANGELOG.md
#	examples/src/main/kotlin/kscience/kmath/stat/DistributionBenchmark.kt
#	kmath-complex/src/commonMain/kotlin/kscience/kmath/complex/ComplexNDField.kt
#	kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt
#	kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NDFieldTest.kt
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Iaroslav Postovalov 2021-01-20 01:10:31 +07:00
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46 changed files with 1137 additions and 611 deletions
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25
CHANGELOG.md
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@ -4,28 +4,29 @@
### Added
- `fun` annotation for SAM interfaces in library
- Explicit `public` visibility for all public APIs
- Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140).
- Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140)
- Automatic README generation for features (#139)
- Native support for `memory`, `core` and `dimensions`
- `kmath-ejml` to supply EJML SimpleMatrix wrapper (https://github.com/mipt-npm/kmath/pull/136).
- `kmath-ejml` to supply EJML SimpleMatrix wrapper (https://github.com/mipt-npm/kmath/pull/136)
- A separate `Symbol` entity, which is used for global unbound symbol.
- A `Symbol` indexing scope.
- Basic optimization API for Commons-math.
- Chi squared optimization for array-like data in CM
- `Fitting` utility object in prob/stat
- ND4J support module submitting `NDStructure` and `NDAlgebra` over `INDArray`.
- Coroutine-deterministic Monte-Carlo scope with a random number generator.
- Some minor utilities to `kmath-for-real`.
- Basic Quaternion vector support in `kmath-complex`.
- ND4J support module submitting `NDStructure` and `NDAlgebra` over `INDArray`
- Coroutine-deterministic Monte-Carlo scope with a random number generator
- Some minor utilities to `kmath-for-real`
- Generic operation result parameter to `MatrixContext`
- New `MatrixFeature` interfaces for matrix decompositions
- Basic Quaternion vector support in `kmath-complex`.
### Changed
- Package changed from `scientifik` to `kscience.kmath`.
- Gradle version: 6.6 -> 6.7.1
- Package changed from `scientifik` to `kscience.kmath`
- Gradle version: 6.6 -> 6.8
- Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
- `Polynomial` secondary constructor made function.
- Kotlin version: 1.3.72 -> 1.4.20
- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library.
- `Polynomial` secondary constructor made function
- Kotlin version: 1.3.72 -> 1.4.21
- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library
- Full autodiff refactoring based on `Symbol`
- `kmath-prob` renamed to `kmath-stat`
- Grid generators moved to `kmath-for-real`
@ -33,6 +34,8 @@
- Optimized dot product for buffer matrices moved to `kmath-for-real`
- EjmlMatrix context is an object
- 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.
- `Complex` and related features moved to a separate module `kmath-complex`
### Deprecated

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-4")
internal val kmathVersion: String by extra("0.2.0-dev-5")
internal val bintrayRepo: String by extra("kscience")
internal val githubProject: String by extra("kmath")

24
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/MultiplicationBenchmark.kt → examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -2,19 +2,22 @@ package kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kscience.kmath.commons.linear.CMMatrixContext
import kscience.kmath.commons.linear.CMMatrixContext.dot
import kscience.kmath.commons.linear.toCM
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.ejml.toEjml
import kscience.kmath.linear.BufferMatrixContext
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
@State(Scope.Benchmark)
class MultiplicationBenchmark {
class DotBenchmark {
companion object {
val random = Random(12224)
val dim = 1000
@ -32,14 +35,14 @@ class MultiplicationBenchmark {
@Benchmark
fun commonsMathMultiplication() {
CMMatrixContext.invoke {
CMMatrixContext {
cmMatrix1 dot cmMatrix2
}
}
@Benchmark
fun ejmlMultiplication() {
EjmlMatrixContext.invoke {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@ -48,13 +51,22 @@ class MultiplicationBenchmark {
fun ejmlMultiplicationwithConversion() {
val ejmlMatrix1 = matrix1.toEjml()
val ejmlMatrix2 = matrix2.toEjml()
EjmlMatrixContext.invoke {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@Benchmark
fun bufferedMultiplication() {
matrix1 dot matrix2
BufferMatrixContext(RealField, Buffer.Companion::real).invoke{
matrix1 dot matrix2
}
}
@Benchmark
fun realMultiplication(){
RealMatrixContext {
matrix1 dot matrix2
}
}
}

25
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LargeNDBenchmark.kt Normal file
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@ -0,0 +1,25 @@
package kscience.kmath.benchmarks
import kscience.kmath.structures.NDField
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import org.openjdk.jmh.infra.Blackhole
import kotlin.random.Random
@State(Scope.Benchmark)
class LargeNDBenchmark {
val arraySize = 10000
val RANDOM = Random(222)
val src1 = DoubleArray(arraySize) { RANDOM.nextDouble() }
val src2 = DoubleArray(arraySize) { RANDOM.nextDouble() }
val field = NDField.real(arraySize)
val kmathArray1 = field.produce { (a) -> src1[a] }
val kmathArray2 = field.produce { (a) -> src2[a] }
@Benchmark
fun test10000(bh: Blackhole) {
bh.consume(field.add(kmathArray1, kmathArray2))
}
}

3
examples/src/main/kotlin/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,8 +1,9 @@
package kscience.kmath.stat
package kscience.kmath.commons.prob
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking
import kscience.kmath.stat.*
import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler
import org.apache.commons.rng.simple.RandomSource
import java.time.Duration

2
examples/src/main/kotlin/kscience/kmath/structures/ComplexND.kt
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@ -13,7 +13,7 @@ fun main() {
val n = 1000
val realField = NDField.real(dim, dim)
val complexField = NDField.complex(dim, dim)
val complexField: ComplexNDField = NDField.complex(dim, dim)
val realTime = measureTimeMillis {
realField {

6
examples/src/main/kotlin/kscience/kmath/structures/NDField.kt
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@ -33,7 +33,7 @@ fun main() {
measureAndPrint("Automatic field addition") {
autoField {
var res: NDBuffer<Double> = one
repeat(n) { res += number(1.0) }
repeat(n) { res += 1.0 }
}
}
@ -52,7 +52,7 @@ fun main() {
measureAndPrint("Nd4j specialized addition") {
nd4jField {
var res = one
repeat(n) { res += 1.0 as Number }
repeat(n) { res += 1.0 }
}
}
@ -73,7 +73,7 @@ fun main() {
genericField {
var res: NDBuffer<Double> = one
repeat(n) {
res += one // couldn't avoid using `one` due to resolution ambiguity }
res += 1.0 // couldn't avoid using `one` due to resolution ambiguity }
}
}
}

5
examples/src/main/kotlin/kscience/kmath/structures/typeSafeDimensions.kt
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@ -4,9 +4,8 @@ import kscience.kmath.dimensions.D2
import kscience.kmath.dimensions.D3
import kscience.kmath.dimensions.DMatrixContext
import kscience.kmath.dimensions.Dimension
import kscience.kmath.operations.RealField
private fun DMatrixContext<Double, RealField>.simple() {
private fun DMatrixContext<Double>.simple() {
val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
@ -18,7 +17,7 @@ private object D5 : Dimension {
override val dim: UInt = 5u
}
private fun DMatrixContext<Double, RealField>.custom() {
private fun DMatrixContext<Double>.custom() {
val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() }
val m2 = produce<D5, D2> { i, j -> (i - j).toDouble() }
val m3 = produce<D2, D2> { i, j -> (i - j).toDouble() }

2
gradle/wrapper/gradle-wrapper.properties vendored
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@ -1,5 +1,5 @@
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-6.7.1-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-6.8-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

24
kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MST.kt
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@ -2,10 +2,9 @@ package kscience.kmath.ast
import kscience.kmath.operations.Algebra
import kscience.kmath.operations.NumericAlgebra
import kscience.kmath.operations.RealField
/**
* A Mathematical Syntax Tree node for mathematical expressions.
* A Mathematical Syntax Tree (MST) node for mathematical expressions.
*
* @author Alexander Nozik
*/
@ -57,21 +56,22 @@ public fun <T> Algebra<T>.evaluate(node: MST): T = when (node) {
?: error("Numeric nodes are not supported by $this")
is MST.Symbolic -> symbol(node.value)
is MST.Unary -> unaryOperationFunction(node.operation)(evaluate(node.value))
is MST.Unary -> when {
this is NumericAlgebra && node.value is MST.Numeric -> unaryOperationFunction(node.operation)(number(node.value.value))
else -> unaryOperationFunction(node.operation)(evaluate(node.value))
}
is MST.Binary -> when {
this !is NumericAlgebra -> binaryOperationFunction(node.operation)(evaluate(node.left), evaluate(node.right))
this is NumericAlgebra && node.left is MST.Numeric && node.right is MST.Numeric ->
binaryOperationFunction(node.operation)(number(node.left.value), number(node.right.value))
node.left is MST.Numeric && node.right is MST.Numeric -> {
val number = RealField
.binaryOperationFunction(node.operation)
.invoke(node.left.value.toDouble(), node.right.value.toDouble())
this is NumericAlgebra && node.left is MST.Numeric ->
leftSideNumberOperationFunction(node.operation)(node.left.value, evaluate(node.right))
number(number)
}
this is NumericAlgebra && node.right is MST.Numeric ->
rightSideNumberOperationFunction(node.operation)(evaluate(node.left), node.right.value)
node.left is MST.Numeric -> leftSideNumberOperationFunction(node.operation)(node.left.value, evaluate(node.right))
node.right is MST.Numeric -> rightSideNumberOperationFunction(node.operation)(evaluate(node.left), node.right.value)
else -> binaryOperationFunction(node.operation)(evaluate(node.left), evaluate(node.right))
}
}

