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[WIP] Features to Attributes refactoring

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This commit is contained in:
Alexander Nozik 2023-07-09 15:51:50 +03:00
parent d3893ab7e6
commit 6da51b7794
34 changed files with 457 additions and 1300 deletions
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4
CHANGELOG.md
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@ -3,9 +3,11 @@
## Unreleased
### Added
- Explicit `mutableStructureND` builders for mutable stucures
- New Attributes-kt module that could be used as stand-alone. It declares type-safe attributes containers.
- Explicit `mutableStructureND` builders for mutable structures
### Changed
- Features replaced with Attributes.
### Deprecated

21
attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attribute.kt
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@ -9,20 +9,31 @@ import kotlin.reflect.KType
public interface Attribute<T>
/**
* An attribute that could be either present or absent
*/
public interface FlagAttribute : Attribute<Unit>
/**
* An attribute with a default value
*/
public interface AttributeWithDefault<T> : Attribute<T> {
public val default: T
}
/**
* Attribute containing a set of values
*/
public interface SetAttribute<V> : Attribute<Set<V>>
/**
* An attribute that has a type parameter for value
* @param type parameter-type
*/
public abstract class PolymorphicAttribute<T>(public val type: KType) : Attribute<T> {
override fun equals(other: Any?): Boolean = (other as? PolymorphicAttribute<*>)?.type == this.type
public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : Attribute<T> {
override fun equals(other: Any?): Boolean = other != null &&
(this::class == other::class) &&
(other as? PolymorphicAttribute<*>)?.type == this.type
override fun hashCode(): Int {
return type.hashCode()
}
override fun hashCode(): Int = this::class.hashCode() + type.hashCode()
}

48
attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt
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@ -24,18 +24,45 @@ public value class Attributes internal constructor(public val content: Map<out A
public fun Attributes.isEmpty(): Boolean = content.isEmpty()
/**
* Get attribute value or default
*/
public fun <T> Attributes.getOrDefault(attribute: AttributeWithDefault<T>): T = get(attribute) ?: attribute.default
public fun <T, A : Attribute<T>> Attributes.withAttribute(
/**
* Check if there is an attribute that matches given key by type and adheres to [predicate].
*/
@Suppress("UNCHECKED_CAST")
public inline fun <T, reified A : Attribute<T>> Attributes.any(predicate: (value: T) -> Boolean): Boolean =
content.any { (mapKey, mapValue) -> mapKey is A && predicate(mapValue as T) }
/**
* Check if there is an attribute of given type (subtypes included)
*/
public inline fun <T, reified A : Attribute<T>> Attributes.any(): Boolean =
content.any { (mapKey, _) -> mapKey is A }
/**
* Check if [Attributes] contains a flag. Multiple keys that are instances of a flag could be present
*/
public inline fun <reified A : FlagAttribute> Attributes.has(): Boolean =
content.keys.any { it is A }
/**
* Create [Attributes] with an added or replaced attribute key.
*/
public fun <T : Any, A : Attribute<T>> Attributes.withAttribute(
attribute: A,
attrValue: T?,
): Attributes = Attributes(
if (attrValue == null) {
content - attribute
} else {
content + (attribute to attrValue)
}
)
attrValue: T,
): Attributes = Attributes(content + (attribute to attrValue))
public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attributes =
withAttribute(attribute, Unit)
/**
* Create new [Attributes] by removing [attribute] key
*/
public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = Attributes(content.minus(attribute))
/**
* Add an element to a [SetAttribute]
@ -63,6 +90,9 @@ public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
)
}
/**
* Create [Attributes] with a single key
*/
public fun <T : Any, A : Attribute<T>> Attributes(
attribute: A,
attrValue: T,

26
attributes-kt/src/commonMain/kotlin/space/kscience/attributes/SafeType.kt Normal file
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@ -0,0 +1,26 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.attributes
import kotlin.reflect.KClass
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
* Safe variant ok Kotlin [KType] that ensures that the type parameter is of the same type ask [kType]
*
* @param kType raw [KType]
*/
public class SafeType<T> @PublishedApi internal constructor(public val kType: KType)
public inline fun <reified T> safeTypeOf(): SafeType<T> = SafeType(typeOf<T>())
/**
* Derive Kotlin [KClass] from this type and fail if the type is not a class (should not happen)
*/
@Suppress("UNCHECKED_CAST")
@UnstableAttributesAPI
public val <T> SafeType<T>.kClass: KClass<T & Any> get() = kType.classifier as KClass<T & Any>

17
attributes-kt/src/commonMain/kotlin/space/kscience/attributes/annotations.kt Normal file
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@ -0,0 +1,17 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.attributes
/**
* Marks declarations that are still experimental in the Attributes-kt APIs, which means that the design of the corresponding
* declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is
* a chance of those declarations will be deprecated in the future or the semantics of their behavior may change
* in some way that may break some code.
*/
@MustBeDocumented
@Retention(value = AnnotationRetention.BINARY)
@RequiresOptIn("This API is unstable and could change in future", RequiresOptIn.Level.WARNING)
public annotation class UnstableAttributesAPI

3
buildSrc/settings.gradle.kts
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@ -1,4 +1,4 @@
enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
rootProject.name = "buildSrc"
dependencyResolutionManagement {
val projectProperties = java.util.Properties()
@ -13,6 +13,7 @@ dependencyResolutionManagement {
val toolsVersion: String = projectProperties["toolsVersion"].toString()
@Suppress("UnstableApiUsage")
repositories {
mavenLocal()
maven("https://repo.kotlin.link")

2
buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt
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@ -56,6 +56,8 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
*/
override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
override val elementType: KType get() = typeOf<$type>()
@Suppress("UNCHECKED_CAST")
override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>

3
gradle.properties
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@ -11,5 +11,4 @@ toolsVersion=0.14.9-kotlin-1.8.20
org.gradle.parallel=true
org.gradle.workers.max=4
org.gradle.configureondemand=true
org.gradle.jvmargs=-Xmx4096m
org.gradle.jvmargs=-Xmx4096m

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-8.1.1-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

