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.math.statistics.distribution.metrics;
031
032import java.util.Random;
033
034import org.openimaj.math.statistics.distribution.MultivariateDistribution;
035import org.openimaj.util.array.ArrayUtils;
036import org.openimaj.util.comparator.DistanceComparator;
037
038/**
039 * By sampling a distribution and calculating the log liklihood 
040 * of those samples against another distribution, construct a distance metric.
041 * 
042 * This function uses {@link MultivariateDistribution#estimateLogProbability(double[][])}
043 * 
044 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
045 */
046public class SampledMultivariateDistanceComparator implements DistanceComparator<MultivariateDistribution>{
047        
048        
049        private static final int DEFAULT_SAMPLE = 1000;
050        private int samples;
051        private Random rng;
052
053        /**
054         * 
055         */
056        public SampledMultivariateDistanceComparator() {
057                this.samples = DEFAULT_SAMPLE;
058                this.rng = new Random();
059        }
060        
061        /**
062         * @param nsamples
063         */
064        public SampledMultivariateDistanceComparator(int nsamples) {
065                this();
066                this.samples = nsamples;
067        }
068        
069        /**
070         * @param seed 
071         * @param nsamples
072         */
073        public SampledMultivariateDistanceComparator(long seed, int nsamples) {
074                this.rng = new Random(seed);
075                this.samples = nsamples;
076        }
077        
078        @Override
079        public double compare(MultivariateDistribution o1, MultivariateDistribution o2) {
080                double[][] X = o1.sample(samples, rng);
081                double[] sampleP = o2.estimateLogProbability(X);
082                return ArrayUtils.sumValues(sampleP);
083        }
084
085        @Override
086        public boolean isDistance() {
087                return false;
088        }
089
090}