31
kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MstAlgebra.kt
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@ -1,5 +1,6 @@
package kscience.kmath.ast
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
/**
@ -25,8 +26,11 @@ public object MstSpace : Space<MST>, NumericAlgebra<MST> {
public override fun number(value: Number): MST.Numeric = MstAlgebra.number(value)
public override fun symbol(value: String): MST.Symbolic = MstAlgebra.symbol(value)
public override fun add(a: MST, b: MST): MST.Binary = binaryOperationFunction(SpaceOperations.PLUS_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = unaryOperationFunction(SpaceOperations.PLUS_OPERATION)(this)
public override operator fun MST.unaryMinus(): MST.Unary = unaryOperationFunction(SpaceOperations.MINUS_OPERATION)(this)
public override operator fun MST.unaryPlus(): MST.Unary =
unaryOperationFunction(SpaceOperations.PLUS_OPERATION)(this)
public override operator fun MST.unaryMinus(): MST.Unary =
unaryOperationFunction(SpaceOperations.MINUS_OPERATION)(this)
public override operator fun MST.minus(b: MST): MST.Binary =
binaryOperationFunction(SpaceOperations.MINUS_OPERATION)(this, b)
@ -44,7 +48,8 @@ public object MstSpace : Space<MST>, NumericAlgebra<MST> {
/**
* [Ring] over [MST] nodes.
*/
public object MstRing : Ring<MST>, NumericAlgebra<MST> {
@OptIn(UnstableKMathAPI::class)
public object MstRing : Ring<MST>, RingWithNumbers<MST> {
public override val zero: MST.Numeric
get() = MstSpace.zero
@ -54,7 +59,9 @@ public object MstRing : Ring<MST>, NumericAlgebra<MST> {
public override fun symbol(value: String): MST.Symbolic = MstSpace.symbol(value)
public override fun add(a: MST, b: MST): MST.Binary = MstSpace.add(a, b)
public override fun multiply(a: MST, k: Number): MST.Binary = MstSpace.multiply(a, k)
public override fun multiply(a: MST, b: MST): MST.Binary = binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
public override fun multiply(a: MST, b: MST): MST.Binary =
binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = MstSpace { +this@unaryPlus }
public override operator fun MST.unaryMinus(): MST.Unary = MstSpace { -this@unaryMinus }
public override operator fun MST.minus(b: MST): MST.Binary = MstSpace { this@minus - b }
@ -69,7 +76,8 @@ public object MstRing : Ring<MST>, NumericAlgebra<MST> {
/**
* [Field] over [MST] nodes.
*/
public object MstField : Field<MST> {
@OptIn(UnstableKMathAPI::class)
public object MstField : Field<MST>, RingWithNumbers<MST> {
public override val zero: MST.Numeric
get() = MstRing.zero
@ -81,7 +89,9 @@ public object MstField : Field<MST> {
public override fun add(a: MST, b: MST): MST.Binary = MstRing.add(a, b)
public override fun multiply(a: MST, k: Number): MST.Binary = MstRing.multiply(a, k)
public override fun multiply(a: MST, b: MST): MST.Binary = MstRing.multiply(a, b)
public override fun divide(a: MST, b: MST): MST.Binary = binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
public override fun divide(a: MST, b: MST): MST.Binary =
binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = MstRing { +this@unaryPlus }
public override operator fun MST.unaryMinus(): MST.Unary = MstRing { -this@unaryMinus }
public override operator fun MST.minus(b: MST): MST.Binary = MstRing { this@minus - b }
@ -89,13 +99,14 @@ public object MstField : Field<MST> {
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstRing.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = MstRing.unaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstRing.unaryOperationFunction(operation)
}
/**
* [ExtendedField] over [MST] nodes.
*/
public object MstExtendedField : ExtendedField<MST> {
public object MstExtendedField : ExtendedField<MST>, NumericAlgebra<MST> {
public override val zero: MST.Numeric
get() = MstField.zero
@ -103,6 +114,7 @@ public object MstExtendedField : ExtendedField<MST> {
get() = MstField.one
public override fun symbol(value: String): MST.Symbolic = MstField.symbol(value)
public override fun number(value: Number): MST.Numeric = MstRing.number(value)
public override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
public override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
public override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg)
@ -132,5 +144,6 @@ public object MstExtendedField : ExtendedField<MST> {
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstField.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary = MstField.unaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstField.unaryOperationFunction(operation)
}

30
kmath-ast/src/jsMain/kotlin/kscience/kmath/estree/estree.kt
View File

@ -1,18 +1,18 @@
package kscience.kmath.estree
import kscience.kmath.ast.MST
import kscience.kmath.ast.MST.*
import kscience.kmath.ast.MstExpression
import kscience.kmath.estree.internal.ESTreeBuilder
import kscience.kmath.estree.internal.estree.BaseExpression
import kscience.kmath.expressions.Expression
import kscience.kmath.operations.Algebra
import kscience.kmath.operations.NumericAlgebra
import kscience.kmath.operations.RealField
@PublishedApi
internal fun <T> MST.compileWith(algebra: Algebra<T>): Expression<T> {
fun ESTreeBuilder<T>.visit(node: MST): BaseExpression = when (node) {
is MST.Symbolic -> {
is Symbolic -> {
val symbol = try {
algebra.symbol(node.value)
} catch (ignored: IllegalStateException) {
@ -25,25 +25,29 @@ internal fun <T> MST.compileWith(algebra: Algebra<T>): Expression<T> {
variable(node.value)
}
is MST.Numeric -> constant(node.value)
is MST.Unary -> call(algebra.unaryOperationFunction(node.operation), visit(node.value))
is Numeric -> constant(node.value)
is MST.Binary -> when {
algebra is NumericAlgebra<T> && node.left is MST.Numeric && node.right is MST.Numeric -> constant(
algebra.number(
RealField
.binaryOperationFunction(node.operation)
.invoke(node.left.value.toDouble(), node.right.value.toDouble())
)
is Unary -> when {
algebra is NumericAlgebra && node.value is Numeric -> constant(
algebra.unaryOperationFunction(node.operation)(algebra.number(node.value.value)))
else -> call(algebra.unaryOperationFunction(node.operation), visit(node.value))
}
is Binary -> when {
algebra is NumericAlgebra && node.left is Numeric && node.right is Numeric -> constant(
algebra
.binaryOperationFunction(node.operation)
.invoke(algebra.number(node.left.value), algebra.number(node.right.value))
)
algebra is NumericAlgebra<T> && node.left is MST.Numeric -> call(
algebra is NumericAlgebra && node.left is Numeric -> call(
algebra.leftSideNumberOperationFunction(node.operation),
visit(node.left),
visit(node.right),
)
algebra is NumericAlgebra<T> && node.right is MST.Numeric -> call(
algebra is NumericAlgebra && node.right is Numeric -> call(
algebra.rightSideNumberOperationFunction(node.operation),
visit(node.left),
visit(node.right),

31
kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/asm.kt
View File

@ -3,11 +3,11 @@ package kscience.kmath.asm
import kscience.kmath.asm.internal.AsmBuilder
import kscience.kmath.asm.internal.buildName
import kscience.kmath.ast.MST
import kscience.kmath.ast.MST.*
import kscience.kmath.ast.MstExpression
import kscience.kmath.expressions.Expression
import kscience.kmath.operations.Algebra
import kscience.kmath.operations.NumericAlgebra
import kscience.kmath.operations.RealField
/**
* Compiles given MST to an Expression using AST compiler.
@ -20,7 +20,7 @@ import kscience.kmath.operations.RealField
@PublishedApi
internal fun <T : Any> MST.compileWith(type: Class<T>, algebra: Algebra<T>): Expression<T> {
fun AsmBuilder<T>.visit(node: MST): Unit = when (node) {
is MST.Symbolic -> {
is Symbolic -> {
val symbol = try {
algebra.symbol(node.value)
} catch (ignored: IllegalStateException) {
@ -33,24 +33,29 @@ internal fun <T : Any> MST.compileWith(type: Class<T>, algebra: Algebra<T>): Exp
loadVariable(node.value)
}
is MST.Numeric -> loadNumberConstant(node.value)
is MST.Unary -> buildCall(algebra.unaryOperationFunction(node.operation)) { visit(node.value) }
is Numeric -> loadNumberConstant(node.value)
is MST.Binary -> when {
algebra is NumericAlgebra<T> && node.left is MST.Numeric && node.right is MST.Numeric -> loadObjectConstant(
algebra.number(
RealField
.binaryOperationFunction(node.operation)
.invoke(node.left.value.toDouble(), node.right.value.toDouble())
)
is Unary -> when {
algebra is NumericAlgebra && node.value is Numeric -> loadObjectConstant(
algebra.unaryOperationFunction(node.operation)(algebra.number(node.value.value)))
else -> buildCall(algebra.unaryOperationFunction(node.operation)) { visit(node.value) }
}
is Binary -> when {
algebra is NumericAlgebra && node.left is Numeric && node.right is Numeric -> loadObjectConstant(
algebra.binaryOperationFunction(node.operation)
.invoke(algebra.number(node.left.value), algebra.number(node.right.value))
)
algebra is NumericAlgebra<T> && node.left is MST.Numeric -> buildCall(algebra.leftSideNumberOperationFunction(node.operation)) {
algebra is NumericAlgebra && node.left is Numeric -> buildCall(
algebra.leftSideNumberOperationFunction(node.operation)) {
visit(node.left)
visit(node.right)
}
algebra is NumericAlgebra<T> && node.right is MST.Numeric -> buildCall(algebra.rightSideNumberOperationFunction(node.operation)) {
algebra is NumericAlgebra && node.right is Numeric -> buildCall(
algebra.rightSideNumberOperationFunction(node.operation)) {
visit(node.left)
visit(node.right)
}

7
kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt
View File

@ -1,7 +1,9 @@
package kscience.kmath.commons.expressions
import kscience.kmath.expressions.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.ExtendedField
import kscience.kmath.operations.RingWithNumbers
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
/**
@ -10,15 +12,18 @@ import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
* @property order The derivation order.
* @property bindings The map of bindings values. All bindings are considered free parameters
*/
@OptIn(UnstableKMathAPI::class)
public class DerivativeStructureField(
public val order: Int,
bindings: Map<Symbol, Double>,
) : ExtendedField<DerivativeStructure>, ExpressionAlgebra<Double, DerivativeStructure> {
) : ExtendedField<DerivativeStructure>, ExpressionAlgebra<Double, DerivativeStructure>, RingWithNumbers<DerivativeStructure> {
public val numberOfVariables: Int = bindings.size
public override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
public override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) }
override fun number(value: Number): DerivativeStructure = const(value.toDouble())
/**
* A class that implements both [DerivativeStructure] and a [Symbol]
*/

49
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
View File

@ -1,41 +1,28 @@
package kscience.kmath.commons.linear
import kscience.kmath.linear.*
import kscience.kmath.linear.DiagonalFeature
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.MatrixWrapper
import kscience.kmath.linear.Point
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.NDStructure
import org.apache.commons.math3.linear.*
import kotlin.reflect.KClass
import kotlin.reflect.cast
public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
public override val rowNum: Int get() = origin.rowDimension
public override val colNum: Int get() = origin.columnDimension
public override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
if (origin is DiagonalMatrix) yield(DiagonalFeature)
}.toHashSet()
public override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
CMMatrix(origin, this.features + features)
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
else -> null
}?.let { type.cast(it) }
public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
public override fun equals(other: Any?): Boolean {
return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
}
public override fun hashCode(): Int {
var result = origin.hashCode()
result = 31 * result + features.hashCode()
return result
}
}
public fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
this
} else {
//TODO add feature analysis
val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
CMMatrix(Array2DRowRealMatrix(array))
}
public fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
@ -60,6 +47,16 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
return CMMatrix(Array2DRowRealMatrix(array))
}
public fun Matrix<Double>.toCM(): CMMatrix = when {
this is CMMatrix -> this
this is MatrixWrapper && matrix is CMMatrix -> matrix as CMMatrix
else -> {
//TODO add feature analysis
val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
CMMatrix(Array2DRowRealMatrix(array))
}
}
public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
CMMatrix(toCM().origin.multiply(other.toCM().origin))

