gov.sandia.cognition.statistics.bayesian

Class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

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<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>

gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

Type Parameters:

ObservationType - Type of observations handled by the MCMC algorithm.

ParameterType - Type of parameters to infer.

All Implemented Interfaces:

AnytimeAlgorithm<DataDistribution<ParameterType>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>, BatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>, BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>, MarkovChainMonteCarlo<ObservationType,ParameterType>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

DirichletProcessMixtureModel, MetropolisHastingsAlgorithm

public abstract class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>
extends AbstractAnytimeBatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>
implements MarkovChainMonteCarlo<ObservationType,ParameterType>

Partial abstract implementation of MarkovChainMonteCarlo.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected ParameterType	currentParameter The current parameters in the random walk.
static int	DEFAULT_NUM_SAMPLES Default number of sample/iterations, 1000.
protected ParameterType	previousParameter The previous parameter in the random walk.
protected java.util.Random	random Random number generator.

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
AbstractMarkovChainMonteCarlo() Creates a new instance of AbstractMarkovChainMonteCarlo

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
abstract ParameterType	createInitialLearnedObject() Creates the initial parameters from which to start the Markov chain.
int	getBurnInIterations() Gets the number of iterations that must transpire before the algorithm begins collection the samples.
ParameterType	getCurrentParameter() Gets the current parameters in the random walk.
int	getIterationsPerSample() Gets the number of iterations that must transpire between capturing samples from the distribution.
ParameterType	getPreviousParameter() Getter for previousParameter
java.util.Random	getRandom() Gets the random number generator used by this object.
DefaultDataDistribution<ParameterType>	getResult() Gets the current result of the algorithm.
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
protected abstract void	mcmcUpdate() Performs a valid MCMC update step.
void	setBurnInIterations(int burnInIterations) Sets the number of iterations that must transpire before the algorithm begins collection the samples.
protected void	setCurrentParameter(ParameterType currentParameter) Setter for currentParameter.
void	setIterationsPerSample(int iterationsPerSample) Sets the number of iterations that must transpire between capturing samples from the distribution.
void	setRandom(java.util.Random random) Sets the random number generator used by this object.
protected void	setResult(DefaultDataDistribution<ParameterType> result) Setter for result
protected boolean	step() Called to take a single step of the learning algorithm.

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.learning.algorithm.BatchLearner

learn

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, stop

Field Detail

DEFAULT_NUM_SAMPLES

public static final int DEFAULT_NUM_SAMPLES

Default number of sample/iterations, 1000.

See Also:

Constant Field Values

random

protected java.util.Random random

Random number generator.

currentParameter

protected ParameterType currentParameter

The current parameters in the random walk.

previousParameter

protected ParameterType previousParameter

The previous parameter in the random walk.

Constructor Detail

AbstractMarkovChainMonteCarlo

public AbstractMarkovChainMonteCarlo()

Creates a new instance of AbstractMarkovChainMonteCarlo

Method Detail

clone

public AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> 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<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>

Returns:

A clone of this object.

getBurnInIterations

public int getBurnInIterations()

Description copied from interface: MarkovChainMonteCarlo

Gets the number of iterations that must transpire before the algorithm begins collection the samples.

Specified by:

getBurnInIterations in interface MarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

The number of iterations that must transpire before the algorithm begins collection the samples.

setBurnInIterations

public void setBurnInIterations(int burnInIterations)

Description copied from interface: MarkovChainMonteCarlo

Sets the number of iterations that must transpire before the algorithm begins collection the samples.

Specified by:

setBurnInIterations in interface MarkovChainMonteCarlo<ObservationType,ParameterType>

Parameters:

burnInIterations - The number of iterations that must transpire before the algorithm begins collection the samples.

getIterationsPerSample

public int getIterationsPerSample()

Description copied from interface: MarkovChainMonteCarlo

Gets the number of iterations that must transpire between capturing samples from the distribution.

Specified by:

getIterationsPerSample in interface MarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

The number of iterations that must transpire between capturing samples from the distribution.

setIterationsPerSample

public void setIterationsPerSample(int iterationsPerSample)

Description copied from interface: MarkovChainMonteCarlo

Sets the number of iterations that must transpire between capturing samples from the distribution.

Specified by:

setIterationsPerSample in interface MarkovChainMonteCarlo<ObservationType,ParameterType>

Parameters:

iterationsPerSample - The number of iterations that must transpire between capturing samples from the distribution.

getResult

public DefaultDataDistribution<ParameterType> getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<DataDistribution<ParameterType>>

Returns:

Current result of the algorithm.

setResult

protected void setResult(DefaultDataDistribution<ParameterType> result)

Setter for result

Parameters:

result - Results to return.

getCurrentParameter

public ParameterType getCurrentParameter()

Description copied from interface: MarkovChainMonteCarlo

Gets the current parameters in the random walk.

Specified by:

getCurrentParameter in interface MarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

The current parameters in the random walk.

setCurrentParameter

protected void setCurrentParameter(ParameterType currentParameter)

Setter for currentParameter.

Parameters:

currentParameter - The current location in the random walk.

getRandom

public java.util.Random getRandom()

Description copied from interface: Randomized

Gets the random number generator used by this object.

Specified by:

getRandom in interface Randomized

Returns:

The random number generator used by this object.

setRandom

public void setRandom(java.util.Random random)

Description copied from interface: Randomized

Sets the random number generator used by this object.

Specified by:

setRandom in interface Randomized

Parameters:

random - The random number generator for this object to use.

mcmcUpdate

protected abstract void mcmcUpdate()

Performs a valid MCMC update step. That is, the function is expected to modify the currentParameter member.

createInitialLearnedObject

public abstract ParameterType createInitialLearnedObject()

Creates the initial parameters from which to start the Markov chain.

Returns:

initial parameters from which to start the Markov chain.

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>,DataDistribution<ParameterType>>

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>,DataDistribution<ParameterType>>

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>,DataDistribution<ParameterType>>

getPreviousParameter

public ParameterType getPreviousParameter()

Getter for previousParameter

Returns:

The previous parameter in the random walk.