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Likelihood.java

/*
 *  Copyright 2006-2007 Columbia University.
 *
 *  This file is part of MEAPsoft.
 *
 *  MEAPsoft is free software; you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License version 2 as
 *  published by the Free Software Foundation.
 *
 *  MEAPsoft is distributed in the hope that it will be useful, but
 *  WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 *  General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with MEAPsoft; if not, write to the Free Software
 *  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 *  02110-1301 USA
 *
 *  See the file "COPYING" for the text of the license.
 */

package com.meapsoft.featextractors;

import java.util.Arrays;

import com.meapsoft.FeatChunk;
import com.meapsoft.FeatFile;

public class Likelihood extends MetaFeatureExtractor 
{       
        public void features(FeatFile featFile, boolean clearOriginalFeatures)
        {               
                int totalChunks = featFile.chunks.size();
                                
                FeatChunk testChunk = (FeatChunk)featFile.chunks.elementAt(0);
                double[] testChunkFeatures = testChunk.getFeatures();
                int numFeatures = testChunkFeatures.length;
                
                // Pass 1 - gather feature statistics
                double sumx[] = new double[numFeatures];
                double sumx2[] = new double[numFeatures];
                double meanx[] = new double[numFeatures];
                double stdx[] = new double[numFeatures];
                double mahaldist[] = new double[totalChunks];
                
                Arrays.fill(sumx, 0);
                Arrays.fill(sumx2, 0);
                Arrays.fill(meanx, 0);
                Arrays.fill(stdx, 0);
                Arrays.fill(mahaldist, 0);
                
                double N = 0.0;
                
                for(int chunk=0; chunk<totalChunks; chunk++) 
                {
                        FeatChunk curChunk = (FeatChunk)featFile.chunks.elementAt(chunk);
                        double[] features = curChunk.getFeatures();
                        
                        for(int feature = 0; feature < numFeatures; feature++)
                        {
                                sumx[feature] += features[feature];
                                sumx2[feature] += features[feature] * features[feature];
                        }
                        N = N + 1.0;
                }
                
                // figure mean and variance
                for(int feature = 0; feature < numFeatures; feature++)
                {
                        meanx[feature] = sumx[feature] / N;
                        stdx[feature]  = Math.sqrt(sumx2[feature]/N - meanx[feature]*meanx[feature]);
                }
                
                // Pass 2 - figure Mahalanobis distance (= unlikeliness score)
                // under diagonal covariance Gaussian for each frame
                // (i.e. Euclidean distance to mean in a variance-normalized space)
                
                for(int chunk=0; chunk<totalChunks; chunk++) 
                {
                        FeatChunk curChunk = (FeatChunk)featFile.chunks.elementAt(chunk);
                        double[] features = curChunk.getFeatures();
                
                        mahaldist[chunk] = 0;
                        
                        for(int feature = 0; feature < numFeatures; feature++)
                        {
                                double dist = (features[feature] - meanx[feature])/stdx[feature];
                                mahaldist[chunk] += dist*dist;
                        }
                        mahaldist[chunk] = Math.sqrt(mahaldist[chunk]/numFeatures);
                        
                        double value = mahaldist[chunk];
                        double[] feats = new double[1];
                        feats[0] = value;
                        
            if(clearOriginalFeatures)
                curChunk.clearFeatures();

                        curChunk.addFeature(feats);
                }
        } 
        
        public String description()
        {
                return "Returns the likelihood of each chunk. Lower numbers mean a segment is more common, " 
            + "higher numbers mean it is more distinct.";
        }
        
}

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