forked from kscience/kmath
Features refactoring.
This commit is contained in:
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ab32cd9561
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4c256a9f14
@ -33,6 +33,7 @@
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- EjmlMatrix context is an object
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- Matrix LUP `inverse` renamed to `inverseWithLUP`
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- `NumericAlgebra` moved outside of regular algebra chain (`Ring` no longer implements it).
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- Features moved to NDStructure and became transparent.
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### Deprecated
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@ -1,42 +1,28 @@
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package kscience.kmath.commons.linear
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import kscience.kmath.linear.*
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import kscience.kmath.linear.DiagonalFeature
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import kscience.kmath.linear.MatrixContext
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import kscience.kmath.linear.MatrixWrapper
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import kscience.kmath.linear.Point
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import kscience.kmath.misc.UnstableKMathAPI
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import kscience.kmath.structures.Matrix
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import kscience.kmath.structures.NDStructure
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import org.apache.commons.math3.linear.*
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import kotlin.reflect.KClass
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import kotlin.reflect.cast
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public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
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public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
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public override val rowNum: Int get() = origin.rowDimension
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public override val colNum: Int get() = origin.columnDimension
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public override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
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if (origin is DiagonalMatrix) yield(DiagonalFeature)
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}.toHashSet()
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public override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
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CMMatrix(origin, this.features + features)
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@UnstableKMathAPI
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override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
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DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
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else -> null
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}?.let { type.cast(it) }
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public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
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public override fun equals(other: Any?): Boolean {
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return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
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}
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public override fun hashCode(): Int {
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var result = origin.hashCode()
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result = 31 * result + features.hashCode()
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return result
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}
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}
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//TODO move inside context
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public fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
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this
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} else {
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//TODO add feature analysis
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val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
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CMMatrix(Array2DRowRealMatrix(array))
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}
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public fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
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@ -61,6 +47,16 @@ public object CMMatrixContext : MatrixContext<Double, CMMatrix> {
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return CMMatrix(Array2DRowRealMatrix(array))
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}
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public fun Matrix<Double>.toCM(): CMMatrix = when {
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this is CMMatrix -> this
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this is MatrixWrapper && matrix is CMMatrix -> matrix as CMMatrix
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else -> {
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//TODO add feature analysis
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val array = Array(rowNum) { i -> DoubleArray(colNum) { j -> get(i, j) } }
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CMMatrix(Array2DRowRealMatrix(array))
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}
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}
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public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
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CMMatrix(toCM().origin.multiply(other.toCM().origin))
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@ -1,10 +1,7 @@
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package kscience.kmath.linear
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import kscience.kmath.operations.Ring
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.BufferFactory
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import kscience.kmath.structures.NDStructure
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import kscience.kmath.structures.asSequence
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import kscience.kmath.structures.*
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/**
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* Basic implementation of Matrix space based on [NDStructure]
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@ -27,8 +24,7 @@ public class BufferMatrix<T : Any>(
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public override val rowNum: Int,
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public override val colNum: Int,
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public val buffer: Buffer<out T>,
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public override val features: Set<MatrixFeature> = emptySet(),
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) : FeaturedMatrix<T> {
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) : Matrix<T> {
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init {
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require(buffer.size == rowNum * colNum) { "Dimension mismatch for matrix structure" }
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@ -36,9 +32,6 @@ public class BufferMatrix<T : Any>(
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override val shape: IntArray get() = intArrayOf(rowNum, colNum)
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public override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
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BufferMatrix(rowNum, colNum, buffer, this.features + features)
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public override operator fun get(index: IntArray): T = get(index[0], index[1])
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public override operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
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@ -50,23 +43,26 @@ public class BufferMatrix<T : Any>(
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if (this === other) return true
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return when (other) {
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is NDStructure<*> -> return NDStructure.equals(this, other)
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is NDStructure<*> -> NDStructure.equals(this, other)
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else -> false
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}
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}
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public override fun hashCode(): Int {
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var result = buffer.hashCode()
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result = 31 * result + features.hashCode()
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override fun hashCode(): Int {
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var result = rowNum
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result = 31 * result + colNum
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result = 31 * result + buffer.hashCode()
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return result
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}
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public override fun toString(): String {
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return if (rowNum <= 5 && colNum <= 5)
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"Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)\n" +
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"Matrix(rowsNum = $rowNum, colNum = $colNum)\n" +
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rows.asSequence().joinToString(prefix = "(", postfix = ")", separator = "\n ") { buffer ->
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buffer.asSequence().joinToString(separator = "\t") { it.toString() }
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}
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else "Matrix(rowsNum = $rowNum, colNum = $colNum, features=$features)"
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else "Matrix(rowsNum = $rowNum, colNum = $colNum)"
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}
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}
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@ -1,13 +1,14 @@
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package kscience.kmath.linear
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import kscience.kmath.misc.UnstableKMathAPI
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import kscience.kmath.operations.*
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import kscience.kmath.structures.*
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/**
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* Common implementation of [LupDecompositionFeature].
