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.commons.math.stat.descriptive.moment.Variance;
034import org.apache.log4j.Logger;
035import org.openimaj.feature.DoubleFV;
036import org.openimaj.feature.FeatureExtractor;
037
038import ch.akuhn.matrix.DenseMatrix;
039import ch.akuhn.matrix.Matrix;
040import ch.akuhn.matrix.SparseMatrix;
041
042/**
043 * Construct a similarity matrix using a Radial Basis Function 
044 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
045 *
046 * @param <T>
047 */
048public class RBFSimilarityDoubleClustererWrapper<T> extends DoubleFVSimilarityFunction<T> {
049        
050        private double[] var;
051        Logger logger = Logger.getLogger(RBFSimilarityDoubleClustererWrapper.class);
052
053        /**
054         * @param extractor
055         */
056        public RBFSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV,T> extractor) {
057                super(extractor);
058        }
059        
060        private void prepareVariance() {
061                this.var = new double[this.feats[0].length];
062                Matrix m = new DenseMatrix(feats);
063                double[] colArr = new double[this.feats.length];
064                Variance v = new Variance();
065                for (int i = 0; i < this.var.length; i++) {
066                        m.column(i).storeOn(colArr, 0);
067                        this.var[i] = v.evaluate(colArr);
068                }
069        }
070
071        @Override
072        protected SparseMatrix similarity() {
073                prepareVariance();
074                int N = feats.length;
075                SparseMatrix sim = new SparseMatrix(N,N);
076                for (int i = 0; i < N; i++) {
077                        double[] di = feats[i];
078                        sim.put(i,i,1);
079                        for (int j = i+1; j < N; j++) {
080                                double[] dj = feats[j];
081                                double expInner = 0;
082                                // -1*sum((data(i,:)-data(j,:)).^2./(2*my_var))
083                                for (int k = 0; k < dj.length; k++) {
084                                        double kv = di[k] - dj[k];
085                                        expInner += (kv * kv) / (2 * this.var[k]);
086                                }
087                                
088                                double v = Math.exp(-1 * expInner);
089                                sim.put(i, j, v);
090                                sim.put(j, i, v);
091                        }
092                }
093                return sim;
094        }
095        
096        
097
098}