gov.sandia.cognition.statistics.distribution

Class MultivariateMixtureDensityModel.PDF<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>

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

Type Parameters:

DistributionType - Type of Distribution in the mixture

All Implemented Interfaces:

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

Direct Known Subclasses:

MixtureOfGaussians.PDF

Enclosing class:

MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>

public static class MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>
extends MultivariateMixtureDensityModel<DistributionType>
implements ProbabilityDensityFunction<Vector>

PDF of the MultivariateMixtureDensityModel

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.MultivariateMixtureDensityModel

MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>

Field Summary

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

distributions, priorWeights

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
double[]	computeRandomVariableLikelihoods(Vector input) Computes the likelihoods of the underlying distributions
double[]	computeRandomVariableProbabilities(Vector input) Computes the probability distribution that the input was generated by the underlying distributions
java.lang.Double	evaluate(Vector input) Evaluates the function on the given input and returns the output.
int	getMostLikelyRandomVariable(Vector input) Gets the index of the most-likely distribution, given the input.
MultivariateMixtureDensityModel.PDF<DistributionType>	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	logEvaluate(Vector input) Evaluate the natural logarithm of the distribution function.

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

clone, convertFromVector, convertToVector, getMean

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.DistributionWithMean

getMean

Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable

clone, convertFromVector, convertToVector

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

sample, sample, sampleInto

Constructor Detail

PDF

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

Creates a new instance of MultivariateMixtureDensityModel

Parameters:

distributions - Underlying distributions from which we sample

PDF

public PDF(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

PDF

public PDF(MultivariateMixtureDensityModel<? extends DistributionType> other)

Copy Constructor

Parameters:

other - MultivariateMixtureDensityModel to copy

Method Detail

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>

Specified by:

getProbabilityFunction in interface ProbabilityDensityFunction<Vector>

Overrides:

getProbabilityFunction in class MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>

Returns:

Distribution function associated with this Distribution.

logEvaluate

public double logEvaluate(Vector input)

Description copied from interface: ProbabilityFunction

Evaluate the natural logarithm of the distribution function. This is sometimes more efficient than evaluating the distribution function itself, and when evaluating the product of many independent or exchangeable samples.

Specified by:

logEvaluate in interface ProbabilityFunction<Vector>

Parameters:

input - The input to be evaluated

Returns:

Natural logarithm of the distribution function.

evaluate

public java.lang.Double evaluate(Vector input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<Vector,java.lang.Double>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

computeRandomVariableProbabilities

public double[] computeRandomVariableProbabilities(Vector input)

Computes the probability distribution that the input was generated by the underlying distributions

Parameters:

input - Input to consider

Returns:

probability distribution that the input was generated by the underlying distributions

computeRandomVariableLikelihoods

public double[] computeRandomVariableLikelihoods(Vector input)

Computes the likelihoods of the underlying distributions

Parameters:

input - Input to consider

Returns:

Vector of likelihoods for the underlying distributions

getMostLikelyRandomVariable

public int getMostLikelyRandomVariable(Vector input)

Gets the index of the most-likely distribution, given the input. That is, find the distribution that most likely generated the input

Parameters:

input - input to consider

Returns:

zero-based index of the most-likely distribution