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*/
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public class LUPDecomposition<T : Any>(
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public val context: MatrixContext<T, FeaturedMatrix<T>>,
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public class LupDecomposition<T : Any>(
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public val context: MatrixContext<T, Matrix<T>>,
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public val elementContext: Field<T>,
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public val lu: Matrix<T>,
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public val pivot: IntArray,
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@ -18,13 +19,13 @@ public class LUPDecomposition<T : Any>(
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*
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* L is a lower-triangular matrix with [Ring.one] in diagonal
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*/
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override val l: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
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override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
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when {
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j < i -> lu[i, j]
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j == i -> elementContext.one
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else -> elementContext.zero
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}
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}
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} + LFeature
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/**
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@ -32,9 +33,9 @@ public class LUPDecomposition<T : Any>(
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*
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* U is an upper-triangular matrix including the diagonal
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*/
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override val u: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
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override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
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if (j >= i) lu[i, j] else elementContext.zero
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}
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} + UFeature
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/**
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* Returns the P rows permutation matrix.
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@ -42,7 +43,7 @@ public class LUPDecomposition<T : Any>(
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* P is a sparse matrix with exactly one element set to [Ring.one] in
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* each row and each column, all other elements being set to [Ring.zero].
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*/
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override val p: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
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override val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
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if (j == pivot[i]) elementContext.one else elementContext.zero
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}
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@ -63,12 +64,12 @@ internal fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, *>.abs
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/**
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* Create a lup decomposition of generic matrix.
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*/
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public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
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public fun <T : Comparable<T>> MatrixContext<T, Matrix<T>>.lup(
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factory: MutableBufferFactory<T>,
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elementContext: Field<T>,
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matrix: Matrix<T>,
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checkSingular: (T) -> Boolean,
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): LUPDecomposition<T> {
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): LupDecomposition<T> {
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require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
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val m = matrix.colNum
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val pivot = IntArray(matrix.rowNum)
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@ -137,23 +138,23 @@ public fun <T : Comparable<T>> MatrixContext<T, FeaturedMatrix<T>>.lup(
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for (row in col + 1 until m) lu[row, col] /= luDiag
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}
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return LUPDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
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return LupDecomposition(this@lup, elementContext, lu.collect(), pivot, even)
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}
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}
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}
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.lup(
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.lup(
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matrix: Matrix<T>,
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noinline checkSingular: (T) -> Boolean,
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): LUPDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
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): LupDecomposition<T> = lup(MutableBuffer.Companion::auto, elementContext, matrix, checkSingular)
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public fun MatrixContext<Double, FeaturedMatrix<Double>>.lup(matrix: Matrix<Double>): LUPDecomposition<Double> =
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public fun MatrixContext<Double, Matrix<Double>>.lup(matrix: Matrix<Double>): LupDecomposition<Double> =
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lup(Buffer.Companion::real, RealField, matrix) { it < 1e-11 }
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public fun <T : Any> LUPDecomposition<T>.solveWithLUP(
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public fun <T : Any> LupDecomposition<T>.solveWithLUP(
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factory: MutableBufferFactory<T>,
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matrix: Matrix<T>
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): FeaturedMatrix<T> {
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matrix: Matrix<T>,
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): Matrix<T> {
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require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
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BufferAccessor2D(matrix.rowNum, matrix.colNum, factory).run {
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@ -198,25 +199,40 @@ public fun <T : Any> LUPDecomposition<T>.solveWithLUP(
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}
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}
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public inline fun <reified T : Any> LUPDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
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public inline fun <reified T : Any> LupDecomposition<T>.solveWithLUP(matrix: Matrix<T>): Matrix<T> =
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solveWithLUP(MutableBuffer.Companion::auto, matrix)
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/**
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* Solve a linear equation **a*x = b** using LUP decomposition
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*/
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.solveWithLUP(
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@OptIn(UnstableKMathAPI::class)
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.solveWithLUP(
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a: Matrix<T>,
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b: Matrix<T>,
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noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
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noinline checkSingular: (T) -> Boolean,
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): FeaturedMatrix<T> {
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): Matrix<T> {
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// Use existing decomposition if it is provided by matrix
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val decomposition = a.getFeature() ?: lup(bufferFactory, elementContext, a, checkSingular)
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return decomposition.solveWithLUP(bufferFactory, b)
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}
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, FeaturedMatrix<T>>.inverseWithLUP(
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public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F, Matrix<T>>.inverseWithLUP(
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matrix: Matrix<T>,
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noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto,
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noinline checkSingular: (T) -> Boolean,
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): FeaturedMatrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
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): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
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public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
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// Use existing decomposition if it is provided by matrix
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val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
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val decomposition: LupDecomposition<Double> = a.getFeature() ?: lup(bufferFactory, RealField, a) { it < 1e-11 }
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return decomposition.solveWithLUP(bufferFactory, b)
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}
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/**
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* Inverses a square matrix using LUP decomposition. Non square matrix will throw a error.
