gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of cognitive-foundry Show documentation
Show all versions of cognitive-foundry Show documentation
A single jar with all the Cognitive Foundry components.
/*
* File: BaumWelchAlgorithm.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Jan 19, 2010, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.learning.algorithm.hmm;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.collection.MultiCollection;
import gov.sandia.cognition.learning.algorithm.BatchLearner;
import gov.sandia.cognition.learning.data.DatasetUtil;
import gov.sandia.cognition.math.RingAccumulator;
import gov.sandia.cognition.math.matrix.Matrix;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.statistics.ComputableDistribution;
import gov.sandia.cognition.statistics.ProbabilityFunction;
import gov.sandia.cognition.util.DefaultPair;
import gov.sandia.cognition.util.DefaultWeightedValue;
import gov.sandia.cognition.util.Pair;
import gov.sandia.cognition.util.WeightedValue;
import java.util.ArrayList;
import java.util.Collection;
/**
* Implements the Baum-Welch algorithm, also known as the "forward-backward
* algorithm", the expectation-maximization algorithm, etc for
* Hidden Markov Models (HMMs). This is the
* standard learning algorithm for HMMs. This implementation allows for
* multiple sequences using the MultiCollection interface.
* @param Type of Observations handled by the HMM.
*/
@PublicationReference(
author="Lawrence R. Rabiner",
title="A tutorial on hidden Markov models and selected applications in speech recognition",
type=PublicationType.Journal,
year=1989,
publication="Proceedings of the IEEE",
pages={257,286},
url="http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf",
notes="Rabiner's transition matrix is transposed from mine."
)
public class BaumWelchAlgorithm
extends AbstractBaumWelchAlgorithm>
{
/**
* Creates a new instance of BaumWelchAlgorithm
*/
public BaumWelchAlgorithm()
{
this( null, null, DEFAULT_REESTIMATE_INITIAL_PROBABILITY );
}
/**
* Creates a new instance of BaumWelchAlgorithm
* @param initialGuess
* Initial guess for the iterations.
* @param distributionLearner
* Learner for the Probability Functions of the HMM.
* @param reestimateInitialProbabilities
* Flag to re-estimate the initial probability Vector.
*/
public BaumWelchAlgorithm(
HiddenMarkovModel initialGuess,
BatchLearner>,? extends ComputableDistribution> distributionLearner,
boolean reestimateInitialProbabilities )
{
super( initialGuess, distributionLearner, reestimateInitialProbabilities );
}
@Override
public BaumWelchAlgorithm clone()
{
return (BaumWelchAlgorithm) super.clone();
}
/**
* Weighted data for the re-estimation procedure.
*/
private transient ArrayList> weightedData;
/**
* Log likelihoods of the various sequences, with the corresponding weights.
*/
private transient ArrayList> sequenceLogLikelihoods;
/**
* Total number of observations in the sequences of data.
*/
private transient int totalNum;
/**
* The multi-collection of sequences
*/
protected transient MultiCollection multicollection;
/**
* The list of all gammas from each sequence
*/
protected transient ArrayList sequenceGammas;
/**
* Allows the algorithm to learn against multiple sequences of data.
* @param data
* Multiple sequences of data against which to train.
* @return
* HMM resulting from the locally maximum likelihood estimate of the
* Baum-Welch algorithm.
*/
public HiddenMarkovModel learn(
MultiCollection data)
{
return super.learn(data);
}
@SuppressWarnings("unchecked")
@Override
protected boolean initializeAlgorithm()
{
this.multicollection = DatasetUtil.asMultiCollection(this.data);
// Let's make sure nobody uses the original data!
this.data = null;
final int numSequences = this.multicollection.getSubCollectionsCount();
this.sequenceLogLikelihoods =
new ArrayList>( numSequences );
this.totalNum = 0;
for( Collection sequence : this.multicollection.subCollections() )
{
this.sequenceLogLikelihoods.add( new DefaultWeightedValue() );
this.totalNum += sequence.size();
}
this.weightedData = new ArrayList>(
this.totalNum );
this.sequenceGammas = new ArrayList( this.totalNum );
for( Collection sequence : this.multicollection.subCollections() )
{
for( ObservationType observation : sequence )
{
this.weightedData.add( new DefaultWeightedValue( observation ) );
this.sequenceGammas.add( null );
}
}
this.result = this.getInitialGuess().clone();
this.lastLogLikelihood = this.updateSequenceLogLikelihoods( this.result );
return (this.result != null);
}
@Override
protected boolean step()
{
final int numSequences = this.multicollection.getSubCollectionsCount();
final boolean updatePi = this.getReestimateInitialProbabilities();
Pair>, ArrayList> pair =
this.computeSequenceParameters();
ArrayList> allGammas = pair.getFirst();
ArrayList sequenceTransitionMatrices = pair.getSecond();
ArrayList firstGammas = (updatePi)
? new ArrayList( numSequences ) : null;
int index = 0;
for( int i = 0; i < numSequences; i++ )
{
ArrayList gammas = allGammas.get(i);
if( updatePi )
{
firstGammas.add( gammas.get(0) );
}
final int Ni = gammas.size();
for( int n = 0; n < Ni; n++ )
{
this.sequenceGammas.set(index, gammas.get(n));
index++;
}
}
Vector pi = this.result.getInitialProbability();
if( this.getReestimateInitialProbabilities() )
{
pi = this.updateInitialProbabilities(firstGammas);
}
Matrix A = this.updateTransitionMatrix(sequenceTransitionMatrices);
ArrayList> fs =
this.updateProbabilityFunctions(this.sequenceGammas);
// See how well we're doing...