26
kmath-complex/src/commonMain/kotlin/kscience/kmath/complex/ComplexNDField.kt
View File

@ -1,27 +1,31 @@
package kscience.kmath.complex
package kscience.kmath.structures
import kscience.kmath.operations.FieldElement
import kscience.kmath.structures.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* Convenience alias for [BufferedNDFieldElement] of [Complex].
*/
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>> {
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) }
@ -30,7 +34,7 @@ public class ComplexNDField(override val shape: IntArray) :
*/
override fun map(
arg: NDBuffer<Complex>,
transform: ComplexField.(Complex) -> Complex
transform: ComplexField.(Complex) -> Complex,
): ComplexNDElement {
check(arg)
val array = buildBuffer(arg.strides.linearSize) { offset -> ComplexField.transform(arg.buffer[offset]) }
@ -44,7 +48,7 @@ public class ComplexNDField(override val shape: IntArray) :
override fun mapIndexed(
arg: NDBuffer<Complex>,
transform: ComplexField.(index: IntArray, Complex) -> Complex
transform: ComplexField.(index: IntArray, Complex) -> Complex,
): ComplexNDElement {
check(arg)
@ -61,7 +65,7 @@ public class ComplexNDField(override val shape: IntArray) :
override fun combine(
a: NDBuffer<Complex>,
b: NDBuffer<Complex>,
transform: ComplexField.(Complex, Complex) -> Complex
transform: ComplexField.(Complex, Complex) -> Complex,
): ComplexNDElement {
check(a, b)
@ -142,7 +146,7 @@ public fun NDField.Companion.complex(vararg shape: Int): ComplexNDField = Comple
public fun NDElement.Companion.complex(
vararg shape: Int,
initializer: ComplexField.(IntArray) -> Complex
initializer: ComplexField.(IntArray) -> Complex,
): ComplexNDElement = NDField.complex(*shape).produce(initializer)
/**

32
kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
View File

@ -7,8 +7,9 @@ import kscience.kmath.operations.*
*
* @param algebra The algebra to provide for Expressions built.
*/
public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(public val algebra: A) :
ExpressionAlgebra<T, Expression<T>> {
public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(
public val algebra: A,
) : ExpressionAlgebra<T, Expression<T>> {
/**
* Builds an Expression of constant expression which does not depend on arguments.
*/
@ -42,8 +43,9 @@ public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(public val
/**
* A context class for [Expression] construction for [Space] algebras.
*/
public open class FunctionalExpressionSpace<T, A : Space<T>>(algebra: A) :
FunctionalExpressionAlgebra<T, A>(algebra), Space<Expression<T>> {
public open class FunctionalExpressionSpace<T, A : Space<T>>(
algebra: A,
) : FunctionalExpressionAlgebra<T, A>(algebra), Space<Expression<T>> {
public override val zero: Expression<T> get() = const(algebra.zero)
/**
@ -71,8 +73,9 @@ public open class FunctionalExpressionSpace<T, A : Space<T>>(algebra: A) :
super<FunctionalExpressionAlgebra>.binaryOperationFunction(operation)
}
public open class FunctionalExpressionRing<T, A>(algebra: A) : FunctionalExpressionSpace<T, A>(algebra),
Ring<Expression<T>> where A : Ring<T>, A : NumericAlgebra<T> {
public open class FunctionalExpressionRing<T, A : Ring<T>>(
algebra: A,
) : FunctionalExpressionSpace<T, A>(algebra), Ring<Expression<T>> {
public override val one: Expression<T>
get() = const(algebra.one)
@ -92,9 +95,8 @@ public open class FunctionalExpressionRing<T, A>(algebra: A) : FunctionalExpress
super<FunctionalExpressionSpace>.binaryOperationFunction(operation)
}
public open class FunctionalExpressionField<T, A>(algebra: A) :
FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>>
where A : Field<T>, A : NumericAlgebra<T> {
public open class FunctionalExpressionField<T, A : Field<T>>(algebra: A) :
FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
/**
* Builds an Expression of division an expression by another one.
*/
@ -111,9 +113,12 @@ public open class FunctionalExpressionField<T, A>(algebra: A) :
super<FunctionalExpressionRing>.binaryOperationFunction(operation)
}
public open class FunctionalExpressionExtendedField<T, A>(algebra: A) :
FunctionalExpressionField<T, A>(algebra),
ExtendedField<Expression<T>> where A : ExtendedField<T>, A : NumericAlgebra<T> {
public open class FunctionalExpressionExtendedField<T, A : ExtendedField<T>>(
algebra: A,
) : FunctionalExpressionField<T, A>(algebra), ExtendedField<Expression<T>> {
override fun number(value: Number): Expression<T> = const(algebra.number(value))
public override fun sin(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
@ -135,7 +140,8 @@ public open class FunctionalExpressionExtendedField<T, A>(algebra: A) :
public override fun exp(arg: Expression<T>): Expression<T> =
unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
public override fun ln(arg: Expression<T>): Expression<T> = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
public override fun ln(arg: Expression<T>): Expression<T> =
unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
super<FunctionalExpressionField>.unaryOperationFunction(operation)

4
kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SimpleAutoDiff.kt
View File

@ -1,6 +1,7 @@
package kscience.kmath.expressions
import kscience.kmath.linear.Point
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kscience.kmath.structures.asBuffer
import kotlin.contracts.InvocationKind
@ -79,10 +80,11 @@ public fun <T : Any, F : Field<T>> F.simpleAutoDiff(
/**
* Represents field in context of which functions can be derived.
*/
@OptIn(UnstableKMathAPI::class)
public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
public val context: F,
bindings: Map<Symbol, T>,
) : Field<AutoDiffValue<T>>, ExpressionAlgebra<T, AutoDiffValue<T>> {
) : Field<AutoDiffValue<T>>, ExpressionAlgebra<T, AutoDiffValue<T>>, RingWithNumbers<AutoDiffValue<T>> {
public override val zero: AutoDiffValue<T>
get() = const(context.zero)

40
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/BufferMatrix.kt
View File

@ -1,6 +1,5 @@
package kscience.kmath.linear
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.structures.*
@ -21,30 +20,11 @@ public class BufferMatrixContext<T : Any, R : Ring<T>>(
public companion object
}
@Suppress("OVERRIDE_BY_INLINE")
public object RealMatrixContext : GenericMatrixContext<Double, RealField, BufferMatrix<Double>> {
public override val elementContext: RealField
get() = RealField
public override inline fun produce(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> {
val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
}
public override inline fun point(size: Int, initializer: (Int) -> Double): Point<Double> =
RealBuffer(size, initializer)
}
public class BufferMatrix<T : Any>(
public override val rowNum: Int,
public override val colNum: Int,
public val buffer: Buffer<out T>,
public override val features: Set<MatrixFeature> = emptySet(),
) : FeaturedMatrix<T> {
) : Matrix<T> {
init {
require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
@ -52,9 +32,6 @@ public class BufferMatrix<T : Any>(
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
public override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
BufferMatrix(rowNum, colNum, buffer, this.features + features)
public override operator fun get(index: IntArray): T = get(index[0], index[1])
public override operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
@ -66,23 +43,26 @@ public class BufferMatrix<T : Any>(
if (this === other) return true
return when (other) {
is NDStructure<*> -> return NDStructure.equals(this, other)
is NDStructure<*> -> NDStructure.equals(this, other)
else -> false
}
}
public override fun hashCode(): Int {
var result = buffer.hashCode()
result = 31 * result + features.hashCode()
override fun hashCode(): Int {
var result = rowNum
result = 31 * result + colNum
result = 31 * result + buffer.hashCode()
return result
}
public override fun toString(): String {
return if (rowNum <= 5 && colNum <= 5)
"Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)\n" +
"Matrix(rowsNum = $rowNum, colNum = $colNum)\n" +
rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") { buffer ->
buffer.asSequence().joinToString(separator = "\t") { it.toString() }
}
else "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)"
else "Matrix(rowsNum = $rowNum, colNum = $colNum)"
}
}

83
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/FeaturedMatrix.kt
View File

@ -1,83 +0,0 @@
package kscience.kmath.linear
import kscience.kmath.operations.Ring
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
import kscience.kmath.structures.asBuffer
import kotlin.math.sqrt
/**
* A 2d structure plus optional matrix-specific features
*/
public interface FeaturedMatrix<T : Any> : Matrix<T> {
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
public val features: Set<MatrixFeature>
/**
* Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
*
* The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
* add only those features that are valid.
*/
public fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T>
public companion object
}
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.
*/
public fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
val size: Int = sqrt(elements.size.toDouble()).toInt()
require(size * size == elements.size) { "The number of elements ${elements.size} is not a full square" }
val buffer = elements.asBuffer()
return BufferMatrix(size, size, buffer)
}
public val Matrix<*>.features: Set<MatrixFeature> get() = (this as? FeaturedMatrix)?.features ?: emptySet()
/**
* Check if matrix has the given feature class
*/
public inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean =
features.find { it is T } != null
/**
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
*/
public inline fun <reified T : Any> Matrix<*>.getFeature(): T? =
features.filterIsInstance<T>().firstOrNull()
/**
* Diagonal matrix of ones. The matrix is virtual no actual matrix is created
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): FeaturedMatrix<T> =
VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
if (i == j) elementContext.one else elementContext.zero
}
/**
* A virtual matrix of zeroes
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
VirtualMatrix(rows, columns) { _, _ -> elementContext.zero }
public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature
/**
* Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
*/
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
return getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
colNum,
rowNum,
setOf(TransposedFeature(this))
) { i, j -> get(j, i) }
}

65
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LUPDecomposition.kt → kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LupDecomposition.kt
View File