31
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
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@ -8,12 +8,14 @@ package space.kscience.kmath.commons.linear
import org.apache.commons.math3.linear.*
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.*
import space.kscience.kmath.nd.StructureFeature
import space.kscience.kmath.nd.StructureAttribute
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.reflect.KClass
import kotlin.reflect.KType
import kotlin.reflect.cast
import kotlin.reflect.typeOf
public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
override val rowNum: Int get() = origin.rowDimension
@ -38,6 +40,8 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMLinearSpace : LinearSpace<Double, DoubleField> {
override val elementAlgebra: DoubleField get() = DoubleField
override val elementType: KType = typeOf<Double>()
override fun buildMatrix(
rows: Int,
columns: Int,
@ -99,7 +103,7 @@ public object CMLinearSpace : LinearSpace<Double, DoubleField> {
v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
override fun <F : StructureAttribute> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
//Return the feature if it is intrinsic to the structure
structure.getFeature(type)?.let { return it }
@ -108,36 +112,37 @@ public object CMLinearSpace : LinearSpace<Double, DoubleField> {
return when (type) {
IsDiagonal::class -> if (origin is DiagonalMatrix) IsDiagonal else null
DeterminantFeature::class, LupDecompositionFeature::class -> object :
DeterminantFeature<Double>,
LupDecompositionFeature<Double> {
Determinant::class, LupDecompositionAttribute::class -> object :
Determinant<Double>,
LupDecompositionAttribute<Double> {
private val lup by lazy { LUDecomposition(origin) }
override val determinant: Double by lazy { lup.determinant }
override val l: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.l).withFeature(LFeature) }
override val u: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.u).withFeature(UFeature) }
override val l: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.l).withAttribute(LowerTriangular) }
override val u: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.u).withAttribute(UpperTriangular) }
override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
CholeskyDecompositionAttribute::class -> object : CholeskyDecompositionAttribute<Double> {
override val l: Matrix<Double> by lazy<Matrix<Double>> {
val cholesky = CholeskyDecomposition(origin)
CMMatrix(cholesky.l).withFeature(LFeature)
CMMatrix(cholesky.l).withAttribute(LowerTriangular)
}
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
QRDecompositionAttribute::class -> object : QRDecompositionAttribute<Double> {
private val qr by lazy { QRDecomposition(origin) }
override val q: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.q).withFeature(OrthogonalFeature) }
override val r: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.r).withFeature(UFeature) }
override val q: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.q).withAttribute(OrthogonalAttribute) }
override val r: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.r).withAttribute(UpperTriangular) }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
SingularValueDecompositionAttribute::class -> object : SingularValueDecompositionAttribute<Double> {
private val sv by lazy { SingularValueDecomposition(origin) }
override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
override val singularValues: Point<Double> by lazy { DoubleBuffer(sv.singularValues) }
}
else -> null
}?.let(type::cast)
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DSAlgebra.kt
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@ -13,6 +13,8 @@ import space.kscience.kmath.structures.MutableBufferFactory
import space.kscience.kmath.structures.asBuffer
import kotlin.math.max
import kotlin.math.min
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
* Class representing both the value and the differentials of a function.

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt
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@ -5,12 +5,15 @@
package space.kscience.kmath.linear
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.VirtualBuffer
import space.kscience.kmath.structures.indices
import kotlin.reflect.KType
import kotlin.reflect.typeOf
public class BufferedLinearSpace<T, out A : Ring<T>>(

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt
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@ -12,6 +12,8 @@ import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.reflect.KType
import kotlin.reflect.typeOf
public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
@ -20,7 +22,7 @@ public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
override fun buildMatrix(
rows: Int,
columns: Int,
initializer: DoubleField.(i: Int, j: Int) -> Double
initializer: DoubleField.(i: Int, j: Int) -> Double,
): Matrix<Double> = DoubleFieldOpsND.structureND(ShapeND(rows, columns)) { (i, j) ->
DoubleField.initializer(i, j)
}.as2D()

40
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
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@ -5,16 +5,15 @@
package space.kscience.kmath.linear
import space.kscience.attributes.SafeType
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureFeature
import space.kscience.kmath.nd.as1D
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.BufferRingOps
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import kotlin.reflect.KClass
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
* Alias for [Structure2D] with more familiar name.
@ -31,13 +30,19 @@ public typealias MutableMatrix<T> = MutableStructure2D<T>
*/
public typealias Point<T> = Buffer<T>
/**
* A marker interface for algebras that operate on matrices
* @param T type of matrix element
*/
public interface MatrixOperations<T>
/**
* Basic operations on matrices and vectors.
*
* @param T the type of items in the matrices.
* @param A the type of ring over [T].
*/
public interface LinearSpace<T, out A : Ring<T>> {
public interface LinearSpace<T, out A : Ring<T>> : MatrixOperations<T> {
public val elementAlgebra: A
/**
@ -167,16 +172,16 @@ public interface LinearSpace<T, out A : Ring<T>> {
public operator fun T.times(v: Point<T>): Point<T> = v * this
/**
* Compute a feature of the structure in this scope. Structure features take precedence other context features.
* Get an attribute value for the structure in this scope. Structure features take precedence other context features.
*
* @param F the type of feature.
* @param A the type of feature.
* @param structure the structure.
* @param type the [KClass] instance of [F].
* @param attribute to be computed
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public fun <F : StructureFeature> computeFeature(structure: Matrix<T>, type: KClass<out F>): F? =
structure.getFeature(type)
public fun <T, A : StructureAttribute<T>> attributeFor(structure: StructureND<*>, attribute: A): T? =
structure.attributes[attribute]
public companion object {
@ -184,23 +189,12 @@ public interface LinearSpace<T, out A : Ring<T>> {
* A structured matrix with custom buffer
*/
public fun <T : Any, A : Ring<T>> buffered(
algebra: A
algebra: A,
): LinearSpace<T, A> = BufferedLinearSpace(BufferRingOps(algebra))
}
}
/**
* Get a feature of the structure in this scope. Structure features take precedence other context features.
*
* @param T the type of items in the matrices.
* @param F the type of feature.
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public inline fun <T : Any, reified F : StructureFeature> LinearSpace<T, *>.computeFeature(structure: Matrix<T>): F? =
computeFeature(structure, F::class)
public inline operator fun <LS : LinearSpace<*, *>, R> LS.invoke(block: LS.() -> R): R = run(block)

