gov.sandia.cognition.learning.algorithm.hmm
Class AbstractBaumWelchAlgorithm<ObservationType,DataType>

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,DataType>

Type Parameters:

ObservationType - Type of Observations handled by the HMM.

DataType - Type of data (Collection of ObservationType, for instance) sent to the learn method.

All Implemented Interfaces:

AnytimeAlgorithm<HiddenMarkovModel<ObservationType>>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<DataType,HiddenMarkovModel<ObservationType>>, BatchLearner<DataType,HiddenMarkovModel<ObservationType>>, CloneableSerializable, Serializable, Cloneable

Direct Known Subclasses:

BaumWelchAlgorithm

public abstract class AbstractBaumWelchAlgorithm<ObservationType,DataType>
extends AbstractAnytimeBatchLearner<DataType,HiddenMarkovModel<ObservationType>>
implements MeasurablePerformanceAlgorithm

Partial implementation of the Baum-Welch algorithm.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

static int	DEFAULT_MAX_ITERATIONS Default maximum number of iterations, 100.
static boolean	DEFAULT_REESTIMATE_INITIAL_PROBABILITY Default flag to re-estimate initial probabilities, true.
protected BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	distributionLearner Learner for the Distribution Functions of the HMM.
protected HiddenMarkovModel<ObservationType>	initialGuess Initial guess for the iterations.
protected double	lastLogLikelihood Last Log Likelihood of the iterations
static String	PERFORMANCE_NAME Name of the performance statistic, "Log Likelihood".
protected boolean	reestimateInitialProbabilities Flag to re-estimate the initial probability Vector.
protected HiddenMarkovModel<ObservationType>	result Result of the Baum-Welch Algorithm

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

AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities)
Creates a new instance of AbstractBaumWelchAlgorithm

Method Summary

AbstractBaumWelchAlgorithm<ObservationType,DataType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	getDistributionLearner() Getter for distributionLearner
HiddenMarkovModel<ObservationType>	getInitialGuess() Getter for initialGuess.
double	getLastLogLikelihood() Gets the log likelihood of the last completed step of the algorithm.
NamedValue<Double>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
boolean	getReestimateInitialProbabilities() Getter for reestimateInitialProbabilities
HiddenMarkovModel<ObservationType>	getResult() Gets the current result of the algorithm.
void	setDistributionLearner(BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner) Setter for distributionLearner
void	setInitialGuess(HiddenMarkovModel<ObservationType> initialGuess) Setter for initialGuess.
void	setReestimateInitialProbabilities(boolean reestimateInitialProbabilities) Setter for reestimateInitialProbabilities

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

cleanupAlgorithm, getData, getKeepGoing, initializeAlgorithm, learn, setData, setKeepGoing, step, 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

DEFAULT_MAX_ITERATIONS

public static final int DEFAULT_MAX_ITERATIONS

Default maximum number of iterations, 100.

See Also:

Constant Field Values

DEFAULT_REESTIMATE_INITIAL_PROBABILITY

public static final boolean DEFAULT_REESTIMATE_INITIAL_PROBABILITY

Default flag to re-estimate initial probabilities, true.

See Also:

Constant Field Values

PERFORMANCE_NAME

public static final String PERFORMANCE_NAME

Name of the performance statistic, "Log Likelihood".

See Also:

Constant Field Values

distributionLearner

protected BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner

Learner for the Distribution Functions of the HMM.

result

protected HiddenMarkovModel<ObservationType> result

Result of the Baum-Welch Algorithm

initialGuess

protected HiddenMarkovModel<ObservationType> initialGuess

Initial guess for the iterations.

lastLogLikelihood

protected double lastLogLikelihood

Last Log Likelihood of the iterations

reestimateInitialProbabilities

protected boolean reestimateInitialProbabilities

Flag to re-estimate the initial probability Vector.

Constructor Detail

AbstractBaumWelchAlgorithm

public AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess,
                                  BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner,
                                  boolean reestimateInitialProbabilities)

Creates a new instance of AbstractBaumWelchAlgorithm

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

clone

public AbstractBaumWelchAlgorithm<ObservationType,DataType> 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 AbstractAnytimeBatchLearner<DataType,HiddenMarkovModel<ObservationType>>

Returns:

A clone of this object.

getPerformance

public NamedValue<Double> getPerformance()

Description copied from interface: MeasurablePerformanceAlgorithm

Gets the name-value pair that describes the current performance of the algorithm. For most algorithms, this is the value that they are attempting to optimize.

Specified by:

getPerformance in interface MeasurablePerformanceAlgorithm

Returns:

The name-value pair that describes the current performance of the algorithm.

getResult

public HiddenMarkovModel<ObservationType> getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<HiddenMarkovModel<ObservationType>>

Returns:

Current result of the algorithm.

getInitialGuess

public HiddenMarkovModel<ObservationType> getInitialGuess()

Getter for initialGuess.

Returns:

Initial guess for the iterations.

setInitialGuess

public void setInitialGuess(HiddenMarkovModel<ObservationType> initialGuess)

Setter for initialGuess.

Parameters:

initialGuess - Initial guess for the iterations.

getReestimateInitialProbabilities

public boolean getReestimateInitialProbabilities()

Getter for reestimateInitialProbabilities

Returns:

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

setReestimateInitialProbabilities

public void setReestimateInitialProbabilities(boolean reestimateInitialProbabilities)

Setter for reestimateInitialProbabilities

Parameters:

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

getDistributionLearner

public BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> getDistributionLearner()

Getter for distributionLearner

Returns:

Learner for the Distribution Functions of the HMM.

setDistributionLearner

public void setDistributionLearner(BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner)

Setter for distributionLearner

Parameters:

distributionLearner - Learner for the Distribution Functions of the HMM.

getLastLogLikelihood

public double getLastLogLikelihood()

Gets the log likelihood of the last completed step of the algorithm.

Returns:

The last log likelihood.