@ -1,31 +1,31 @@
package kscience.kmath.linear
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kscience.kmath.structures.*
/**
* Common implementation of [LUPDecompositionFeature]
* Common implementation of [LupDecompositionFeature].
*/
public class LUPDecomposition<T : Any>(
public val context: MatrixContext<T, FeaturedMatrix<T>>,
public class LupDecomposition<T : Any>(
public val context: MatrixContext<T, Matrix<T>>,
public val elementContext: Field<T>,
public val lu: Structure2D<T>,
public val lu: Matrix<T>,
public val pivot: IntArray,
private val even: Boolean,
) : LUPDecompositionFeature<T>, DeterminantFeature<T> {
) : LupDecompositionFeature<T>, DeterminantFeature<T> {
/**
* Returns the matrix L of the decomposition.
*
* L is a lower-triangular matrix with [Ring.one] in diagonal
*/
override val l: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
when {
j < i -> lu[i, j]
j == i -> elementContext.one
else -> elementContext.zero
}
}
} + LFeature
/**
@ -33,9 +33,9 @@ public class LUPDecomposition<T : Any>(
*
* U is an upper-triangular matrix including the diagonal
*/
override val u: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
if (j >= i) lu[i, j] else elementContext.zero
}
} + UFeature
/**
* Returns the P rows permutation matrix.
@ -43,7 +43,7 @@ public class LUPDecomposition<T : Any>(
* P is a sparse matrix with exactly one element set to [Ring.one] in
* each row and each column, all other elements being set to [Ring.zero].
*/
override val p: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
override val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
if (j == pivot[i]) elementContext.one else elementContext.zero
}
@ -64,12 +64,12 @@ internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs
/**
* Create a lup decomposition of generic matrix.
*/
public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
factory: MutableBufferFactory<T>,
elementContext: Field<T>,
matrix: Matrix<T>,
checkSingular: (T) -> Boolean,
): LUPDecomposition<T> {
): LupDecomposition<T> {
require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
val m = matrix.colNum
val pivot = IntArray(matrix.rowNum)
@ -138,20 +138,23 @@ public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
for (row in col + 1 until m) lu[row, col] /= luDiag
}
return LUPDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
return LupDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
}
}
}
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.lup(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.lup(
matrix: Matrix<T>,
noinline checkSingular: (T) -> Boolean,
): LUPDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
public fun MatrixContext<Double, FeaturedMatrix<Double>>.lup(matrix: Matrix<Double>): LUPDecomposition<Double> =
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(factory: MutableBufferFactory<T>, matrix: Matrix<T>): FeaturedMatrix<T> {
public fun <T : Any> LupDecomposition<T>.solveWithLUP(
factory: MutableBufferFactory<T>,
matrix: Matrix<T>,
): Matrix<T> {
require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
@ -196,34 +199,40 @@ public fun <T : Any> LUPDecomposition<T>.solveWithLUP(factory: MutableBufferFact
}
}
public inline fun <reified T : Any> LUPDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
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
*/
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.solveWithLUP(
@OptIn(UnstableKMathAPI::class)
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,
noinline checkSingular: (T) -> Boolean,
): FeaturedMatrix<T> {
): 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)
}
public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(a, b) { it < 1e-11 }
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<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,
): FeaturedMatrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
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)
}
/**
* Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
*/
public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), Buffer.Companion::real) { it < 1e-11 }
public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): Matrix<Double> =
solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))

15
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixBuilder.kt
View File

@ -1,12 +1,9 @@
package kscience.kmath.linear
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.Structure2D
import kscience.kmath.structures.asBuffer
import kscience.kmath.structures.*
public class MatrixBuilder(public val rows: Int, public val columns: Int) {
public operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
public operator fun <T : Any> invoke(vararg elements: T): Matrix<T> {
require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
val buffer = elements.asBuffer()
return BufferMatrix(rows, columns, buffer)
@ -17,7 +14,7 @@ public class MatrixBuilder(public val rows: Int, public val columns: Int) {
public fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
public fun <T : Any> Structure2D.Companion.row(vararg values: T): FeaturedMatrix<T> {
public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(1, values.size, buffer)
}
@ -26,12 +23,12 @@ public inline fun <reified T : Any> Structure2D.Companion.row(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
): FeaturedMatrix<T> {
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(1, size, buffer)
}
public fun <T : Any> Structure2D.Companion.column(vararg values: T): FeaturedMatrix<T> {
public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(values.size, 1, buffer)
}
@ -40,7 +37,7 @@ public inline fun <reified T : Any> Structure2D.Companion.column(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T
): FeaturedMatrix<T> {
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(size, 1, buffer)
}

16
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt
View File

@ -18,6 +18,11 @@ public interface MatrixContext<T : Any, out M : Matrix<T>> : SpaceOperations<Mat
*/
public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): M
/**
* Produce a point compatible with matrix space (and possibly optimized for it)
*/
public fun point(size: Int, initializer: (Int) -> T): Point<T> = Buffer.boxing(size, initializer)
@Suppress("UNCHECKED_CAST")
public override fun binaryOperationFunction(operation: String): (left: Matrix<T>, right: Matrix<T>) -> M =
when (operation) {
@ -62,10 +67,6 @@ public interface MatrixContext<T : Any, out M : Matrix<T>> : SpaceOperations<Mat
public operator fun T.times(m: Matrix<T>): M = m * this
public companion object {
/**
* Non-boxing double matrix
*/
public val real: RealMatrixContext = RealMatrixContext
/**
* A structured matrix with custom buffer
@ -89,11 +90,6 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> :
*/
public val elementContext: R
/**
* Produce a point compatible with matrix space
*/
public fun point(size: Int, initializer: (Int) -> T): Point<T>
public override infix fun Matrix<T>.dot(other: Matrix<T>): M {
//TODO add typed error
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
@ -137,8 +133,6 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> :
public override fun multiply(a: Matrix<T>, k: Number): M =
produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
public operator fun Number.times(matrix: FeaturedMatrix<T>): M = multiply(matrix, this)
public override operator fun Matrix<T>.times(value: T): M =
produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
}

152
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixFeatures.kt
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@ -1,62 +1,158 @@
package kscience.kmath.linear
import kscience.kmath.structures.Matrix
/**
* A marker interface representing some matrix feature like diagonal, sparse, zero, etc. Features used to optimize matrix
* operations performance in some cases.
* 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.
*/
public interface MatrixFeature
/**
* The matrix with this feature is considered to have only diagonal non-null elements
* Matrices with this feature are considered to have only diagonal non-null elements.
*/
public object DiagonalFeature : MatrixFeature
/**
* Matrix with this feature has all zero elements
*/
public object ZeroFeature : MatrixFeature
/**
* Matrix with this feature have unit elements on diagonal and zero elements in all other places
*/
public object UnitFeature : MatrixFeature
/**
* Inverted matrix feature
*/
public interface InverseMatrixFeature<T : Any> : MatrixFeature {
public val inverse: FeaturedMatrix<T>
public interface DiagonalFeature : MatrixFeature{
public companion object: DiagonalFeature
}
/**
* A determinant container
* Matrices with this feature have all zero elements.
*/
public object ZeroFeature : DiagonalFeature
/**
* Matrices with this feature have unit elements on diagonal and zero elements in all other places.
*/
public object UnitFeature : DiagonalFeature
/**
* Matrices with this feature can be inverted: [inverse] = `a`<sup>-1</sup> where `a` is the owning matrix.
*
* @param T the type of matrices' items.
*/
public interface InverseMatrixFeature<T : Any> : MatrixFeature {
/**
* The inverse matrix of the matrix that owns this feature.
*/
public val inverse: Matrix<T>
}
/**
* Matrices with this feature can compute their determinant.
*/
public interface DeterminantFeature<T : Any> : MatrixFeature {
/**
* The determinant of the matrix that owns this feature.
*/
public val determinant: T
}
/**
* Produces a [DeterminantFeature] where the [DeterminantFeature.determinant] is [determinant].
*
* @param determinant the value of determinant.
* @return a new [DeterminantFeature].
*/
@Suppress("FunctionName")
public fun <T : Any> DeterminantFeature(determinant: T): DeterminantFeature<T> = object : DeterminantFeature<T> {
override val determinant: T = determinant
}
/**
* Lower triangular matrix
* Matrices with this feature are lower triangular ones.
*/
public object LFeature : MatrixFeature
/**
* Upper triangular feature
* Matrices with this feature are upper triangular ones.
*/
public object UFeature : MatrixFeature
/**
* TODO add documentation
* Matrices with this feature support LU factorization with partial pivoting: *[p] &middot; a = [l] &middot; [u]* where
* *a* is the owning matrix.
*
* @param T the type of matrices' items.
*/
public interface LUPDecompositionFeature<T : Any> : MatrixFeature {
public val l: FeaturedMatrix<T>
public val u: FeaturedMatrix<T>
public val p: FeaturedMatrix<T>
public interface LupDecompositionFeature<T : Any> : MatrixFeature {
/**
* The lower triangular matrix in this decomposition. It may have [LFeature].
*/
public val l: Matrix<T>
/**
* The upper triangular matrix in this decomposition. It may have [UFeature].
*/
public val u: Matrix<T>
/**
* The permutation matrix in this decomposition.
*/
public val p: Matrix<T>
}
/**
* Matrices with this feature are orthogonal ones: *a &middot; a<sup>T</sup> = u* where *a* is the owning matrix, *u*
* is the unit matrix ([UnitFeature]).
*/
public object OrthogonalFeature : MatrixFeature
/**
* Matrices with this feature support QR factorization: *a = [q] &middot; [r]* where *a* is the owning matrix.
*
* @param T the type of matrices' items.
*/
public interface QRDecompositionFeature<T : Any> : MatrixFeature {
/**
* The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
*/
public val q: Matrix<T>
/**
* The upper triangular matrix in this decomposition. It may have [UFeature].
*/
public val r: Matrix<T>
}
/**
* Matrices with this feature support Cholesky factorization: *a = [l] &middot; [l]<sup>H</sup>* where *a* is the
* owning matrix.
*
* @param T the type of matrices' items.
*/
public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
/**
* The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
*/
public val l: Matrix<T>
}
/**
* Matrices with this feature support SVD: *a = [u] &middot; [s] &middot; [v]<sup>H</sup>* where *a* is the owning
* matrix.
*
* @param T the type of matrices' items.
*/
public interface SingularValueDecompositionFeature<T : Any> : MatrixFeature {
/**
* The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
*/
public val u: Matrix<T>
/**
* The matrix in this decomposition. Its main diagonal elements are singular values.
*/
public val s: Matrix<T>
/**
* The matrix in this decomposition. It is unitary, and it consists from right singular vectors.
*/
public val v: Matrix<T>
/**
* The buffer of singular values of this SVD.
*/
public val singularValues: Point<T>
}
//TODO add sparse matrix feature

