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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math4.linear;
import java.util.Arrays;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.util.FastMath;
import org.apache.commons.math4.exception.util.LocalizedFormats;
/**
* Calculates the QR-decomposition of a matrix.
* The QR-decomposition of a matrix A consists of two matrices Q and R
* that satisfy: A = QR, Q is orthogonal (QTQ = I), and R is
* upper triangular. If A is m×n, Q is m×m and R m×n.
* This class compute the decomposition using Householder reflectors.
* For efficiency purposes, the decomposition in packed form is transposed.
* This allows inner loop to iterate inside rows, which is much more cache-efficient
* in Java.
* This class is based on the class with similar name from the
* JAMA library, with the
* following changes:
*
* - a {@link #getQT() getQT} method has been added,
* - the {@code solve} and {@code isFullRank} methods have been replaced
* by a {@link #getSolver() getSolver} method and the equivalent methods
* provided by the returned {@link DecompositionSolver}.
*
*
* @see MathWorld
* @see Wikipedia
*
* @since 1.2 (changed to concrete class in 3.0)
*/
public class QRDecomposition {
/**
* A packed TRANSPOSED representation of the QR decomposition.
* The elements BELOW the diagonal are the elements of the UPPER triangular
* matrix R, and the rows ABOVE the diagonal are the Householder reflector vectors
* from which an explicit form of Q can be recomputed if desired.
*/
private double[][] qrt;
/** The diagonal elements of R. */
private double[] rDiag;
/** Cached value of Q. */
private RealMatrix cachedQ;
/** Cached value of QT. */
private RealMatrix cachedQT;
/** Cached value of R. */
private RealMatrix cachedR;
/** Cached value of H. */
private RealMatrix cachedH;
/** Singularity threshold. */
private final double threshold;
/**
* Calculates the QR-decomposition of the given matrix.
* The singularity threshold defaults to zero.
*
* @param matrix The matrix to decompose.
*
* @see #QRDecomposition(RealMatrix,double)
*/
public QRDecomposition(RealMatrix matrix) {
this(matrix, 0d);
}
/**
* Calculates the QR-decomposition of the given matrix.
*
* @param matrix The matrix to decompose.
* @param threshold Singularity threshold.
* The matrix will be considered singular if the absolute value of
* any of the diagonal elements of the "R" matrix is smaller than
* the threshold.
*/
public QRDecomposition(RealMatrix matrix,
double threshold) {
this.threshold = threshold;
final int m = matrix.getRowDimension();
final int n = matrix.getColumnDimension();
qrt = matrix.transpose().getData();
rDiag = new double[FastMath.min(m, n)];
cachedQ = null;
cachedQT = null;
cachedR = null;
cachedH = null;
decompose(qrt);
}
/** Decompose matrix.
* @param matrix transposed matrix
* @since 3.2
*/
protected void decompose(double[][] matrix) {
for (int minor = 0; minor < FastMath.min(matrix.length, matrix[0].length); minor++) {
performHouseholderReflection(minor, matrix);
}
}
/** Perform Householder reflection for a minor A(minor, minor) of A.
* @param minor minor index
* @param matrix transposed matrix
* @since 3.2
*/
protected void performHouseholderReflection(int minor, double[][] matrix) {
final double[] qrtMinor = matrix[minor];
/*
* Let x be the first column of the minor, and a^2 = |x|^2.
* x will be in the positions qr[minor][minor] through qr[m][minor].
* The first column of the transformed minor will be (a,0,0,..)'
* The sign of a is chosen to be opposite to the sign of the first
* component of x. Let's find a:
*/
double xNormSqr = 0;
for (int row = minor; row < qrtMinor.length; row++) {
final double c = qrtMinor[row];
xNormSqr += c * c;
}
final double a = (qrtMinor[minor] > 0) ? -FastMath.sqrt(xNormSqr) : FastMath.sqrt(xNormSqr);
rDiag[minor] = a;
if (a != 0.0) {
/*
* Calculate the normalized reflection vector v and transform
* the first column. We know the norm of v beforehand: v = x-ae
* so |v|^2 = = -2a+a^2 =
* a^2+a^2-2a = 2a*(a - ).
* Here is now qr[minor][minor].
* v = x-ae is stored in the column at qr:
*/
qrtMinor[minor] -= a; // now |v|^2 = -2a*(qr[minor][minor])
/*
* Transform the rest of the columns of the minor:
* They will be transformed by the matrix H = I-2vv'/|v|^2.
* If x is a column vector of the minor, then
* Hx = (I-2vv'/|v|^2)x = x-2vv'x/|v|^2 = x - 2/|v|^2 v.
* Therefore the transformation is easily calculated by
* subtracting the column vector (2/|v|^2)v from x.