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*/
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public fun RealMatrixContext.inverseWithLUP(matrix: Matrix<Double>): Matrix<Double> =
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solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum))
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@ -1,12 +1,9 @@
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package kscience.kmath.linear
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.BufferFactory
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import kscience.kmath.structures.Structure2D
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import kscience.kmath.structures.asBuffer
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import kscience.kmath.structures.*
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public class MatrixBuilder(public val rows: Int, public val columns: Int) {
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public operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
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public operator fun <T : Any> invoke(vararg elements: T): Matrix<T> {
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require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
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val buffer = elements.asBuffer()
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return BufferMatrix(rows, columns, buffer)
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@ -17,7 +14,7 @@ public class MatrixBuilder(public val rows: Int, public val columns: Int) {
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public fun Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder = MatrixBuilder(rows, columns)
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public fun <T : Any> Structure2D.Companion.row(vararg values: T): FeaturedMatrix<T> {
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public fun <T : Any> Structure2D.Companion.row(vararg values: T): Matrix<T> {
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val buffer = values.asBuffer()
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return BufferMatrix(1, values.size, buffer)
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}
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@ -26,12 +23,12 @@ public inline fun <reified T : Any> Structure2D.Companion.row(
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size: Int,
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factory: BufferFactory<T> = Buffer.Companion::auto,
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noinline builder: (Int) -> T
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): FeaturedMatrix<T> {
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): Matrix<T> {
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val buffer = factory(size, builder)
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return BufferMatrix(1, size, buffer)
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}
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public fun <T : Any> Structure2D.Companion.column(vararg values: T): FeaturedMatrix<T> {
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public fun <T : Any> Structure2D.Companion.column(vararg values: T): Matrix<T> {
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val buffer = values.asBuffer()
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return BufferMatrix(values.size, 1, buffer)
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}
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@ -40,7 +37,7 @@ public inline fun <reified T : Any> Structure2D.Companion.column(
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size: Int,
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factory: BufferFactory<T> = Buffer.Companion::auto,
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noinline builder: (Int) -> T
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): FeaturedMatrix<T> {
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): Matrix<T> {
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val buffer = factory(size, builder)
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return BufferMatrix(size, 1, buffer)
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}
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@ -133,8 +133,6 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>, out M : Matrix<T>> :
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public override fun multiply(a: Matrix<T>, k: Number): M =
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produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
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public operator fun Number.times(matrix: FeaturedMatrix<T>): M = multiply(matrix, this)
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public override operator fun Matrix<T>.times(value: T): M =
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produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
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}
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@ -11,17 +11,19 @@ public interface MatrixFeature
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/**
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* Matrices with this feature are considered to have only diagonal non-null elements.
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*/
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public object DiagonalFeature : MatrixFeature
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public interface DiagonalFeature : MatrixFeature{
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public companion object: DiagonalFeature
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}
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/**
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* Matrices with this feature have all zero elements.
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*/
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public object ZeroFeature : MatrixFeature
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public object ZeroFeature : DiagonalFeature
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/**
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* Matrices with this feature have unit elements on diagonal and zero elements in all other places.
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*/
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public object UnitFeature : MatrixFeature
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public object UnitFeature : DiagonalFeature
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/**
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* Matrices with this feature can be inverted: [inverse] = `a`<sup>-1</sup> where `a` is the owning matrix.
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@ -76,17 +78,17 @@ public interface LupDecompositionFeature<T : Any> : MatrixFeature {
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/**
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* The lower triangular matrix in this decomposition. It may have [LFeature].
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*/
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public val l: FeaturedMatrix<T>
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public val l: Matrix<T>
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/**
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* The upper triangular matrix in this decomposition. It may have [UFeature].
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*/
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public val u: FeaturedMatrix<T>
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public val u: Matrix<T>
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/**
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* The permutation matrix in this decomposition.
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*/
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public val p: FeaturedMatrix<T>
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public val p: Matrix<T>
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}
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/**
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@ -104,12 +106,12 @@ public interface QRDecompositionFeature<T : Any> : MatrixFeature {
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/**
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* The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
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*/
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public val q: FeaturedMatrix<T>
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public val q: Matrix<T>
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/**
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* The upper triangular matrix in this decomposition. It may have [UFeature].
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*/
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public val r: FeaturedMatrix<T>
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public val r: Matrix<T>
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}
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/**
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@ -122,7 +124,7 @@ public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
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/**
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* The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
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*/
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public val l: FeaturedMatrix<T>
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public val l: Matrix<T>
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}
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/**
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@ -135,17 +137,17 @@ public interface SingularValueDecompositionFeature<T : Any> : MatrixFeature {
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/**
|
||||
* The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
|
||||
*/
|
||||
public val u: FeaturedMatrix<T>
|
||||
public val u: Matrix<T>
|
||||
|
||||
/**
|
||||
* The matrix in this decomposition. Its main diagonal elements are singular values.
|
||||
*/
|
||||
public val s: FeaturedMatrix<T>
|
||||
public val s: Matrix<T>
|
||||
|
||||
/**
|
||||
* The matrix in this decomposition. It is unitary, and it consists from right singular vectors.