97
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixWrapper.kt Normal file
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@ -0,0 +1,97 @@
package kscience.kmath.linear
import kscience.kmath.misc.UnstableKMathAPI
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
/**
* A [Matrix] that holds [MatrixFeature] objects.
*
* @param T the type of items.
*/
public class MatrixWrapper<T : Any>(
public val matrix: Matrix<T>,
public val features: Set<MatrixFeature>,
) : Matrix<T> by matrix {
/**
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
*/
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = type.safeCast(features.find { type.isInstance(it) })
override fun equals(other: Any?): Boolean = matrix == other
override fun hashCode(): Int = matrix.hashCode()
override fun toString(): String {
return "MatrixWrapper(matrix=$matrix, features=$features)"
}
}
/**
* Add a single feature to a [Matrix]
*/
public operator fun <T : Any> Matrix<T>.plus(newFeature: MatrixFeature): MatrixWrapper<T> = if (this is MatrixWrapper) {
MatrixWrapper(matrix, features + newFeature)
} else {
MatrixWrapper(this, setOf(newFeature))
}
/**
* Add a collection of features to a [Matrix]
*/
public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeature>): MatrixWrapper<T> =
if (this is MatrixWrapper) {
MatrixWrapper(matrix, features + newFeatures)
} else {
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.
*/
public fun <T : Any> Structure2D.Companion.square(vararg elements: T): Matrix<T> {
val size: Int = sqrt(elements.size.toDouble()).toInt()
require(size * size == elements.size) { "The number of elements ${elements.size} is not a full square" }
val buffer = elements.asBuffer()
return BufferMatrix(size, size, buffer)
}
/**
* Diagonal matrix of ones. The matrix is virtual no actual matrix is created
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): Matrix<T> =
VirtualMatrix(rows, columns) { i, j ->
if (i == j) elementContext.one else elementContext.zero
} + UnitFeature
/**
* A virtual matrix of zeroes
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): Matrix<T> =
VirtualMatrix(rows, columns) { _, _ -> elementContext.zero } + ZeroFeature
public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature
/**
* Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
*/
@OptIn(UnstableKMathAPI::class)
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
return getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
colNum,
rowNum,
) { i, j -> get(j, i) } + TransposedFeature(this)
}

68
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/RealMatrixContext.kt Normal file
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@ -0,0 +1,68 @@
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(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> {
val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
}
private fun Matrix<Double>.wrap(): BufferMatrix<Double> = 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<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
return produce(rowNum, other.colNum) { i, j ->
var res = 0.0
for (l in 0 until colNum) {
res += get(i, l) * other.get(l, j)
}
res
}
}
public override infix fun Matrix<Double>.dot(vector: Point<Double>): Point<Double> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
return RealBuffer(rowNum) { i ->
var res = 0.0
for (j in 0 until colNum) {
res += get(i, j) * vector[j]
}
res
}
}
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}" }
return produce(a.rowNum, a.colNum) { i, j ->
a[i, j] + b[i, j]
}
}
override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> =
produce(rowNum, colNum) { i, j -> get(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() }
}
/**
* Partially optimized real-valued matrix
*/
public val MatrixContext.Companion.real: RealMatrixContext get() = RealMatrixContext

31
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/VirtualMatrix.kt
View File

@ -5,31 +5,16 @@ import kscience.kmath.structures.Matrix
public class VirtualMatrix<T : Any>(
override val rowNum: Int,
override val colNum: Int,
override val features: Set<MatrixFeature> = emptySet(),
public val generator: (i: Int, j: Int) -> T
) : FeaturedMatrix<T> {
public constructor(
rowNum: Int,
colNum: Int,
vararg features: MatrixFeature,
generator: (i: Int, j: Int) -> T
) : this(
rowNum,
colNum,
setOf(*features),
generator
)
) : Matrix<T> {
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
override operator fun get(i: Int, j: Int): T = generator(i, j)
override fun suggestFeature(vararg features: MatrixFeature): VirtualMatrix<T> =
VirtualMatrix(rowNum, colNum, this.features + features, generator)
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other !is FeaturedMatrix<*>) return false
if (other !is Matrix<*>) return false
if (rowNum != other.rowNum) return false
if (colNum != other.colNum) return false
@ -40,21 +25,9 @@ public class VirtualMatrix<T : Any>(
override fun hashCode(): Int {
var result = rowNum
result = 31 * result + colNum
result = 31 * result + features.hashCode()
result = 31 * result + generator.hashCode()
return result
}
public companion object {
/**
* Wrap a matrix adding additional features to it
*/
public fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): FeaturedMatrix<T> {
return if (matrix is VirtualMatrix)
VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features, matrix.generator)
else
VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features) { i, j -> matrix[i, j] }
}
}
}

117
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt
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@ -88,85 +88,6 @@ public interface Algebra<T> {
public fun binaryOperation(operation: String, left: T, right: T): T = binaryOperationFunction(operation)(left, right)
}
/**
* An algebraic structure where elements can have numeric representation.
*
* @param T the type of element of this structure.
*/
public interface NumericAlgebra<T> : Algebra<T> {
/**
* Wraps a number to [T] object.
*
* @param value the number to wrap.
* @return an object.
*/
public fun number(value: Number): T
/**
* Dynamically dispatches a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [leftSideNumberOperation] overload:
* i.e. `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b)`.
*
* @param operation the name of operation.
* @return an operation.
*/
public fun leftSideNumberOperationFunction(operation: String): (left: Number, right: T) -> T =
{ l, r -> binaryOperationFunction(operation)(number(l), r) }
/**
* Dynamically invokes a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with second [leftSideNumberOperation] overload:
* i.e. `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @param left the first argument of operation.
* @param right the second argument of operation.
* @return a result of operation.
*/
public fun leftSideNumberOperation(operation: String, left: Number, right: T): T =
leftSideNumberOperationFunction(operation)(left, right)
/**
* Dynamically dispatches a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [rightSideNumberOperationFunction] overload:
* i.e. `rightSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @return an operation.
*/
public fun rightSideNumberOperationFunction(operation: String): (left: T, right: Number) -> T =
{ l, r -> binaryOperationFunction(operation)(l, number(r)) }
/**
* Dynamically invokes a binary operation with the certain name with numeric second argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [rightSideNumberOperationFunction] overload:
* i.e. `rightSideNumberOperationFunction(a)(b, c) == rightSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @param left the first argument of operation.
* @param right the second argument of operation.
* @return a result of operation.
*/
public fun rightSideNumberOperation(operation: String, left: T, right: Number): T =
rightSideNumberOperationFunction(operation)(left, right)
}
/**
* Call a block with an [Algebra] as receiver.
*/
@ -341,47 +262,11 @@ public interface RingOperations<T> : SpaceOperations<T> {
*
* @param T the type of element of this ring.
*/
public interface Ring<T> : Space<T>, RingOperations<T>, NumericAlgebra<T> {
public interface Ring<T> : Space<T>, RingOperations<T> {
/**
* neutral operation for multiplication
*/
public val one: T
public override fun number(value: Number): T = one * value.toDouble()
/**
* Addition of element and scalar.
*
* @receiver the addend.
* @param b the augend.
*/
public operator fun T.plus(b: Number): T = this + number(b)
/**
* Addition of scalar and element.
*
* @receiver the addend.
* @param b the augend.
*/
public operator fun Number.plus(b: T): T = b + this
/**
* Subtraction of element from number.
*
* @receiver the minuend.
* @param b the subtrahend.
* @receiver the difference.
*/
public operator fun T.minus(b: Number): T = this - number(b)
/**
* Subtraction of number from element.
*
* @receiver the minuend.
* @param b the subtrahend.
* @receiver the difference.
*/
public operator fun Number.minus(b: T): T = -b + this
}
/**

4
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/BigInt.kt
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@ -1,5 +1,6 @@
package kscience.kmath.operations
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.BigInt.Companion.BASE
import kscience.kmath.operations.BigInt.Companion.BASE_SIZE
import kscience.kmath.structures.*
@ -16,7 +17,8 @@ public typealias TBase = ULong
*
* @author Robert Drynkin (https://github.com/robdrynkin) and Peter Klimai (https://github.com/pklimai)
*/
public object BigIntField : Field<BigInt> {
@OptIn(UnstableKMathAPI::class)
public object BigIntField : Field<BigInt>, RingWithNumbers<BigInt> {
override val zero: BigInt = BigInt.ZERO
override val one: BigInt = BigInt.ONE

219
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt Normal file
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@ -0,0 +1,219 @@
package kscience.kmath.operations
import kscience.kmath.memory.MemoryReader
import kscience.kmath.memory.MemorySpec
import kscience.kmath.memory.MemoryWriter
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.MemoryBuffer
import kscience.kmath.structures.MutableBuffer
import kscience.kmath.structures.MutableMemoryBuffer
import kotlin.math.*
/**
* This complex's conjugate.
*/
public val Complex.conjugate: Complex
get() = Complex(re, -im)
/**
* This complex's reciprocal.
*/
public val Complex.reciprocal: Complex
get() {
val scale = re * re + im * im
return Complex(re / scale, -im / scale)
}
/**
* Absolute value of complex number.
*/
public val Complex.r: Double
get() = sqrt(re * re + im * im)
/**
* An angle between vector represented by complex number and X axis.
*/
public val Complex.theta: Double
get() = atan(im / re)
private val PI_DIV_2 = Complex(PI / 2, 0)
/**
* A field of [Complex].
*/
public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, RingWithNumbers<Complex> {
override val zero: Complex = 0.0.toComplex()
override val one: Complex = 1.0.toComplex()
/**
* The imaginary unit.
*/
public val i: Complex = Complex(0.0, 1.0)
override fun add(a: Complex, b: Complex): Complex = Complex(a.re + b.re, a.im + b.im)
override fun multiply(a: Complex, k: Number): Complex = Complex(a.re * k.toDouble(), a.im * k.toDouble())
override fun multiply(a: Complex, b: Complex): Complex =
Complex(a.re * b.re - a.im * b.im, a.re * b.im + a.im * b.re)
override fun divide(a: Complex, b: Complex): Complex = when {
b.re.isNaN() || b.im.isNaN() -> Complex(Double.NaN, Double.NaN)
(if (b.im < 0) -b.im else +b.im) < (if (b.re < 0) -b.re else +b.re) -> {
val wr = b.im / b.re
val wd = b.re + wr * b.im
if (wd.isNaN() || wd == 0.0)
Complex(Double.NaN, Double.NaN)
else
Complex((a.re + a.im * wr) / wd, (a.im - a.re * wr) / wd)
}
b.im == 0.0 -> Complex(Double.NaN, Double.NaN)
else -> {
val wr = b.re / b.im
val wd = b.im + wr * b.re
if (wd.isNaN() || wd == 0.0)
Complex(Double.NaN, Double.NaN)
else
Complex((a.re * wr + a.im) / wd, (a.im * wr - a.re) / wd)
}
}
override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2
override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2
override fun tan(arg: Complex): Complex {
val e1 = exp(-i * arg)
val e2 = exp(i * arg)
return i * (e1 - e2) / (e1 + e2)
}
override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg)
override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg)
override fun atan(arg: Complex): Complex {
val iArg = i * arg
return i * (ln(1 - iArg) - ln(1 + iArg)) / 2
}
override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0)
arg.re.pow(pow.toDouble()).toComplex()
else
exp(pow * ln(arg))
override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im))
override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re)
/**
* Adds complex number to real one.
*
* @receiver the addend.
* @param c the augend.
* @return the sum.
*/
public operator fun Double.plus(c: Complex): Complex = add(this.toComplex(), c)
/**
* Subtracts complex number from real one.
*
* @receiver the minuend.
* @param c the subtrahend.
* @return the difference.
*/
public operator fun Double.minus(c: Complex): Complex = add(this.toComplex(), -c)
/**
* Adds real number to complex one.
*
* @receiver the addend.
* @param d the augend.
* @return the sum.
*/
public operator fun Complex.plus(d: Double): Complex = d + this
/**
* Subtracts real number from complex one.
*
* @receiver the minuend.
* @param d the subtrahend.
* @return the difference.
*/
public operator fun Complex.minus(d: Double): Complex = add(this, -d.toComplex())
/**
* Multiplies real number by complex one.
*
* @receiver the multiplier.
* @param c the multiplicand.
* @receiver the product.
*/
public operator fun Double.times(c: Complex): Complex = Complex(c.re * this, c.im * this)
override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg)
override fun symbol(value: String): Complex = if (value == "i") i else super<ExtendedField>.symbol(value)
}
/**
* Represents `double`-based complex number.
*
* @property re The real part.
* @property im The imaginary part.
*/
public data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Complex, ComplexField>,
Comparable<Complex> {
public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble())
override val context: ComplexField get() = ComplexField
override fun unwrap(): Complex = this
override fun Complex.wrap(): Complex = this
override fun compareTo(other: Complex): Int = r.compareTo(other.r)
override fun toString(): String {
return "($re + i*$im)"
}
public companion object : MemorySpec<Complex> {
override val objectSize: Int
get() = 16
override fun MemoryReader.read(offset: Int): Complex = Complex(readDouble(offset), readDouble(offset + 8))
override fun MemoryWriter.write(offset: Int, value: Complex) {
writeDouble(offset, value.re)
writeDouble(offset + 8, value.im)
}
}
}
/**
* Creates a complex number with real part equal to this real.
*
* @receiver the real part.
* @return the new complex number.
*/
public fun Number.toComplex(): Complex = Complex(this, 0.0)
/**
* Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
* specified [init] function.
*/
public inline fun Buffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> =
MemoryBuffer.create(Complex, size, init)
/**
* Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
* specified [init] function.
*/
public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): MutableBuffer<Complex> =
MutableMemoryBuffer.create(Complex, size, init)

