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WIP vector space refactor

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This commit is contained in:
Alexander Nozik 2021-03-12 22:52:18 +03:00
parent f449bdd58f
commit 9bc8e8fbf9
25 changed files with 352 additions and 566 deletions
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26
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -4,11 +4,11 @@ import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMMatrixContext
import space.kscience.kmath.ejml.EjmlMatrixContext
import space.kscience.kmath.linear.BufferMatrixContext
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpace
import space.kscience.kmath.linear.BufferLinearSpace
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.RealMatrixContext
import space.kscience.kmath.linear.RealLinearSpace
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
@ -24,44 +24,44 @@ internal class DotBenchmark {
val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
val ejmlMatrix1 = EjmlLinearSpace { matrix1.toEjml() }
val ejmlMatrix2 = EjmlLinearSpace { matrix2.toEjml() }
}
@Benchmark
fun cmDot(blackhole: Blackhole) {
CMMatrixContext {
CMLinearSpace {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
}
@Benchmark
fun ejmlDot(blackhole: Blackhole) {
EjmlMatrixContext {
EjmlLinearSpace {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
}
@Benchmark
fun ejmlDotWithConversion(blackhole: Blackhole) {
EjmlMatrixContext {
EjmlLinearSpace {
blackhole.consume(matrix1 dot matrix2)
}
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) {
BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
BufferLinearSpace(RealField, Buffer.Companion::real).invoke {
blackhole.consume(matrix1 dot matrix2)
}
}
@Benchmark
fun realDot(blackhole: Blackhole) {
RealMatrixContext {
RealLinearSpace {
blackhole.consume(matrix1 dot matrix2)
}
}

13
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
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@ -4,13 +4,12 @@ import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMMatrixContext
import space.kscience.kmath.commons.linear.CMMatrixContext.dot
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.commons.linear.inverse
import space.kscience.kmath.ejml.EjmlMatrixContext
import space.kscience.kmath.ejml.EjmlLinearSpace
import space.kscience.kmath.ejml.inverse
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.MatrixContext
import space.kscience.kmath.linear.inverseWithLup
import space.kscience.kmath.linear.real
import kotlin.random.Random
@ -29,19 +28,19 @@ internal class LinearAlgebraBenchmark {
@Benchmark
fun kmathLupInversion(blackhole: Blackhole) {
blackhole.consume(MatrixContext.real.inverseWithLup(matrix))
blackhole.consume(LinearSpace.real.inverseWithLup(matrix))
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) {
with(CMMatrixContext) {
with(CMLinearSpace) {
blackhole.consume(inverse(matrix))
}
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) {
with(EjmlMatrixContext) {
with(EjmlLinearSpace) {
blackhole.consume(inverse(matrix))
}
}

2
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
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@ -7,7 +7,7 @@ import kotlin.system.measureTimeMillis
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = NDStructure.build(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
val structure = NDStructure.buffered(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
structure.mapToBuffer { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } }
println("Structure mapping finished in $time1 millis")

15
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
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@ -3,6 +3,7 @@ package space.kscience.kmath.commons.linear
import org.apache.commons.math3.linear.*
import space.kscience.kmath.linear.*
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.structures.RealBuffer
import kotlin.reflect.KClass
import kotlin.reflect.cast
@ -55,7 +56,7 @@ public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
public fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
public class CMVector(public val origin: RealVector) : Point<Double> {
public class CMVector(public val origin: RealVector) : Vector<Double> {
public override val size: Int get() = origin.dimension
public override operator fun get(index: Int): Double = origin.getEntry(index)
@ -70,8 +71,8 @@ public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
public override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
public object CMLinearSpace : LinearSpace<Double, RealField> {
public override fun buildMatrix(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
val array = Array(rows) { i -> DoubleArray(columns) { j -> initializer(i, j) } }
return CMMatrix(Array2DRowRealMatrix(array))
}
@ -86,17 +87,15 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
}
}
override fun scale(a: Matrix<Double>, value: Double): Matrix<Double> = a.toCM().times(value)
public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
CMMatrix(toCM().origin.multiply(other.toCM().origin))
public override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
public override fun Matrix<Double>.dot(vector: Vector<Double>): CMVector =
CMVector(toCM().origin.preMultiply(vector.toCM().origin))
public override operator fun Matrix<Double>.unaryMinus(): CMMatrix =
produce(rowNum, colNum) { i, j -> -get(i, j) }
buildMatrix(rowNum, colNum) { i, j -> -get(i, j) }
public override fun add(a: Matrix<Double>, b: Matrix<Double>): CMMatrix =
CMMatrix(a.toCM().origin.multiply(b.toCM().origin))
@ -108,7 +107,7 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
// CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
public override operator fun Matrix<Double>.times(value: Double): CMMatrix =
produce(rowNum, colNum) { i, j -> get(i, j) * value }
buildMatrix(rowNum, colNum) { i, j -> get(i, j) * value }
}
public operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =

8
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMSolver.kt
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@ -12,7 +12,7 @@ public enum class CMDecomposition {
CHOLESKY
}
public fun CMMatrixContext.solver(
public fun CMLinearSpace.solver(
a: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP
): DecompositionSolver = when (decomposition) {
@ -23,19 +23,19 @@ public fun CMMatrixContext.solver(
CMDecomposition.CHOLESKY -> CholeskyDecomposition(a.toCM().origin).solver
}
public fun CMMatrixContext.solve(
public fun CMLinearSpace.solve(
a: Matrix<Double>,
b: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP
): CMMatrix = solver(a, decomposition).solve(b.toCM().origin).asMatrix()
public fun CMMatrixContext.solve(
public fun CMLinearSpace.solve(
a: Matrix<Double>,
b: Point<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP
): CMVector = solver(a, decomposition).solve(b.toCM().origin).toPoint()
public fun CMMatrixContext.inverse(
public fun CMLinearSpace.inverse(
a: Matrix<Double>,
decomposition: CMDecomposition = CMDecomposition.LUP
): CMMatrix = solver(a, decomposition).inverse.asMatrix()

144
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferMatrix.kt
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@ -1,144 +0,0 @@
package space.kscience.kmath.linear
import space.kscience.kmath.nd.NDStructure
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.asSequence
/**
* Alias for [Structure2D] with more familiar name.
*
* @param T the type of items.
*/
public typealias Matrix<T> = Structure2D<T>
/**
* Basic implementation of Matrix space based on [NDStructure]
*/
public class BufferMatrixContext<T : Any, A>(
public override val elementContext: A,
private val bufferFactory: BufferFactory<T>,
) : GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A : ScaleOperations<T> {
public override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): BufferMatrix<T> {
val buffer = bufferFactory(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
}
override fun scale(a: Matrix<T>, value: Double): Matrix<T> = elementContext {
produce(a.rowNum, a.colNum) { i, j ->
a[i, j] * value
}
}
public override fun point(size: Int, initializer: (Int) -> T): Point<T> = bufferFactory(size, initializer)
private fun Matrix<T>.toBufferMatrix(): BufferMatrix<T> = if (this is BufferMatrix) this else {
produce(rowNum, colNum) { i, j -> get(i, j) }
}
public fun one(rows: Int, columns: Int): Matrix<Double> = VirtualMatrix(rows, columns) { i, j ->
if (i == j) 1.0 else 0.0
} + DiagonalFeature
public override infix fun Matrix<T>.dot(other: Matrix<T>): BufferMatrix<T> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val bufferMatrix = toBufferMatrix()
val otherBufferMatrix = other.toBufferMatrix()
return elementContext {
produce(rowNum, other.colNum) { i, j ->
var res = one
for (l in 0 until colNum) {
res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
}
res
}
}
}
public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val bufferMatrix = toBufferMatrix()
return elementContext {
bufferFactory(rowNum) { i ->
var res = one
for (j in 0 until colNum) {
res += bufferMatrix[i, j] * vector[j]
}
res
}
}
}
override fun add(a: Matrix<T>, b: Matrix<T>): BufferMatrix<T> {
require(a.rowNum == b.rowNum) { "Row number mismatch in matrix addition. Left side: ${a.rowNum}, right side: ${b.rowNum}" }
require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
val aBufferMatrix = a.toBufferMatrix()
val bBufferMatrix = b.toBufferMatrix()
return elementContext {
produce(a.rowNum, a.colNum) { i, j ->
aBufferMatrix[i, j] + bBufferMatrix[i, j]
}
}
}
// override fun multiply(a: Matrix<T>, k: Number): BufferMatrix<T> {
// val aBufferMatrix = a.toBufferMatrix()
// return elementContext {
// produce(a.rowNum, a.colNum) { i, j -> aBufferMatrix[i, j] * k.toDouble() }
// }
// }
public companion object
}
public class BufferMatrix<T : Any>(
public override val rowNum: Int,
public override val colNum: Int,
public val buffer: Buffer<T>,
) : Matrix<T> {
init {
require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
}
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
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]
public 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))
}
public override fun equals(other: Any?): Boolean {
if (this === other) return true
return when (other) {
is NDStructure<*> -> NDStructure.contentEquals(this, other)
else -> false
}
}
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)\n" +
rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") { buffer ->
buffer.asSequence().joinToString(separator = "\t") { it.toString() }
}
else "Matrix(rowsNum = $rowNum, colNum = $colNum)"
}
}

8
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearAlgebra.kt
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@ -1,7 +1,7 @@
package space.kscience.kmath.linear
import space.kscience.kmath.nd.as1D
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.VirtualBuffer
public typealias Point<T> = Buffer<T>
@ -10,16 +10,16 @@ public typealias Point<T> = Buffer<T>
*/
public interface LinearSolver<T : Any> {
public fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>
public fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.asMatrix()).asPoint()
public fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.asMatrix()).asVector()
public fun inverse(a: Matrix<T>): Matrix<T>
}
/**
* Convert matrix to vector if it is possible
*/
public fun <T : Any> Matrix<T>.asPoint(): Point<T> =
public fun <T : Any> Matrix<T>.asVector(): Vector<T> =
if (this.colNum == 1)
VirtualBuffer(rowNum) { get(it, 0) }
as1D()
else
error("Can't convert matrix with more than one column to vector")

