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Implement decomposition features by CMMatrix

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Iaroslav Postovalov 2021-01-19 01:09:44 +07:00
parent 758508ba96
commit 53db4489f8
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3 changed files with 42 additions and 6 deletions
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38
kmath-commons/src/main/kotlin/kscience/kmath/commons/linear/CMMatrix.kt
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@ -3,15 +3,45 @@ package kscience.kmath.commons.linear
import kscience.kmath.linear.*
import kscience.kmath.structures.Matrix
import kscience.kmath.structures.NDStructure
import kscience.kmath.structures.RealBuffer
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> = emptySet()) :
FeaturedMatrix<Double> {
public override val rowNum: Int get() = origin.rowDimension
public override val colNum: Int get() = origin.columnDimension
public override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
if (origin is DiagonalMatrix) yield(DiagonalFeature)
}.toHashSet()
public override val features: Set<MatrixFeature> = features union hashSetOf(
*if (origin is DiagonalMatrix) arrayOf(DiagonalFeature) else emptyArray(),
object : DeterminantFeature<Double>, LupDecompositionFeature<Double> {
private val lup by lazy { LUDecomposition(origin) }
override val determinant: Double by lazy { lup.determinant }
override val l: FeaturedMatrix<Double> by lazy { CMMatrix(lup.l, setOf(LFeature)) }
override val u: FeaturedMatrix<Double> by lazy { CMMatrix(lup.u, setOf(UFeature)) }
override val p: FeaturedMatrix<Double> by lazy { CMMatrix(lup.p) }
},
object : CholeskyDecompositionFeature<Double> {
override val l: FeaturedMatrix<Double> by lazy {
val cholesky = CholeskyDecomposition(origin)
CMMatrix(cholesky.l, setOf(LFeature))
}
},
object : QRDecompositionFeature<Double> {
private val qr by lazy { QRDecomposition(origin) }
override val q: FeaturedMatrix<Double> by lazy { CMMatrix(qr.q, setOf(OrthogonalFeature)) }
override val r: FeaturedMatrix<Double> by lazy { CMMatrix(qr.r, setOf(UFeature)) }
},
object : SingularValueDecompositionFeature<Double> {
private val sv by lazy { SingularValueDecomposition(origin) }
override val u: FeaturedMatrix<Double> by lazy { CMMatrix(sv.u) }
override val s: FeaturedMatrix<Double> by lazy { CMMatrix(sv.s) }
override val v: FeaturedMatrix<Double> by lazy { CMMatrix(sv.v) }
override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
},
)
public override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
CMMatrix(origin, this.features + features)

2
kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixFeatures.kt
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@ -35,6 +35,8 @@ 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 {
/**

8
kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrix.kt
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@ -48,8 +48,12 @@ public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFea
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))) }
override val q: FeaturedMatrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(qr.getQ(null, false)), setOf(OrthogonalFeature))
}
override val r: FeaturedMatrix<Double> by lazy {
EjmlMatrix(SimpleMatrix(qr.getR(null, false)), setOf(UFeature))
}
},
object : CholeskyDecompositionFeature<Double> {