188
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt
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@ -3,67 +3,92 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@file:Suppress("UnusedReceiverParameter")
package space.kscience.kmath.linear
import space.kscience.attributes.PolymorphicAttribute
import space.kscience.attributes.SafeType
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.BufferAccessor2D
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import space.kscience.kmath.structures.*
/**
* Common implementation of [LupDecompositionFeature].
* 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.
* @param l The lower triangular matrix in this decomposition. It may have [LowerTriangular].
* @param u The upper triangular matrix in this decomposition. It may have [UpperTriangular].
* @param p he permutation matrix in this decomposition. May have [Determinant] attribute
*/
public class LupDecomposition<T : Any>(
public val context: LinearSpace<T, *>,
public val elementContext: Field<T>,
public val lu: Matrix<T>,
public val pivot: IntArray,
private val even: Boolean,
) : 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: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
when {
j < i -> lu[i, j]
j == i -> elementContext.one
else -> elementContext.zero
}
}.withFeature(LFeature)
public data class LupDecomposition<T>(
public val l: Matrix<T>,
public val u: Matrix<T>,
public val p: Matrix<T>,
)
/**
* Returns the matrix U of the decomposition.
*
* U is an upper-triangular matrix including the diagonal
*/
override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
if (j >= i) lu[i, j] else elementContext.zero
}.withFeature(UFeature)
public class LupDecompositionAttribute<T>(type: SafeType<LupDecomposition<T>>) :
PolymorphicAttribute<LupDecomposition<T>>(type),
MatrixAttribute<LupDecomposition<T>>
/**
* Returns the P rows permutation matrix.
*
* 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: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
if (j == pivot[i]) elementContext.one else elementContext.zero
}
public val <T> MatrixOperations<T>.LUP: LupDecompositionAttribute<T>
get() = LupDecompositionAttribute(safeTypeOf())
/**
* Return the determinant of the matrix
* @return determinant of the matrix
*/
override val determinant: T by lazy {
elementContext { (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] } }
}
}
///**
// * Common implementation of [LupDecomposition].
// */
//private class LupDecompositionImpl<T : Any>(
// public val elementContext: Field<T>,
// public val lu: Matrix<T>,
// public val pivot: IntBuffer,
// private val even: Boolean,
//) : LupDecomposition<T> {
// /**
// * Returns the matrix L of the decomposition.
// *
// * L is a lower-triangular matrix with [Ring.one] in diagonal
// */
// 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
// }
// }.withFeature(LowerTriangular)
//
//
// /**
// * Returns the matrix U of the decomposition.
// *
// * U is an upper-triangular matrix including the diagonal
// */
// override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
// if (j >= i) lu[i, j] else elementContext.zero
// }.withFeature(UpperTriangular)
//
// /**
// * Returns the P rows permutation matrix.
// *
// * 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: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
// if (j == pivot[i]) elementContext.one else elementContext.zero
// }
//
// /**
// * Return the determinant of the matrix
// * @return determinant of the matrix
// */
// override val determinant: T by lazy {
// elementContext { (0 until lu.shape[0]).fold(if(even) one else -one) { value, i -> value * lu[i, i] } }
// }
//
//}
@PublishedApi
internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
@ -73,7 +98,6 @@ internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
* Create a lup decomposition of generic matrix.
*/
public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
factory: MutableBufferFactory<T>,
matrix: Matrix<T>,
checkSingular: (T) -> Boolean,
): LupDecomposition<T> {
@ -82,15 +106,15 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
val pivot = IntArray(matrix.rowNum)
//TODO just waits for multi-receivers
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory).run {
elementAlgebra {
val lu = create(matrix)
// Initialize permutation array and parity
// Initialize the permutation array and parity
for (row in 0 until m) pivot[row] = row
var even = true
// Initialize permutation array and parity
// Initialize the permutation array and parity
for (row in 0 until m) pivot[row] = row
// Loop over columns
@ -145,46 +169,57 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
for (row in col + 1 until m) lu[row, col] /= luDiag
}
return LupDecomposition(this@lup, elementAlgebra, lu.collect(), pivot, even)
val l: MatrixWrapper<T> = VirtualMatrix(rowNum, colNum) { i, j ->
when {
j < i -> lu[i, j]
j == i -> one
else -> zero
}
}.withAttribute(LowerTriangular)
val u = VirtualMatrix(rowNum, colNum) { i, j ->
if (j >= i) lu[i, j] else zero
}.withAttribute(UpperTriangular)
val p = VirtualMatrix(rowNum, colNum) { i, j ->
if (j == pivot[i]) one else zero
}.withAttribute(Determinant, if (even) one else -one)
return LupDecomposition(l, u, p)
}
}
}
public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
matrix: Matrix<T>,
noinline checkSingular: (T) -> Boolean,
): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, matrix, checkSingular)
public fun LinearSpace<Double, DoubleField>.lup(
matrix: Matrix<Double>,
singularityThreshold: Double = 1e-11,
): LupDecomposition<Double> =
lup(::DoubleBuffer, matrix) { it < singularityThreshold }
): LupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
internal fun <T : Any> LupDecomposition<T>.solve(
factory: MutableBufferFactory<T>,
internal fun <T : Any, A : Field<T>> LinearSpace<T, A>.solve(
lup: LupDecomposition<T>,
matrix: Matrix<T>,
): Matrix<T> {
require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
require(matrix.rowNum == lup.l.rowNum) { "Matrix dimension mismatch. Expected ${lup.l.rowNum}, but got ${matrix.colNum}" }
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
elementContext {
BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory).run {
elementAlgebra {
// Apply permutations to b
val bp = create { _, _ -> zero }
for (row in pivot.indices) {
for (row in 0 until rowNum) {
val bpRow = bp.row(row)
val pRow = pivot[row]
for (col in 0 until matrix.colNum) bpRow[col] = matrix[pRow, col]
}
// Solve LY = b
for (col in pivot.indices) {
for (col in 0 until colNum) {
val bpCol = bp.row(col)
for (i in col + 1 until pivot.size) {
for (i in col + 1 until colNum) {
val bpI = bp.row(i)
val luICol = lu[i, col]
val luICol = lup.l[i, col]
for (j in 0 until matrix.colNum) {
bpI[j] -= bpCol[j] * luICol
}
@ -192,19 +227,19 @@ internal fun <T : Any> LupDecomposition<T>.solve(
}
// Solve UX = Y
for (col in pivot.size - 1 downTo 0) {
for (col in colNum - 1 downTo 0) {
val bpCol = bp.row(col)
val luDiag = lu[col, col]
val luDiag = lup.u[col, col]
for (j in 0 until matrix.colNum) bpCol[j] /= luDiag
for (i in 0 until col) {
val bpI = bp.row(i)
val luICol = lu[i, col]
val luICol = lup.u[i, col]
for (j in 0 until matrix.colNum) bpI[j] -= bpCol[j] * luICol
}
}
return context.buildMatrix(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
return buildMatrix(matrix.rowNum, matrix.colNum) { i, j -> bp[i, j] }
}
}
}
@ -214,17 +249,16 @@ internal fun <T : Any> LupDecomposition<T>.solve(
*/
@OptIn(UnstableKMathAPI::class)
public fun <T : Comparable<T>, F : Field<T>> LinearSpace<T, F>.lupSolver(
bufferFactory: MutableBufferFactory<T>,
singularityCheck: (T) -> Boolean,
): LinearSolver<T> = object : LinearSolver<T> {
override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
// Use existing decomposition if it is provided by matrix
val decomposition = computeFeature(a) ?: lup(bufferFactory, a, singularityCheck)
return decomposition.solve(bufferFactory, b)
val decomposition = attributeFor(a, LUP) ?: lup(a, singularityCheck)
return solve(decomposition, b)
}
override fun inverse(matrix: Matrix<T>): Matrix<T> = solve(matrix, one(matrix.rowNum, matrix.colNum))
}
public fun LinearSpace<Double, DoubleField>.lupSolver(singularityThreshold: Double = 1e-11): LinearSolver<Double> =
lupSolver(::DoubleBuffer) { it < singularityThreshold }
lupSolver { it < singularityThreshold }