|
||||
*/
|
||||
public val v: FeaturedMatrix<T>
|
||||
public val v: Matrix<T>
|
||||
|
||||
/**
|
||||
* The buffer of singular values of this SVD.
|
||||
|
@ -1,35 +1,57 @@
|
||||
package kscience.kmath.linear
|
||||
|
||||
import kscience.kmath.misc.UnstableKMathAPI
|
||||
import kscience.kmath.operations.Ring
|
||||
import kscience.kmath.structures.Matrix
|
||||
import kscience.kmath.structures.Structure2D
|
||||
import kscience.kmath.structures.asBuffer
|
||||
import kscience.kmath.structures.getFeature
|
||||
import kotlin.math.sqrt
|
||||
import kotlin.reflect.KClass
|
||||
import kotlin.reflect.safeCast
|
||||
|
||||
/**
|
||||
* A [Matrix] that holds [MatrixFeature] objects.
|
||||
*
|
||||
* @param T the type of items.
|
||||
*/
|
||||
public interface FeaturedMatrix<T : Any> : Matrix<T> {
|
||||
public override val shape: IntArray get() = intArrayOf(rowNum, colNum)
|
||||
public class MatrixWrapper<T : Any>(
|
||||
public val matrix: Matrix<T>,
|
||||
public val features: Set<MatrixFeature>,
|
||||
) : Matrix<T> by matrix {
|
||||
|
||||
/**
|
||||
* The set of features this matrix possesses.
|
||||
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
|
||||
*/
|
||||
public val features: Set<MatrixFeature>
|
||||
@UnstableKMathAPI
|
||||
override fun <T : Any> getFeature(type: KClass<T>): T? = type.safeCast(features.find { type.isInstance(it) })
|
||||
|
||||
/**
|
||||
* Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
|
||||
*
|
||||
* The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
|
||||
* add only those features that are valid.
|
||||
*/
|
||||
public fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T>
|
||||
|
||||
public companion object
|
||||
override fun equals(other: Any?): Boolean = matrix == other
|
||||
override fun hashCode(): Int = matrix.hashCode()
|
||||
override fun toString(): String {
|
||||
return "MatrixWrapper(matrix=$matrix, features=$features)"
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a single feature to a [Matrix]
|
||||
*/
|
||||
public operator fun <T : Any> Matrix<T>.plus(newFeature: MatrixFeature): MatrixWrapper<T> = if (this is MatrixWrapper) {
|
||||
MatrixWrapper(matrix, features + newFeature)
|
||||
} else {
|
||||
MatrixWrapper(this, setOf(newFeature))
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a collection of features to a [Matrix]
|
||||
*/
|
||||
public operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeature>): MatrixWrapper<T> =
|
||||
if (this is MatrixWrapper) {
|
||||
MatrixWrapper(matrix, features + newFeatures)
|
||||
} else {
|
||||
MatrixWrapper(this, newFeatures.toSet())
|
||||
}
|
||||
|
||||
public inline fun Structure2D.Companion.real(
|
||||
rows: Int,
|
||||
columns: Int,
|
||||
@ -39,51 +61,37 @@ public inline fun Structure2D.Companion.real(
|
||||
/**
|
||||
* Build a square matrix from given elements.
|
||||
*/
|
||||
public fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
|
||||
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)
|
||||
}
|
||||
|
||||
public val Matrix<*>.features: Set<MatrixFeature> get() = (this as? FeaturedMatrix)?.features ?: emptySet()
|
||||
|
||||
/**
|
||||
* Check if matrix has the given feature class
|
||||
*/
|
||||
public inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean =
|
||||
features.find { it is T } != null
|
||||
|
||||
/**
|
||||
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
|
||||
*/
|
||||
public inline fun <reified T : Any> Matrix<*>.getFeature(): T? =
|
||||
features.filterIsInstance<T>().firstOrNull()
|
||||
|
||||
/**
|
||||
* 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> =
|
||||
VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
|
||||
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.one(rows: Int, columns: Int): Matrix<T> =
|
||||
VirtualMatrix(rows, columns) { i, j ->
|
||||
if (i == j) elementContext.one else elementContext.zero
|
||||
}
|
||||
} + UnitFeature
|
||||
|
||||
|
||||
/**
|
||||
* A virtual matrix of zeroes
|
||||
*/
|
||||
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
|
||||
VirtualMatrix(rows, columns) { _, _ -> elementContext.zero }
|
||||
public fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R, *>.zero(rows: Int, columns: Int): Matrix<T> =
|
||||
VirtualMatrix(rows, columns) { _, _ -> elementContext.zero } + ZeroFeature
|
||||
|
||||
public class TransposedFeature<T : Any>(public val original: Matrix<T>) : MatrixFeature
|
||||
|
||||
/**
|
||||
* Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
|
||||
*/
|
||||
@OptIn(UnstableKMathAPI::class)
|
||||
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