125
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/NumericAlgebra.kt Normal file
View File

@ -0,0 +1,125 @@
package kscience.kmath.operations
import kscience.kmath.misc.UnstableKMathAPI
/**
* An algebraic structure where elements can have numeric representation.
*
* @param T the type of element of this structure.
*/
public interface NumericAlgebra<T> : Algebra<T> {
/**
* Wraps a number to [T] object.
*
* @param value the number to wrap.
* @return an object.
*/
public fun number(value: Number): T
/**
* Dynamically dispatches a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [leftSideNumberOperation] overload:
* i.e. `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b)`.
*
* @param operation the name of operation.
* @return an operation.
*/
public fun leftSideNumberOperationFunction(operation: String): (left: Number, right: T) -> T =
{ l, r -> binaryOperationFunction(operation)(number(l), r) }
/**
* Dynamically invokes a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with second [leftSideNumberOperation] overload:
* i.e. `leftSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @param left the first argument of operation.
* @param right the second argument of operation.
* @return a result of operation.
*/
public fun leftSideNumberOperation(operation: String, left: Number, right: T): T =
leftSideNumberOperationFunction(operation)(left, right)
/**
* Dynamically dispatches a binary operation with the certain name with numeric first argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [rightSideNumberOperationFunction] overload:
* i.e. `rightSideNumberOperationFunction(a)(b, c) == leftSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @return an operation.
*/
public fun rightSideNumberOperationFunction(operation: String): (left: T, right: Number) -> T =
{ l, r -> binaryOperationFunction(operation)(l, number(r)) }
/**
* Dynamically invokes a binary operation with the certain name with numeric second argument.
*
* This function must follow two properties:
*
* 1. In case if operation is not defined in the structure, the function throws [kotlin.IllegalStateException].
* 2. This function is symmetric with the other [rightSideNumberOperationFunction] overload:
* i.e. `rightSideNumberOperationFunction(a)(b, c) == rightSideNumberOperation(a, b, c)`.
*
* @param operation the name of operation.
* @param left the first argument of operation.
* @param right the second argument of operation.
* @return a result of operation.
*/
public fun rightSideNumberOperation(operation: String, left: T, right: Number): T =
rightSideNumberOperationFunction(operation)(left, right)
}
/**
* A combination of [NumericAlgebra] and [Ring] that adds intrinsic simple operations on numbers like `T+1`
* TODO to be removed and replaced by extensions after multiple receivers are there
*/
@UnstableKMathAPI
public interface RingWithNumbers<T>: Ring<T>, NumericAlgebra<T>{
public override fun number(value: Number): T = one * value
/**
* Addition of element and scalar.
*
* @receiver the addend.
* @param b the augend.
*/
public operator fun T.plus(b: Number): T = this + number(b)
/**
* Addition of scalar and element.
*
* @receiver the addend.
* @param b the augend.
*/
public operator fun Number.plus(b: T): T = b + this
/**
* Subtraction of element from number.
*
* @receiver the minuend.
* @param b the subtrahend.
* @receiver the difference.
*/
public operator fun T.minus(b: Number): T = this - number(b)
/**
* Subtraction of number from element.
*
* @receiver the minuend.
* @param b the subtrahend.
* @receiver the difference.
*/
public operator fun Number.minus(b: T): T = -b + this
}

31
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/NumberAlgebra.kt → kmath-core/src/commonMain/kotlin/kscience/kmath/operations/numbers.kt
View File

@ -37,7 +37,7 @@ public interface ExtendedFieldOperations<T> :
/**
* Advanced Number-like field that implements basic operations.
*/
public interface ExtendedField<T> : ExtendedFieldOperations<T>, Field<T> {
public interface ExtendedField<T> : ExtendedFieldOperations<T>, Field<T>, NumericAlgebra<T> {
public override fun sinh(arg: T): T = (exp(arg) - exp(-arg)) / 2
public override fun cosh(arg: T): T = (exp(arg) + exp(-arg)) / 2
public override fun tanh(arg: T): T = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
@ -80,6 +80,8 @@ public object RealField : ExtendedField<Double>, Norm<Double, Double> {
public override val one: Double
get() = 1.0
override fun number(value: Number): Double = value.toDouble()
public override fun binaryOperationFunction(operation: String): (left: Double, right: Double) -> Double =
when (operation) {
PowerOperations.POW_OPERATION -> ::power
@ -131,10 +133,13 @@ public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
public override val one: Float
get() = 1.0f
public override fun binaryOperationFunction(operation: String): (left: Float, right: Float) -> Float = when (operation) {
PowerOperations.POW_OPERATION -> ::power
else -> super.binaryOperationFunction(operation)
}
override fun number(value: Number): Float = value.toFloat()
public override fun binaryOperationFunction(operation: String): (left: Float, right: Float) -> Float =
when (operation) {
PowerOperations.POW_OPERATION -> ::power
else -> super.binaryOperationFunction(operation)
}
public override inline fun add(a: Float, b: Float): Float = a + b
public override inline fun multiply(a: Float, k: Number): Float = a * k.toFloat()
@ -174,13 +179,15 @@ public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
* A field for [Int] without boxing. Does not produce corresponding ring element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object IntRing : Ring<Int>, Norm<Int, Int> {
public object IntRing : Ring<Int>, Norm<Int, Int>, NumericAlgebra<Int> {
public override val zero: Int
get() = 0
public override val one: Int
get() = 1
override fun number(value: Number): Int = value.toInt()
public override inline fun add(a: Int, b: Int): Int = a + b
public override inline fun multiply(a: Int, k: Number): Int = k.toInt() * a
@ -198,13 +205,15 @@ public object IntRing : Ring<Int>, Norm<Int, Int> {
* A field for [Short] without boxing. Does not produce appropriate ring element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object ShortRing : Ring<Short>, Norm<Short, Short> {
public object ShortRing : Ring<Short>, Norm<Short, Short>, NumericAlgebra<Short> {
public override val zero: Short
get() = 0
public override val one: Short
get() = 1
override fun number(value: Number): Short = value.toShort()
public override inline fun add(a: Short, b: Short): Short = (a + b).toShort()
public override inline fun multiply(a: Short, k: Number): Short = (a * k.toShort()).toShort()
@ -222,13 +231,15 @@ public object ShortRing : Ring<Short>, Norm<Short, Short> {
* A field for [Byte] without boxing. Does not produce appropriate ring element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
public object ByteRing : Ring<Byte>, Norm<Byte, Byte>, NumericAlgebra<Byte> {
public override val zero: Byte
get() = 0
public override val one: Byte
get() = 1
override fun number(value: Number): Byte = value.toByte()
public override inline fun add(a: Byte, b: Byte): Byte = (a + b).toByte()
public override inline fun multiply(a: Byte, k: Number): Byte = (a * k.toByte()).toByte()
@ -246,13 +257,15 @@ public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
* A field for [Double] without boxing. Does not produce appropriate ring element.
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object LongRing : Ring<Long>, Norm<Long, Long> {
public object LongRing : Ring<Long>, Norm<Long, Long>, NumericAlgebra<Long> {
public override val zero: Long
get() = 0L
public override val one: Long
get() = 1L
override fun number(value: Number): Long = value.toLong()
public override inline fun add(a: Long, b: Long): Long = a + b
public override inline fun multiply(a: Long, k: Number): Long = a * k.toLong()

15
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt
View File

@ -1,5 +1,6 @@
package kscience.kmath.structures
import kscience.kmath.misc.UnstableKMathAPI
import kotlin.jvm.JvmName
import kotlin.native.concurrent.ThreadLocal
import kotlin.reflect.KClass
@ -38,9 +39,17 @@ public interface NDStructure<T> {
*/
public fun elements(): Sequence<Pair<IntArray, T>>
//force override equality and hash code
public override fun equals(other: Any?): Boolean
public override fun hashCode(): Int
/**
* Feature is additional property or hint that does not directly affect the structure, but could in some cases help
* optimize operations and performance. If the feature is not present, null is defined.
*/
@UnstableKMathAPI
public fun <T : Any> getFeature(type: KClass<T>): T? = null
public companion object {
/**
* Indicates whether some [NDStructure] is equal to another one.
@ -120,6 +129,9 @@ public interface NDStructure<T> {
*/
public operator fun <T> NDStructure<T>.get(vararg index: Int): T = get(index)
@UnstableKMathAPI
public inline fun <reified T : Any> NDStructure<*>.getFeature(): T? = getFeature(T::class)
/**
* Represents mutable [NDStructure].
*/
@ -133,6 +145,9 @@ public interface MutableNDStructure<T> : NDStructure<T> {
public operator fun set(index: IntArray, value: T)
}
/**
* Transform a structure element-by element in place.
*/
public inline fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T): Unit =
elements().forEach { (index, oldValue) -> this[index] = action(index, oldValue) }

