gov.sandia.cognition.statistics.bayesian

Class DirichletProcessMixtureModel.MultivariateMeanUpdater

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater

All Implemented Interfaces:

DirichletProcessMixtureModel.Updater<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

DirichletProcessMixtureModel<ObservationType>

public static class DirichletProcessMixtureModel.MultivariateMeanUpdater
extends AbstractCloneableSerializable
implements DirichletProcessMixtureModel.Updater<Vector>

Updater that creates specified clusters with identical covariances

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected MultivariateGaussianMeanBayesianEstimator	estimator Bayesian estimator for the parameters

Constructor Summary

Constructors

Constructor and Description
MultivariateMeanUpdater() Default constructor
MultivariateMeanUpdater(int dimensionality) Creates a new instance of MeanCovarianceUpdater
MultivariateMeanUpdater(MultivariateGaussianMeanBayesianEstimator estimator) Creates a new instance of MeanUpdater

Method Summary

Modifier and Type	Method and Description
DirichletProcessMixtureModel.MultivariateMeanUpdater	clone() This makes public the clone method on the Object class and removes the exception that it throws.
MultivariateGaussian.PDF	createClusterPosterior(java.lang.Iterable<? extends Vector> values, java.util.Random random) Updates the cluster from the values assigned to it
MultivariateGaussian.PDF	createPriorPredictive(java.lang.Iterable<? extends Vector> data) Creates the prior predictive distribution from the data.

Methods inherited from class java.lang.Object

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

Field Detail

estimator

protected MultivariateGaussianMeanBayesianEstimator estimator

Bayesian estimator for the parameters

Constructor Detail

MultivariateMeanUpdater

public MultivariateMeanUpdater()

Default constructor

MultivariateMeanUpdater

public MultivariateMeanUpdater(int dimensionality)

Creates a new instance of MeanCovarianceUpdater

Parameters:

dimensionality - Dimensionality of the Vectors

MultivariateMeanUpdater

public MultivariateMeanUpdater(MultivariateGaussianMeanBayesianEstimator estimator)

Creates a new instance of MeanUpdater

Parameters:

estimator - Bayesian estimator for the parameters

Method Detail

clone

public DirichletProcessMixtureModel.MultivariateMeanUpdater 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.

createPriorPredictive

public MultivariateGaussian.PDF createPriorPredictive(java.lang.Iterable<? extends Vector> data)

Description copied from interface: DirichletProcessMixtureModel.Updater

Creates the prior predictive distribution from the data.

Specified by:

createPriorPredictive in interface DirichletProcessMixtureModel.Updater<Vector>

Parameters:

data - Data from which to create the prior predictive

Returns:

Prior predictive distribution from the data

createClusterPosterior

public MultivariateGaussian.PDF createClusterPosterior(java.lang.Iterable<? extends Vector> values,
                                                       java.util.Random random)

Description copied from interface: DirichletProcessMixtureModel.Updater

Updates the cluster from the values assigned to it

Specified by:

createClusterPosterior in interface DirichletProcessMixtureModel.Updater<Vector>

Parameters:

values - Values assigned to the cluster

random - Random number generator

Returns:

Updated cluster value