|
||||
return getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
|
||||
colNum,
|
||||
rowNum,
|
||||
setOf(TransposedFeature(this))
|
||||
) { i, j -> get(j, i) }
|
||||
) { i, j -> get(j, i) } + TransposedFeature(this)
|
||||
}
|
@ -1,9 +1,6 @@
|
||||
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")
|
||||
@ -22,9 +19,9 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
|
||||
produce(rowNum, colNum) { i, j -> get(i, j) }
|
||||
}
|
||||
|
||||
public fun one(rows: Int, columns: Int): FeaturedMatrix<Double> = VirtualMatrix(rows, columns, DiagonalFeature) { 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<Double>.dot(other: Matrix<Double>): BufferMatrix<Double> {
|
||||
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
|
||||
@ -69,16 +66,3 @@ public object RealMatrixContext : MatrixContext<Double, BufferMatrix<Double>> {
|
||||
* 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))
|
||||
|
@ -5,31 +5,16 @@ import kscience.kmath.structures.Matrix
|
||||
public class VirtualMatrix<T : Any>(
|
||||
override val rowNum: Int,
|
||||
override val colNum: Int,
|
||||
override val features: Set<MatrixFeature> = emptySet(),
|
||||
public val generator: (i: Int, j: Int) -> T
|
||||
) : FeaturedMatrix<T> {
|
||||
public constructor(
|
||||
rowNum: Int,
|
||||
colNum: Int,
|
||||
vararg features: MatrixFeature,
|
||||
generator: (i: Int, j: Int) -> T
|
||||
) : this(
|
||||
rowNum,
|
||||
colNum,
|
||||
setOf(*features),
|
||||
generator
|
||||
)
|
||||
) : Matrix<T> {
|
||||
|
||||
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
|
||||
|
||||
override operator fun get(i: Int, j: Int): T = generator(i, j)
|
||||
|
||||
override fun suggestFeature(vararg features: MatrixFeature): VirtualMatrix<T> =
|
||||
VirtualMatrix(rowNum, colNum, this.features + features, generator)
|
||||
|
||||
override fun equals(other: Any?): Boolean {
|
||||
if (this === other) return true
|
||||
if (other !is FeaturedMatrix<*>) return false
|
||||
if (other !is Matrix<*>) return false
|
||||
|
||||
if (rowNum != other.rowNum) return false
|
||||
if (colNum != other.colNum) return false
|
||||
@ -40,21 +25,9 @@ public class VirtualMatrix<T : Any>(
|
||||
override fun hashCode(): Int {
|
||||
var result = rowNum
|
||||
result = 31 * result + colNum
|
||||
result = 31 * result + features.hashCode()
|
||||
result = 31 * result + generator.hashCode()
|
||||
return result
|
||||
}
|
||||
|
||||
|
||||
public companion object {
|
||||
/**
|
||||
* Wrap a matrix adding additional features to it
|
||||
*/
|
||||
public fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): FeaturedMatrix<T> {
|
||||
return if (matrix is VirtualMatrix)
|
||||
VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features, matrix.generator)
|
||||
else
|
||||
VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features) { i, j -> matrix[i, j] }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
@ -179,13 +179,15 @@ public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
|
||||
* A field for [Int] without boxing. Does not produce corresponding ring element.
|
||||
*/
|
||||
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
|
||||
public object IntRing : Ring<Int>, Norm<Int, Int> {
|
||||
public object IntRing : Ring<Int>, Norm<Int, Int>, NumericAlgebra<Int> {
|
||||
public override val zero: Int
|
||||
get() = 0
|
||||
|
||||
public override val one: Int
|
||||
get() = 1
|
||||
|
||||
override fun number(value: Number): Int = value.toInt()
|
||||
|
||||
public override inline fun add(a: Int, b: Int): Int = a + b
|
||||
public override inline fun multiply(a: Int, k: Number): Int = k.toInt() * a
|
||||
|
||||
@ -203,13 +205,15 @@ public object IntRing : Ring<Int>, Norm<Int, Int> {
|
||||
* A field for [Short] without boxing. Does not produce appropriate ring element.
|
||||
*/
|
||||
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
|
||||
public object ShortRing : Ring<Short>, Norm<Short, Short> {
|
||||
public object ShortRing : Ring<Short>, Norm<Short, Short>, NumericAlgebra<Short> {
|
||||
public override val zero: Short
|
||||
get() = 0
|
||||
|
||||
public override val one: Short
|
||||
get() = 1
|
||||
|
||||
override fun number(value: Number): Short = value.toShort()
|
||||
|
||||
public override inline fun add(a: Short, b: Short): Short = (a + b).toShort()
|
||||
public override inline fun multiply(a: Short, k: Number): Short = (a * k.toShort()).toShort()
|
||||
|
||||
@ -227,13 +231,15 @@ public object ShortRing : Ring<Short>, Norm<Short, Short> {
|
||||
* A field for [Byte] without boxing. Does not produce appropriate ring element.