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

@ -150,6 +150,8 @@ public class RealBufferField(public val size: Int) : ExtendedField<Buffer<Double
public override val zero: Buffer<Double> by lazy { RealBuffer(size) { 0.0 } }
public override val one: Buffer<Double> by lazy { RealBuffer(size) { 1.0 } }
override fun number(value: Number): Buffer<Double> = RealBuffer(size) { value.toDouble() }
public override fun add(a: Buffer<Double>, b: Buffer<Double>): RealBuffer {
require(a.size == size) { "The buffer size ${a.size} does not match context size $size" }
return RealBufferFieldOperations.add(a, b)

16
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/RealNDField.kt
View File

@ -1,13 +1,17 @@
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>> {
ExtendedNDField<Double, RealField, NDBuffer<Double>>,
RingWithNumbers<NDBuffer<Double>>{
override val strides: Strides = DefaultStrides(shape)
@ -15,7 +19,12 @@ public class RealNDField(override val shape: IntArray) :
override val zero: RealNDElement by lazy { produce { zero } }
override val one: RealNDElement by lazy { produce { one } }
public inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Double): Buffer<Double> =
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble()
return produce { d }
}
private inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Double): Buffer<Double> =
RealBuffer(DoubleArray(size) { initializer(it) })
/**
@ -59,7 +68,8 @@ public class RealNDField(override val shape: IntArray) :
check(a, b)
return BufferedNDFieldElement(
this,
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) }
)
}
override fun NDBuffer<Double>.toElement(): FieldElement<NDBuffer<Double>, *, out BufferedNDField<Double, RealField>> =

54
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure2D.kt
View File

@ -1,12 +1,42 @@
package kscience.kmath.structures
/**
* A structure that is guaranteed to be two-dimensional
* A structure that is guaranteed to be two-dimensional.
*
* @param T the type of items.
*/
public interface Structure2D<T> : NDStructure<T> {
public val rowNum: Int get() = shape[0]
public val colNum: Int get() = shape[1]
/**
* The number of rows in this structure.
*/
public val rowNum: Int
/**
* The number of columns in this structure.
*/
public val colNum: Int
public override val shape: IntArray get() = intArrayOf(rowNum, colNum)
/**
* The buffer of rows of this structure. It gets elements from the structure dynamically.
*/
public val rows: Buffer<Buffer<T>>
get() = VirtualBuffer(rowNum) { i -> VirtualBuffer(colNum) { j -> get(i, j) } }
/**
* The buffer of columns of this structure. It gets elements from the structure dynamically.
*/
public val columns: Buffer<Buffer<T>>
get() = VirtualBuffer(colNum) { j -> VirtualBuffer(rowNum) { i -> get(i, j) } }
/**
* Retrieves an element from the structure by two indices.
*
* @param i the first index.
* @param j the second index.
* @return an element.
*/
public operator fun get(i: Int, j: Int): T
override operator fun get(index: IntArray): T {
@ -14,15 +44,9 @@ public interface Structure2D<T> : NDStructure<T> {
return get(index[0], index[1])
}
public val rows: Buffer<Buffer<T>>
get() = VirtualBuffer(rowNum) { i -> VirtualBuffer(colNum) { j -> get(i, j) } }
public val columns: Buffer<Buffer<T>>
get() = VirtualBuffer(colNum) { j -> VirtualBuffer(rowNum) { i -> get(i, j) } }
override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
for (i in (0 until rowNum))
for (j in (0 until colNum)) yield(intArrayOf(i, j) to get(i, j))
for (i in 0 until rowNum)
for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
}
public companion object
@ -34,6 +58,9 @@ public interface Structure2D<T> : NDStructure<T> {
private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
override val shape: IntArray get() = structure.shape
override val rowNum: Int get() = shape[0]
override val colNum: Int get() = shape[1]
override operator fun get(i: Int, j: Int): T = structure[i, j]
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
@ -47,4 +74,9 @@ public fun <T> NDStructure<T>.as2D(): Structure2D<T> = if (shape.size == 2)
else
error("Can't create 2d-structure from ${shape.size}d-structure")
/**
* Alias for [Structure2D] with more familiar name.
*
* @param T the type of items.
*/
public typealias Matrix<T> = Structure2D<T>

5
kmath-core/src/commonTest/kotlin/kscience/kmath/structures/NDFieldTest.kt
View File

@ -1,14 +1,13 @@
package kscience.kmath.structures
import kscience.kmath.testutils.FieldVerifier
import kscience.kmath.operations.invoke
import kscience.kmath.operations.internal.FieldVerifier
import kotlin.test.Test
import kotlin.test.assertEquals
internal class NDFieldTest {
@Test
fun verify() {
(NDField.real(12, 32)) { FieldVerifier(this, one + 3, one - 23, one * 12, 6.66) }
NDField.real(12, 32).run { FieldVerifier(this, one + 3, one - 23, one * 12, 6.66) }
}
@Test

8
kmath-core/src/jvmMain/kotlin/kscience/kmath/operations/BigNumbers.kt
View File

@ -7,7 +7,7 @@ import java.math.MathContext
/**
* A field over [BigInteger].
*/
public object JBigIntegerField : Field<BigInteger> {
public object JBigIntegerField : Field<BigInteger>, NumericAlgebra<BigInteger> {
public override val zero: BigInteger
get() = BigInteger.ZERO
@ -28,9 +28,9 @@ public object JBigIntegerField : Field<BigInteger> {
*
* @property mathContext the [MathContext] to use.
*/
public abstract class JBigDecimalFieldBase internal constructor(public val mathContext: MathContext = MathContext.DECIMAL64) :
Field<BigDecimal>,
PowerOperations<BigDecimal> {
public abstract class JBigDecimalFieldBase internal constructor(
private val mathContext: MathContext = MathContext.DECIMAL64,
) : Field<BigDecimal>, PowerOperations<BigDecimal>, NumericAlgebra<BigDecimal> {
public override val zero: BigDecimal
get() = BigDecimal.ZERO

41
kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Wrappers.kt
View File

@ -1,11 +1,6 @@
package kscience.kmath.dimensions
import kscience.kmath.linear.GenericMatrixContext
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.linear.transpose
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.linear.*
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
@ -42,9 +37,11 @@ public interface DMatrix<T, R : Dimension, C : Dimension> : Structure2D<T> {
* An inline wrapper for a Matrix
*/
public inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
private val structure: Structure2D<T>
private val structure: Structure2D<T>,
) : DMatrix<T, R, C> {
override val shape: IntArray get() = structure.shape
override val rowNum: Int get() = shape[0]
override val colNum: Int get() = shape[1]
override operator fun get(i: Int, j: Int): T = structure[i, j]
}
@ -81,7 +78,7 @@ public inline class DPointWrapper<T, D : Dimension>(public val point: Point<T>)
/**
* Basic operations on dimension-safe matrices. Operates on [Matrix]
*/
public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: GenericMatrixContext<T, Ri, Matrix<T>>) {
public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T, Matrix<T>>) {
public inline fun <reified R : Dimension, reified C : Dimension> Matrix<T>.coerce(): DMatrix<T, R, C> {
require(rowNum == Dimension.dim<R>().toInt()) {
"Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum"
@ -115,7 +112,7 @@ public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: Ge
}
public inline infix fun <reified R1 : Dimension, reified C1 : Dimension, reified C2 : Dimension> DMatrix<T, R1, C1>.dot(
other: DMatrix<T, C1, C2>
other: DMatrix<T, C1, C2>,
): DMatrix<T, R1, C2> = context { this@dot dot other }.coerce()
public inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> =
@ -139,18 +136,20 @@ public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: Ge
public inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> =
context { (this@transpose as Matrix<T>).transpose() }.coerce()
/**
* A square unit matrix
*/
public inline fun <reified D : Dimension> one(): DMatrix<T, D, D> = produce { i, j ->
if (i == j) context.elementContext.one else context.elementContext.zero
}
public inline fun <reified R : Dimension, reified C : Dimension> zero(): DMatrix<T, R, C> = produce { _, _ ->
context.elementContext.zero
}
public companion object {
public val real: DMatrixContext<Double, RealField> = DMatrixContext(MatrixContext.real)
public val real: DMatrixContext<Double> = DMatrixContext(MatrixContext.real)
}
}
/**
* A square unit matrix
*/
public inline fun <reified D : Dimension> DMatrixContext<Double>.one(): DMatrix<Double, D, D> = produce { i, j ->
if (i == j) 1.0 else 0.0
}
public inline fun <reified R : Dimension, reified C : Dimension> DMatrixContext<Double>.zero(): DMatrix<Double, R, C> =
produce { _, _ ->
0.0
}

1
kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
View File

@ -3,6 +3,7 @@ package kscience.dimensions
import kscience.kmath.dimensions.D2
import kscience.kmath.dimensions.D3
import kscience.kmath.dimensions.DMatrixContext
import kscience.kmath.dimensions.one
import kotlin.test.Test
internal class DMatrixContextTest {

110
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt
View File

@ -1,12 +1,13 @@
package kscience.kmath.ejml
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.simple.SimpleMatrix
import kscience.kmath.linear.DeterminantFeature
import kscience.kmath.linear.FeaturedMatrix
import kscience.kmath.linear.LUPDecompositionFeature
import kscience.kmath.linear.MatrixFeature
import kscience.kmath.structures.NDStructure
import kotlin.reflect.KClass
import kotlin.reflect.cast
/**
* Represents featured matrix over EJML [SimpleMatrix].
@ -14,58 +15,65 @@ import kscience.kmath.structures.NDStructure
* @property origin the underlying [SimpleMatrix].
* @author Iaroslav Postovalov
*/
public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
public override val rowNum: Int
get() = origin.numRows()
public inline class EjmlMatrix(
public val origin: SimpleMatrix,
) : Matrix<Double> {
public override val rowNum: Int get() = origin.numRows()
public override val colNum: Int
get() = origin.numCols()
public override val colNum: Int get() = origin.numCols()
public override val shape: IntArray
get() = intArrayOf(origin.numRows(), origin.numCols())
public override val features: Set<MatrixFeature> = setOf(
object : LUPDecompositionFeature<Double>, DeterminantFeature<Double> {
override val determinant: Double
get() = origin.determinant()
private val lup by lazy {
val ludecompositionF64 = DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
.also { it.decompose(origin.ddrm.copy()) }
Triple(
EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))),
EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))),
EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))),
)
@UnstableKMathAPI
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)
.apply { decompose(origin.ddrm.copy()) }
}
override val l: FeaturedMatrix<Double>
get() = lup.second
override val u: FeaturedMatrix<Double>
get() = lup.third
override val p: FeaturedMatrix<Double>
get() = lup.first
override val u: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
override val s: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
override val v: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
override val singularValues: Point<Double> by lazy { RealBuffer(svd.singularValues) }
}
) union features.orEmpty()
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy {
DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
}
public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
EjmlMatrix(origin, this.features + features)
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))) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
DecompositionFactory_DDRM.chol(rowNum, true).apply { decompose(origin.ddrm.copy()) }
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()) }
}
override val l: Matrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(lup.getLower(null))) + LFeature
}
override val u: Matrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(lup.getUpper(null))) + UFeature
}
override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
}
else -> null
}?.let{type.cast(it)}
public override operator fun get(i: Int, j: Int): Double = origin[i, j]
public override fun equals(other: Any?): Boolean {
if (other is EjmlMatrix) return origin.isIdentical(other.origin, 0.0)
return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
}
public override fun hashCode(): Int {
var result = origin.hashCode()
result = 31 * result + features.hashCode()
return result
}
public override fun toString(): String = "EjmlMatrix(origin=$origin, features=$features)"
}

