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.experiment.gmm.retrieval;
031
032import org.openimaj.feature.FeatureVector;
033import org.openimaj.feature.local.LocalFeature;
034import org.openimaj.feature.local.list.LocalFeatureList;
035import org.openimaj.math.statistics.distribution.MixtureOfGaussians;
036import org.openimaj.ml.gmm.GaussianMixtureModelEM;
037import org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType;
038import org.openimaj.util.array.ArrayUtils;
039import org.openimaj.util.function.Function;
040
041import Jama.Matrix;
042
043/**
044 * This function turns a list of features to a gaussian mixture model
045 * 
046 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
047 */
048public class GMMFromFeatures implements Function<
049                        LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>>, 
050                        MixtureOfGaussians
051                >
052{
053        
054        /**
055         * default number of guassians to train agains
056         */
057        public static final int DEFAULT_COMPONENTS = 10;
058        /**
059         * default covariance type
060         */
061        public static final CovarianceType DEFAULT_COVARIANCE = GaussianMixtureModelEM.CovarianceType.Spherical;
062        
063        private GaussianMixtureModelEM gmm;
064        /**
065         * Defaults to {@link #DEFAULT_COMPONENTS} and 
066         */
067        public GMMFromFeatures() {
068                this.gmm = new GaussianMixtureModelEM(DEFAULT_COMPONENTS, DEFAULT_COVARIANCE);
069        }
070        
071        /**
072         * @param type
073         */
074        public GMMFromFeatures(CovarianceType type) {
075                this.gmm = new GaussianMixtureModelEM(DEFAULT_COMPONENTS, type);
076        }
077        
078        /**
079         * @param nComps
080         */
081        public GMMFromFeatures(int nComps) {
082                this.gmm = new GaussianMixtureModelEM(nComps, DEFAULT_COVARIANCE);
083        }
084        
085        /**
086         * @param nComps
087         * @param type
088         */
089        public GMMFromFeatures(int nComps,CovarianceType type) {
090                this.gmm = new GaussianMixtureModelEM(nComps, type);
091        }
092        
093        @Override
094        public MixtureOfGaussians apply(LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> features) {
095                System.out.println("Creating double array...");
096                double[][] doubleFeatures = new double[features.size()][];
097                int i = 0;
098                for (LocalFeature<?,?> localFeature : features) {                       
099                        doubleFeatures[i] = ArrayUtils.divide(localFeature.getFeatureVector().asDoubleVector(), 128);
100                        i++;
101                }
102                System.out.println(String.format("Launching EM with double array: %d x %d",doubleFeatures.length,doubleFeatures[0].length));
103                return this.gmm.estimate(new Matrix(doubleFeatures));
104        }
105
106
107
108}