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixBuilder.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.linear
import space.kscience.attributes.FlagAttribute
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.structures.BufferAccessor2D
@ -49,10 +50,10 @@ public inline fun <T : Any> LinearSpace<T, Ring<T>>.column(
public fun <T : Any> LinearSpace<T, Ring<T>>.column(vararg values: T): Matrix<T> = column(values.size, values::get)
public object SymmetricMatrixFeature : MatrixFeature
public object Symmetric : MatrixAttribute<Unit>, FlagAttribute
/**
* Naive implementation of a symmetric matrix builder, that adds a [SymmetricMatrixFeature] tag. The resulting matrix contains
* Naive implementation of a symmetric matrix builder, that adds a [Symmetric] tag. The resulting matrix contains
* full `size^2` number of elements, but caches elements during calls to save [builder] calls. [builder] is always called in the
* upper triangle region meaning that `i <= j`
*/
@ -72,6 +73,6 @@ public fun <T : Any, A : Ring<T>> MatrixBuilder<T, A>.symmetric(
} else {
cached
}
}.withFeature(SymmetricMatrixFeature)
}.withAttribute(Symmetric)
}
}

158
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt
View File

@ -3,20 +3,27 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@file:OptIn(UnstableKMathAPI::class)
@file:Suppress("UnusedReceiverParameter")
package space.kscience.kmath.linear
import space.kscience.attributes.Attribute
import space.kscience.attributes.*
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.nd.StructureAttribute
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
* 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<T> : Attribute<T>
public interface MatrixAttribute<T> : StructureAttribute<T>
/**
* Matrices with this feature are considered to have only diagonal non-zero elements.
*/
public interface IsDiagonal : MatrixFeature<Unit> {
public interface IsDiagonal : MatrixAttribute<Unit>, FlagAttribute {
public companion object : IsDiagonal
}
@ -31,130 +38,110 @@ public object IsZero : IsDiagonal
public object IsUnit : IsDiagonal
/**
* Matrices with this feature can be inverted: *[inverse] = a<sup>&minus;1</sup>* where *a* is the owning matrix.
* Matrices with this feature can be inverted.
*
* @param T the type of matrices' items.
*/
public class Inverted<T> private constructor() : MatrixFeature<Matrix<T>> {
internal val instance: Inverted<Nothing> = Inverted()
}
public class Inverted<T>(type: SafeType<Matrix<T>>) :
PolymorphicAttribute<Matrix<T>>(type),
MatrixAttribute<Matrix<T>>
@Suppress("UNCHECKED_CAST")
public val <T> LinearSpace<T, *>.Inverted: Inverted<T> get() = Inverted.instance as Inverted<T>
public val <T> MatrixOperations<T>.Inverted: Inverted<T> get() = Inverted(safeTypeOf())
/**
* Matrices with this feature can compute their determinant.
*
* @param T the type of matrices' items.
*/
public class DeterminantFeature<T : Any> : MatrixFeature<T>
public class Determinant<T>(type: SafeType<T>) :
PolymorphicAttribute<T>(type),
MatrixAttribute<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
}
public inline val <reified T> MatrixOperations<T>.Determinant: Determinant<T> get() = Determinant(safeTypeOf())
/**
* Matrices with this feature are lower triangular ones.
*/
public object LFeature : MatrixFeature<Unit>
public object LowerTriangular : MatrixAttribute<Unit>, FlagAttribute
/**
* Matrices with this feature are upper triangular ones.
*/
public object UFeature : MatrixFeature<Unit>
public object UpperTriangular : MatrixAttribute<Unit>, FlagAttribute
/**
* Matrices with this feature support LU factorization: *a = [l] &middot; [u]* where *a* is the owning matrix.
* @param l The lower triangular matrix in this decomposition. It may have [LowerTriangular].
* @param u The upper triangular matrix in this decomposition. It may have [UpperTriangular].
*/
public data class LUDecomposition<T>(val l: Matrix<T>, val u: Matrix<T>)
/**
* Matrices with this feature support LU factorization: *a = [l] &middot; [u]* where *a* is the owning matrix.
*
* @param T the type of matrices' items.
*/
public interface LUDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The lower triangular matrix in this decomposition. It may have [LFeature].
*/
public val l: Matrix<T>
public class LuDecompositionAttribute<T>(type: SafeType<LUDecomposition<T>>) :
PolymorphicAttribute<LUDecomposition<T>>(type),
MatrixAttribute<LUDecomposition<T>>
/**
* The upper triangular matrix in this decomposition. It may have [UFeature].
*/
public val u: Matrix<T>
}
public val <T> MatrixOperations<T>.LU: LuDecompositionAttribute<T> get() = LuDecompositionAttribute(safeTypeOf())
/**
* 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<out 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 ([IsUnit]).
*/
public object OrthogonalFeature : MatrixFeature
public object OrthogonalAttribute : MatrixAttribute<Unit>, FlagAttribute
/**
* 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<out T : Any> : MatrixFeature {
public interface QRDecomposition<out T> {
/**
* The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
* The orthogonal matrix in this decomposition. It may have [OrthogonalAttribute].
*/
public val q: Matrix<T>
/**
* The upper triangular matrix in this decomposition. It may have [UFeature].
* The upper triangular matrix in this decomposition. It may have [UpperTriangular].
*/
public val r: Matrix<T>
}
/**
* Matrices with this feature support QR factorization: *a = [QR.q] &middot; [QR.r]* where *a* is the owning matrix.
*
* @param T the type of matrices' items.
*/
public class QRDecompositionAttribute<T>(type: SafeType<QRDecomposition<T>>) :
PolymorphicAttribute<QRDecomposition<T>>(type),
MatrixAttribute<QRDecomposition<T>>
public val <T> MatrixOperations<T>.QR: QRDecompositionAttribute<T>
get() = QRDecompositionAttribute(safeTypeOf())
public interface CholeskyDecomposition<T> {
/**
* The triangular matrix in this decomposition. It may have either [UpperTriangular] or [LowerTriangular].
*/
public val l: 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<out T : Any> : MatrixFeature {
/**
* The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
*/
public val l: Matrix<T>
}
public class CholeskyDecompositionAttribute<T>(type: SafeType<CholeskyDecomposition<T>>) :
PolymorphicAttribute<CholeskyDecomposition<T>>(type),
MatrixAttribute<CholeskyDecomposition<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<out T : Any> : MatrixFeature {
public val <T> MatrixOperations<T>.Cholesky: CholeskyDecompositionAttribute<T>
get() = CholeskyDecompositionAttribute(safeTypeOf())
public interface SingularValueDecomposition<T> {
/**
* The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
* The matrix in this decomposition. It is unitary, and it consists of left singular vectors.
*/
public val u: Matrix<T>
@ -164,14 +151,27 @@ public interface SingularValueDecompositionFeature<out T : Any> : MatrixFeature
public val s: Matrix<T>
/**
* The matrix in this decomposition. It is unitary, and it consists from right singular vectors.
* The matrix in this decomposition. It is unitary, and it consists of right singular vectors.
*/
public val v: Matrix<T>
/**
* The buffer of singular values of this SVD.
* The buffer of singular values for this SVD.
*/
public val singularValues: Point<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 class SingularValueDecompositionAttribute<T>(type: SafeType<SingularValueDecomposition<T>>) :
PolymorphicAttribute<SingularValueDecomposition<T>>(type),
MatrixAttribute<SingularValueDecomposition<T>>
public val <T> MatrixOperations<T>.SVD: SingularValueDecompositionAttribute<T>
get() = SingularValueDecompositionAttribute(safeTypeOf())
//TODO add sparse matrix feature

