fr.cnes.sirius.patrius.math.distribution

Class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution>

java.lang.Object

fr.cnes.sirius.patrius.math.distribution.AbstractMultivariateRealDistribution

fr.cnes.sirius.patrius.math.distribution.MixtureMultivariateRealDistribution<T>

Type Parameters:

T - Type of the mixture components.

All Implemented Interfaces:

MultivariateRealDistribution

public class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution>
extends AbstractMultivariateRealDistribution

Class for representing mixture model distributions.

Since:

3.1

Version:

$Id: MixtureMultivariateRealDistribution.java 18108 2017-10-04 06:45:27Z bignon $

Field Summary

Fields inherited from class fr.cnes.sirius.patrius.math.distribution.AbstractMultivariateRealDistribution

random

Constructor Summary

Constructors

Constructor and Description
MixtureMultivariateRealDistribution(List<Pair<Double,T>> components) Creates a mixture model from a list of distributions and their associated weights.
MixtureMultivariateRealDistribution(RandomGenerator rng, List<Pair<Double,T>> components) Creates a mixture model from a list of distributions and their associated weights.

Method Summary

Modifier and Type	Method and Description
double	density(double[] values) Returns the probability density function (PDF) of this distribution evaluated at the specified point x.
List<Pair<Double,T>>	getComponents() Gets the distributions that make up the mixture model.
void	reseedRandomGenerator(long seed) Reseeds the random generator used to generate samples.
double[]	sample() Generates a random value vector sampled from this distribution.

Methods inherited from class fr.cnes.sirius.patrius.math.distribution.AbstractMultivariateRealDistribution

getDimension, sample

Methods inherited from class java.lang.Object

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

Constructor Detail

MixtureMultivariateRealDistribution

public MixtureMultivariateRealDistribution(List<Pair<Double,T>> components)

Creates a mixture model from a list of distributions and their associated weights.

Parameters:

components - List of (weight, distribution) pairs from which to sample.

MixtureMultivariateRealDistribution

public MixtureMultivariateRealDistribution(RandomGenerator rng,
                                           List<Pair<Double,T>> components)

Creates a mixture model from a list of distributions and their associated weights.

Parameters:

rng - Random number generator.

components - Distributions from which to sample.

Throws:

NotPositiveException - if any of the weights is negative.

DimensionMismatchException - if not all components have the same number of variables.

Method Detail

density

public double density(double[] values)

Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the cumulative distribution function. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.

Parameters:

values - Point at which the PDF is evaluated.

Returns:

the value of the probability density function at point x.

sample

public double[] sample()

Generates a random value vector sampled from this distribution.

Specified by:

sample in interface MultivariateRealDistribution

Specified by:

sample in class AbstractMultivariateRealDistribution

Returns:

a random value vector.

reseedRandomGenerator

public void reseedRandomGenerator(long seed)

Reseeds the random generator used to generate samples.

Specified by:

reseedRandomGenerator in interface MultivariateRealDistribution

Overrides:

reseedRandomGenerator in class AbstractMultivariateRealDistribution

Parameters:

seed - Seed with which to initialize the random number generator.

getComponents

public List<Pair<Double,T>> getComponents()

Gets the distributions that make up the mixture model.

Returns:

the component distributions and associated weights.