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Implemented #131

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This commit is contained in:
Alexander Nozik 2020-12-01 21:21:56 +03:00
parent 5368bb5d4a
commit 712df04170
13 changed files with 112 additions and 89 deletions
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7
CHANGELOG.md
View File

@ -16,13 +16,14 @@
- ND4J support module submitting `NDStructure` and `NDAlgebra` over `INDArray`.
- Coroutine-deterministic Monte-Carlo scope with a random number generator.
- Some minor utilities to `kmath-for-real`.
- Generic operation result parameter to `MatrixContext`
### Changed
- Package changed from `scientifik` to `kscience.kmath`.
- Gradle version: 6.6 -> 6.7
- Gradle version: 6.6 -> 6.7.1
- Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
- `Polynomial` secondary constructor made function.
- Kotlin version: 1.3.72 -> 1.4.20-M1
- Kotlin version: 1.3.72 -> 1.4.20
- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library.
- Full autodiff refactoring based on `Symbol`
- `kmath-prob` renamed to `kmath-stat`
@ -30,6 +31,7 @@
- Use `Point<Double>` instead of specialized type in `kmath-for-real`
- Optimized dot product for buffer matrices moved to `kmath-for-real`
- EjmlMatrix context is an object
- Matrix LUP `inverse` renamed to `inverseWithLUP`
### Deprecated
@ -37,6 +39,7 @@
- `kmath-koma` module because it doesn't support Kotlin 1.4.
- Support of `legacy` JS backend (we will support only IR)
- `toGrid` method.
- Public visibility of `BufferAccessor2D`
### Fixed
- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)

2
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
View File

@ -29,7 +29,7 @@ class LinearAlgebraBenchmark {
@Benchmark
fun kmathLUPInversion() {
MatrixContext.real.inverse(matrix)
MatrixContext.real.inverseWithLUP(matrix)
}
@Benchmark

7
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
View File

@ -5,8 +5,7 @@ import kscience.kmath.structures.Matrix
import kscience.kmath.structures.NDStructure
import org.apache.commons.math3.linear.*
public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) :
FeaturedMatrix<Double> {
public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
public override val rowNum: Int get() = origin.rowDimension
public override val colNum: Int get() = origin.columnDimension
@ -55,7 +54,7 @@ public fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMMatrixContext : MatrixContext<Double> {
public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
public override fun produce(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))
@ -79,7 +78,7 @@ public object CMMatrixContext : MatrixContext<Double> {
public override fun multiply(a: Matrix<Double>, k: Number): CMMatrix =
CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
public override operator fun Matrix<Double>.times(value: Double): Matrix<Double> =
public override operator fun Matrix<Double>.times(value: Double): CMMatrix =
produce(rowNum, colNum) { i, j -> get(i, j) * value }
}

4
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/BufferMatrix.kt
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@ -10,7 +10,7 @@ import kscience.kmath.structures.*
public class BufferMatrixContext<T : Any, R : Ring<T>>(
public override val elementContext: R,
private val bufferFactory: BufferFactory<T>,
) : GenericMatrixContext<T, R> {
) : GenericMatrixContext<T, R, BufferMatrix<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)
@ -22,7 +22,7 @@ public class BufferMatrixContext<T : Any, R : Ring<T>>(
}
@Suppress("OVERRIDE_BY_INLINE")
public object RealMatrixContext : GenericMatrixContext<Double, RealField> {
public object RealMatrixContext : GenericMatrixContext<Double, RealField, BufferMatrix<Double>> {
public override val elementContext: RealField
get() = RealField

4
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/FeaturedMatrix.kt
View File

@ -57,7 +57,7 @@ public inline fun <reified T : Any> Matrix<*>.getFeature(): T? =
/**
* Diagonal matrix of ones. The matrix is virtual no actual matrix is created
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.one(rows: Int, columns: Int): FeaturedMatrix<T> =
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): FeaturedMatrix<T> =
VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
if (i == j) elementContext.one else elementContext.zero
}
@ -66,7 +66,7 @@ public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.one(rows: Int, colu
/**
* A virtual matrix of zeroes
*/
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
VirtualMatrix(rows, columns) { _, _ -> elementContext.zero }
public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature

86
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LUPDecomposition.kt
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@ -1,25 +1,18 @@
package kscience.kmath.linear
import kscience.kmath.operations.Field
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.operations.invoke
import kscience.kmath.structures.BufferAccessor2D
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
import kotlin.reflect.KClass
import kscience.kmath.operations.*
import kscience.kmath.structures.*
/**
* Common implementation of [LUPDecompositionFeature]
*/
public class LUPDecomposition<T : Any>(
public val context: GenericMatrixContext<T, out Field<T>>,
public val context: MatrixContext<T, FeaturedMatrix<T>>,
public val elementContext: Field<T>,
public val lu: Structure2D<T>,
public val pivot: IntArray,
private val even: Boolean
private val even: Boolean,
) : LUPDecompositionFeature<T>, DeterminantFeature<T> {
public val elementContext: Field<T>
get() = context.elementContext
/**
* Returns the matrix L of the decomposition.
@ -64,23 +57,25 @@ public class LUPDecomposition<T : Any>(
}
public fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.abs(value: T): T =
@PublishedApi
internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs(value: T): T =
if (value > elementContext.zero) value else elementContext { -value }
/**
* Create a lup decomposition of generic matrix
* Create a lup decomposition of generic matrix.
*/
public inline fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
type: KClass<T>,
public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
factory: MutableBufferFactory<T>,
elementContext: Field<T>,
matrix: Matrix<T>,
checkSingular: (T) -> Boolean
checkSingular: (T) -> Boolean,
): LUPDecomposition<T> {
require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
val m = matrix.colNum
val pivot = IntArray(matrix.rowNum)
//TODO just waits for KEEP-176
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
elementContext {
val lu = create(matrix)
@ -112,14 +107,14 @@ public inline fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.l
luRow[col] = sum
// maintain best permutation choice
if (this@lup.abs(sum) > largest) {
largest = this@lup.abs(sum)
if (abs(sum) > largest) {
largest = abs(sum)
max = row
}
}
// Singularity check
check(!checkSingular(this@lup.abs(lu[max, col]))) { "The matrix is singular" }
check(!checkSingular(abs(lu[max, col]))) { "The matrix is singular" }
// Pivot if necessary
if (max != col) {
@ -143,23 +138,23 @@ public inline fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.l
for (row in col + 1 until m) lu[row, col] /= luDiag
}
return LUPDecomposition(this@lup, lu.collect(), pivot, even)
return LUPDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
}
}
}
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.lup(
matrix: Matrix<T>,
checkSingular: (T) -> Boolean
): LUPDecomposition<T> = lup(T::class, matrix, checkSingular)
noinline checkSingular: (T) -> Boolean,
): LUPDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
public fun GenericMatrixContext<Double, RealField>.lup(matrix: Matrix<Double>): LUPDecomposition<Double> =
lup(Double::class, matrix) { it < 1e-11 }
public fun MatrixContext<Double, FeaturedMatrix<Double>>.lup(matrix: Matrix<Double>): LUPDecomposition<Double> =
lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
public fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Matrix<T> {
public fun <T : Any> LUPDecomposition<T>.solveWithLUP(factory: MutableBufferFactory<T>, matrix: Matrix<T>): FeaturedMatrix<T> {
require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
elementContext {
// Apply permutations to b
val bp = create { _, _ -> zero }
@ -201,27 +196,34 @@ public fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T
}
}
public inline fun <reified T : Any> LUPDecomposition<T>.solve(matrix: Matrix<T>): Matrix<T> = solve(T::class, matrix)
public inline fun <reified T : Any> LUPDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
solveWithLUP(MutableBuffer.Companion::auto, matrix)
/**
* Solve a linear equation **a*x = b**
* Solve a linear equation **a*x = b** using LUP decomposition
*/
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.solve(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.solveWithLUP(
a: Matrix<T>,
b: Matrix<T>,
checkSingular: (T) -> Boolean
): Matrix<T> {
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
): FeaturedMatrix<T> {
// Use existing decomposition if it is provided by matrix
val decomposition = a.getFeature() ?: lup(T::class, a, checkSingular)
return decomposition.solve(T::class, b)
val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
return decomposition.solveWithLUP(bufferFactory, b)
}
public fun RealMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> = solve(a, b) { it < 1e-11 }
public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(a, b) { it < 1e-11 }
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.inverse(
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.inverseWithLUP(
matrix: Matrix<T>,
checkSingular: (T) -> Boolean
): Matrix<T> = solve(matrix, one(matrix.rowNum, matrix.colNum), checkSingular)
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
): FeaturedMatrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
public fun RealMatrixContext.inverse(matrix: Matrix<Double>): Matrix<Double> =
solve(matrix, one(matrix.rowNum, matrix.colNum)) { it < 1e-11 }
/**
* Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
*/
public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), Buffer.Companion::real) { it < 1e-11 }

