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.FeatureExtractor;
033import org.openimaj.feature.FeatureVector;
034import org.openimaj.feature.local.LocalFeature;
035import org.openimaj.feature.local.list.LocalFeatureList;
036import org.openimaj.math.statistics.distribution.MixtureOfGaussians;
037import org.openimaj.ml.gmm.GaussianMixtureModelEM;
038import org.openimaj.ml.gmm.GaussianMixtureModelEM.CovarianceType;
039import org.openimaj.util.array.ArrayUtils;
040import org.openimaj.util.function.Function;
041
042import Jama.Matrix;
043
044/**
045 * This function turns a list of features to a gaussian mixture model
046 * 
047 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
048 */
049public class GMMFromFeatures implements Function<
050                        LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>>, 
051                        MixtureOfGaussians
052                >
053{
054        
055        /**
056         * default number of guassians to train agains
057         */
058        public static final int DEFAULT_COMPONENTS = 10;
059        /**
060         * default covariance type
061         */
062        public static final CovarianceType DEFAULT_COVARIANCE = GaussianMixtureModelEM.CovarianceType.Spherical;
063        
064        private GaussianMixtureModelEM gmm;
065        /**
066         * Defaults to {@link #DEFAULT_COMPONENTS} and 
067         */
068        public GMMFromFeatures() {
069                this.gmm = new GaussianMixtureModelEM(DEFAULT_COMPONENTS, DEFAULT_COVARIANCE);
070        }
071        
072        /**
073         * @param type
074         */
075        public GMMFromFeatures(CovarianceType type) {
076                this.gmm = new GaussianMixtureModelEM(DEFAULT_COMPONENTS, type);
077        }
078        
079        /**
080         * @param nComps
081         */
082        public GMMFromFeatures(int nComps) {
083                this.gmm = new GaussianMixtureModelEM(nComps, DEFAULT_COVARIANCE);
084        }
085        
086        /**
087         * @param nComps
088         * @param type
089         */
090        public GMMFromFeatures(int nComps,CovarianceType type) {
091                this.gmm = new GaussianMixtureModelEM(nComps, type);
092        }
093        
094        @Override
095        public MixtureOfGaussians apply(LocalFeatureList<? extends LocalFeature<?,? extends FeatureVector>> features) {
096                System.out.println("Creating double array...");
097                double[][] doubleFeatures = new double[features.size()][];
098                int i = 0;
099                for (LocalFeature<?,?> localFeature : features) {                 
100                        doubleFeatures[i] = ArrayUtils.divide(localFeature.getFeatureVector().asDoubleVector(), 128);
101                        i++;
102                }
103                System.out.println(String.format("Launching EM with double array: %d x %d",doubleFeatures.length,doubleFeatures[0].length));
104                return this.gmm.estimate(new Matrix(doubleFeatures));
105        }
106
107
108
109}