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

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.MultivariateMeanUpdater
All Implemented Interfaces:
DirichletProcessMixtureModel.Updater<Vector>, CloneableSerializable, Serializable, 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
protected  MultivariateGaussianMeanBayesianEstimator estimator
          Bayesian estimator for the parameters
 
Constructor Summary
DirichletProcessMixtureModel.MultivariateMeanUpdater()
          Default constructor
DirichletProcessMixtureModel.MultivariateMeanUpdater(int dimensionality)
          Creates a new instance of MeanCovarianceUpdater
DirichletProcessMixtureModel.MultivariateMeanUpdater(MultivariateGaussianMeanBayesianEstimator estimator)
          Creates a new instance of MeanUpdater
 
Method Summary
 DirichletProcessMixtureModel.MultivariateMeanUpdater clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 MultivariateGaussian.PDF createClusterPosterior(Iterable<? extends Vector> values, Random random)
          Updates the cluster from the values assigned to it
 MultivariateGaussian.PDF createPriorPredictive(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

DirichletProcessMixtureModel.MultivariateMeanUpdater

public DirichletProcessMixtureModel.MultivariateMeanUpdater()
Default constructor


DirichletProcessMixtureModel.MultivariateMeanUpdater

public DirichletProcessMixtureModel.MultivariateMeanUpdater(int dimensionality)
Creates a new instance of MeanCovarianceUpdater

Parameters:
dimensionality - Dimensionality of the Vectors

DirichletProcessMixtureModel.MultivariateMeanUpdater

public DirichletProcessMixtureModel.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(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(Iterable<? extends Vector> values,
                                                       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