Implement decomposition features by CMMatrix
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@ -3,15 +3,45 @@ package kscience.kmath.commons.linear
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import kscience.kmath.linear.*
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import kscience.kmath.structures.Matrix
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import kscience.kmath.structures.NDStructure
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import kscience.kmath.structures.RealBuffer
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import org.apache.commons.math3.linear.*
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public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
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public class CMMatrix(public val origin: RealMatrix, features: Set<MatrixFeature> = emptySet()) :
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FeaturedMatrix<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 val features: Set<MatrixFeature> = features union hashSetOf(
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*if (origin is DiagonalMatrix) arrayOf(DiagonalFeature) else emptyArray(),
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object : DeterminantFeature<Double>, LupDecompositionFeature<Double> {
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private val lup by lazy { LUDecomposition(origin) }
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override val determinant: Double by lazy { lup.determinant }
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override val l: FeaturedMatrix<Double> by lazy { CMMatrix(lup.l, setOf(LFeature)) }
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override val u: FeaturedMatrix<Double> by lazy { CMMatrix(lup.u, setOf(UFeature)) }
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override val p: FeaturedMatrix<Double> by lazy { CMMatrix(lup.p) }
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},
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object : CholeskyDecompositionFeature<Double> {
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override val l: FeaturedMatrix<Double> by lazy {
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val cholesky = CholeskyDecomposition(origin)
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CMMatrix(cholesky.l, setOf(LFeature))
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}
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},
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object : QRDecompositionFeature<Double> {
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private val qr by lazy { QRDecomposition(origin) }
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override val q: FeaturedMatrix<Double> by lazy { CMMatrix(qr.q, setOf(OrthogonalFeature)) }
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override val r: FeaturedMatrix<Double> by lazy { CMMatrix(qr.r, setOf(UFeature)) }
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},
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object : SingularValueDecompositionFeature<Double> {
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private val sv by lazy { SingularValueDecomposition(origin) }
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override val u: FeaturedMatrix<Double> by lazy { CMMatrix(sv.u) }
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override val s: FeaturedMatrix<Double> by lazy { CMMatrix(sv.s) }
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override val v: FeaturedMatrix<Double> by lazy { CMMatrix(sv.v) }
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override val singularValues: Point<Double> by lazy { RealBuffer(sv.singularValues) }
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},
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)
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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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@ -35,6 +35,8 @@ public interface InverseMatrixFeature<T : Any> : MatrixFeature {
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/**
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* Matrices with this feature can compute their determinant.
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*
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* @param T the type of matrices' items.
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*/
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public interface DeterminantFeature<T : Any> : MatrixFeature {
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/**
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@ -48,8 +48,12 @@ public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFea
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DecompositionFactory_DDRM.qr().apply { decompose(origin.ddrm.copy()) }
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}
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override val q: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getQ(null, false))) }
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override val r: FeaturedMatrix<Double> by lazy { EjmlMatrix(SimpleMatrix(qr.getR(null, false))) }
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override val q: FeaturedMatrix<Double> by lazy {
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EjmlMatrix(SimpleMatrix(qr.getQ(null, false)), setOf(OrthogonalFeature))
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}
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override val r: FeaturedMatrix<Double> by lazy {
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EjmlMatrix(SimpleMatrix(qr.getR(null, false)), setOf(UFeature))
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}
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},
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object : CholeskyDecompositionFeature<Double> {
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