gov.sandia.cognition.statistics.bayesian

Class DirichletProcessMixtureModel.Sample<ObservationType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

Type Parameters:

ObservationType - Type of observations handled by the mixture model

All Implemented Interfaces:

CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

DirichletProcessMixtureModel<ObservationType>

public static class DirichletProcessMixtureModel.Sample<ObservationType>
extends AbstractCloneableSerializable

A sample from the Dirichlet Process Mixture Model.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	alpha Scaling parameter which defines the strength of the base distribution, must be greater than zero.
protected java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>>	clusters Point mass realizations from the base distribution.

Constructor Summary

Constructors

Constructor and Description
Sample(double alpha, java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> clusters) Creates a new instance of Sample

Method Summary

Modifier and Type	Method and Description
DirichletProcessMixtureModel.Sample<ObservationType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
double	computePosteriorLogLikelihood(int numObservations, double logConditional) Computes the posterior log likelihood of the Sample
double	computePosteriorLogLikelihood(java.lang.Iterable<? extends ObservationType> data) Computes the posterior log likelihood of the data given the clusters and the prior probability of the clustering from a Chinese Restaurant Process
double	getAlpha() Getter for alpha
java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>>	getClusters() Getter for clusters
int	getNumClusters() Gets the number of clusters in the Sample
java.lang.Double	getPosteriorLogLikelihood() Gets the posterior log-likelihood.
void	removeUnusedClusters() Removes the unused clusters from the Sample.
protected void	setAlpha(double alpha) Setter for alpha
protected void	setClusters(java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> clusters) Setter for clusters
void	setPosteriorLogLikelihood(java.lang.Double posteriorLogLikelihood) sets the posterior log-likelihood.

Methods inherited from class java.lang.Object

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

Field Detail

alpha

protected double alpha

Scaling parameter which defines the strength of the base distribution, must be greater than zero.

clusters

protected java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> clusters

Point mass realizations from the base distribution.

Constructor Detail

Sample

public Sample(double alpha,
              java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> clusters)

Creates a new instance of Sample

Parameters:

alpha - Scaling parameter which defines the strength of the base distribution, must be greater than zero.

clusters - Point mass realizations from the base distribution.

Method Detail

clone

public DirichletProcessMixtureModel.Sample<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 AbstractCloneableSerializable

Returns:

A clone of this object.

computePosteriorLogLikelihood

public double computePosteriorLogLikelihood(java.lang.Iterable<? extends ObservationType> data)

Computes the posterior log likelihood of the data given the clusters and the prior probability of the clustering from a Chinese Restaurant Process

Parameters:

data - Data to consider

Returns:

Posterior log likelihood of the data

computePosteriorLogLikelihood

public double computePosteriorLogLikelihood(int numObservations,
                                            double logConditional)

Computes the posterior log likelihood of the Sample

Parameters:

numObservations - Number of observations in the Sample

logConditional - Log conditional likelihood of the data given the sample

Returns:

Posterior log likelihood

removeUnusedClusters

public void removeUnusedClusters()

Removes the unused clusters from the Sample.

getAlpha

public double getAlpha()

Getter for alpha

Returns:

Scaling parameter which defines the strength of the base distribution, must be greater than zero.

setAlpha

protected void setAlpha(double alpha)

Setter for alpha

Parameters:

alpha - Scaling parameter which defines the strength of the base distribution, must be greater than zero.

getNumClusters

public int getNumClusters()

Gets the number of clusters in the Sample

Returns:

Number of clusters in the Sample.

getClusters

public java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> getClusters()

Getter for clusters

Returns:

Point mass realizations from the base distribution.

setClusters

protected void setClusters(java.util.ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>> clusters)

Setter for clusters

Parameters:

clusters - Point mass realizations from the base distribution.

getPosteriorLogLikelihood

public java.lang.Double getPosteriorLogLikelihood()

Gets the posterior log-likelihood.

Returns:

The posterior log-likelihood.

setPosteriorLogLikelihood

public void setPosteriorLogLikelihood(java.lang.Double posteriorLogLikelihood)

sets the posterior log-likelihood.

Parameters:

posteriorLogLikelihood - The posterior log-likelihood.