213
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt Normal file
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@ -0,0 +1,213 @@
package space.kscience.kmath.linear
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import kotlin.reflect.KClass
/**
* Alias for [Structure2D] with more familiar name.
*
* @param T the type of items.
*/
public typealias Matrix<T> = Structure2D<T>
/**
* Alias for [Structure1D] with more familiar name.
*
* @param T the type of items.
*/
public typealias Vector<T> = Structure1D<T>
/**
* Basic operations on matrices and vectors. Operates on [Matrix].
*
* @param T the type of items in the matrices.
* @param M the type of operated matrices.
*/
public interface LinearSpace<T : Any, A : Ring<T>> {
public val elementAlgebra: A
/**
* Produces a matrix with this context and given dimensions.
*/
public fun buildMatrix(rows: Int, columns: Int, initializer: A.(i: Int, j: Int) -> T): Matrix<T>
/**
* Produces a point compatible with matrix space (and possibly optimized for it).
*/
public fun buildVector(size: Int, initializer: A.(Int) -> T): Vector<T> =
buildMatrix(1, size) { _, j -> initializer(j) }.as1D()
public operator fun Matrix<T>.unaryMinus(): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
-get(i, j)
}
public operator fun Vector<T>.unaryMinus(): Vector<T> = buildVector(size) {
-get(it)
}
/**
* Matrix sum
*/
public operator fun Matrix<T>.plus(other: Matrix<T>): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
get(i, j) + other[i, j]
}
/**
* Vector sum
*/
public operator fun Vector<T>.plus(other: Vector<T>): Vector<T> = buildVector(size) {
get(it) + other[it]
}
/**
* Matrix subtraction
*/
public operator fun Matrix<T>.minus(other: Matrix<T>): Matrix<T> = buildMatrix(rowNum, colNum) { i, j ->
get(i, j) - other[i, j]
}
/**
* Vector subtraction
*/
public operator fun Vector<T>.minus(other: Vector<T>): Vector<T> = buildVector(size) {
get(it) - other[it]
}
/**
* Computes the dot product of this matrix and another one.
*
* @receiver the multiplicand.
* @param other the multiplier.
* @return the dot product.
*/
public infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
return elementAlgebra {
buildMatrix(rowNum, other.colNum) { i, j ->
var res = zero
for (l in 0 until colNum) {
res += this@dot[i, l] * other[l, j]
}
res
}
}
}
/**
* Computes the dot product of this matrix and a vector.
*
* @receiver the multiplicand.
* @param vector the multiplier.
* @return the dot product.
*/
public infix fun Matrix<T>.dot(vector: Vector<T>): Vector<T> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
return elementAlgebra {
buildVector(rowNum) { i ->
var res = one
for (j in 0 until colNum) {
res += this@dot[i, j] * vector[j]
}
res
}
}
}
/**
* Multiplies a matrix by its element.
*
* @receiver the multiplicand.
* @param value the multiplier.
* @receiver the product.
*/
public operator fun Matrix<T>.times(value: T): Matrix<T> =
buildMatrix(rowNum, colNum) { i, j -> get(i, j) * value }
/**
* Multiplies an element by a matrix of it.
*
* @receiver the multiplicand.
* @param m the multiplier.
* @receiver the product.
*/
public operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
/**
* Multiplies a vector by its element.
*
* @receiver the multiplicand.
* @param value the multiplier.
* @receiver the product.
*/
public operator fun Vector<T>.times(value: T): Vector<T> =
buildVector(size) { i -> get(i) * value }
/**
* Multiplies an element by a vector of it.
*
* @receiver the multiplicand.
* @param v the multiplier.
* @receiver the product.
*/
public operator fun T.times(v: Vector<T>): Vector<T> = v * this
/**
* Gets a feature from the matrix. This function may return some additional features to
* [space.kscience.kmath.nd.NDStructure.getFeature].
*
* @param F the type of feature.
* @param m the matrix.
* @param type the [KClass] instance of [F].
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public fun <F : Any> getFeature(m: Matrix<T>, type: KClass<F>): F? = m.getFeature(type)
public companion object {
/**
* A structured matrix with custom buffer
*/
public fun <T : Any, A : Ring<T>> buffered(
algebra: A,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): LinearSpace<T, A> = object : LinearSpace<T, A> {
override val elementAlgebra: A = algebra
override fun buildMatrix(
rows: Int, columns: Int,
initializer: A.(i: Int, j: Int) -> T,
): Matrix<T> = NDStructure.buffered(intArrayOf(rows, columns)) { (i, j) ->
algebra.initializer(i, j)
}.as2D()
}
/**
* Automatic buffered matrix, unboxed if it is possible
*/
public inline fun <reified T : Any, A : Ring<T>> auto(ring: A): LinearSpace<T, A> =
buffered(ring, Buffer.Companion::auto)
}
}
/**
* Gets a feature from the matrix. This function may return some additional features to
* [space.kscience.kmath.nd.NDStructure.getFeature].
*
* @param T the type of items in the matrices.
* @param M the type of operated matrices.
* @param F the type of feature.
* @receiver the [LinearSpace] of [T].
* @param m the matrix.
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public inline fun <T : Any, reified F : Any> LinearSpace<T, *>.getFeature(m: Matrix<T>): F? = getFeature(m, F::class)