47
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt
View File

@ -5,19 +5,20 @@
package space.kscience.kmath.linear
import space.kscience.attributes.Attribute
import space.kscience.attributes.Attributes
import space.kscience.attributes.withAttribute
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.misc.FeatureSet
import space.kscience.kmath.operations.Ring
/**
* A [Matrix] that holds [MatrixFeature] objects.
* A [Matrix] that holds [MatrixAttribute] objects.
*
* @param T the type of items.
*/
public class MatrixWrapper<out T : Any> internal constructor(
public val origin: Matrix<T>,
public val attributes: Attributes,
override val attributes: Attributes,
) : Matrix<T> by origin {
override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$attributes)"
@ -34,23 +35,31 @@ public val <T : Any> Matrix<T>.origin: Matrix<T>
/**
* Add a single feature to a [Matrix]
*/
public fun <T : Any> Matrix<T>.withFeature(newFeature: MatrixFeature): MatrixWrapper<T> = if (this is MatrixWrapper) {
MatrixWrapper(origin, attributes.with(newFeature))
public fun <T : Any, A : Attribute<T>> Matrix<T>.withAttribute(
attribute: A,
attrValue: T,
): MatrixWrapper<T> = if (this is MatrixWrapper) {
MatrixWrapper(origin, attributes.withAttribute(attribute,attrValue))
} else {
MatrixWrapper(this, FeatureSet.of(newFeature))
MatrixWrapper(this, Attributes(attribute, attrValue))
}
@Deprecated("To be replaced by withFeature")
public operator fun <T : Any> Matrix<T>.plus(newFeature: MatrixFeature): MatrixWrapper<T> = withFeature(newFeature)
public fun <T : Any, A : Attribute<Unit>> Matrix<T>.withAttribute(
attribute: A,
): MatrixWrapper<T> = if (this is MatrixWrapper) {
MatrixWrapper(origin, attributes.withAttribute(attribute))
} else {
MatrixWrapper(this, Attributes(attribute, Unit))
}
/**
* Add a collection of features to a [Matrix]
* Modify matrix attributes
*/
public fun <T : Any> Matrix<T>.withFeatures(newFeatures: Iterable<MatrixFeature>): MatrixWrapper<T> =
public fun <T : Any> Matrix<T>.modifyAttributes(modifier: (Attributes) -> Attributes): MatrixWrapper<T> =
if (this is MatrixWrapper) {
MatrixWrapper(origin, attributes.with(newFeatures))
MatrixWrapper(origin, modifier(attributes))
} else {
MatrixWrapper(this, FeatureSet.of(newFeatures))
MatrixWrapper(this, modifier(Attributes.EMPTY))
}
/**
@ -59,9 +68,9 @@ public fun <T : Any> Matrix<T>.withFeatures(newFeatures: Iterable<MatrixFeature>
public fun <T : Any> LinearSpace<T, Ring<T>>.one(
rows: Int,
columns: Int,
): Matrix<T> = VirtualMatrix(rows, columns) { i, j ->
): MatrixWrapper<T> = VirtualMatrix(rows, columns) { i, j ->
if (i == j) elementAlgebra.one else elementAlgebra.zero
}.withFeature(IsUnit)
}.withAttribute(IsUnit)
/**
@ -70,16 +79,16 @@ public fun <T : Any> LinearSpace<T, Ring<T>>.one(
public fun <T : Any> LinearSpace<T, Ring<T>>.zero(
rows: Int,
columns: Int,
): Matrix<T> = VirtualMatrix(rows, columns) { _, _ ->
): MatrixWrapper<T> = VirtualMatrix(rows, columns) { _, _ ->
elementAlgebra.zero
}.withFeature(IsZero)
}.withAttribute(IsZero)
public class TransposedFeature<out T : Any>(public val original: Matrix<T>) : MatrixFeature
public class TransposedAttribute<out T : Any>(public val original: Matrix<T>) : MatrixAttribute
/**
* Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
*/
@Suppress("UNCHECKED_CAST")
@OptIn(UnstableKMathAPI::class)
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> = getFeature(TransposedFeature::class)?.original as? Matrix<T>
?: VirtualMatrix(colNum, rowNum) { i, j -> get(j, i) }.withFeature(TransposedFeature(this))
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> = getFeature(TransposedAttribute::class)?.original as? Matrix<T>
?: VirtualMatrix(colNum, rowNum) { i, j -> get(j, i) }.withAttribute(TransposedAttribute(this))

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

@ -5,6 +5,7 @@
package space.kscience.kmath.linear
import space.kscience.attributes.Attributes
import space.kscience.kmath.nd.ShapeND
@ -16,6 +17,7 @@ import space.kscience.kmath.nd.ShapeND
public class VirtualMatrix<out T : Any>(
override val rowNum: Int,
override val colNum: Int,
override val attributes: Attributes = Attributes.EMPTY,
public val generator: (i: Int, j: Int) -> T,
) : Matrix<T> {
@ -24,5 +26,8 @@ public class VirtualMatrix<out T : Any>(
override operator fun get(i: Int, j: Int): T = generator(i, j)
}
