kmath-core/space.kscience.kmath.linear/LinearSpace

LinearSpace

interface LinearSpace<T, out A : Ring<T>>(source)

Basic operations on matrices and vectors.

Parameters

T

the type of items in the matrices.

A

the type of ring over T.

Inheritors

Types

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object Companion

Functions

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abstract fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix<T>

Produces a matrix with this context and given dimensions.

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abstract fun buildVector(size: Int, initializer: A.(Int) -> T): Point<T>

Produces a point compatible with matrix space (and possibly optimized for it).

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fun <T : Any> LinearSpace<T, Ring<T>>.column(vararg values: T): Matrix<T>
inline fun <T : Any> LinearSpace<T, Ring<T>>.column(size: Int, crossinline builder: (Int) -> T): Matrix<T>
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@UnstableKMathAPI
open fun <F : StructureFeature> computeFeature(structure: Matrix<T>, type: KClass<out F>): F?

Compute a feature of the structure in this scope. Structure features take precedence other context features.

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@UnstableKMathAPI
inline fun <T : Any, F : StructureFeature> LinearSpace<T, *>.computeFeature(structure: Matrix<T>): F?

Get a feature of the structure in this scope. Structure features take precedence other context features.

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@PerformancePitfall
fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.contentEquals(st1: StructureND<T>, st2: StructureND<T>): Boolean

Indicates whether some StructureND is equal to another one.

@PerformancePitfall
fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.contentEquals(st1: StructureND<T>, st2: StructureND<T>, absoluteTolerance: T): Boolean

Indicates whether some StructureND is equal to another one with absoluteTolerance.

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open infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>

Computes the dot product of this matrix and another one.

open infix fun Matrix<T>.dot(vector: Point<T>): Point<T>

Computes the dot product of this matrix and a vector.

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inline fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(matrix: Matrix<T>, noinline checkSingular: (T) -> Boolean): LupDecomposition<T>
fun LinearSpace<Double, DoubleField>.lup(matrix: Matrix<Double>, singularityThreshold: Double = 1.0E-11): LupDecomposition<Double>

fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(factory: MutableBufferFactory<T>, matrix: Matrix<T>, checkSingular: (T) -> Boolean): LupDecomposition<T>

Create a lup decomposition of generic matrix.

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fun LinearSpace<Double, DoubleField>.lupSolver(singularityThreshold: Double = 1.0E-11): LinearSolver<Double>

fun <T : Comparable<T>, F : Field<T>> LinearSpace<T, F>.lupSolver(bufferFactory: MutableBufferFactory<T>, singularityCheck: (T) -> Boolean): LinearSolver<T>

Produce a generic solver based on LUP decomposition

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@UnstableKMathAPI
fun <T : Any, A : Ring<T>> LinearSpace<T, A>.matrix(rows: Int, columns: Int): MatrixBuilder<T, A>

Create a matrix builder with given number of rows and columns

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open operator fun Matrix<T>.minus(other: Matrix<T>): Matrix<T>

Matrix subtraction

open operator fun Point<T>.minus(other: Point<T>): Point<T>

Vector subtraction

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fun <T : Any> LinearSpace<T, Ring<T>>.one(rows: Int, columns: Int): Matrix<T>

Diagonal matrix of ones. The matrix is virtual no actual matrix is created.

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open operator fun Matrix<T>.plus(other: Matrix<T>): Matrix<T>

Matrix sum

open operator fun Point<T>.plus(other: Point<T>): Point<T>

Vector sum

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fun <T : Any> LinearSpace<T, Ring<T>>.row(vararg values: T): Matrix<T>
inline fun <T : Any> LinearSpace<T, Ring<T>>.row(size: Int, crossinline builder: (Int) -> T): Matrix<T>
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open operator fun T.times(m: Matrix<T>): Matrix<T>

Multiplies an element by a matrix of it.

open operator fun T.times(v: Point<T>): Point<T>

Multiplies an element by a vector of it.

open operator fun Matrix<T>.times(value: T): Matrix<T>

Multiplies a matrix by its element.

open operator fun Point<T>.times(value: T): Point<T>

Multiplies a vector by its element.

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open operator fun Matrix<T>.unaryMinus(): Matrix<T>
open operator fun Point<T>.unaryMinus(): Point<T>
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@UnstableKMathAPI
fun <T : Any> LinearSpace<T, Ring<T>>.vector(vararg elements: T): Point<T>
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fun <T : Any> LinearSpace<T, Ring<T>>.zero(rows: Int, columns: Int): Matrix<T>

A virtual matrix of zeroes

Properties

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abstract val elementAlgebra: A