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
032
033import org.apache.log4j.Logger;
034import org.openimaj.feature.DoubleFV;
035import org.openimaj.feature.DoubleFVComparison;
036import org.openimaj.feature.FeatureExtractor;
037import org.openimaj.ml.clustering.SimilarityClusterer;
038
039import ch.akuhn.matrix.SparseMatrix;
040
041/**
042 * Wraps the functionality of a {@link SimilarityClusterer} around a dataset
043 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
044 *
045 * @param <T>
046 */
047public class NormalisedSimilarityDoubleClustererWrapper<T> extends DoubleFVSimilarityFunction<T> {
048        
049        private double eps;
050
051
052
053        /**
054         * 
055         * @param extractor
056         * @param eps
057         */
058        public NormalisedSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV,T> extractor, double eps) {
059                super(extractor);
060                this.eps = eps;
061        }
062
063        Logger logger = Logger.getLogger(NormalisedSimilarityDoubleClustererWrapper.class);
064
065
066        
067        protected SparseMatrix similarity() {
068                final SparseMatrix mat = new SparseMatrix(feats.length,feats.length);
069                final DoubleFVComparison dist = DoubleFVComparison.EUCLIDEAN;
070                double maxD = 0;
071                for (int i = 0; i < feats.length; i++) {
072                        for (int j = i; j < feats.length; j++) {
073                                double d = dist.compare(feats[i], feats[j]);
074                                if(d>eps ) 
075                                        d = Double.NaN;
076                                else{
077                                        maxD = Math.max(d, maxD);
078                                }
079                                mat.put(i, j, d);
080                                mat.put(j, i, d);
081                        }
082                }
083                SparseMatrix mat_norm = new SparseMatrix(feats.length,feats.length);
084                for (int i = 0; i < feats.length; i++) {
085                        for (int j = i; j < feats.length; j++) {
086                                double d = mat.get(i, j);
087                                if(Double.isNaN(d)){
088                                        continue;
089                                }
090                                else{
091                                        d/=maxD;
092                                }
093                                mat_norm.put(i, j, 1-d);
094                                mat_norm.put(j, i, 1-d);
095                        }
096                }
097                return mat_norm;
098        }
099        
100        
101
102}