public fun <T : Any> MatrixBuilder<T, *>.virtual(generator: (i: Int, j: Int) -> T): VirtualMatrix<T> =
VirtualMatrix(rows, columns, generator)
public fun <T : Any> MatrixBuilder<T, *>.virtual(
attributes: Attributes = Attributes.EMPTY,
generator: (i: Int, j: Int) -> T,
): VirtualMatrix<T> =
VirtualMatrix(rows, columns, attributes, generator)

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt
View File

@ -79,7 +79,7 @@ public interface AlgebraND<T, out C : Algebra<T>> : Algebra<StructureND<T>> {
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public fun <F : StructureFeature> getFeature(structure: StructureND<T>, type: KClass<out F>): F? =
public fun <F : StructureAttribute> getFeature(structure: StructureND<T>, type: KClass<out F>): F? =
structure.getFeature(type)
public companion object
@ -93,7 +93,7 @@ public interface AlgebraND<T, out C : Algebra<T>> : Algebra<StructureND<T>> {
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public inline fun <T : Any, reified F : StructureFeature> AlgebraND<T, *>.getFeature(structure: StructureND<T>): F? =
public inline fun <T : Any, reified F : StructureAttribute> AlgebraND<T, *>.getFeature(structure: StructureND<T>): F? =
getFeature(structure, F::class)
/**

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
View File

@ -34,9 +34,9 @@ public open class BufferND<out T>(
/**
* Create a generic [BufferND] using provided [initializer]
*/
public fun <T> BufferND(
public inline fun <reified T> BufferND(
shape: ShapeND,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
bufferFactory: BufferFactory<T> = BufferFactory.auto(),
initializer: (IntArray) -> T,
): BufferND<T> {
val strides = Strides(shape)

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
View File

@ -110,7 +110,7 @@ private value class Structure2DWrapper<out T>(val structure: StructureND<T>) : S
@PerformancePitfall
override operator fun get(i: Int, j: Int): T = structure[i, j]
override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? = structure.getFeature(type)
override fun <F : StructureAttribute> getFeature(type: KClass<out F>): F? = structure.getFeature(type)
@PerformancePitfall
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()

22
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.nd
import space.kscience.attributes.Attribute
import space.kscience.attributes.AttributeContainer
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.operations.Ring
@ -15,9 +16,8 @@ import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import kotlin.jvm.JvmName
import kotlin.math.abs
import kotlin.reflect.KClass
public interface StructureFeature<T> : Attribute<T>
public interface StructureAttribute<T> : Attribute<T>
/**
* Represents n-dimensional structure i.e., multidimensional container of items of the same type and size. The number
@ -122,9 +122,14 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
*/
public fun <T> buffered(
strides: Strides,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
initializer: (IntArray) -> T,
): BufferND<T> = BufferND(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
): BufferND<T> = BufferND(strides, Buffer.boxing(strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> buffered(
shape: ShapeND,
initializer: (IntArray) -> T,
): BufferND<T> = buffered(ColumnStrides(shape), initializer)
/**
* Inline create NDStructure with non-boxing buffer implementation if it is possible
@ -135,16 +140,11 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
): BufferND<T> = BufferND(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
public inline fun <T : Any> auto(
type: KClass<T>,
type: SafeType<T>,
strides: Strides,
crossinline initializer: (IntArray) -> T,
): BufferND<T> = BufferND(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> buffered(
shape: ShapeND,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
initializer: (IntArray) -> T,
): BufferND<T> = buffered(ColumnStrides(shape), bufferFactory, initializer)
public inline fun <reified T : Any> auto(
shape: ShapeND,
@ -159,7 +159,7 @@ public interface StructureND<out T> : AttributeContainer, WithShape {
auto(ColumnStrides(ShapeND(shape)), initializer)
public inline fun <T : Any> auto(
type: KClass<T>,
type: SafeType<T>,
vararg shape: Int,
crossinline initializer: (IntArray) -> T,
): BufferND<T> = auto(type, ColumnStrides(ShapeND(shape)), initializer)