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

@ -12,7 +12,7 @@ import space.kscience.kmath.structures.MutableBufferFactory
* Common implementation of [LupDecompositionFeature].
*/
public class LupDecomposition<T : Any>(
public val context: MatrixContext<T, Matrix<T>>,
public val context: LinearSpace<T, *>,
public val elementContext: Field<T>,
public val lu: Matrix<T>,
public val pivot: IntArray,
@ -62,15 +62,14 @@ public class LupDecomposition<T : Any>(
}
@PublishedApi
internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs(value: T): T =
if (value > elementContext.zero) value else elementContext { -value }
internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
if (value > elementAlgebra.zero) value else elementAlgebra { -value }
/**
* Create a lup decomposition of generic matrix.
*/
public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
factory: MutableBufferFactory<T>,
elementContext: Field<T>,
matrix: Matrix<T>,
checkSingular: (T) -> Boolean,
): LupDecomposition<T> {
@ -80,7 +79,7 @@ public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
//TODO just waits for KEEP-176
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
elementContext {
elementAlgebra {
val lu = create(matrix)
// Initialize permutation array and parity
@ -142,18 +141,18 @@ public fun <T : Comparable<T>> MatrixContext<T, Matrix<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, elementAlgebra, lu.collect(), pivot, even)
}
}
}
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.lup(
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, elementContext, matrix, checkSingular)
): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, matrix, checkSingular)
public fun MatrixContext<Double, Matrix<Double>>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
public fun LinearSpace<Double, RealField>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
lup(Buffer.Companion::real, matrix) { it < 1e-11 }
public fun <T : Any> LupDecomposition<T>.solveWithLup(
factory: MutableBufferFactory<T>,
@ -198,7 +197,7 @@ public fun <T : Any> LupDecomposition<T>.solveWithLup(
}
}
return context.produce(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
return context.buildMatrix(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
}
}
}
@ -210,18 +209,18 @@ public inline fun <reified T : Any> LupDecomposition<T>.solveWithLup(matrix: Mat
* Solves a system of linear equations *ax = b** using LUP decomposition.
*/
@OptIn(UnstableKMathAPI::class)
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLup(
public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.solveWithLup(
a: Matrix<T>,
b: Matrix<T>,
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
): Matrix<T> {
// Use existing decomposition if it is provided by matrix
val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
val decomposition = a.getFeature() ?: lup(bufferFactory, a, checkSingular)
return decomposition.solveWithLup(bufferFactory, b)
}
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLup(
public inline fun <reified T : Comparable<T>> LinearSpace<T, Field<T>>.inverseWithLup(
matrix: Matrix<T>,
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
@ -229,15 +228,15 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
@OptIn(UnstableKMathAPI::class)
public fun RealMatrixContext.solveWithLup(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
public fun RealLinearSpace.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 }
val decomposition: LupDecomposition<Double> = a.getFeature() ?: lup(bufferFactory, 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>): Matrix<Double> =
public fun RealLinearSpace.inverseWithLup(matrix: Matrix<Double>): Matrix<Double> =
solveWithLup(matrix, one(matrix.rowNum, matrix.colNum))

52
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixBuilder.kt
View File

@ -1,46 +1,30 @@
package space.kscience.kmath.linear
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.Ring
public class MatrixBuilder(public val rows: Int, public val columns: Int) {
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)
}
//TODO add specific matrix builder functions like diagonal, etc
@UnstableKMathAPI
public fun <T : Any> LinearSpace<T, Ring<T>>.matrix(rows: Int, columns: Int, vararg elements: T): Matrix<T> {
require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
return buildMatrix(rows, columns) { i, j -> elements[i * columns + j] }
}
public fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(1, values.size, buffer)
@UnstableKMathAPI
public fun <T : Any> LinearSpace<T, Ring<T>>.vector(vararg elements: T): Vector<T> {
return buildVector(elements.size, elements::get)
}
public inline fun <reified T : Any> Structure2D.Companion.row(
public inline fun <T : Any> LinearSpace<T, Ring<T>>.row(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T,
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(1, size, buffer)
}
crossinline builder: (Int) -> T,
): Matrix<T> = buildMatrix(1, size) { _, j -> builder(j) }
public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
val buffer = values.asBuffer()
return BufferMatrix(values.size, 1, buffer)
}
public fun <T : Any> LinearSpace<T, Ring<T>>.row(vararg values: T): Matrix<T> = row(values.size, values::get)
public inline fun <reified T : Any> Structure2D.Companion.column(
public inline fun <T : Any> LinearSpace<T, Ring<T>>.column(
size: Int,
factory: BufferFactory<T> = Buffer.Companion::auto,
noinline builder: (Int) -> T,
): Matrix<T> {
val buffer = factory(size, builder)
return BufferMatrix(size, 1, buffer)
}
crossinline builder: (Int) -> T,
): Matrix<T> = buildMatrix(size, 1) { i, _ -> builder(i) }
public fun <T : Any> LinearSpace<T, Ring<T>>.column(vararg values: T): Matrix<T> = column(values.size, values::get)

