gov.sandia.cognition.statistics.distribution

Class MultivariatePolyaDistribution.PMF

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution

gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution.PMF

All Implemented Interfaces:

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

Enclosing class:

MultivariatePolyaDistribution

public static class MultivariatePolyaDistribution.PMF
extends MultivariatePolyaDistribution
implements ProbabilityMassFunction<Vector>, VectorInputEvaluator<Vector,java.lang.Double>

PMF of the MultivariatePolyaDistribution

See Also:

Serialized Form

Nested Class Summary

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

MultivariatePolyaDistribution.PMF

Field Summary

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

DEFAULT_DIMENSIONALITY, DEFAULT_NUM_TRIALS, parameters

Constructor Summary

Constructors

Constructor and Description
PMF() Creates a new instance of DirichletDistribution
PMF(int dimensionality, int numTrials) Creates a new instance of MultivariatePolyaDistribution
PMF(MultivariatePolyaDistribution other) Copy Constructor.
PMF(Vector parameters, int numTrials) Creates a new instance of MultivariatePolyaDistribution

Method Summary

Modifier and Type	Method and Description
java.lang.Double	evaluate(Vector input) Evaluates the function on the given input and returns the output.
double	getEntropy() Gets the entropy of the values in the histogram.
MultivariatePolyaDistribution.PMF	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.MultivariatePolyaDistribution

clone, convertFromVector, convertToVector, getDomain, getDomainSize, getInputDimensionality, getMean, getNumTrials, getParameters, sampleInto, setNumTrials, setParameters, toString

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

sample, 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.DiscreteDistribution

getDomain, getDomainSize

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

sample, sample, sampleInto

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

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

getInputDimensionality

Constructor Detail

PMF

public PMF()

Creates a new instance of DirichletDistribution

PMF

public PMF(int dimensionality,
           int numTrials)

Creates a new instance of MultivariatePolyaDistribution

Parameters:

dimensionality - Dimensionality of the distribution

numTrials - Number of trials in the distribution, must be greater than 0.

PMF

public PMF(Vector parameters,
           int numTrials)

Creates a new instance of MultivariatePolyaDistribution

Parameters:

parameters - Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

numTrials - Number of trials in the distribution, must be greater than 0.

PMF

public PMF(MultivariatePolyaDistribution other)

Copy Constructor.

Parameters:

other - MultivariatePolyaDistribution to copy.

Method Detail

getProbabilityFunction

public MultivariatePolyaDistribution.PMF 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 DiscreteDistribution<Vector>

Specified by:

getProbabilityFunction in interface ProbabilityMassFunction<Vector>

Overrides:

getProbabilityFunction in class MultivariatePolyaDistribution

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.

getEntropy

public double getEntropy()

Description copied from interface: ProbabilityMassFunction

Gets the entropy of the values in the histogram.

Specified by:

getEntropy in interface ProbabilityMassFunction<Vector>

Returns:

The entropy of the values in the histogram.