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Merge pull request #189 from mipt-npm/commandertvis/cm-decompositions

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Support of new matrix decomposition features for CMMatrix
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Alexander Nozik 2021-01-28 19:58:07 +03:00 committed by GitHub
commit d9afcf019a
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4 changed files with 54 additions and 16 deletions
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40
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
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@ -1,11 +1,9 @@
package kscience.kmath.commons.linear
import kscience.kmath.linear.DiagonalFeature
import kscience.kmath.linear.MatrixContext
import kscience.kmath.linear.Point
import kscience.kmath.linear.origin
import kscience.kmath.linear.*
import kscience.kmath.misc.UnstableKMathAPI
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.RealBuffer
import org.apache.commons.math3.linear.*
import kotlin.reflect.KClass
import kotlin.reflect.cast
@ -17,8 +15,40 @@ public inline class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
DiagonalFeature::class -> if (origin is DiagonalMatrix) DiagonalFeature else null
DeterminantFeature::class, LupDecompositionFeature::class -> object :
DeterminantFeature<Double>,
LupDecompositionFeature<Double> {
private val lup by lazy { LUDecomposition(origin) }
override val determinant: Double by lazy { lup.determinant }
override val l: Matrix<Double> by lazy { CMMatrix(lup.l) + LFeature }
override val u: Matrix<Double> by lazy { CMMatrix(lup.u) + UFeature }
override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
override val l: Matrix<Double> by lazy {
val cholesky = CholeskyDecomposition(origin)
CMMatrix(cholesky.l) + LFeature
}
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy { QRDecomposition(origin) }
override val q: Matrix<Double> by lazy { CMMatrix(qr.q) + OrthogonalFeature }
override val r: Matrix<Double> by lazy { CMMatrix(qr.r) + UFeature }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
private val sv by lazy { SingularValueDecomposition(origin) }
override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
}
else -> null
}?.let { type.cast(it) }
}?.let(type::cast)
public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
}

6
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixFeatures.kt
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@ -11,8 +11,8 @@ public interface MatrixFeature
/**
* Matrices with this feature are considered to have only diagonal non-null elements.
*/
public interface DiagonalFeature : MatrixFeature{
public companion object: DiagonalFeature
public interface DiagonalFeature : MatrixFeature {
public companion object : DiagonalFeature
}
/**
@ -39,6 +39,8 @@ public interface InverseMatrixFeature<T : Any> : MatrixFeature {
/**
* Matrices with this feature can compute their determinant.
*
* @param T the type of matrices' items.
*/
public interface DeterminantFeature<T : Any> : MatrixFeature {
/**

22
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt
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@ -16,21 +16,20 @@ import kotlin.reflect.cast
* @property origin the underlying [SimpleMatrix].
* @author Iaroslav Postovalov
*/
public class EjmlMatrix(
public val origin: SimpleMatrix,
) : Matrix<Double> {
public class EjmlMatrix(public val origin: SimpleMatrix) : Matrix<Double> {
public override val rowNum: Int get() = origin.numRows()
public override val colNum: Int get() = origin.numCols()
@UnstableKMathAPI
override fun <T : Any> getFeature(type: KClass<T>): T? = when (type) {
public 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)
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
private val svd by lazy {
DecompositionFactory_DDRM.svd(origin.numRows(), origin.numCols(), true, true, false)
@ -42,14 +41,19 @@ public class EjmlMatrix(
override val v: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(svd.getV(null, false))) }
override val singularValues: Point<Double> by lazy { RealBuffer(svd.singularValues) }
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy {
DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
}
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))) }
override val q: Matrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) + OrthogonalFeature
}
override val r: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) + UFeature }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
@ -58,6 +62,7 @@ public class EjmlMatrix(
EjmlMatrix(SimpleMatrix(cholesky.getT(null))) + LFeature
}
}
LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
private val lup by lazy {
DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols()).apply { decompose(origin.ddrm.copy()) }
@ -73,8 +78,9 @@ public class EjmlMatrix(
override val p: Matrix<Double> by lazy { EjmlMatrix(SimpleMatrix(lup.getRowPivot(null))) }
}
else -> null
}?.let { type.cast(it) }
}?.let(type::cast)
public override operator fun get(i: Int, j: Int): Double = origin[i, j]