*
* Let 2/|v|^2 = alpha. From above we have
* |v|^2 = -2a*(qr[minor][minor]), so
* alpha = -/(a*qr[minor][minor])
*/
for (int col = minor+1; col < matrix.length; col++) {
final double[] qrtCol = matrix[col];
double alpha = 0;
for (int row = minor; row < qrtCol.length; row++) {
alpha -= qrtCol[row] * qrtMinor[row];
}
alpha /= a * qrtMinor[minor];
// Subtract the column vector alpha*v from x.
for (int row = minor; row < qrtCol.length; row++) {
qrtCol[row] -= alpha * qrtMinor[row];
}
}
}
}
/**
* Returns the matrix R of the decomposition.
* R is an upper-triangular matrix
* @return the R matrix
*/
public RealMatrix getR() {
if (cachedR == null) {
// R is supposed to be m x n
final int n = qrt.length;
final int m = qrt[0].length;
double[][] ra = new double[m][n];
// copy the diagonal from rDiag and the upper triangle of qr
for (int row = FastMath.min(m, n) - 1; row >= 0; row--) {
ra[row][row] = rDiag[row];
for (int col = row + 1; col < n; col++) {
ra[row][col] = qrt[col][row];
}
}
cachedR = MatrixUtils.createRealMatrix(ra);
}
// return the cached matrix
return cachedR;
}
/**
* Returns the matrix Q of the decomposition.
* Q is an orthogonal matrix
* @return the Q matrix
*/
public RealMatrix getQ() {
if (cachedQ == null) {
cachedQ = getQT().transpose();
}
return cachedQ;
}
/**
* Returns the transpose of the matrix Q of the decomposition.
* Q is an orthogonal matrix
* @return the transpose of the Q matrix, QT
*/
public RealMatrix getQT() {
if (cachedQT == null) {
// QT is supposed to be m x m
final int n = qrt.length;
final int m = qrt[0].length;
double[][] qta = new double[m][m];
/*
* Q = Q1 Q2 ... Q_m, so Q is formed by first constructing Q_m and then
* applying the Householder transformations Q_(m-1),Q_(m-2),...,Q1 in
* succession to the result
*/
for (int minor = m - 1; minor >= FastMath.min(m, n); minor--) {
qta[minor][minor] = 1.0d;
}
for (int minor = FastMath.min(m, n)-1; minor >= 0; minor--){
final double[] qrtMinor = qrt[minor];
qta[minor][minor] = 1.0d;
if (qrtMinor[minor] != 0.0) {
for (int col = minor; col < m; col++) {
double alpha = 0;
for (int row = minor; row < m; row++) {
alpha -= qta[col][row] * qrtMinor[row];
}
alpha /= rDiag[minor] * qrtMinor[minor];
for (int row = minor; row < m; row++) {
qta[col][row] += -alpha * qrtMinor[row];
}
}
}
}
cachedQT = MatrixUtils.createRealMatrix(qta);
}
// return the cached matrix
return cachedQT;
}
/**
* Returns the Householder reflector vectors.
* H is a lower trapezoidal matrix whose columns represent
* each successive Householder reflector vector. This matrix is used
* to compute Q.
* @return a matrix containing the Householder reflector vectors
*/
public RealMatrix getH() {
if (cachedH == null) {
final int n = qrt.length;
final int m = qrt[0].length;
double[][] ha = new double[m][n];
for (int i = 0; i < m; ++i) {
for (int j = 0; j < FastMath.min(i + 1, n); ++j) {
ha[i][j] = qrt[j][i] / -rDiag[j];
}
}
cachedH = MatrixUtils.createRealMatrix(ha);
}
// return the cached matrix
return cachedH;
}
/**
* Get a solver for finding the A × X = B solution in least square sense.
*
* Least Square sense means a solver can be computed for an overdetermined system,
* (i.e. a system with more equations than unknowns, which corresponds to a tall A
* matrix with more rows than columns). In any case, if the matrix is singular
* within the tolerance set at {@link QRDecomposition#QRDecomposition(RealMatrix,
* double) construction}, an error will be triggered when
* the {@link DecompositionSolver#solve(RealVector) solve} method will be called.
*
* @return a solver
*/
public DecompositionSolver getSolver() {
return new Solver(qrt, rDiag, threshold);
}
/** Specialized solver. */
private static class Solver implements DecompositionSolver {
/**
* A packed TRANSPOSED representation of the QR decomposition.
* The elements BELOW the diagonal are the elements of the UPPER triangular
* matrix R, and the rows ABOVE the diagonal are the Householder reflector vectors
* from which an explicit form of Q can be recomputed if desired.
*/
private final double[][] qrt;
/** The diagonal elements of R. */
private final double[] rDiag;
/** Singularity threshold. */
private final double threshold;
/**
* Build a solver from decomposed matrix.
*
* @param qrt Packed TRANSPOSED representation of the QR decomposition.
* @param rDiag Diagonal elements of R.
* @param threshold Singularity threshold.