boolean gettingBetter;
// If somebody asks for a single iteration, then just assume they want
// the re-estimated parameter
if( this.getMaxIterations() <= 1 )
{
this.result.emissionFunctions = fs;
this.result.initialProbability = pi;
this.result.transitionProbability = A;
gettingBetter = true;
}
// If somebody wants multiple steps, then check and see if our
// current candidate is actually better. This can happen due to
// numerical round-off or asymptotic behavior.
else
{
HiddenMarkovModel candidate = this.result.clone();
candidate.emissionFunctions = fs;
candidate.initialProbability = pi;
candidate.transitionProbability = A;
double logLikelihood = this.updateSequenceLogLikelihoods( candidate );
gettingBetter = (logLikelihood > this.lastLogLikelihood) ||
(this.getIteration() <= 1);
if( gettingBetter )
{
this.result = candidate;
this.lastLogLikelihood = logLikelihood;
}
}
return gettingBetter;
}
@Override
protected void cleanupAlgorithm()
{
this.multicollection = null;
this.weightedData = null;
this.sequenceLogLikelihoods = null;
this.totalNum = 0;
}
/**
* Computes the gammas and A matrices for each sequence.
* @return
* Gammas and A matrices for each sequence
*/
protected Pair>,ArrayList> computeSequenceParameters()
{
final int numSequences = this.multicollection.getSubCollectionsCount();
ArrayList> allGammas =
new ArrayList>( numSequences );
ArrayList sequenceTransitionMatrices =
new ArrayList( numSequences );
final boolean normalize = true;
int k = 0;
for( Collection sequence : this.multicollection.subCollections() )
{
double sequenceWeight = this.sequenceLogLikelihoods.get(k).getWeight();
ArrayList b =
this.result.computeObservationLikelihoods( sequence );
ArrayList> alphas =
this.result.computeForwardProbabilities( b, normalize );
ArrayList> betas =
this.result.computeBackwardProbabilities( b, alphas );
ArrayList gammas =
this.result.computeStateObservationLikelihood(alphas, betas, sequenceWeight);
allGammas.add( gammas );
Matrix A = this.result.computeTransitions( alphas, betas, b );
if( sequenceWeight != 1.0 )
{
A.scaleEquals(sequenceWeight);
}
sequenceTransitionMatrices.add( A );
k++;
}
return DefaultPair.create( allGammas, sequenceTransitionMatrices );
}
/**
* Updates the probability function from the concatenated gammas from
* all sequences
* @param sequenceGammas
* Concatenated gammas from all sequences
* @return
* Maximum Likelihood probability functions
*/
protected ArrayList> updateProbabilityFunctions(
ArrayList sequenceGammas )
{
final int numStates = this.result.getNumStates();
ArrayList> fs =
new ArrayList>( numStates );
for( int i = 0; i < numStates; i++ )
{
int index = 0;
for( int n = 0; n < sequenceGammas.size(); n++ )
{
final double g = sequenceGammas.get(n).getElement(i);
this.weightedData.get(index).setWeight(g);
index++;
}
ProbabilityFunction f =
this.distributionLearner.learn( this.weightedData ).getProbabilityFunction();
fs.add( f );
}
return fs;
}
/**
* Computes an updated transition matrix from the scaled estimates
* @param sequenceTransitionMatrices
* Scaled estimates from each sequence
* @return
* Overall Maximum Likelihood estimate of the transition matrix
*/
protected Matrix updateTransitionMatrix(
ArrayList sequenceTransitionMatrices )
{
RingAccumulator As =
new RingAccumulator( sequenceTransitionMatrices );
Matrix A = As.getSum();
this.result.normalizeTransitionMatrix(A);
return A;
}
/**
* Updates the initial probabilities from sequenceGammas
* @param firstGammas
* The first gamma of the each sequence
* @return
* Updated initial probability Vector for the HMM.
*/
protected Vector updateInitialProbabilities(
ArrayList firstGammas )
{
RingAccumulator pi = new RingAccumulator();
for( int k = 0; k < firstGammas.size(); k++ )
{
pi.accumulate( firstGammas.get(k) );
}
Vector pisum = pi.getSum();
pisum.scaleEquals( 1.0 / pisum.norm1() );
return pisum;
}
/**
* Updates the internal sequence likelihoods for the given HMM
* @param hmm
* Hidden Markov model to consider
* @return
* log likelihood of the observations sequences given the HMM.
*/
protected double updateSequenceLogLikelihoods(
HiddenMarkovModel hmm )
{
int k = 0;
double maxLogLikelihood = Double.NEGATIVE_INFINITY;
double totalLogLikelihood = 0.0;
for( Collection sequence : this.multicollection.subCollections() )
{
double logLikelihood = hmm.computeObservationLogLikelihood(sequence);
if( maxLogLikelihood < logLikelihood )
{
maxLogLikelihood = logLikelihood;
}
this.sequenceLogLikelihoods.get(k).setValue(logLikelihood);
totalLogLikelihood += logLikelihood;
k++;
}
// Subtract off the maximum log-likleihood to at least make sure the
// weights go from 1.0 to almost zero.
final int numSequences = this.multicollection.getSubCollectionsCount();
for( k = 0; k < numSequences; k++ )
{
// The weight is the INVERSE of the sequence Probability!!
DefaultWeightedValue wv = this.sequenceLogLikelihoods.get(k);
final double logLikelihood = wv.getValue();
final double weight = 1.0/Math.exp(logLikelihood - maxLogLikelihood);
wv.setWeight(weight);
}
return totalLogLikelihood;
}
}