gov.sandia.cognition.statistics.distribution

Class ScalarMixtureDensityModel.PDF

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.LinearMixtureModel<java.lang.Double,SmoothUnivariateDistribution>

gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel

gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.PDF

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, Vectorizable, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, ProbabilityDensityFunction<java.lang.Double>, ProbabilityFunction<java.lang.Double>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, UnivariateProbabilityDensityFunction, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

ScalarMixtureDensityModel

public static class ScalarMixtureDensityModel.PDF
extends ScalarMixtureDensityModel
implements UnivariateProbabilityDensityFunction

PDF of the SMDM

See Also:

Serialized Form

Nested Class Summary

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

ScalarMixtureDensityModel.CDF, ScalarMixtureDensityModel.EMLearner, ScalarMixtureDensityModel.PDF

Field Summary

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

distributions, priorWeights

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
double	evaluate(double input) Produces a double output for the given double input
java.lang.Double	evaluate(java.lang.Double input) Evaluates the function on the given input and returns the output.
double	evaluateAsDouble(java.lang.Double input) Evaluates the scalar function as a double.
ScalarMixtureDensityModel.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	logEvaluate(double input) Evaluate the natural logarithm of the distribution function.
double	logEvaluate(java.lang.Double input) Evaluate the natural logarithm of the distribution function.

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

clone, convertFromVector, convertToVector, getCDF, getMaxSupport, getMean, getMeanAsDouble, getMinSupport, getVariance, sampleAsDouble, sampleAsDoubles, sampleInto

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.SmoothUnivariateDistribution

getCDF, getMean, sampleAsDouble, sampleAsDoubles, sampleInto

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

getMaxSupport, getMeanAsDouble, getMinSupport, getVariance

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

sample, sample, sampleInto

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

clone, convertFromVector, convertToVector

Constructor Detail

PDF

public PDF()

Creates a new instance of ScalarMixtureDensityModel

PDF

public PDF(SmoothUnivariateDistribution... distributions)

Creates a new instance of ScalarMixtureDensityModel

Parameters:

distributions - Distributions that comprise the SMDM with equal prior weight

PDF

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

Creates a new instance of ScalarMixtureDensityModel

Parameters:

distributions - Distributions that comprise the SMDM with equal prior weight

PDF

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

Creates a new instance of ScalarMixtureDensityModel

Parameters:

distributions - Distributions that comprise the SMDM

priorWeights - Weights proportionate by which the distributions are sampled

PDF

public PDF(ScalarMixtureDensityModel other)

Copy constructor

Parameters:

other - SMDM to copy

Method Detail

logEvaluate

public double logEvaluate(java.lang.Double 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<java.lang.Double>

Parameters:

input - The input to be evaluated

Returns:

Natural logarithm of the distribution function.

evaluate

public java.lang.Double evaluate(java.lang.Double input)

Description copied from interface: Evaluator

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

Specified by:

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

Specified by:

evaluate in interface ScalarFunction<java.lang.Double>

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

evaluateAsDouble

public double evaluateAsDouble(java.lang.Double input)

Description copied from interface: ScalarFunction

Evaluates the scalar function as a double.

Specified by:

evaluateAsDouble in interface ScalarFunction<java.lang.Double>

Specified by:

evaluateAsDouble in interface UnivariateScalarFunction

Parameters:

input - The input value.

Returns:

The scalar output calculated from the given input.

evaluate

public double evaluate(double input)

Description copied from interface: UnivariateScalarFunction

Produces a double output for the given double input

Specified by:

evaluate in interface UnivariateScalarFunction

Parameters:

input - Input to the Evaluator

Returns:

output at the given input

getProbabilityFunction

public ScalarMixtureDensityModel.PDF 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<java.lang.Double>

Specified by:

getProbabilityFunction in interface ProbabilityDensityFunction<java.lang.Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Specified by:

getProbabilityFunction in interface UnivariateProbabilityDensityFunction

Overrides:

getProbabilityFunction in class ScalarMixtureDensityModel

Returns:

Distribution function associated with this Distribution.

logEvaluate

public double logEvaluate(double input)

Description copied from interface: UnivariateProbabilityDensityFunction

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 UnivariateProbabilityDensityFunction

Parameters:

input - The input value.

Returns:

The natural logarithm of the distribution function.