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Changes for upcoming GSL extension project #148

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CommanderTvis merged 98 commits from gsl-experiment into dev 2021-02-19 13:34:27 +03:00
Conversation 3 Commits 98 Files Changed 7 +135 -78
10 changed files with 135 additions and 78 deletions
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22
examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/MultiplicationBenchmark.kt → examples/src/benchmarks/kotlin/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -2,19 +2,22 @@ package kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kscience.kmath.commons.linear.CMMatrixContext
import kscience.kmath.commons.linear.CMMatrixContext.dot
import kscience.kmath.commons.linear.toCM
import kscience.kmath.ejml.EjmlMatrixContext
import kscience.kmath.ejml.toEjml
import kscience.kmath.linear.BufferMatrixContext
import kscience.kmath.linear.RealMatrixContext
import kscience.kmath.linear.real
import kscience.kmath.operations.RealField
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.Matrix
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlin.random.Random
@State(Scope.Benchmark)
class MultiplicationBenchmark {
class DotBenchmark {
companion object {
val random = Random(12224)
val dim = 1000
@ -32,14 +35,14 @@ class MultiplicationBenchmark {
@Benchmark
fun commonsMathMultiplication() {
CMMatrixContext.invoke {
CMMatrixContext {
cmMatrix1 dot cmMatrix2
}
}
@Benchmark
fun ejmlMultiplication() {
EjmlMatrixContext.invoke {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@ -48,13 +51,22 @@ class MultiplicationBenchmark {
fun ejmlMultiplicationwithConversion() {
val ejmlMatrix1 = matrix1.toEjml()
val ejmlMatrix2 = matrix2.toEjml()
EjmlMatrixContext.invoke {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@Benchmark
fun bufferedMultiplication() {
BufferMatrixContext(RealField, Buffer.Companion::real).invoke{
matrix1 dot matrix2
}
}
@Benchmark
fun realMultiplication(){
RealMatrixContext {
matrix1 dot matrix2
}
}
}

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

1
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
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@ -29,6 +29,7 @@ public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature
}
}
//TODO move inside context
public fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
this
} else {

24
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/BufferMatrix.kt
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@ -1,8 +1,10 @@
package kscience.kmath.linear
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.structures.*
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.NDStructure
import kscience.kmath.structures.asSequence
/**
* Basic implementation of Matrix space based on [NDStructure]
@ -21,24 +23,6 @@ public class BufferMatrixContext<T : Any, R : Ring<T>>(
public companion object
}
@Suppress("OVERRIDE_BY_INLINE")
public object RealMatrixContext : GenericMatrixContext<Double, RealField, BufferMatrix<Double>> {
public override val elementContext: RealField
get() = RealField
public override inline fun produce(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> {
val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
}
public override inline fun point(size: Int, initializer: (Int) -> Double): Point<Double> =
RealBuffer(size, initializer)
}
public class BufferMatrix<T : Any>(
public override val rowNum: Int,
public override val colNum: Int,

9
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/LUPDecomposition.kt
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@ -213,17 +213,8 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
return decomposition.solveWithLUP(bufferFactory, b)
}
public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(a, b) { it < 1e-11 }
public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.inverseWithLUP(
matrix: Matrix<T>,
noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
noinline checkSingular: (T) -> Boolean,
): FeaturedMatrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
/**
* 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 }

14
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt
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@ -18,6 +18,11 @@ public interface MatrixContext<T : Any, out M : Matrix<T>> : SpaceOperations<Mat
*/
public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): M
/**
* Produce a point compatible with matrix space (and possibly optimized for it)
*/
public fun point(size: Int, initializer: (Int) -> T): Point<T> = Buffer.boxing(size, initializer)
@Suppress("UNCHECKED_CAST")
public override fun binaryOperationFunction(operation: String): (left: Matrix<T>, right: Matrix<T>) -> M =
when (operation) {
@ -62,10 +67,6 @@ public interface MatrixContext<T : Any, out M : Matrix<T>> : SpaceOperations<Mat
public operator fun T.times(m: Matrix<T>): M = m * this
public companion object {
/**
* Non-boxing double matrix
*/
public val real: RealMatrixContext = RealMatrixContext
/**
* A structured matrix with custom buffer
@ -89,11 +90,6 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> :
*/
public val elementContext: R
/**
* Produce a point compatible with matrix space
*/
public fun point(size: Int, initializer: (Int) -> T): Point<T>
public override infix fun Matrix<T>.dot(other: Matrix<T>): M {
//TODO add typed error
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }

84
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/RealMatrixContext.kt Normal file
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@ -0,0 +1,84 @@
package kscience.kmath.linear
import kscience.kmath.operations.RealField
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.MutableBuffer
import kscience.kmath.structures.MutableBufferFactory
import kscience.kmath.structures.RealBuffer
@Suppress("OVERRIDE_BY_INLINE")
public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
public override inline fun produce(
rows: Int,
columns: Int,
initializer: (i: Int, j: Int) -> Double,
): BufferMatrix<Double> {
val buffer = RealBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
return BufferMatrix(rows, columns, buffer)
}
private fun Matrix<Double>.wrap(): BufferMatrix<Double> = if (this is BufferMatrix) this else {
produce(rowNum, colNum) { i, j -> get(i, j) }
}
public fun one(rows: Int, columns: Int): FeaturedMatrix<Double> = VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
if (i == j) 1.0 else 0.0
}
public override infix fun Matrix<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
return produce(rowNum, other.colNum) { i, j ->
var res = 0.0
for (l in 0 until colNum) {
res += get(i, l) * other.get(l, j)
}
res
}
}
public override infix fun Matrix<Double>.dot(vector: Point<Double>): Point<Double> {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
return RealBuffer(rowNum) { i ->
var res = 0.0
for (j in 0 until colNum) {
res += get(i, j) * vector[j]
}
res
}
}
override fun add(a: Matrix<Double>, b: Matrix<Double>): BufferMatrix<Double> {
require(a.rowNum == b.rowNum) { "Row number mismatch in matrix addition. Left side: ${a.rowNum}, right side: ${b.rowNum}" }
require(a.colNum == b.colNum) { "Column number mismatch in matrix addition. Left side: ${a.colNum}, right side: ${b.colNum}" }
return produce(a.rowNum, a.colNum) { i, j ->
a[i, j] + b[i, j]
}
}
override fun Matrix<Double>.times(value: Double): BufferMatrix<Double> =
produce(rowNum, colNum) { i, j -> get(i, j) * value }
override fun multiply(a: Matrix<Double>, k: Number): BufferMatrix<Double> =
produce(a.rowNum, a.colNum) { i, j -> a.get(i, j) * k.toDouble() }
}
/**
* Partially optimized real-valued matrix
*/
public val MatrixContext.Companion.real: RealMatrixContext get() = RealMatrixContext
public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): FeaturedMatrix<Double> {
// Use existing decomposition if it is provided by matrix
val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
val decomposition = a.getFeature() ?: lup(bufferFactory, RealField, a) { it < 1e-11 }
return decomposition.solveWithLUP(bufferFactory, b)
}
/**
* Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
*/
public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): FeaturedMatrix<Double> =
solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))

38
kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Wrappers.kt
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@ -1,11 +1,6 @@
package kscience.kmath.dimensions
import kscience.kmath.linear.GenericMatrixContext
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.linear.transpose
import kscience.kmath.operations.RealField
import kscience.kmath.operations.Ring
import kscience.kmath.linear.*
import kscience.kmath.operations.invoke
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.Structure2D
@ -42,7 +37,7 @@ 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>(
private 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 +76,7 @@ public inline class DPointWrapper<T, D : Dimension>(public val point: Point<T>)
/**
* Basic operations on dimension-safe matrices. Operates on [Matrix]
*/
public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: GenericMatrixContext<T, Ri, Matrix<T>>) {
public inline class DMatrixContext<T : Any>(public val context: MatrixContext<T, Matrix<T>>) {
public inline fun <reified R : Dimension, reified C : Dimension> Matrix<T>.coerce(): DMatrix<T, R, C> {
require(rowNum == Dimension.dim<R>().toInt()) {
"Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum"
@ -115,7 +110,7 @@ public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: Ge
}
public inline infix fun <reified R1 : Dimension, reified C1 : Dimension, reified C2 : Dimension> DMatrix<T, R1, C1>.dot(
other: DMatrix<T, C1, C2>
other: DMatrix<T, C1, C2>,
): DMatrix<T, R1, C2> = context { this@dot dot other }.coerce()
public inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> =
@ -139,18 +134,19 @@ public inline class DMatrixContext<T : Any, Ri : Ring<T>>(public val context: Ge
public inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> =
context { (this@transpose as Matrix<T>).transpose() }.coerce()
/**
* A square unit matrix
*/
public inline fun <reified D : Dimension> one(): DMatrix<T, D, D> = produce { i, j ->
if (i == j) context.elementContext.one else context.elementContext.zero
}
public inline fun <reified R : Dimension, reified C : Dimension> zero(): DMatrix<T, R, C> = produce { _, _ ->
context.elementContext.zero
}
public companion object {
public val real: DMatrixContext<Double, RealField> = DMatrixContext(MatrixContext.real)
public val real: DMatrixContext<Double> = DMatrixContext(MatrixContext.real)
}
}
/**
* A square unit matrix
*/
public inline fun <reified D : Dimension> DMatrixContext<Double>.one(): DMatrix<Double, D, D> = produce { i, j ->
if (i == j) 1.0 else 0.0
}
public inline fun <reified R : Dimension, reified C : Dimension> DMatrixContext<Double>.zero(): DMatrix<Double, R, C> = produce { _, _ ->
0.0
}

1
kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
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@ -3,6 +3,7 @@ package kscience.dimensions
import kscience.kmath.dimensions.D2
import kscience.kmath.dimensions.D3
import kscience.kmath.dimensions.DMatrixContext
import kscience.kmath.dimensions.one
import kotlin.test.Test
internal class DMatrixContextTest {

11
kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt
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@ -1,13 +1,7 @@
package kscience.kmath.real
import kscience.kmath.linear.FeaturedMatrix
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.RealMatrixContext.elementContext
import kscience.kmath.linear.VirtualMatrix
import kscience.kmath.linear.inverseWithLUP
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.operations.invoke
import kscience.kmath.operations.sum
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.RealBuffer
import kscience.kmath.structures.asIterable
@ -122,8 +116,7 @@ public fun RealMatrix.extractColumn(columnIndex: Int): RealMatrix =
extractColumns(columnIndex..columnIndex)
public fun RealMatrix.sumByColumn(): RealBuffer = RealBuffer(colNum) { j ->
val column = columns[j]
elementContext { sum(column.asIterable()) }
columns[j].asIterable().sum()
}
public fun RealMatrix.minByColumn(): RealBuffer = RealBuffer(colNum) { j ->