|
||||
*/
|
||||
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
|
||||
public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
|
||||
public object ByteRing : Ring<Byte>, Norm<Byte, Byte>, NumericAlgebra<Byte> {
|
||||
public override val zero: Byte
|
||||
get() = 0
|
||||
|
||||
public override val one: Byte
|
||||
get() = 1
|
||||
|
||||
override fun number(value: Number): Byte = value.toByte()
|
||||
|
||||
public override inline fun add(a: Byte, b: Byte): Byte = (a + b).toByte()
|
||||
public override inline fun multiply(a: Byte, k: Number): Byte = (a * k.toByte()).toByte()
|
||||
|
||||
@ -251,13 +257,15 @@ public object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
|
||||
* A field for [Double] without boxing. Does not produce appropriate ring element.
|
||||
*/
|
||||
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
|
||||
public object LongRing : Ring<Long>, Norm<Long, Long> {
|
||||
public object LongRing : Ring<Long>, Norm<Long, Long>, NumericAlgebra<Long> {
|
||||
public override val zero: Long
|
||||
get() = 0L
|
||||
|
||||
public override val one: Long
|
||||
get() = 1L
|
||||
|
||||
override fun number(value: Number): Long = value.toLong()
|
||||
|
||||
public override inline fun add(a: Long, b: Long): Long = a + b
|
||||
public override inline fun multiply(a: Long, k: Number): Long = a * k.toLong()
|
||||
|
||||
|
@ -1,5 +1,6 @@
|
||||
package kscience.kmath.structures
|
||||
|
||||
import kscience.kmath.misc.UnstableKMathAPI
|
||||
import kotlin.jvm.JvmName
|
||||
import kotlin.native.concurrent.ThreadLocal
|
||||
import kotlin.reflect.KClass
|
||||
@ -42,6 +43,13 @@ public interface NDStructure<T> {
|
||||
public override fun equals(other: Any?): Boolean
|
||||
public override fun hashCode(): Int
|
||||
|
||||
/**
|
||||
* Feature is additional property or hint that does not directly affect the structure, but could in some cases help
|
||||
* optimize operations and performance. If the feature is not present, null is defined.
|
||||
*/
|
||||
@UnstableKMathAPI
|
||||
public fun <T : Any> getFeature(type: KClass<T>): T? = null
|
||||
|
||||
public companion object {
|
||||
/**
|
||||
* Indicates whether some [NDStructure] is equal to another one.
|
||||
@ -121,6 +129,9 @@ public interface NDStructure<T> {
|
||||
*/
|
||||
public operator fun <T> NDStructure<T>.get(vararg index: Int): T = get(index)
|
||||
|
||||
@UnstableKMathAPI
|
||||
public inline fun <reified T : Any> NDStructure<*>.getFeature(): T? = getFeature(T::class)
|
||||
|
||||
/**
|
||||
* Represents mutable [NDStructure].
|
||||
*/
|
||||
|
@ -9,12 +9,14 @@ public interface Structure2D<T> : NDStructure<T> {
|
||||
/**
|
||||
* The number of rows in this structure.
|
||||
*/
|
||||
public val rowNum: Int get() = shape[0]
|
||||
public val rowNum: Int
|
||||
|
||||
/**
|
||||
* The number of columns in this structure.
|
||||
*/
|
||||
public val colNum: Int get() = shape[1]
|
||||
public val colNum: Int
|
||||
|
||||
public override val shape: IntArray get() = intArrayOf(rowNum, colNum)
|
||||
|
||||
/**
|
||||
* The buffer of rows of this structure. It gets elements from the structure dynamically.
|
||||
@ -56,6 +58,9 @@ public interface Structure2D<T> : NDStructure<T> {
|
||||
private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
|
||||
override val shape: IntArray get() = structure.shape
|
||||
|
||||
override val rowNum: Int get() = shape[0]
|
||||
override val colNum: Int get() = shape[1]
|
||||
|
||||
override operator fun get(i: Int, j: Int): T = structure[i, j]
|
||||
|
||||
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
|
||||
|
@ -40,6 +40,8 @@ public inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
|
||||
private val structure: Structure2D<T>,
|
||||
) : DMatrix<T, R, C> {
|
||||
override val shape: IntArray get() = structure.shape
|
||||
override val rowNum: Int get() = shape[0]
|
||||
override val colNum: Int get() = shape[1]
|
||||
override operator fun get(i: Int, j: Int): T = structure[i, j]
|
||||
}
|
||||
|
||||
@ -147,6 +149,7 @@ public inline fun <reified D : Dimension> DMatrixContext<Double>.one(): DMatrix<
|
||||
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
|
||||
}
|
||||
public inline fun <reified R : Dimension, reified C : Dimension> DMatrixContext<Double>.zero(): DMatrix<Double, R, C> =
|
||||
produce { _, _ ->
|
||||
0.0
|
||||
}
|
@ -1,10 +1,13 @@
|
||||
package kscience.kmath.ejml
|
||||
|
||||
import kscience.kmath.linear.*
|
||||
import kscience.kmath.structures.NDStructure
|
||||
import kscience.kmath.misc.UnstableKMathAPI
|
||||
import kscience.kmath.structures.Matrix
|
||||
import kscience.kmath.structures.RealBuffer
|
||||
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
|
||||
import org.ejml.simple.SimpleMatrix
|
||||
import kotlin.reflect.KClass
|
||||
import kotlin.reflect.cast
|
||||
|
||||
/**
|
||||
* Represents featured matrix over EJML [SimpleMatrix].
