gov.sandia.cognition.learning.algorithm.hmm

Class ParallelBaumWelchAlgorithm<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>

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

Type Parameters:

ObservationType - Type of Observations handled by the HMM.

All Implemented Interfaces:

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

@PublicationReference(author="William Turin",
                      title="Unidirectional and Parallel Baum\u2013Welch Algorithms",
                      type=Journal,
                      publication="IEEE Transactions on Speech and Audio Processing",
                      year=1998,
                      url="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00725318")
public class ParallelBaumWelchAlgorithm<ObservationType>
extends BaumWelchAlgorithm<ObservationType>
implements ParallelAlgorithm

A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType> Re-estimates the PDF from the gammas.

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.ArrayList<ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType>>	distributionEstimatorTasks Tasks for re-estimating the PDFs.

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

multicollection, sequenceGammas

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
ParallelBaumWelchAlgorithm() Default constructor
ParallelBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of ParallelBaumWelchAlgorithm

Method Summary

Modifier and Type	Method and Description
protected java.util.ArrayList<ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType>>	createDistributionEstimatorTasks() Creates the DistributionEstimatorTask
int	getNumThreads() Gets the number of threads in the thread pool.
java.util.concurrent.ThreadPoolExecutor	getThreadPool() Gets the thread pool for the algorithm to use.
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
void	setThreadPool(java.util.concurrent.ThreadPoolExecutor threadPool) Sets the thread pool for the algorithm to use.
protected java.util.ArrayList<ProbabilityFunction<ObservationType>>	updateProbabilityFunctions(java.util.ArrayList<Vector> sequenceGammas) Updates the probability function from the concatenated gammas from all sequences

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

cleanupAlgorithm, clone, computeSequenceParameters, learn, step, updateInitialProbabilities, updateSequenceLogLikelihoods, updateTransitionMatrix

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.util.CloneableSerializable

clone

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

distributionEstimatorTasks

protected transient java.util.ArrayList<ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType>> distributionEstimatorTasks

Tasks for re-estimating the PDFs.

Constructor Detail

ParallelBaumWelchAlgorithm

public ParallelBaumWelchAlgorithm()

Default constructor

ParallelBaumWelchAlgorithm

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

Creates a new instance of ParallelBaumWelchAlgorithm

Parameters:

initialGuess - Initial guess for the iterations.

distributionLearner - Learner for the Distribution Functions of the HMM.

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

Method Detail

getThreadPool

public java.util.concurrent.ThreadPoolExecutor getThreadPool()

Description copied from interface: ParallelAlgorithm

Gets the thread pool for the algorithm to use.

Specified by:

getThreadPool in interface ParallelAlgorithm

Returns:

Thread pool used for parallelization.

setThreadPool

public void setThreadPool(java.util.concurrent.ThreadPoolExecutor threadPool)

Description copied from interface: ParallelAlgorithm

Sets the thread pool for the algorithm to use.

Specified by:

setThreadPool in interface ParallelAlgorithm

Parameters:

threadPool - Thread pool used for parallelization.

getNumThreads

public int getNumThreads()

Description copied from interface: ParallelAlgorithm

Gets the number of threads in the thread pool.

Specified by:

getNumThreads in interface ParallelAlgorithm

Returns:

Number of threads in the thread pool

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.

Overrides:

initializeAlgorithm in class BaumWelchAlgorithm<ObservationType>

Returns:

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

updateProbabilityFunctions

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

Description copied from class: BaumWelchAlgorithm

Updates the probability function from the concatenated gammas from all sequences

Overrides:

updateProbabilityFunctions in class BaumWelchAlgorithm<ObservationType>

Parameters:

sequenceGammas - Concatenated gammas from all sequences

Returns:

Maximum Likelihood probability functions

createDistributionEstimatorTasks

protected java.util.ArrayList<ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType>> createDistributionEstimatorTasks()

Creates the DistributionEstimatorTask

Returns:

DistributionEstimatorTask.