gov.sandia.cognition.learning.algorithm.hmm

Class BaumWelchAlgorithm<ObservationType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<DataType,HiddenMarkovModel<ObservationType>>

gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm<ObservationType,java.util.Collection<? extends ObservationType>>

gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm<ObservationType>

Type Parameters:

ObservationType - Type of Observations handled by the HMM.

All Implemented Interfaces:

AnytimeAlgorithm<HiddenMarkovModel<ObservationType>>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends ObservationType>,HiddenMarkovModel<ObservationType>>, BatchLearner<java.util.Collection<? extends ObservationType>,HiddenMarkovModel<ObservationType>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

ParallelBaumWelchAlgorithm

@PublicationReference(author="Lawrence R. Rabiner",
                      title="A tutorial on hidden Markov models and selected applications in speech recognition",
                      type=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<ObservationType>
extends AbstractBaumWelchAlgorithm<ObservationType,java.util.Collection<? extends ObservationType>>

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.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected MultiCollection<? extends ObservationType>	multicollection The multi-collection of sequences
protected java.util.ArrayList<Vector>	sequenceGammas The list of all gammas from each sequence

Fields inherited from class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm

DEFAULT_MAX_ITERATIONS, DEFAULT_REESTIMATE_INITIAL_PROBABILITY, distributionLearner, initialGuess, lastLogLikelihood, PERFORMANCE_NAME, reestimateInitialProbabilities, result

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
BaumWelchAlgorithm() Creates a new instance of BaumWelchAlgorithm
BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of BaumWelchAlgorithm

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
BaumWelchAlgorithm<ObservationType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
protected Pair<java.util.ArrayList<java.util.ArrayList<Vector>>,java.util.ArrayList<Matrix>>	computeSequenceParameters() Computes the gammas and A matrices for each sequence.
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
HiddenMarkovModel<ObservationType>	learn(MultiCollection<ObservationType> data) Allows the algorithm to learn against multiple sequences of data.
protected boolean	step() Called to take a single step of the learning algorithm.
protected Vector	updateInitialProbabilities(java.util.ArrayList<Vector> firstGammas) Updates the initial probabilities from sequenceGammas
protected java.util.ArrayList<ProbabilityFunction<ObservationType>>	updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas) Updates the probability function from the concatenated gammas from all sequences
protected double	updateSequenceLogLikelihoods(HiddenMarkovModel<ObservationType> hmm) Updates the internal sequence likelihoods for the given HMM
protected Matrix	updateTransitionMatrix(java.util.ArrayList<Matrix> sequenceTransitionMatrices) Computes an updated transition matrix from the scaled estimates

Methods inherited from class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm

getDistributionLearner, getInitialGuess, getLastLogLikelihood, getPerformance, getReestimateInitialProbabilities, getResult, setDistributionLearner, setInitialGuess, setReestimateInitialProbabilities

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid

Field Detail

multicollection

protected transient MultiCollection<? extends ObservationType> multicollection

The multi-collection of sequences

sequenceGammas

protected transient java.util.ArrayList<Vector> sequenceGammas

The list of all gammas from each sequence

Constructor Detail

BaumWelchAlgorithm

public BaumWelchAlgorithm()

Creates a new instance of BaumWelchAlgorithm

BaumWelchAlgorithm

public BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess,
                          BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner,
                          boolean reestimateInitialProbabilities)

Creates a new instance of BaumWelchAlgorithm

Parameters:

initialGuess - Initial guess for the iterations.

distributionLearner - Learner for the Probability Functions of the HMM.

reestimateInitialProbabilities - Flag to re-estimate the initial probability Vector.

Method Detail

clone

public BaumWelchAlgorithm<ObservationType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractBaumWelchAlgorithm<ObservationType,java.util.Collection<? extends ObservationType>>

Returns:

A clone of this object.

learn

public HiddenMarkovModel<ObservationType> learn(MultiCollection<ObservationType> data)

Allows the algorithm to learn against multiple sequences of data.

Parameters:

data - Multiple sequences of data against which to train.

Returns:

HMM resulting from the locally maximum likelihood estimate of the Baum-Welch algorithm.

initializeAlgorithm

protected boolean initializeAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.

Specified by:

initializeAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends ObservationType>,HiddenMarkovModel<ObservationType>>

Returns:

True if the learning algorithm can be run and false if it cannot.

step

protected boolean step()

Description copied from class: AbstractAnytimeBatchLearner

Called to take a single step of the learning algorithm.

Specified by:

step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends ObservationType>,HiddenMarkovModel<ObservationType>>

Returns:

True if another step can be taken and false it the algorithm should halt.

cleanupAlgorithm

protected void cleanupAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to clean up the learning algorithm's state after learning has finished.

Specified by:

cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends ObservationType>,HiddenMarkovModel<ObservationType>>

computeSequenceParameters

protected Pair<java.util.ArrayList<java.util.ArrayList<Vector>>,java.util.ArrayList<Matrix>> computeSequenceParameters()

Computes the gammas and A matrices for each sequence.

Returns:

Gammas and A matrices for each sequence

updateProbabilityFunctions

protected java.util.ArrayList<ProbabilityFunction<ObservationType>> updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas)

Updates the probability function from the concatenated gammas from all sequences

Parameters:

sequenceGammas - Concatenated gammas from all sequences

Returns:

Maximum Likelihood probability functions

updateTransitionMatrix

protected Matrix updateTransitionMatrix(java.util.ArrayList<Matrix> sequenceTransitionMatrices)

Computes an updated transition matrix from the scaled estimates

Parameters:

sequenceTransitionMatrices - Scaled estimates from each sequence

Returns:

Overall Maximum Likelihood estimate of the transition matrix

updateInitialProbabilities

protected Vector updateInitialProbabilities(java.util.ArrayList<Vector> firstGammas)

Updates the initial probabilities from sequenceGammas

Parameters:

firstGammas - The first gamma of the each sequence

Returns:

Updated initial probability Vector for the HMM.

updateSequenceLogLikelihoods

protected double updateSequenceLogLikelihoods(HiddenMarkovModel<ObservationType> hmm)

Updates the internal sequence likelihoods for the given HMM

Parameters:

hmm - Hidden Markov model to consider

Returns:

log likelihood of the observations sequences given the HMM.