no.uib.cipr.matrix.SVD Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of mtj Show documentation
Show all versions of mtj Show documentation
A comprehensive collection of matrix data structures, linear solvers, least squares methods,
eigenvalue, and singular value decompositions.
Forked from: https://github.com/fommil/matrix-toolkits-java
and added support for eigenvalue computation of general matrices
The newest version!
/*
* Copyright (C) 2003-2006 Bjørn-Ove Heimsund
*
* This file is part of MTJ.
*
* This library is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published by the
* Free Software Foundation; either version 2.1 of the License, or (at your
* option) any later version.
*
* This library is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
* for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this library; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
package no.uib.cipr.matrix;
import com.github.fommil.netlib.LAPACK;
import org.netlib.util.intW;
/**
* Computes singular value decompositions
*/
public class SVD {
/**
* Work array
*/
private final double[] work;
/**
* Work array
*/
private final int[] iwork;
/**
* Matrix dimension
*/
private final int m, n;
/**
* Compute the singular vectors fully?
*/
private final boolean vectors;
/**
* Job to do
*/
private final JobSVD job;
/**
* The singular values
*/
private final double[] S;
/**
* Singular vectors
*/
private final DenseMatrix U, Vt;
/**
* Creates an empty SVD which will compute all singular values and vectors
*
* @param m
* Number of rows
* @param n
* Number of columns
*/
public SVD(int m, int n) {
this(m, n, true);
}
/**
* Creates an empty SVD
*
* @param m
* Number of rows
* @param n
* Number of columns
* @param vectors
* True to compute the singular vectors, false for just the
* singular values
*/
public SVD(int m, int n, boolean vectors) {
this.m = m;
this.n = n;
this.vectors = vectors;
// Allocate space for the decomposition
S = new double[Math.min(m, n)];
if (vectors) {
U = new DenseMatrix(m, m);
Vt = new DenseMatrix(n, n);
} else
U = Vt = null;
job = vectors ? JobSVD.All : JobSVD.None;
// Find workspace requirements
iwork = new int[8 * Math.min(m, n)];
// Query optimal workspace
double[] worksize = new double[1];
intW info = new intW(0);
LAPACK.getInstance().dgesdd(job.netlib(), m, n, new double[0],
Matrices.ld(m), new double[0], new double[0], Matrices.ld(m),
new double[0], Matrices.ld(n), worksize, -1, iwork, info);
// Allocate workspace
int lwork = -1;
if (info.val != 0) {
if (vectors)
lwork = 3
* Math.min(m, n)
* Math.min(m, n)
+ Math.max(
Math.max(m, n),
4 * Math.min(m, n) * Math.min(m, n) + 4
* Math.min(m, n));
else
lwork = 3
* Math.min(m, n)
* Math.min(m, n)
+ Math.max(
Math.max(m, n),
5 * Math.min(m, n) * Math.min(m, n) + 4
* Math.min(m, n));
} else
lwork = (int) worksize[0];
lwork = Math.max(lwork, 1);
work = new double[lwork];
}
/**
* Convenience method for computing a full SVD
*
* @param A
* Matrix to decompose, not modified
* @return Newly allocated factorization
* @throws NotConvergedException
*/
public static SVD factorize(Matrix A) throws NotConvergedException {
return new SVD(A.numRows(), A.numColumns()).factor(new DenseMatrix(A));
}
/**
* Computes an SVD
*
* @param A
* Matrix to decompose. Size must conform, and it will be
* overwritten on return. Pass a copy to avoid this
* @return The current decomposition
* @throws NotConvergedException
*/
public SVD factor(DenseMatrix A) throws NotConvergedException {
if (A.numRows() != m)
throw new IllegalArgumentException("A.numRows() != m");
else if (A.numColumns() != n)
throw new IllegalArgumentException("A.numColumns() != n");
intW info = new intW(0);
LAPACK.getInstance().dgesdd(job.netlib(), m, n, A.getData(),
Matrices.ld(m), S, vectors ? U.getData() : new double[0],
Matrices.ld(m), vectors ? Vt.getData() : new double[0],
Matrices.ld(n), work, work.length, iwork, info);
if (info.val > 0)
throw new NotConvergedException(
NotConvergedException.Reason.Iterations);
else if (info.val < 0)
throw new IllegalArgumentException();
return this;
}
/**
* True if singular vectors are stored
*/
public boolean hasSingularVectors() {
return U != null;
}
/**
* Returns the left singular vectors, column-wise. Not available for partial
* decompositions
*
* @return Matrix of size m*m
*/
public DenseMatrix getU() {
return U;
}
/**
* Returns the right singular vectors, row-wise. Not available for partial
* decompositions
*
* @return Matrix of size n*n
*/
public DenseMatrix getVt() {
return Vt;
}
/**
* Returns the singular values (stored in descending order)
*
* @return Array of size min(m,n)
*/
public double[] getS() {
return S;
}
}