37
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt
View File

@ -12,15 +12,16 @@ import kscience.kmath.structures.asSequence
/**
* Basic operations on matrices. Operates on [Matrix]
*/
public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
public interface MatrixContext<T : Any, out M : Matrix<T>> : SpaceOperations<Matrix<T>> {
/**
* Produce a matrix with this context and given dimensions
*/
public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T>
public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): M
public override fun binaryOperation(operation: String, left: Matrix<T>, right: Matrix<T>): Matrix<T> = when (operation) {
@Suppress("UNCHECKED_CAST")
public override fun binaryOperation(operation: String, left: Matrix<T>, right: Matrix<T>): M = when (operation) {
"dot" -> left dot right
else -> super.binaryOperation(operation, left, right)
else -> super.binaryOperation(operation, left, right) as M
}
/**
@ -30,7 +31,7 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
* @param other the multiplier.
* @return the dot product.
*/
public infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>
public infix fun Matrix<T>.dot(other: Matrix<T>): M
/**
* Computes the dot product of this matrix and a vector.
@ -48,7 +49,7 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
* @param value the multiplier.
* @receiver the product.
*/
public operator fun Matrix<T>.times(value: T): Matrix<T>
public operator fun Matrix<T>.times(value: T): M
/**
* Multiplies an element by a matrix of it.
@ -57,7 +58,7 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
* @param value the multiplier.
* @receiver the product.
*/
public operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
public operator fun T.times(m: Matrix<T>): M = m * this
public companion object {
/**
@ -70,18 +71,18 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
*/
public fun <T : Any, R : Ring<T>> buffered(
ring: R,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
): GenericMatrixContext<T, R> = BufferMatrixContext(ring, bufferFactory)
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): GenericMatrixContext<T, R, BufferMatrix<T>> = BufferMatrixContext(ring, bufferFactory)
/**
* Automatic buffered matrix, unboxed if it is possible
*/
public inline fun <reified T : Any, R : Ring<T>> auto(ring: R): GenericMatrixContext<T, R> =
public inline fun <reified T : Any, R : Ring<T>> auto(ring: R): GenericMatrixContext<T, R, BufferMatrix<T>> =
buffered(ring, Buffer.Companion::auto)
}
}
public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> : MatrixContext<T, M> {
/**
* The ring context for matrix elements
*/
@ -92,7 +93,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
*/
public fun point(size: Int, initializer: (Int) -> T): Point<T>
public override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
public override infix fun Matrix<T>.dot(other: Matrix<T>): M {
//TODO add typed error
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
@ -113,10 +114,10 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
}
}
public override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
public override operator fun Matrix<T>.unaryMinus(): M =
produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
public override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
public override fun add(a: Matrix<T>, b: Matrix<T>): M {
require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
"Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
}
@ -124,7 +125,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
}
public override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
public override operator fun Matrix<T>.minus(b: Matrix<T>): M {
require(rowNum == b.rowNum && colNum == b.colNum) {
"Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
}
@ -132,11 +133,11 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
}
public override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
public override fun multiply(a: Matrix<T>, k: Number): M =
produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
public operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
public operator fun Number.times(matrix: FeaturedMatrix<T>): M = multiply(matrix, this)
public override operator fun Matrix<T>.times(value: T): Matrix<T> =
public override operator fun Matrix<T>.times(value: T): M =
produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
}

5
kmath-core/src/commonMain/kotlin/kscience/kmath/operations/AlgebraExtensions.kt
View File

@ -38,6 +38,11 @@ public fun <T> Space<T>.average(data: Iterable<T>): T = sum(data) / data.count()
*/
public fun <T> Space<T>.average(data: Sequence<T>): T = sum(data) / data.count()
/**
* Absolute of the comparable [value]
*/
public fun <T : Comparable<T>> Space<T>.abs(value: T): T = if (value > zero) value else -value
/**
* Returns the sum of all elements in the iterable in provided space.
*

22
kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BufferAccessor2D.kt
View File

