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;
031import java.io.File;
032import java.io.IOException;
033import java.util.ArrayList;
034import java.util.Arrays;
035import java.util.Collection;
036
037import org.apache.log4j.Logger;
038import org.openimaj.ml.clustering.SimilarityClusterer;
039
040import com.jmatio.io.MatFileWriter;
041import com.jmatio.types.MLArray;
042import com.jmatio.types.MLDouble;
043import com.jmatio.types.MLInt32;
044
045import ch.akuhn.matrix.DenseMatrix;
046import ch.akuhn.matrix.SparseMatrix;
047import ch.akuhn.matrix.eigenvalues.Eigenvalues;
048
049/**
050 * Built from a mixture of this tutorial:
051 *      - http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5B0%5D.pdf
052 * And this implementation:
053 *  - https://github.com/peterklipfel/AutoponicsVision/blob/master/SpectralClustering.java
054 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
055 *
056 */
057public class DoubleSpectralClustering implements SimilarityClusterer<SpectralIndexedClusters>{
058        final static Logger logger = Logger.getLogger(DoubleSpectralClustering.class);
059        protected SpectralClusteringConf<double[]> conf;
060
061        /**
062         * @param conf
063         * cluster the eigen vectors
064         */
065        public DoubleSpectralClustering(SpectralClusteringConf<double[]> conf) {
066                this.conf = conf;
067        }
068        
069        protected DoubleSpectralClustering() {
070        }
071        
072        @Override
073        public SpectralIndexedClusters clusterSimilarity(SparseMatrix sim) {
074                return cluster(sim);
075        }
076        
077        @Override
078        public SpectralIndexedClusters cluster(SparseMatrix data) {
079                // Get the laplacian, solve the eigen problem and choose the best 
080                // Use the lowest eigen valued cols as the features, each row is a data item in the reduced feature space
081                Eigenvalues eig = spectralCluster(data);
082                PreparedSpectralClustering prep = new PreparedSpectralClustering(conf);
083                return prep.cluster(eig);
084        }
085
086        
087
088        protected Eigenvalues spectralCluster(SparseMatrix data) {
089                // Compute the laplacian of the graph
090                final SparseMatrix laplacian = laplacian(data);
091                Eigenvalues eig = laplacianEigenVectors(laplacian);
092                
093                return eig;
094        }
095
096        protected Eigenvalues laplacianEigenVectors(final SparseMatrix laplacian) {
097                // Calculate the eigvectors
098                Eigenvalues eig = conf.eigenChooser.prepare(laplacian);
099                eig.run();
100                return eig;
101        }
102
103        protected SparseMatrix laplacian(SparseMatrix data) {
104                return conf.laplacian.laplacian(data);
105        }
106
107        @Override
108        public int[][] performClustering(SparseMatrix data) {
109                return this.cluster(data).clusters();
110        }
111        
112        @Override
113        public String toString() {
114                return String.format("%s: {Laplacian: %s, EigenChooser: %s, SpatialClusterer: %s}",simpleName(this),simpleName(conf.laplacian),simpleName(conf.eigenChooser),conf.internal);
115        }
116
117        private String simpleName(Object o) {
118                return o.getClass().getSimpleName();
119        }
120}