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt
View File

@ -9,14 +9,14 @@ import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.Ring.Companion.optimizedPower
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.reflect.KType
/**
* Represents an algebraic structure.
*
* @param T the type of element of this structure.
* @param T the type of element which Algebra operates on.
*/
public interface Algebra<T> {
/**
* Provide a factory for buffers, associated with this [Algebra]
*/
@ -67,12 +67,12 @@ public interface Algebra<T> {
*
* @param operation the name of operation.
* @param arg the argument of operation.
* @return a result of operation.
* @return the result of the operation.
*/
public fun unaryOperation(operation: String, arg: T): T = unaryOperationFunction(operation)(arg)
/**
* Dynamically dispatches a binary operation with the certain name.
* Dynamically dispatches a binary operation with a certain name.
*
* Implementations must fulfil the following requirements:
*
@ -87,7 +87,7 @@ public interface Algebra<T> {
error("Binary operation '$operation' not defined in $this")
/**
* Dynamically invokes a binary operation with the certain name.
* Dynamically invokes a binary operation with a certain name.
*
* Implementations must fulfil the following requirements:
*

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt
View File

@ -8,6 +8,7 @@ package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.reflect.KType
public interface WithSize {
public val size: Int
@ -18,6 +19,9 @@ public interface WithSize {
*/
public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
public val elementAlgebra: A
public val elementType: KType
public val elementBufferFactory: MutableBufferFactory<T> get() = elementAlgebra.bufferFactory
public fun buffer(size: Int, vararg elements: T): Buffer<T> {

22
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
View File

@ -5,10 +5,12 @@
package space.kscience.kmath.structures
import space.kscience.attributes.SafeType
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.operations.WithSize
import space.kscience.kmath.operations.asSequence
import kotlin.jvm.JvmInline
import kotlin.reflect.KClass
import kotlin.reflect.typeOf
/**
* Function that produces [Buffer] from its size and function that supplies values.
@ -99,13 +101,13 @@ public interface Buffer<out T> : WithSize {
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<T>, size: Int, initializer: (Int) -> T): Buffer<T> =
when (type) {
Double::class -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
Short::class -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
Int::class -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
Long::class -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>
Float::class -> MutableBuffer.float(size) { initializer(it) as Float } as Buffer<T>
public inline fun <T> auto(type: SafeType<T>, size: Int, initializer: (Int) -> T): Buffer<T> =
when (type.kType) {
typeOf<Double>() -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
typeOf<Short>() -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
typeOf<Int>() -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
typeOf<Long>() -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>
typeOf<Float>() -> MutableBuffer.float(size) { initializer(it) as Float } as Buffer<T>
else -> boxing(size, initializer)
}
@ -115,8 +117,8 @@ public interface Buffer<out T> : WithSize {
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): Buffer<T> =
auto(T::class, size, initializer)
public inline fun <reified T> auto(size: Int, initializer: (Int) -> T): Buffer<T> =
auto(safeTypeOf<T>(), size, initializer)
}
}

9
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt
View File

@ -27,12 +27,9 @@ internal class BufferAccessor2D<T>(
fun create(mat: Structure2D<T>): MutableBuffer<T> = create { i, j -> mat[i, j] }
//TODO optimize wrapper
fun MutableBuffer<T>.collect(): Structure2D<T> = StructureND.buffered(
ColumnStrides(ShapeND(rowNum, colNum)),
factory
) { (i, j) ->
get(i, j)
}.as2D()
fun MutableBuffer<T>.toStructure2D(): Structure2D<T> = StructureND.buffered(
ColumnStrides(ShapeND(rowNum, colNum))
) { (i, j) -> get(i, j) }.as2D()
inner class Row(val buffer: MutableBuffer<T>, val rowIndex: Int) : MutableBuffer<T> {
override val size: Int get() = colNum

25
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/MutableBuffer.kt
View File

@ -5,7 +5,9 @@
package space.kscience.kmath.structures
import kotlin.reflect.KClass
import space.kscience.attributes.SafeType
import space.kscience.attributes.safeTypeOf
import kotlin.reflect.typeOf
/**
* A generic mutable random-access structure for both primitives and objects.
@ -74,13 +76,13 @@ public interface MutableBuffer<T> : Buffer<T> {
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
when (type) {
Double::class -> double(size) { initializer(it) as Double } as MutableBuffer<T>
Short::class -> short(size) { initializer(it) as Short } as MutableBuffer<T>
Int::class -> int(size) { initializer(it) as Int } as MutableBuffer<T>
Float::class -> float(size) { initializer(it) as Float } as MutableBuffer<T>
Long::class -> long(size) { initializer(it) as Long } as MutableBuffer<T>
public inline fun <T> auto(type: SafeType<T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
when (type.kType) {
typeOf<Double>() -> double(size) { initializer(it) as Double } as MutableBuffer<T>
typeOf<Short>() -> short(size) { initializer(it) as Short } as MutableBuffer<T>
typeOf<Int>() -> int(size) { initializer(it) as Int } as MutableBuffer<T>
typeOf<Float>() -> float(size) { initializer(it) as Float } as MutableBuffer<T>
typeOf<Long>() -> long(size) { initializer(it) as Long } as MutableBuffer<T>
else -> boxing(size, initializer)
}
@ -90,11 +92,10 @@ public interface MutableBuffer<T> : Buffer<T> {
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
auto(T::class, size, initializer)
public inline fun <reified T> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
auto(safeTypeOf<T>(), size, initializer)
}
}
public sealed interface PrimitiveBuffer<T>: MutableBuffer<T>
public sealed interface PrimitiveBuffer<T> : MutableBuffer<T>

