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.ml.clustering.spectral;
031
032import org.apache.logging.log4j.Logger;
033import org.apache.logging.log4j.LogManager;
034import org.openimaj.ml.clustering.SimilarityClusterer;
035
036import ch.akuhn.matrix.SparseMatrix;
037import ch.akuhn.matrix.eigenvalues.Eigenvalues;
038
039/**
040 * Built from a mixture of this tutorial:
041 *      - http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5B0%5D.pdf
042 * And this implementation:
043 *  - https://github.com/peterklipfel/AutoponicsVision/blob/master/SpectralClustering.java
044 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
045 *
046 */
047public class DoubleSpectralClustering implements SimilarityClusterer<SpectralIndexedClusters>{
048        final static Logger logger = LogManager.getLogger(DoubleSpectralClustering.class);
049        protected SpectralClusteringConf<double[]> conf;
050
051        /**
052         * @param conf
053         * cluster the eigen vectors
054         */
055        public DoubleSpectralClustering(SpectralClusteringConf<double[]> conf) {
056                this.conf = conf;
057        }
058        
059        protected DoubleSpectralClustering() {
060        }
061        
062        @Override
063        public SpectralIndexedClusters clusterSimilarity(SparseMatrix sim) {
064                return cluster(sim);
065        }
066        
067        @Override
068        public SpectralIndexedClusters cluster(SparseMatrix data) {
069                // Get the laplacian, solve the eigen problem and choose the best 
070                // Use the lowest eigen valued cols as the features, each row is a data item in the reduced feature space
071                Eigenvalues eig = spectralCluster(data);
072                PreparedSpectralClustering prep = new PreparedSpectralClustering(conf);
073                return prep.cluster(eig);
074        }
075
076        
077
078        protected Eigenvalues spectralCluster(SparseMatrix data) {
079                // Compute the laplacian of the graph
080                final SparseMatrix laplacian = laplacian(data);
081                Eigenvalues eig = laplacianEigenVectors(laplacian);
082                
083                return eig;
084        }
085
086        protected Eigenvalues laplacianEigenVectors(final SparseMatrix laplacian) {
087                // Calculate the eigvectors
088                Eigenvalues eig = conf.eigenChooser.prepare(laplacian);
089                eig.run();
090                return eig;
091        }
092
093        protected SparseMatrix laplacian(SparseMatrix data) {
094                return conf.laplacian.laplacian(data);
095        }
096
097        @Override
098        public int[][] performClustering(SparseMatrix data) {
099                return this.cluster(data).clusters();
100        }
101        
102        @Override
103        public String toString() {
104                return String.format("%s: {Laplacian: %s, EigenChooser: %s, SpatialClusterer: %s}",simpleName(this),simpleName(conf.laplacian),simpleName(conf.eigenChooser),conf.internal);
105        }
106
107        private String simpleName(Object o) {
108                return o.getClass().getSimpleName();
109        }
110}