173
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixContext.kt
View File

@ -1,173 +0,0 @@
package space.kscience.kmath.linear
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.asSequence
import kotlin.reflect.KClass
/**
* Basic operations on matrices. Operates on [Matrix].
*
* @param T the type of items in the matrices.
* @param M the type of operated matrices.
*/
public interface MatrixContext<T : Any, out M : Matrix<T>> : GroupOperations<Matrix<T>>, ScaleOperations<Matrix<T>> {
/**
* Produces a matrix with this context and given dimensions.
*/
public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): M
/**
* Produces 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) {
"dot" -> { left, right -> left dot right }
else -> super<GroupOperations>.binaryOperationFunction(operation) as (Matrix<T>, Matrix<T>) -> M
}
/**
* Computes the dot product of this matrix and another one.
*
* @receiver the multiplicand.
* @param other the multiplier.
* @return the dot product.
*/
public infix fun Matrix<T>.dot(other: Matrix<T>): M
/**
* Computes the dot product of this matrix and a vector.
*
* @receiver the multiplicand.
* @param vector the multiplier.
* @return the dot product.
*/
public infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
/**
* Multiplies a matrix by its element.
*
* @receiver the multiplicand.
* @param value the multiplier.
* @receiver the product.
*/
public operator fun Matrix<T>.times(value: T): M
/**
* Multiplies an element by a matrix of it.
*
* @receiver the multiplicand.
* @param m the multiplier.
* @receiver the product.
*/
public operator fun T.times(m: Matrix<T>): M = m * this
/**
* Gets a feature from the matrix. This function may return some additional features to
* [kscience.kmath.nd.NDStructure.getFeature].
*
* @param F the type of feature.
* @param m the matrix.
* @param type the [KClass] instance of [F].
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public fun <F : Any> getFeature(m: Matrix<T>, type: KClass<F>): F? = m.getFeature(type)
public companion object {
/**
* A structured matrix with custom buffer
*/
public fun <T : Any, A> buffered(
ring: A,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A: ScaleOperations<T> = BufferMatrixContext(ring, bufferFactory)
/**
* Automatic buffered matrix, unboxed if it is possible
*/
public inline fun <reified T : Any, A> auto(ring: A): GenericMatrixContext<T, A, BufferMatrix<T>> where A : Ring<T>, A: ScaleOperations<T> =
buffered(ring, Buffer.Companion::auto)
}
}
/**
* Gets a feature from the matrix. This function may return some additional features to
* [kscience.kmath.nd.NDStructure.getFeature].
*
* @param T the type of items in the matrices.
* @param M the type of operated matrices.
* @param F the type of feature.
* @receiver the [MatrixContext] of [T].
* @param m the matrix.
* @return a feature object or `null` if it isn't present.
*/
@UnstableKMathAPI
public inline fun <T : Any, reified F : Any> MatrixContext<T, *>.getFeature(m: Matrix<T>): F? =
getFeature(m, F::class)
/**
* Partial implementation of [MatrixContext] for matrices of [Ring].
*
* @param T the type of items in the matrices.
* @param A the type of ring of matrix elements.
* @param M the type of operated matrices.
*/
public interface GenericMatrixContext<T : Any, A, out M : Matrix<T>> : MatrixContext<T, M> where A : Ring<T>, A : ScaleOperations<T>{
/**
* The ring over matrix elements.
*/
public val elementContext: A
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}, ${other.colNum})" }
return produce(rowNum, other.colNum) { i, j ->
val row = rows[i]
val column = other.columns[j]
elementContext { sum(row.asSequence().zip(column.asSequence(), ::multiply)) }
}
}
public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
//TODO add typed error
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
return point(rowNum) { i ->
val row = rows[i]
elementContext { sum(row.asSequence().zip(vector.asSequence(), ::multiply)) }
}
}
public override operator fun Matrix<T>.unaryMinus(): M =
produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
public override fun add(a: Matrix<T>, b: Matrix<T>): M {
require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
"Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
}
return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
}
public override operator fun Matrix<T>.minus(b: Matrix<T>): M {
require(rowNum == b.rowNum && colNum == b.colNum) {
"Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
}
return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
}
//
// public override fun multiply(a: Matrix<T>, k: Number): M =
// produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
public override operator fun Matrix<T>.times(value: T): M =
produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
}

30
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt
View File

@ -1,14 +1,9 @@
package space.kscience.kmath.linear
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.getFeature
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.asBuffer
import kotlin.math.sqrt
import kotlin.reflect.KClass
import kotlin.reflect.safeCast
/**
* A [Matrix] that holds [MatrixFeature] objects.
@ -24,7 +19,8 @@ public class MatrixWrapper<T : Any> internal constructor(
* 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) })
@Suppress("UNCHECKED_CAST")
override fun <T : Any> getFeature(type: KClass<T>): T? = features.singleOrNull { type.isInstance(it) } as? T
?: origin.getFeature(type)
override fun equals(other: Any?): Boolean = origin == other
@ -61,35 +57,25 @@ public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeatu
MatrixWrapper(this, newFeatures.toSet())
}
/**
* 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, A> GenericMatrixContext<T, A, *>.one(
public fun <T : Any> LinearSpace<T, Ring<T>>.one(
rows: Int,
columns: Int,
): Matrix<T> where A : Ring<T>, A : ScaleOperations<T> = VirtualMatrix(rows, columns) { i, j ->
if (i == j) elementContext.one else elementContext.zero
): Matrix<T> = VirtualMatrix(rows, columns) { i, j ->
if (i == j) elementAlgebra.one else elementAlgebra.zero
} + UnitFeature
/**
* A virtual matrix of zeroes
*/
public fun <T : Any, A> GenericMatrixContext<T, A, *>.zero(
public fun <T : Any> LinearSpace<T, Ring<T>>.zero(
rows: Int,
columns: Int,
): Matrix<T> where A : Ring<T>, A : ScaleOperations<T> = VirtualMatrix(rows, columns) { _, _ ->
elementContext.zero
): Matrix<T> = VirtualMatrix(rows, columns) { _, _ ->
elementAlgebra.zero
} + ZeroFeature
public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature

23
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/RealMatrixContext.kt → kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/RealLinearSpace.kt
View File

