T - Type of the mixture components.public class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution> extends AbstractMultivariateRealDistribution
random| 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. | 
| 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. | 
getDimension, samplepublic MixtureMultivariateRealDistribution(List<Pair<Double,T>> components)
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 sample() and AbstractMultivariateRealDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
components - List of (weight, distribution) pairs from which to sample.public MixtureMultivariateRealDistribution(RandomGenerator rng, List<Pair<Double,T>> components)
rng - Random number generator.components - Distributions from which to sample.NotPositiveException - if any of the weights is negative.DimensionMismatchException - if not all components have the same
 number of variables.public double density(double[] values)
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.values - Point at which the PDF is evaluated.x.public double[] sample()
sample in interface MultivariateRealDistributionsample in class AbstractMultivariateRealDistributionpublic void reseedRandomGenerator(long seed)
reseedRandomGenerator in interface MultivariateRealDistributionreseedRandomGenerator in class AbstractMultivariateRealDistributionseed - Seed with which to initialize the random number generator.Copyright © 2003–2016 The Apache Software Foundation. All rights reserved.