36
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt
View File

@ -1,16 +1,14 @@
package kscience.kmath.ejml
import kscience.kmath.linear.InverseMatrixFeature
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.MatrixWrapper
import kscience.kmath.linear.Point
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.getFeature
import org.ejml.simple.SimpleMatrix
/**
* Converts this matrix to EJML one.
*/
public fun Matrix<Double>.toEjml(): EjmlMatrix =
if (this is EjmlMatrix) this else EjmlMatrixContext.produce(rowNum, colNum) { i, j -> get(i, j) }
/**
* Represents context of basic operations operating with [EjmlMatrix].
*
@ -18,6 +16,15 @@ public fun Matrix<Double>.toEjml(): EjmlMatrix =
*/
public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix> {
/**
* Converts this matrix to EJML one.
*/
public fun Matrix<Double>.toEjml(): EjmlMatrix = when {
this is EjmlMatrix -> this
this is MatrixWrapper && matrix is EjmlMatrix -> matrix as EjmlMatrix
else -> produce(rowNum, colNum) { i, j -> get(i, j) }
}
/**
* Converts this vector to EJML one.
*/
@ -33,6 +40,11 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix> {
}
})
override fun point(size: Int, initializer: (Int) -> Double): Point<Double> =
EjmlVector(SimpleMatrix(size, 1).also {
(0 until it.numRows()).forEach { row -> it[row, 0] = initializer(row) }
})
public override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
@ -74,11 +86,7 @@ public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMa
public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
/**
* Returns the inverse of given matrix: b = a^(-1).
*
* @param a the matrix.
* @return the inverse of this matrix.
* @author Iaroslav Postovalov
*/
public fun EjmlMatrixContext.inverse(a: Matrix<Double>): EjmlMatrix = EjmlMatrix(a.toEjml().origin.invert())
@OptIn(UnstableKMathAPI::class)
public fun EjmlMatrix.inverted(): EjmlMatrix = getFeature<InverseMatrixFeature<Double>>()!!.inverse as EjmlMatrix
public fun EjmlMatrixContext.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()

9
kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlMatrixTest.kt
View File

@ -1,9 +1,10 @@
package kscience.kmath.ejml
import kscience.kmath.linear.DeterminantFeature
import kscience.kmath.linear.LUPDecompositionFeature
import kscience.kmath.linear.LupDecompositionFeature
import kscience.kmath.linear.MatrixFeature
import kscience.kmath.linear.getFeature
import kscience.kmath.linear.plus
import kscience.kmath.structures.getFeature
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.simple.SimpleMatrix
import kotlin.random.Random
@ -44,7 +45,7 @@ internal class EjmlMatrixTest {
val w = EjmlMatrix(m)
val det = w.getFeature<DeterminantFeature<Double>>() ?: fail()
assertEquals(m.determinant(), det.determinant)
val lup = w.getFeature<LUPDecompositionFeature<Double>>() ?: fail()
val lup = w.getFeature<LupDecompositionFeature<Double>>() ?: fail()
val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows(), m.numCols())
.also { it.decompose(m.ddrm.copy()) }
@ -58,7 +59,7 @@ internal class EjmlMatrixTest {
@Test
fun suggestFeature() {
assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
assertNotNull((EjmlMatrix(randomMatrix) + SomeFeature).getFeature<SomeFeature>())
}
@Test

11
kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt
View File

@ -1,14 +1,12 @@
package kscience.kmath.real
import kscience.kmath.linear.FeaturedMatrix
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.RealMatrixContext.elementContext
import kscience.kmath.linear.VirtualMatrix
import kscience.kmath.linear.inverseWithLUP
import kscience.kmath.linear.real
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.invoke
import kscience.kmath.operations.sum
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
import kscience.kmath.structures.asIterable
import kotlin.math.pow
@ -25,7 +23,7 @@ import kotlin.math.pow
* Functions that help create a real (Double) matrix
*/
public typealias RealMatrix = FeaturedMatrix<Double>
public typealias RealMatrix = Matrix<Double>
public fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum, initializer)
@ -122,8 +120,7 @@ public fun RealMatrix.extractColumn(columnIndex: Int): RealMatrix =
extractColumns(columnIndex..columnIndex)
public fun RealMatrix.sumByColumn(): RealBuffer = RealBuffer(colNum) { j ->
val column = columns[j]
elementContext { sum(column.asIterable()) }
columns[j].asIterable().sum()
}
public fun RealMatrix.minByColumn(): RealBuffer = RealBuffer(colNum) { j ->

3
kmath-for-real/src/commonTest/kotlin/kaceince/kmath/real/RealMatrixTest.kt
View File

@ -1,6 +1,5 @@
package kaceince.kmath.real
import kscience.kmath.linear.VirtualMatrix
import kscience.kmath.linear.build
import kscience.kmath.real.*
import kscience.kmath.structures.Matrix
@ -42,7 +41,7 @@ internal class RealMatrixTest {
1.0, 0.0, 0.0,
0.0, 1.0, 2.0
)
assertEquals(VirtualMatrix.wrap(matrix2), matrix1.repeatStackVertical(3))
assertEquals(matrix2, matrix1.repeatStackVertical(3))
}
@Test

46
kmath-nd4j/src/main/kotlin/kscience.kmath.nd4j/Nd4jArrayAlgebra.kt
View File

@ -1,5 +1,6 @@
package kscience.kmath.nd4j
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.*
import kscience.kmath.structures.NDAlgebra
import kscience.kmath.structures.NDField
@ -35,7 +36,7 @@ public interface Nd4jArrayAlgebra<T, C> : NDAlgebra<T, C, Nd4jArrayStructure<T>>
public override fun mapIndexed(
arg: Nd4jArrayStructure<T>,
transform: C.(index: IntArray, T) -> T
transform: C.(index: IntArray, T) -> T,
): Nd4jArrayStructure<T> {
check(arg)
val new = Nd4j.create(*shape).wrap()
@ -46,7 +47,7 @@ public interface Nd4jArrayAlgebra<T, C> : NDAlgebra<T, C, Nd4jArrayStructure<T>>
public override fun combine(
a: Nd4jArrayStructure<T>,
b: Nd4jArrayStructure<T>,
transform: C.(T, T) -> T
transform: C.(T, T) -> T,
): Nd4jArrayStructure<T> {
check(a, b)
val new = Nd4j.create(*shape).wrap()
@ -61,8 +62,8 @@ 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> : NDSpace<T, S, Nd4jArrayStructure<T>>,
Nd4jArrayAlgebra<T, S> where S : Space<T> {
public interface Nd4jArraySpace<T, S : Space<T>> : NDSpace<T, S, Nd4jArrayStructure<T>>, Nd4jArrayAlgebra<T, S> {
public override val zero: Nd4jArrayStructure<T>
get() = Nd4j.zeros(*shape).wrap()
@ -103,7 +104,9 @@ public interface Nd4jArraySpace<T, S> : NDSpace<T, S, Nd4jArrayStructure<T>>,
* @param T the type of the element contained in ND structure.
* @param R the type of ring of structure elements.
*/
public interface Nd4jArrayRing<T, R> : NDRing<T, R, Nd4jArrayStructure<T>>, Nd4jArraySpace<T, R> where R : Ring<T> {
@OptIn(UnstableKMathAPI::class)
public interface Nd4jArrayRing<T, R : Ring<T>> : NDRing<T, R, Nd4jArrayStructure<T>>, Nd4jArraySpace<T, R> {
public override val one: Nd4jArrayStructure<T>
get() = Nd4j.ones(*shape).wrap()
@ -111,21 +114,21 @@ public interface Nd4jArrayRing<T, R> : NDRing<T, R, Nd4jArrayStructure<T>>, Nd4j
check(a, b)
return a.ndArray.mul(b.ndArray).wrap()
}
public override operator fun Nd4jArrayStructure<T>.minus(b: Number): Nd4jArrayStructure<T> {
check(this)
return ndArray.sub(b).wrap()
}
public override operator fun Nd4jArrayStructure<T>.plus(b: Number): Nd4jArrayStructure<T> {
check(this)
return ndArray.add(b).wrap()
}
public override operator fun Number.minus(b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
check(b)
return b.ndArray.rsub(this).wrap()
}
//
// public override operator fun Nd4jArrayStructure<T>.minus(b: Number): Nd4jArrayStructure<T> {
// check(this)
// return ndArray.sub(b).wrap()
// }
//
// public override operator fun Nd4jArrayStructure<T>.plus(b: Number): Nd4jArrayStructure<T> {
// check(this)
// return ndArray.add(b).wrap()
// }
//
// public override operator fun Number.minus(b: Nd4jArrayStructure<T>): Nd4jArrayStructure<T> {
// check(b)
// return b.ndArray.rsub(this).wrap()
// }
public companion object {
private val intNd4jArrayRingCache: ThreadLocal<MutableMap<IntArray, IntNd4jArrayRing>> =
@ -165,7 +168,8 @@ public interface Nd4jArrayRing<T, R> : NDRing<T, R, Nd4jArrayStructure<T>>, Nd4j
* @param N the type of ND structure.
* @param F the type field of structure elements.
*/
public interface Nd4jArrayField<T, F> : NDField<T, F, Nd4jArrayStructure<T>>, Nd4jArrayRing<T, F> where F : Field<T> {
public interface Nd4jArrayField<T, F : Field<T>> : NDField<T, F, Nd4jArrayStructure<T>>, 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()