gov.sandia.cognition.statistics.distribution

Class LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

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

Type Parameters:

DataType - Type of data in this mixture model

DistributionType - The type of the internal distributions inside the mixture.

All Implemented Interfaces:

Distribution<DataType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

MultivariateMixtureDensityModel, ScalarMixtureDensityModel

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

A linear mixture of RandomVariables, with a prior probability distribution. The posterior pdf is: p(x|this) = \sum_{y\in this} p(x|y,this)P(y|this), where p(x|y,this) is the pdf of the underlying RandomVariable, and P(y|this) is the prior probability of RandomVariable y in this.

Since:

1.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.ArrayList<? extends DistributionType>	distributions Underlying distributions from which we sample
protected double[]	priorWeights Weights proportionate by which the distributions are sampled

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
LinearMixtureModel<DataType,DistributionType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
int	getDistributionCount() Gets the number of distributions in the model
java.util.ArrayList<? extends DistributionType>	getDistributions() Getter for distributions
double[]	getPriorWeights() Getter for priorWeights
double	getPriorWeightSum() Computes the sum of the prior weights
DataType	sample(java.util.Random random) Draws a single random sample from the distribution.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super DataType> output) Draws multiple random samples from the distribution and puts the result into the given collection.
void	setDistributions(java.util.ArrayList<? extends DistributionType> distributions) Setter for distributions
void	setPriorWeights(double[] priorWeights) Getter for priorWeights
java.lang.String	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

Field Detail

distributions

protected java.util.ArrayList<? extends DistributionType extends Distribution<DataType>> distributions

Underlying distributions from which we sample

priorWeights

protected double[] priorWeights

Weights proportionate by which the distributions are sampled

Constructor Detail

LinearMixtureModel

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

Creates a new instance of LinearMixtureModel

Parameters:

distributions - Underlying distributions from which we sample

LinearMixtureModel

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

Creates a new instance of LinearMixtureModel

Parameters:

distributions - Underlying distributions from which we sample

priorWeights - Weights proportionate by which the distributions are sampled

Method Detail

clone

public LinearMixtureModel<DataType,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 CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getDistributions

public java.util.ArrayList<? extends DistributionType> getDistributions()

Getter for distributions

Returns:

Underlying distributions from which we sample

setDistributions

public void setDistributions(java.util.ArrayList<? extends DistributionType> distributions)

Setter for distributions

Parameters:

distributions - Underlying distributions from which we sample

getDistributionCount

public int getDistributionCount()

Gets the number of distributions in the model

Returns:

Number of distributions in the model

sample

public DataType sample(java.util.Random random)

Description copied from interface: Distribution

Draws a single random sample from the distribution.

Specified by:

sample in interface Distribution<DataType>

Overrides:

sample in class AbstractDistribution<DataType>

Parameters:

random - Random-number generator to use in order to generate random numbers.

Returns:

Sample drawn according to this distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super DataType> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

getPriorWeights

public double[] getPriorWeights()

Getter for priorWeights

Returns:

Weights proportionate by which the distributions are sampled

setPriorWeights

public void setPriorWeights(double[] priorWeights)

Getter for priorWeights

Parameters:

priorWeights - Weights proportionate by which the distributions are sampled

getPriorWeightSum

public double getPriorWeightSum()

Computes the sum of the prior weights

Returns:

Sum of the prior weights