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 org.openimaj.math.matrix.ThinSingularValueDecomposition;
033
034import Jama.Matrix;
035
036/**
037 * Compute the PCA using a thin SVD to extract the best-n principal
038 * components directly. 
039 * 
040 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
041 */
042public class ThinSvdPrincipalComponentAnalysis extends PrincipalComponentAnalysis {
043        int ndims;
044        
045        /**
046         * Construct a {@link ThinSvdPrincipalComponentAnalysis} that
047         * will extract the n best eigenvectors.
048         * @param ndims the number of eigenvectors to select.
049         */
050        public ThinSvdPrincipalComponentAnalysis(int ndims) {
051                this.ndims = ndims;
052        }
053        
054        @Override
055        public void learnBasisNorm(Matrix data) {
056                ThinSingularValueDecomposition svd = new ThinSingularValueDecomposition(data, ndims);
057                basis = svd.Vt.transpose();
058                
059                eigenvalues = svd.S;
060                double normEig = 1.0 / (data.getRowDimension() - 1);
061                for (int i=0; i<eigenvalues.length; i++) 
062                        eigenvalues[i] = eigenvalues[i] * eigenvalues[i] * normEig;
063        }
064}