gov.sandia.cognition.statistics.distribution

Class MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<DataType>

gov.sandia.cognition.statistics.distribution.LinearMixtureModel<Vector,DistributionType>

gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel<DistributionType>

Type Parameters:

DistributionType - Type of Distribution in the mixture

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

MultivariateMixtureDensityModel.PDF

@PublicationReference(author="Wikipedia",
                      title="Mixture Model",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Mixture_model")
public class MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>
extends LinearMixtureModel<Vector,DistributionType>
implements ClosedFormComputableDistribution<Vector>

A LinearMixtureModel of multivariate distributions with associated PDFs.

Since:

3.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>> PDF of the MultivariateMixtureDensityModel

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.distribution.LinearMixtureModel

distributions, priorWeights

Constructor Summary

Constructors

Constructor and Description
MultivariateMixtureDensityModel(java.util.Collection<? extends DistributionType> distributions) Creates a new instance of MultivariateMixtureDensityModel
MultivariateMixtureDensityModel(java.util.Collection<? extends DistributionType> distributions, double[] priorWeights) Creates a new instance of MultivariateMixtureDensityModel
MultivariateMixtureDensityModel(MultivariateMixtureDensityModel<? extends DistributionType> other) Copy Constructor

Method Summary

Modifier and Type	Method and Description
MultivariateMixtureDensityModel<DistributionType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
Vector	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
MultivariateMixtureDensityModel.PDF<DistributionType>	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.

Methods inherited from class gov.sandia.cognition.statistics.distribution.LinearMixtureModel

getDistributionCount, getDistributions, getPriorWeights, getPriorWeightSum, sample, sampleInto, setDistributions, setPriorWeights, toString

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample, sample, sampleInto

Constructor Detail

MultivariateMixtureDensityModel

public MultivariateMixtureDensityModel(java.util.Collection<? extends DistributionType> distributions)

Creates a new instance of MultivariateMixtureDensityModel

Parameters:

distributions - Underlying distributions from which we sample

MultivariateMixtureDensityModel

public MultivariateMixtureDensityModel(java.util.Collection<? extends DistributionType> distributions,
                                       double[] priorWeights)

Creates a new instance of MultivariateMixtureDensityModel

Parameters:

distributions - Underlying distributions from which we sample

priorWeights - Weights proportionate by which the distributions are sampled

MultivariateMixtureDensityModel

public MultivariateMixtureDensityModel(MultivariateMixtureDensityModel<? extends DistributionType> other)

Copy Constructor

Parameters:

other - MultivariateMixtureDensityModel to copy

Method Detail

clone

public MultivariateMixtureDensityModel<DistributionType> 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 Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class LinearMixtureModel<Vector,DistributionType extends ClosedFormComputableDistribution<Vector>>

Returns:

A clone of this object.

getMean

public Vector getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<Vector>

Returns:

Mean of the distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getProbabilityFunction

public MultivariateMixtureDensityModel.PDF<DistributionType> getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Vector>

Returns:

Distribution function associated with this Distribution.