@ -1,34 +1,37 @@
package space.kscience.kmath.linear
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.RealBuffer
public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, ScaleOperations<Matrix<Double>> {
public object RealLinearSpace : LinearSpace<Double, RealField>, ScaleOperations<Matrix<Double>> {
public override fun produce(
override val elementAlgebra: RealField get() = RealField
public override fun buildMatrix(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> {
): Matrix<Double> {
val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
return BufferMatrix(rows, columns, buffer)
}
public fun Matrix<Double>.toBufferMatrix(): BufferMatrix<Double> = if (this is BufferMatrix) this else {
produce(rowNum, colNum) { i, j -> get(i, j) }
buildMatrix(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
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = produce(rowNum, colNum) { i, j -> -get(i, j) }
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = buildMatrix(rowNum, colNum) { i, j -> -get(i, j) }
public override infix fun Matrix<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val bufferMatrix = toBufferMatrix()
val otherBufferMatrix = other.toBufferMatrix()
return produce(rowNum, other.colNum) { i, j ->
return buildMatrix(rowNum, other.colNum) { i, j ->
var res = 0.0
for (l in 0 until colNum) {
res += bufferMatrix[i, l] * otherBufferMatrix[l, j]
@ -54,14 +57,14 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, S
require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
val aBufferMatrix = a.toBufferMatrix()
val bBufferMatrix = b.toBufferMatrix()
return produce(a.rowNum, a.colNum) { i, j ->
return buildMatrix(a.rowNum, a.colNum) { i, j ->
aBufferMatrix[i, j] + bBufferMatrix[i, j]
}
}
override fun scale(a: Matrix<Double>, value: Double): BufferMatrix<Double> {
val bufferMatrix = a.toBufferMatrix()
return produce(a.rowNum, a.colNum) { i, j -> bufferMatrix[i, j] * value }
return buildMatrix(a.rowNum, a.colNum) { i, j -> bufferMatrix[i, j] * value }
}
override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> = scale(this, value)
@ -82,4 +85,4 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>>, S
/**
* Partially optimized real-valued matrix
*/
public val MatrixContext.Companion.real: RealMatrixContext get() = RealMatrixContext
public val LinearSpace.Companion.real: RealLinearSpace get() = RealLinearSpace

72
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/VectorSpace.kt
View File

@ -1,72 +0,0 @@
package space.kscience.kmath.linear
import space.kscience.kmath.operations.Group
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
/**
* A linear space for vectors.
* Could be used on any point-like structure
*/
public interface VectorSpace<T : Any, A> : Group<Point<T>>, ScaleOperations<Point<T>>
where A : Group<T>, A : ScaleOperations<T> {
public val size: Int
public val algebra: A
override val zero: Point<T> get() = produce { algebra.zero }
public fun produce(initializer: A.(Int) -> T): Point<T>
override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { algebra { a[it] + b[it] } }
override fun scale(a: Point<T>, value: Double): Point<T> = produce { algebra.scale(a[it], value) }
override fun Point<T>.unaryMinus(): Point<T> = produce { -get(it) }
//TODO add basis
public companion object {
private val realSpaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
/**
* Non-boxing double vector space
*/
public fun real(size: Int): BufferVectorSpace<Double, RealField> = realSpaceCache.getOrPut(size) {
BufferVectorSpace(
size,
RealField,
Buffer.Companion::auto
)
}
/**
* A structured vector space with custom buffer
*/
public fun <T : Any, A> buffered(
size: Int,
space: A,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): BufferVectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> =
BufferVectorSpace(size, space, bufferFactory)
/**
* Automatic buffered vector, unboxed if it is possible
*/
public inline fun <reified T : Any, A> auto(
size: Int,
space: A,
): VectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> =
buffered(size, space, Buffer.Companion::auto)
}
}
public class BufferVectorSpace<T : Any, A>(
override val size: Int,
override val algebra: A,
public val bufferFactory: BufferFactory<T>,
) : VectorSpace<T, A> where A : Group<T>, A : ScaleOperations<T> {
override fun produce(initializer: A.(Int) -> T): Buffer<T> = bufferFactory(size) { algebra.initializer(it) }
}

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

@ -31,6 +31,4 @@ public class VirtualMatrix<T : Any>(
result = 31 * result + generator.hashCode()
return result
}
}

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/NDStructure.kt
View File

@ -74,7 +74,7 @@ public interface NDStructure<T> {
*
* Strides should be reused if possible.
*/
public fun <T> build(
public fun <T> buffered(
strides: Strides,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
initializer: (IntArray) -> T,
@ -94,11 +94,11 @@ public interface NDStructure<T> {
crossinline initializer: (IntArray) -> T,
): NDBuffer<T> = NDBuffer(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
public fun <T> build(
public fun <T> buffered(
shape: IntArray,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
initializer: (IntArray) -> T,
): NDBuffer<T> = build(DefaultStrides(shape), bufferFactory, initializer)
): NDBuffer<T> = buffered(DefaultStrides(shape), bufferFactory, initializer)
public inline fun <reified T : Any> auto(
shape: IntArray,

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
View File

@ -46,7 +46,11 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
* Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
*/
public fun <T> NDStructure<T>.as1D(): Structure1D<T> = if (shape.size == 1) {
if (this is NDBuffer) Buffer1DWrapper(this.buffer) else Structure1DWrapper(this)
when (this) {
is Structure1DWrapper -> this
is NDBuffer -> Buffer1DWrapper(this.buffer)
else -> Structure1DWrapper(this)
}
} else
error("Can't create 1d-structure from ${shape.size}d-structure")