@ -1,25 +1,31 @@
package kscience.kmath.structures
import kotlin.reflect.KClass
/**
* A context that allows to operate on a [MutableBuffer] as on 2d array
*/
public class BufferAccessor2D<T : Any>(public val type: KClass<T>, public val rowNum: Int, public val colNum: Int) {
internal class BufferAccessor2D<T : Any>(
public val rowNum: Int,
public val colNum: Int,
val factory: MutableBufferFactory<T>,
) {
public operator fun Buffer<T>.get(i: Int, j: Int): T = get(i + colNum * j)
public operator fun MutableBuffer<T>.set(i: Int, j: Int, value: T) {
set(i + colNum * j, value)
}
public inline fun create(init: (i: Int, j: Int) -> T): MutableBuffer<T> =
MutableBuffer.auto(type, rowNum * colNum) { offset -> init(offset / colNum, offset % colNum) }
public inline fun create(crossinline init: (i: Int, j: Int) -> T): MutableBuffer<T> =
factory(rowNum * colNum) { offset -> init(offset / colNum, offset % colNum) }
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.auto(type, rowNum, colNum) { (i, j) -> get(i, j) }.as2D()
public fun MutableBuffer<T>.collect(): Structure2D<T> = NDStructure.build(
DefaultStrides(intArrayOf(rowNum, colNum)),
factory
) { (i, j) ->
get(i, j)
}.as2D()
public inner class Row(public val buffer: MutableBuffer<T>, public val rowIndex: Int) : MutableBuffer<T> {
override val size: Int get() = colNum
@ -30,7 +36,7 @@ public class BufferAccessor2D<T : Any>(public val type: KClass<T>, public val ro
buffer[rowIndex, index] = value
}
override fun copy(): MutableBuffer<T> = MutableBuffer.auto(type, colNum) { get(it) }
override fun copy(): MutableBuffer<T> = factory(colNum) { get(it) }
override operator fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
}

4
kmath-core/src/commonTest/kotlin/kscience/kmath/linear/RealLUSolverTest.kt
View File

@ -9,7 +9,7 @@ class RealLUSolverTest {
@Test
fun testInvertOne() {
val matrix = MatrixContext.real.one(2, 2)
val inverted = MatrixContext.real.inverse(matrix)
val inverted = MatrixContext.real.inverseWithLUP(matrix)
assertEquals(matrix, inverted)
}
@ -37,7 +37,7 @@ class RealLUSolverTest {
1.0, 3.0
)
val inverted = MatrixContext.real.inverse(matrix)
val inverted = MatrixContext.real.inverseWithLUP(matrix)
val expected = Matrix.square(
0.375, -0.125,

4
kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Wrappers.kt
View File

@ -42,7 +42,7 @@ public interface DMatrix<T, R : Dimension, C : Dimension> : Structure2D<T> {
* An inline wrapper for a Matrix
*/
public inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
public val structure: Structure2D<T>
private val structure: Structure2D<T>
) : DMatrix<T, R, C> {
override val shape: IntArray get() = structure.shape
override operator fun get(i: Int, j: Int): T = structure[i, j]
@ -81,7 +81,7 @@ public inline class DPointWrapper<T, D : Dimension>(public val point: Point<T>)
/**
* Basic operations on dimension-safe matrices. Operates on [Matrix]
*/
public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: GenericMatrixContext<T, Ri>) {
public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: GenericMatrixContext<T, Ri, 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"

6
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt
View File

@ -1,11 +1,9 @@
package kscience.kmath.ejml
import org.ejml.simple.SimpleMatrix
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.operations.Space
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import org.ejml.simple.SimpleMatrix
/**
* Converts this matrix to EJML one.
@ -18,7 +16,7 @@ public fun Matrix<Double>.toEjml(): EjmlMatrix =
*
* @author Iaroslav Postovalov
*/
public object EjmlMatrixContext : MatrixContext<Double> {
public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix> {
/**
* Converts this vector to EJML one.

13
kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt
View File

@ -1,12 +1,16 @@
package kscience.kmath.real
import kscience.kmath.linear.FeaturedMatrix
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.RealMatrixContext.elementContext
import kscience.kmath.linear.VirtualMatrix
import kscience.kmath.linear.inverseWithLUP
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.invoke
import kscience.kmath.operations.sum
import kscience.kmath.structures.*
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.RealBuffer
import kscience.kmath.structures.asIterable
import kotlin.math.pow
/*
@ -21,7 +25,7 @@ import kotlin.math.pow
* Functions that help create a real (Double) matrix
*/
public typealias RealMatrix = Matrix<Double>
public typealias RealMatrix = FeaturedMatrix<Double>
public fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum, initializer)
@ -148,6 +152,11 @@ public inline fun RealMatrix.map(transform: (Double) -> Double): RealMatrix =
transform(get(i, j))
}
/**
* Inverse a square real matrix using LUP decomposition
*/
public fun RealMatrix.inverseWithLUP(): RealMatrix = MatrixContext.real.inverseWithLUP(this)
//extended operations
public fun RealMatrix.pow(p: Double): RealMatrix = map { it.pow(p) }