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