4
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt
View File

@ -12,6 +12,8 @@ import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.operations.Ring
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
* [LinearSpace] implementation specialized for a certain EJML type.
@ -43,5 +45,5 @@ public abstract class EjmlLinearSpace<T : Any, out A : Ring<T>, out M : org.ejml
@Suppress("UNCHECKED_CAST")
@UnstableKMathAPI
public fun EjmlMatrix<T, *>.inverse(): Structure2D<Double> =
computeFeature(this, InverseMatrixFeature::class)?.inverse as Structure2D<Double>
attributeFor(this, InverseMatrixFeature::class)?.inverse as Structure2D<Double>
}

1003
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt
View File

File diff suppressed because it is too large Load Diff

4
kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt
View File

@ -61,9 +61,9 @@ internal class EjmlMatrixTest {
fun features() {
val m = randomMatrix
val w = EjmlDoubleMatrix(m)
val det: DeterminantFeature<Double> = EjmlLinearSpaceDDRM.computeFeature(w) ?: fail()
val det: Determinant<Double> = EjmlLinearSpaceDDRM.attributeFor(w) ?: fail()
assertEquals(CommonOps_DDRM.det(m), det.determinant)
val lup: LupDecompositionFeature<Double> = EjmlLinearSpaceDDRM.computeFeature(w) ?: fail()
val lup: LupDecompositionAttribute<Double> = EjmlLinearSpaceDDRM.attributeFor(w) ?: fail()
val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols)
.also { it.decompose(m.copy()) }

1
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt
View File

@ -16,7 +16,6 @@ import kotlin.math.pow
public typealias DoubleVector = Point<Double>
@Suppress("FunctionName")
public fun DoubleVector(vararg doubles: Double): DoubleVector = doubles.asBuffer()
/**

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

@ -7,11 +7,14 @@
package space.kscience.kmath.functions
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.operations.invoke
import kotlin.math.max
import kotlin.math.min
import kotlin.reflect.KType
import kotlin.reflect.typeOf
/**
@ -48,7 +51,7 @@ public data class Polynomial<out C>(
*
* @usesMathJax
*/
public val coefficients: List<C>
public val coefficients: List<C>,
) {
override fun toString(): String = "Polynomial$coefficients"
}
@ -62,16 +65,17 @@ public data class Polynomial<out C>(
* @param ring underlying ring of constants of type [A].
*/
public open class PolynomialSpace<C, A>(
/**
* Underlying ring of constants. Its operations on constants are used by local operations on constants and polynomials.
*/
public val ring: A,
) : Ring<Polynomial<C>>, ScaleOperations<Polynomial<C>> where A : Ring<C>, A : ScaleOperations<C> {
@UnstableKMathAPI
override val elementType: KType get() = typeOf<Polynomial<C>>()
/**
* Instance of zero constant (zero of the underlying ring).
*/
public val constantZero: C get() = ring.zero
/**
* Instance of unit constant (unit of the underlying ring).
*/
@ -95,6 +99,7 @@ public open class PolynomialSpace<C, A>(
)
}
}
/**
* Returns difference between the constant represented as a polynomial and the polynomial.
*/
@ -115,6 +120,7 @@ public open class PolynomialSpace<C, A>(
)
}
}
/**
* Returns product of the constant represented as a polynomial and the polynomial.
*/
@ -147,6 +153,7 @@ public open class PolynomialSpace<C, A>(
)
}
}
/**
* Returns difference between the constant represented as a polynomial and the polynomial.
*/
@ -165,6 +172,7 @@ public open class PolynomialSpace<C, A>(
)
}
}
/**
* Returns product of the constant represented as a polynomial and the polynomial.
*/
@ -183,6 +191,7 @@ public open class PolynomialSpace<C, A>(
* Converts the constant [value] to polynomial.
*/
public fun number(value: C): Polynomial<C> = Polynomial(listOf(value))
/**
* Converts the constant to polynomial.
*/
@ -194,6 +203,7 @@ public open class PolynomialSpace<C, A>(
public override operator fun Polynomial<C>.unaryMinus(): Polynomial<C> = ring {
Polynomial(coefficients.map { -it })
}
/**
* Returns sum of the polynomials.
*/
@ -210,6 +220,7 @@ public open class PolynomialSpace<C, A>(
}
)
}
/**
* Returns difference of the polynomials.
*/
@ -226,6 +237,7 @@ public open class PolynomialSpace<C, A>(
}
)
}
/**
* Returns product of the polynomials.
*/
@ -245,6 +257,7 @@ public open class PolynomialSpace<C, A>(
* Instance of zero polynomial (zero of the polynomial ring).
*/
override val zero: Polynomial<C> = Polynomial(emptyList())
/**
* Instance of unit polynomial (unit of the polynomial ring).
*/

7
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt
View File

@ -5,13 +5,12 @@
package space.kscience.kmath.distributions
import space.kscience.kmath.chains.BlockingDoubleChain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.operations.DoubleField.pow
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.samplers.GaussianSampler
import space.kscience.kmath.samplers.*
import space.kscience.kmath.samplers.InternalErf
import space.kscience.kmath.samplers.NormalizedGaussianSampler
import space.kscience.kmath.samplers.ZigguratNormalizedGaussianSampler
import kotlin.math.*
/**
@ -24,7 +23,7 @@ public class NormalDistribution(public val sampler: GaussianSampler) : Distribut
return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI)))
}
override fun sample(generator: RandomGenerator): Chain<Double> = sampler.sample(generator)
override fun sample(generator: RandomGenerator): BlockingDoubleChain = sampler.sample(generator)
override fun cumulative(arg: Double): Double {
val dev = arg - sampler.mean