12
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
View File

@ -1,7 +1,5 @@
package space.kscience.kmath.nd
import space.kscience.kmath.linear.BufferMatrix
import space.kscience.kmath.linear.RealMatrixContext
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.VirtualBuffer
@ -54,15 +52,7 @@ public interface Structure2D<T> : NDStructure<T> {
for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
}
public companion object {
public inline fun real(
rows: Int,
columns: Int,
crossinline init: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> = RealMatrixContext.produce(rows,columns) { i, j ->
init(i, j)
}
}
public companion object
}
/**

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/BufferAccessor2D.kt
View File

@ -25,7 +25,7 @@ internal class BufferAccessor2D<T : Any>(
public fun create(mat: Structure2D<T>): MutableBuffer<T> = create { i, j -> mat[i, j] }
//TODO optimize wrapper
public fun MutableBuffer<T>.collect(): Structure2D<T> = NDStructure.build(
public fun MutableBuffer<T>.collect(): Structure2D<T> = NDStructure.buffered(
DefaultStrides(intArrayOf(rowNum, colNum)),
factory
) { (i, j) ->

6
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt
View File

@ -10,7 +10,7 @@ import kotlin.test.assertEquals
class MatrixTest {
@Test
fun testTranspose() {
val matrix = MatrixContext.real.one(3, 3)
val matrix = LinearSpace.real.one(3, 3)
val transposed = matrix.transpose()
assertEquals(matrix, transposed)
}
@ -39,7 +39,7 @@ class MatrixTest {
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
var res = this
repeat(power - 1) {
res = RealMatrixContext.invoke { res dot this@pow }
res = RealLinearSpace.invoke { res dot this@pow }
}
return res
}
@ -52,7 +52,7 @@ class MatrixTest {
val firstMatrix = NDStructure.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D()
val secondMatrix = NDStructure.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D()
MatrixContext.real.run {
LinearSpace.real.run {
// val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
// val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }
val result = firstMatrix dot secondMatrix

8
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/RealLUSolverTest.kt
View File

@ -7,8 +7,8 @@ class RealLUSolverTest {
@Test
fun testInvertOne() {
val matrix = MatrixContext.real.one(2, 2)
val inverted = MatrixContext.real.inverseWithLup(matrix)
val matrix = LinearSpace.real.one(2, 2)
val inverted = LinearSpace.real.inverseWithLup(matrix)
assertEquals(matrix, inverted)
}
@ -19,7 +19,7 @@ class RealLUSolverTest {
1.0, 3.0
)
MatrixContext.real.run {
LinearSpace.real.run {
val lup = lup(matrix)
//Check determinant
@ -36,7 +36,7 @@ class RealLUSolverTest {
1.0, 3.0
)
val inverted = MatrixContext.real.inverseWithLup(matrix)
val inverted = LinearSpace.real.inverseWithLup(matrix)
val expected = Matrix.square(
0.375, -0.125,

8
kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt
View File

@ -77,7 +77,7 @@ 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>(public val context: MatrixContext<T, Matrix<T>>) {
public inline class DMatrixContext<T : Any>(public val context: LinearSpace<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"
@ -96,14 +96,14 @@ public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T,
public inline fun <reified R : Dimension, reified C : Dimension> produce(noinline initializer: (i: Int, j: Int) -> T): DMatrix<T, R, C> {
val rows = Dimension.dim<R>()
val cols = Dimension.dim<C>()
return context.produce(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
return context.buildMatrix(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
}
public inline fun <reified D : Dimension> point(noinline initializer: (Int) -> T): DPoint<T, D> {
val size = Dimension.dim<D>()
return DPoint.coerceUnsafe(
context.point(
context.buildVector(
size.toInt(),
initializer
)
@ -136,7 +136,7 @@ public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T,
context { (this@transpose as Matrix<T>).transpose() }.coerce()
public companion object {
public val real: DMatrixContext<Double> = DMatrixContext(MatrixContext.real)
public val real: DMatrixContext<Double> = DMatrixContext(LinearSpace.real)
}
}

16
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrixContext.kt → kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt
View File

@ -11,7 +11,7 @@ import space.kscience.kmath.operations.ScaleOperations
*
* @author Iaroslav Postovalov
*/
public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperations<Matrix<Double>> {
public object EjmlLinearSpace : LinearSpace<Double, EjmlMatrix>, ScaleOperations<Matrix<Double>> {
/**
* Converts this matrix to EJML one.
@ -19,7 +19,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
@OptIn(UnstableKMathAPI::class)
public fun Matrix<Double>.toEjml(): EjmlMatrix = when (val matrix = origin) {
is EjmlMatrix -> matrix
else -> produce(rowNum, colNum) { i, j -> get(i, j) }
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
/**
@ -30,14 +30,14 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
(0 until it.numRows()).forEach { row -> it[row, 0] = get(row) }
})
override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
override fun buildMatrix(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
EjmlMatrix(SimpleMatrix(rows, columns).also {
(0 until rows).forEach { row ->
(0 until columns).forEach { col -> it[row, col] = initializer(row, col) }
}
})
override fun point(size: Int, initializer: (Int) -> Double): Point<Double> =
override fun buildVector(size: Int, initializer: (Int) -> Double): Point<Double> =
EjmlVector(SimpleMatrix(size, 1).also {
(0 until it.numRows()).forEach { row -> it[row, 0] = initializer(row) }
})
@ -58,7 +58,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
EjmlMatrix(toEjml().origin - b.toEjml().origin)
public override fun scale(a: Matrix<Double>, value: Double): EjmlMatrix =
produce(a.rowNum, a.colNum) { i, j -> a[i, j] * value }
buildMatrix(a.rowNum, a.colNum) { i, j -> a[i, j] * value }
public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix =
EjmlMatrix(toEjml().origin.scale(value))
@ -72,7 +72,7 @@ public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix>, ScaleOperat
* @return the solution for 'x' that is n by p.
* @author Iaroslav Postovalov
*/
public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
EjmlMatrix(a.toEjml().origin.solve(b.toEjml().origin))
/**
@ -83,10 +83,10 @@ public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMa
* @return the solution for 'x' that is n by p.
* @author Iaroslav Postovalov
*/
public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
@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()
public fun EjmlLinearSpace.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()

