/* 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. */
/**
@author Aron Culotta <a href="mailto:culotta@cs.umass.edu">culotta@cs.umass.edu</a>
*/
package cc.mallet.fst.confidence;
import java.util.logging.*;
import java.util.*;
import cc.mallet.fst.*;
import cc.mallet.pipe.iterator.*;
import cc.mallet.types.*;
import cc.mallet.util.MalletLogger;
/**
Estimates the confidence of an entire sequence by the ration of the
probabilities of the first and second best Viterbi paths.
*/
public class ViterbiRatioConfidenceEstimator extends TransducerSequenceConfidenceEstimator
{
private static Logger logger = MalletLogger.getLogger(
SegmentProductConfidenceEstimator.class.getName());
public ViterbiRatioConfidenceEstimator (Transducer model) {
super(model);
}
/**
Calculates the confidence in the tagging of an {@link Instance}.
*/
public double estimateConfidenceFor (Instance instance,
Object[] startTags,
Object[] inTags) {
SumLatticeDefault lattice = new SumLatticeDefault (model, (Sequence)instance.getData());
//ViterbiPathNBest bestViterbis = new ViterbiPathNBest (model, (Sequence)instance.getData(), 2);
//double[] costs = bestViterbis.costNBest();
MaxLatticeDefault vlat = new MaxLatticeDefault (model, (Sequence)instance.getData(), null, 2);
List<SequencePairAlignment<Object,Object>> alignments = vlat.bestOutputAlignments(2);
double cost1 = alignments.get(0).getWeight();
double cost2 = alignments.get(1).getWeight();
double latticeCost = lattice.getTotalWeight();
return (Math.exp (-cost1 + latticeCost) / Math.exp(-cost2 + latticeCost));
}
}