|
||||
@ -12,85 +15,65 @@ import org.ejml.simple.SimpleMatrix
|
||||
* @property origin the underlying [SimpleMatrix].
|
||||
* @author Iaroslav Postovalov
|
||||
*/
|
||||
public class EjmlMatrix(
|
||||
public inline class EjmlMatrix(
|
||||
public val origin: SimpleMatrix,
|
||||
features: Set<MatrixFeature> = emptySet()
|
||||
) : FeaturedMatrix<Double> {
|
||||
public override val rowNum: Int
|
||||
get() = origin.numRows()
|
||||
) : Matrix<Double> {
|
||||
public override val rowNum: Int get() = origin.numRows()
|
||||
|
||||
public override val colNum: Int
|
||||
get() = origin.numCols()
|
||||
public override val colNum: Int get() = origin.numCols()
|
||||
|
||||
public override val shape: IntArray by lazy { intArrayOf(rowNum, colNum) }
|
||||
|
||||
public override val features: Set<MatrixFeature> = hashSetOf(
|
||||
object : InverseMatrixFeature<Double> {
|
||||
override val inverse: FeaturedMatrix<Double> by lazy { EjmlMatrix(origin.invert()) }
|
||||
},
|
||||
|
||||
object : DeterminantFeature<Double> {
|
||||
@UnstableKMathAPI
|
||||
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
|
||||
InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
|
||||
override val inverse: Matrix<Double> by lazy { EjmlMatrix(origin.invert()) }
|
||||
}
|
||||
DeterminantFeature::class -> object : DeterminantFeature<Double> {
|
||||
override val determinant: Double by lazy(origin::determinant)
|
||||
},
|
||||
|
||||
object : SingularValueDecompositionFeature<Double> {
|
||||
}
|
||||
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
|
||||
private val svd by lazy {
|
||||
DecompositionFactory_DDRM.svd(origin.numRows(), origin.numCols(), true, true, false)
|
||||
.apply { decompose(origin.ddrm.copy()) }
|
||||
}
|
||||
|
||||
override val u: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
|
||||
override val s: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
|
||||
override val v: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
|
||||
override val u: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getU(null, false))) }
|
||||
override val s: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getW(null))) }
|
||||
override val v: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
|
||||
override val singularValues: Point<Double> by lazy { RealBuffer(svd.singularValues) }
|
||||
},
|
||||
|
||||
object : QRDecompositionFeature<Double> {
|
||||
}
|
||||
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
|
||||
private val qr by lazy {
|
||||
DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
|
||||
}
|
||||
|
||||
override val q: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
|
||||
override val r: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
|
||||
},
|
||||
|
||||
object : CholeskyDecompositionFeature<Double> {
|
||||
override val l: FeaturedMatrix<Double> by lazy {
|
||||
override val q: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
|
||||
override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
|
||||
}
|
||||
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
|
||||
override val l: Matrix<Double> by lazy {
|
||||
val cholesky =
|
||||
DecompositionFactory_DDRM.chol(rowNum, true).apply { decompose(origin.ddrm.copy()) }
|
||||
|
||||
EjmlMatrix(SimpleMatrix(cholesky.getT(null)), setOf(LFeature))
|
||||
EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
|
||||
}
|
||||
},
|
||||
|
||||
object : LupDecompositionFeature<Double> {
|
||||
}
|
||||
LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
|
||||
private val lup by lazy {
|
||||
DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols()).apply { decompose(origin.ddrm.copy()) }
|
||||
}
|
||||
|
||||
override val l: FeaturedMatrix<Double> by lazy {
|
||||
EjmlMatrix(SimpleMatrix(lup.getLower(null)), setOf(LFeature))
|
||||
override val l: Matrix<Double> by lazy {
|
||||
EjmlMatrix(SimpleMatrix(lup.getLower(null))) + LFeature
|
||||
}
|
||||
|
||||
override val u: FeaturedMatrix<Double> by lazy {
|
||||
EjmlMatrix(SimpleMatrix(lup.getUpper(null)), setOf(UFeature))
|
||||
override val u: Matrix<Double> by lazy {
|
||||
EjmlMatrix(SimpleMatrix(lup.getUpper(null))) + UFeature
|
||||
}
|
||||
|
||||
override val p: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
|
||||
},
|
||||
) union features
|
||||
|
||||
public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
|
||||
EjmlMatrix(origin, this.features + features)
|
||||
override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
|
||||
}
|
||||
else -> null
|
||||
}?.let{type.cast(it)}
|
||||
|
||||
public override operator fun get(i: Int, j: Int): Double = origin[i, j]
|
||||
|
||||
public override fun equals(other: Any?): Boolean {
|
||||
if (other is EjmlMatrix) return origin.isIdentical(other.origin, 0.0)
|
||||
return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
|
||||
}
|
||||
|
||||
public override fun hashCode(): Int = origin.hashCode()
|
||||
|
||||
public override fun toString(): String = "EjmlMatrix($origin)"
|
||||
}
|
||||
|
@ -2,23 +2,29 @@ package kscience.kmath.ejml
|
||||
|
||||
import kscience.kmath.linear.InverseMatrixFeature
|
||||
import kscience.kmath.linear.MatrixContext
|
||||
import kscience.kmath.linear.MatrixWrapper
|
||||
import kscience.kmath.linear.Point
|
||||
import kscience.kmath.linear.getFeature
|
||||
import kscience.kmath.misc.UnstableKMathAPI
|
||||
import kscience.kmath.structures.Matrix
|
||||
import kscience.kmath.structures.getFeature
|
||||
import org.ejml.simple.SimpleMatrix
|
||||
|
||||
/**
|
||||
* Converts this matrix to EJML one.