34
kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/RealMatrix.kt
View File

@ -22,14 +22,14 @@ import kotlin.math.pow
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)
LinearSpace.real.buildMatrix(rowNum, colNum, initializer)
public fun Array<DoubleArray>.toMatrix(): RealMatrix {
return MatrixContext.real.produce(size, this[0].size) { row, col -> this[row][col] }
return LinearSpace.real.buildMatrix(size, this[0].size) { row, col -> this[row][col] }
}
public fun Sequence<DoubleArray>.toMatrix(): RealMatrix = toList().let {
MatrixContext.real.produce(it.size, it[0].size) { row, col -> it[row][col] }
LinearSpace.real.buildMatrix(it.size, it[0].size) { row, col -> it[row][col] }
}
public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix =
@ -42,37 +42,37 @@ public fun RealMatrix.repeatStackVertical(n: Int): RealMatrix =
*/
public operator fun RealMatrix.times(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
this[row, col] * double
}
public operator fun RealMatrix.plus(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
this[row, col] + double
}
public operator fun RealMatrix.minus(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
this[row, col] - double
}
public operator fun RealMatrix.div(double: Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col ->
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col ->
this[row, col] / double
}
public operator fun Double.times(matrix: RealMatrix): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
this * matrix[row, col]
}
public operator fun Double.plus(matrix: RealMatrix): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
this + matrix[row, col]
}
public operator fun Double.minus(matrix: RealMatrix): RealMatrix =
MatrixContext.real.produce(matrix.rowNum, matrix.colNum) { row, col ->
LinearSpace.real.buildMatrix(matrix.rowNum, matrix.colNum) { row, col ->
this - matrix[row, col]
}
@ -87,20 +87,20 @@ public operator fun Double.minus(matrix: RealMatrix): RealMatrix =
@UnstableKMathAPI
public operator fun RealMatrix.times(other: RealMatrix): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col -> this[row, col] * other[row, col] }
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col -> this[row, col] * other[row, col] }
public operator fun RealMatrix.plus(other: RealMatrix): RealMatrix =
MatrixContext.real.add(this, other)
LinearSpace.real.add(this, other)
public operator fun RealMatrix.minus(other: RealMatrix): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { row, col -> this[row, col] - other[row, col] }
LinearSpace.real.buildMatrix(rowNum, colNum) { row, col -> this[row, col] - other[row, col] }
/*
* Operations on columns
*/
public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
LinearSpace.real.buildMatrix(rowNum, colNum + 1) { row, col ->
if (col < colNum)
this[row, col]
else
@ -108,7 +108,7 @@ public inline fun RealMatrix.appendColumn(crossinline mapper: (Buffer<Double>) -
}
public fun RealMatrix.extractColumns(columnRange: IntRange): RealMatrix =
MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
LinearSpace.real.buildMatrix(rowNum, columnRange.count()) { row, col ->
this[row, columnRange.first + col]
}
@ -141,14 +141,14 @@ public fun RealMatrix.max(): Double? = elements().map { (_, value) -> value }.ma
public fun RealMatrix.average(): Double = elements().map { (_, value) -> value }.average()
public inline fun RealMatrix.map(crossinline transform: (Double) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum) { i, j ->
LinearSpace.real.buildMatrix(rowNum, colNum) { i, j ->
transform(get(i, j))
}
/**
* Inverse a square real matrix using LUP decomposition
*/
public fun RealMatrix.inverseWithLup(): RealMatrix = MatrixContext.real.inverseWithLup(this)
public fun RealMatrix.inverseWithLup(): RealMatrix = LinearSpace.real.inverseWithLup(this)
//extended operations

4
kmath-for-real/src/commonTest/kotlin/kaceince/kmath/real/RealVectorTest.kt
View File

@ -1,6 +1,6 @@
package kaceince.kmath.real
import space.kscience.kmath.linear.MatrixContext
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.asMatrix
import space.kscience.kmath.linear.real
import space.kscience.kmath.linear.transpose
@ -32,7 +32,7 @@ internal class RealVectorTest {
val vector2 = Buffer.real(5) { 5 - it.toDouble() }
val matrix1 = vector1.asMatrix()
val matrix2 = vector2.asMatrix().transpose()
val product = MatrixContext.real { matrix1 dot matrix2 }
val product = LinearSpace.real { matrix1 dot matrix2 }
assertEquals(5.0, product[1, 0])
assertEquals(6.0, product[2, 2])
}