Package org.hipparchus.distribution.multivariate

Class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution>

java.lang.Object
org.hipparchus.distribution.multivariate.AbstractMultivariateRealDistribution
org.hipparchus.distribution.multivariate.MixtureMultivariateRealDistribution<T>
Type Parameters:
T - Type of the mixture components.
All Implemented Interfaces:
MultivariateRealDistribution
Direct Known Subclasses:
MixtureMultivariateNormalDistribution
public class MixtureMultivariateRealDistribution<T extends MultivariateRealDistribution> extends AbstractMultivariateRealDistribution
Class for representing mixture model distributions.

  • Constructor Details

    • MixtureMultivariateRealDistribution

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

      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.

      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:
      MathIllegalArgumentException - if any of the weights is negative.
      MathIllegalArgumentException - if not all components have the same number of variables.

  • Method Details

    • 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.