*/
private Solver(final double[][] qrt,
final double[] rDiag,
final double threshold) {
this.qrt = qrt;
this.rDiag = rDiag;
this.threshold = threshold;
}
/** {@inheritDoc} */
@Override
public boolean isNonSingular() {
return !checkSingular(rDiag, threshold, false);
}
/** {@inheritDoc} */
@Override
public RealVector solve(RealVector b) {
final int n = qrt.length;
final int m = qrt[0].length;
if (b.getDimension() != m) {
throw new DimensionMismatchException(b.getDimension(), m);
}
checkSingular(rDiag, threshold, true);
final double[] x = new double[n];
final double[] y = b.toArray();
// apply Householder transforms to solve Q.y = b
for (int minor = 0; minor < FastMath.min(m, n); minor++) {
final double[] qrtMinor = qrt[minor];
double dotProduct = 0;
for (int row = minor; row < m; row++) {
dotProduct += y[row] * qrtMinor[row];
}
dotProduct /= rDiag[minor] * qrtMinor[minor];
for (int row = minor; row < m; row++) {
y[row] += dotProduct * qrtMinor[row];
}
}
// solve triangular system R.x = y
for (int row = rDiag.length - 1; row >= 0; --row) {
y[row] /= rDiag[row];
final double yRow = y[row];
final double[] qrtRow = qrt[row];
x[row] = yRow;
for (int i = 0; i < row; i++) {
y[i] -= yRow * qrtRow[i];
}
}
return new ArrayRealVector(x, false);
}
/** {@inheritDoc} */
@Override
public RealMatrix solve(RealMatrix b) {
final int n = qrt.length;
final int m = qrt[0].length;
if (b.getRowDimension() != m) {
throw new DimensionMismatchException(b.getRowDimension(), m);
}
checkSingular(rDiag, threshold, true);
final int columns = b.getColumnDimension();
final int blockSize = BlockRealMatrix.BLOCK_SIZE;
final int cBlocks = (columns + blockSize - 1) / blockSize;
final double[][] xBlocks = BlockRealMatrix.createBlocksLayout(n, columns);
final double[][] y = new double[b.getRowDimension()][blockSize];
final double[] alpha = new double[blockSize];
for (int kBlock = 0; kBlock < cBlocks; ++kBlock) {
final int kStart = kBlock * blockSize;
final int kEnd = FastMath.min(kStart + blockSize, columns);
final int kWidth = kEnd - kStart;
// get the right hand side vector
b.copySubMatrix(0, m - 1, kStart, kEnd - 1, y);
// apply Householder transforms to solve Q.y = b
for (int minor = 0; minor < FastMath.min(m, n); minor++) {
final double[] qrtMinor = qrt[minor];
final double factor = 1.0 / (rDiag[minor] * qrtMinor[minor]);
Arrays.fill(alpha, 0, kWidth, 0.0);
for (int row = minor; row < m; ++row) {
final double d = qrtMinor[row];
final double[] yRow = y[row];
for (int k = 0; k < kWidth; ++k) {
alpha[k] += d * yRow[k];
}
}
for (int k = 0; k < kWidth; ++k) {
alpha[k] *= factor;
}
for (int row = minor; row < m; ++row) {
final double d = qrtMinor[row];
final double[] yRow = y[row];
for (int k = 0; k < kWidth; ++k) {
yRow[k] += alpha[k] * d;
}
}
}
// solve triangular system R.x = y
for (int j = rDiag.length - 1; j >= 0; --j) {
final int jBlock = j / blockSize;
final int jStart = jBlock * blockSize;
final double factor = 1.0 / rDiag[j];
final double[] yJ = y[j];
final double[] xBlock = xBlocks[jBlock * cBlocks + kBlock];
int index = (j - jStart) * kWidth;
for (int k = 0; k < kWidth; ++k) {
yJ[k] *= factor;
xBlock[index++] = yJ[k];
}
final double[] qrtJ = qrt[j];
for (int i = 0; i < j; ++i) {
final double rIJ = qrtJ[i];
final double[] yI = y[i];
for (int k = 0; k < kWidth; ++k) {
yI[k] -= yJ[k] * rIJ;
}
}
}
}
return new BlockRealMatrix(n, columns, xBlocks, false);
}
/**
* {@inheritDoc}
* @throws SingularMatrixException if the decomposed matrix is singular.
*/
@Override
public RealMatrix getInverse() {
return solve(MatrixUtils.createRealIdentityMatrix(qrt[0].length));
}
/**
* Check singularity.
*
* @param diag Diagonal elements of the R matrix.
* @param min Singularity threshold.
* @param raise Whether to raise a {@link SingularMatrixException}
* if any element of the diagonal fails the check.
* @return {@code true} if any element of the diagonal is smaller
* or equal to {@code min}.
* @throws SingularMatrixException if the matrix is singular and
* {@code raise} is {@code true}.
*/
private static boolean checkSingular(double[] diag,
double min,
boolean raise) {
final int len = diag.length;
for (int i = 0; i < len; i++) {
final double d = diag[i];
if (FastMath.abs(d) <= min) {
if (raise) {
final SingularMatrixException e = new SingularMatrixException();
e.getContext().addMessage(LocalizedFormats.NUMBER_TOO_SMALL, d, min);
e.getContext().addMessage(LocalizedFormats.INDEX, i);
throw e;
} else {
return true;
}
}
}
return false;
}
}
}