balancedwinnow.java

常用机器学习算法,java编写源代码,内含常用分类算法,包括说明文档

/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
   This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
   http://www.cs.umass.edu/~mccallum/mallet
   This software is provided under the terms of the Common Public License,
   version 1.0, as published by http://www.opensource.org.  For further
   information, see the file `LICENSE' included with this distribution. */


package edu.umass.cs.mallet.base.classify;

import edu.umass.cs.mallet.base.classify.Classifier;
import edu.umass.cs.mallet.base.types.Instance;
import edu.umass.cs.mallet.base.types.Alphabet;
import edu.umass.cs.mallet.base.types.FeatureVector;
import edu.umass.cs.mallet.base.types.Labeling;
import edu.umass.cs.mallet.base.types.LabelVector;
import edu.umass.cs.mallet.base.types.MatrixOps;
import edu.umass.cs.mallet.base.pipe.Pipe;

/** 
 * Classification methods of BalancedWinnow2 algorithm.
 * @see BalancedWinnowTrainer
 * @author Gary Huang <a href="mailto:[e-mail]">[e-mail]</a>
 */
public class BalancedWinnow extends Classifier
{
    double [][] m_weights;
	
    /**
     * Passes along data pipe and weights from 
     * {@link #BalancedWinnowTrainer BalancedWinnowTrainer}
     * @param dataPipe needed for dictionary, labels, feature vectors, etc
     * @param weights weights calculated during training phase
     */
    public BalancedWinnow (Pipe dataPipe, double [][] weights)
    {
        super (dataPipe);
        m_weights = new double[weights.length][weights[0].length];
        for (int i = 0; i < weights.length; i++)
	  for (int j = 0; j < weights[0].length; j++)
	      m_weights[i][j] = weights[i][j];
    }
	
    /**
     * Classifies an instance using BalancedWinnow's weights
     *
     * <p>Returns a Classification containing the normalized
     * dot products between class weight vectors and the instance 
     * feature vector.
     *
     * <p>One can obtain the confidence of the classification by 
     * calculating weight(j')/weight(j), where j' is the 
     * highest weight prediction and j is the 2nd-highest.
     * Another possibility is to calculate 
     * <br><center>e^{dot(w_j', x} / sum_j[e^{dot(w_j, x)}]</center></br>
     */
    public Classification classify (Instance instance)
    {
        int numClasses = getLabelAlphabet().size();
        double[] scores = new double[numClasses];
        FeatureVector fv = (FeatureVector) instance.getData (this.instancePipe);
        // Make sure the feature vector's feature dictionary matches
        // what we are expecting from our data pipe (and thus our notion
        // of feature probabilities.
        assert (fv.getAlphabet () == this.instancePipe.getDataAlphabet());
        int fvisize = fv.numLocations();
        // Take dot products
        double sum = 0;
        for (int fvi = 0; fvi < fvisize; fvi++) {
	  int fi = fv.indexAtLocation (fvi);
	  for (int ci = 0; ci < numClasses; ci++) {
	      scores[ci] += m_weights[ci][fi];
	      sum += m_weights[ci][fi];
	  }
        }
        MatrixOps.timesEquals(scores, 1.0 / sum);
        // Create and return a Classification object
        return new Classification (instance, this,
			     new LabelVector (getLabelAlphabet(),
					  scores));
    }
}