gov.sandia.cognition.statistics.bayesian

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

gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo<ObservationType,DirichletProcessMixtureModel.Sample<ObservationType>>

gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel<ObservationType>

gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel<ObservationType>

Type Parameters:

ObservationType - Type of observations handled by the algorithm

All Implemented Interfaces:

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

public class ParallelDirichletProcessMixtureModel<ObservationType>
extends DirichletProcessMixtureModel<ObservationType>
implements ParallelAlgorithm

A Parallelized version of vanilla Dirichlet Process Mixture Model learning. In particular, this class parallelizes the assignment of observations to clusters and the Gibbs sampling updating of clusters from their constituent observations.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected class	ParallelDirichletProcessMixtureModel.ClusterUpdaterTask Tasks that update the values of the clusters for Gibbs sampling
static class	ParallelDirichletProcessMixtureModel.DPMMAssignments Assignments from the DPMM
protected class	ParallelDirichletProcessMixtureModel.ObservationAssignmentTask Task that assign observations to cluster indices

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

DirichletProcessMixtureModel.DPMMCluster<ObservationType>, DirichletProcessMixtureModel.DPMMLogConditional, DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater, DirichletProcessMixtureModel.MultivariateMeanUpdater, DirichletProcessMixtureModel.Sample<ObservationType>, DirichletProcessMixtureModel.Updater<ObservationType>

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.ArrayList<ParallelDirichletProcessMixtureModel.ObservationAssignmentTask>	assignmentTasks Tasks that assign observations to clusters
protected java.util.ArrayList<ParallelDirichletProcessMixtureModel.ClusterUpdaterTask>	clusterUpdaterTasks Tasks that update the values of the clusters for Gibbs sampling

Fields inherited from class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

alphaInverseSampler, clusterWeights, conditionalPriorPredictive, DEFAULT_ALPHA, DEFAULT_NUM_INITIAL_CLUSTERS, DEFAULT_REESTIMATE_ALPHA, etaSampler, initialAlpha, reestimateAlpha, updater

Fields inherited from class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

currentParameter, DEFAULT_NUM_SAMPLES, previousParameter, random

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
ParallelDirichletProcessMixtureModel() Creates a new instance of ParallelDirichletProcessMixtureModel

Method Summary

Modifier and Type	Method and Description
protected java.util.ArrayList<java.util.Collection<ObservationType>>	assignObservationsToClusters(int K, DirichletProcessMixtureModel.DPMMLogConditional logConditional) Assigns observations to each of the K clusters, plus the as-yet-uncreated new cluster
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.
void	setThreadPool(java.util.concurrent.ThreadPoolExecutor threadPool) Sets the thread pool for the algorithm to use.
protected java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>>	updateClusters(java.util.ArrayList<java.util.Collection<ObservationType>> clusterAssignments) Update each cluster according to the data assigned to it

Methods inherited from class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel

assignObservationToCluster, clone, createCluster, createInitialLearnedObject, getInitialAlpha, getNumInitialClusters, getReestimateAlpha, getUpdater, mcmcUpdate, setInitialAlpha, setNumInitialClusters, setReestimateAlpha, setUpdater, updateAlpha

Methods inherited from class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

cleanupAlgorithm, getBurnInIterations, getCurrentParameter, getIterationsPerSample, getPreviousParameter, getRandom, getResult, initializeAlgorithm, setBurnInIterations, setCurrentParameter, setIterationsPerSample, setRandom, setResult, step

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.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

assignmentTasks

protected transient java.util.ArrayList<ParallelDirichletProcessMixtureModel.ObservationAssignmentTask> assignmentTasks

Tasks that assign observations to clusters

clusterUpdaterTasks

protected transient java.util.ArrayList<ParallelDirichletProcessMixtureModel.ClusterUpdaterTask> clusterUpdaterTasks

Tasks that update the values of the clusters for Gibbs sampling

Constructor Detail

ParallelDirichletProcessMixtureModel

public ParallelDirichletProcessMixtureModel()

Creates a new instance of ParallelDirichletProcessMixtureModel

Method Detail

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

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.

assignObservationsToClusters

protected java.util.ArrayList<java.util.Collection<ObservationType>> assignObservationsToClusters(int K,
                                                                                                  DirichletProcessMixtureModel.DPMMLogConditional logConditional)

Description copied from class: DirichletProcessMixtureModel

Assigns observations to each of the K clusters, plus the as-yet-uncreated new cluster

Overrides:

assignObservationsToClusters in class DirichletProcessMixtureModel<ObservationType>

Parameters:

K - Number of clusters

logConditional - The log of the conditional.

Returns:

Assignments from observations to clusters

updateClusters

protected java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> updateClusters(java.util.ArrayList<java.util.Collection<ObservationType>> clusterAssignments)

Description copied from class: DirichletProcessMixtureModel

Update each cluster according to the data assigned to it

Overrides:

updateClusters in class DirichletProcessMixtureModel<ObservationType>

Parameters:

clusterAssignments - Observations assigned to each cluster

Returns:

Cluster that contains an update parameter estimate and weighted by the number of observations assigned to the cluster