001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.math.matrix.algorithm.pca;
031
032import java.util.Arrays;
033
034import no.uib.cipr.matrix.NotConvergedException;
035import Jama.Matrix;
036
037/**
038 * Compute the PCA using an SVD without actually constructing the covariance
039 * matrix. This class performs a full SVD extracting all singular values and
040 * vectors. If you know apriori how many principle components (or have an upper
041 * bound on the number), then use a {@link ThinSvdPrincipalComponentAnalysis}
042 * instead.
043 *
044 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
045 */
046public class SvdPrincipalComponentAnalysis extends PrincipalComponentAnalysis {
047        int ndims;
048
049        /**
050         * Construct a {@link SvdPrincipalComponentAnalysis} that will extract all
051         * the eigenvectors.
052         */
053        public SvdPrincipalComponentAnalysis() {
054                this(-1);
055        }
056
057        /**
058         * Construct a {@link SvdPrincipalComponentAnalysis} that will extract the n
059         * best eigenvectors.
060         * 
061         * @param ndims
062         *            the number of eigenvectors to select.
063         */
064        public SvdPrincipalComponentAnalysis(int ndims) {
065                this.ndims = ndims;
066        }
067
068        @Override
069        public void learnBasisNorm(Matrix norm) {
070                try {
071                        final no.uib.cipr.matrix.DenseMatrix mjtA = new no.uib.cipr.matrix.DenseMatrix(norm.getArray());
072                        final no.uib.cipr.matrix.EconomySVD svd = no.uib.cipr.matrix.EconomySVD.factorize(mjtA);
073
074                        final no.uib.cipr.matrix.DenseMatrix output = svd.getVt();
075
076                        final int dims = ndims < 0 ? svd.getS().length : ndims;
077
078                        basis = new Matrix(output.numColumns(), dims);
079                        eigenvalues = Arrays.copyOf(svd.getS(), dims);
080
081                        final double normEig = 1.0 / (norm.getRowDimension() - 1);
082                        for (int i = 0; i < eigenvalues.length; i++)
083                                eigenvalues[i] = eigenvalues[i] * eigenvalues[i] * normEig;
084
085                        final double[][] basisData = basis.getArray();
086                        for (int j = 0; j < output.numColumns(); j++)
087                                for (int i = 0; i < dims; i++)
088                                        basisData[j][i] = output.get(i, j);
089
090                } catch (final NotConvergedException e) {
091                        throw new RuntimeException(e);
092                }
093        }
094}