|
||||
*/
|
||||
public fun Matrix<Double>.toEjml(): EjmlMatrix =
|
||||
if (this is EjmlMatrix) this else EjmlMatrixContext.produce(rowNum, colNum) { i, j -> get(i, j) }
|
||||
|
||||
/**
|
||||
* Represents context of basic operations operating with [EjmlMatrix].
|
||||
*
|
||||
* @author Iaroslav Postovalov
|
||||
*/
|
||||
public object EjmlMatrixContext : MatrixContext<Double, EjmlMatrix> {
|
||||
|
||||
/**
|
||||
* Converts this matrix to EJML one.
|
||||
*/
|
||||
public fun Matrix<Double>.toEjml(): EjmlMatrix = when {
|
||||
this is EjmlMatrix -> this
|
||||
this is MatrixWrapper && matrix is EjmlMatrix -> matrix as EjmlMatrix
|
||||
else -> produce(rowNum, colNum) { i, j -> get(i, j) }
|
||||
}
|
||||
|
||||
/**
|
||||
* Converts this vector to EJML one.
|
||||
*/
|
||||
@ -80,6 +86,7 @@ public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMa
|
||||
public fun EjmlMatrixContext.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()
|
@ -3,7 +3,8 @@ package kscience.kmath.ejml
|
||||
import kscience.kmath.linear.DeterminantFeature
|
||||
import kscience.kmath.linear.LupDecompositionFeature
|
||||
import kscience.kmath.linear.MatrixFeature
|
||||
import kscience.kmath.linear.getFeature
|
||||
import kscience.kmath.linear.plus
|
||||
import kscience.kmath.structures.getFeature
|
||||
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
|
||||
import org.ejml.simple.SimpleMatrix
|
||||
import kotlin.random.Random
|
||||
@ -58,7 +59,7 @@ internal class EjmlMatrixTest {
|
||||
|
||||
@Test
|
||||
fun suggestFeature() {
|
||||
assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
|
||||
assertNotNull((EjmlMatrix(randomMatrix) + SomeFeature).getFeature<SomeFeature>())
|
||||
}
|
||||
|
||||
@Test
|
||||
|
@ -1,8 +1,12 @@
|
||||
package kscience.kmath.real
|
||||
|
||||
import kscience.kmath.linear.*
|
||||
import kscience.kmath.linear.MatrixContext
|
||||
import kscience.kmath.linear.VirtualMatrix
|
||||
import kscience.kmath.linear.inverseWithLUP
|
||||
import kscience.kmath.linear.real
|
||||
import kscience.kmath.misc.UnstableKMathAPI
|
||||
import kscience.kmath.structures.Buffer
|
||||
import kscience.kmath.structures.Matrix
|
||||
import kscience.kmath.structures.RealBuffer
|
||||
import kscience.kmath.structures.asIterable
|
||||
import kotlin.math.pow
|
||||
@ -19,7 +23,7 @@ import kotlin.math.pow
|
||||
* Functions that help create a real (Double) matrix
|
||||
*/
|
||||
|
||||
public typealias RealMatrix = FeaturedMatrix<Double>
|
||||
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)
|
||||
|
@ -1,6 +1,5 @@
|
||||
package kaceince.kmath.real
|
||||
|
||||
import kscience.kmath.linear.VirtualMatrix
|
||||
import kscience.kmath.linear.build
|
||||
import kscience.kmath.real.*
|
||||
import kscience.kmath.structures.Matrix
|
||||
@ -42,7 +41,7 @@ internal class RealMatrixTest {
|
||||
1.0, 0.0, 0.0,
|
||||
0.0, 1.0, 2.0
|
||||
)
|
||||
assertEquals(VirtualMatrix.wrap(matrix2), matrix1.repeatStackVertical(3))
|
||||
assertEquals(matrix2, matrix1.repeatStackVertical(3))
|
||||
}
|
||||
|
||||
@Test
|
||||
|
Loading…
Reference in New Issue
Block a user