Package weka.core

Class RandomVariates

java.lang.Object
java.util.Random
weka.core.RandomVariates
All Implemented Interfaces:
Serializable, RandomGenerator, RevisionHandler
public final class RandomVariates extends Random implements RevisionHandler
Class implementing some simple random variates generator.
Version:
$Revision: 8034 $
Author:
Xin Xu (xx5@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • RandomVariates

      public RandomVariates()
      Simply the constructor of super class

    • RandomVariates

      public RandomVariates(long seed)
      Simply the constructor of super class
      Parameters:
      seed - the seed in this random object

  • Method Details

    • nextExponential

      public double nextExponential()
      Generate a value of a variate following standard exponential distribution using simple inverse method.

      Variates related to standard Exponential can be generated using simple transformations.

      Specified by:
      nextExponential in interface RandomGenerator
      Returns:
      a value of the variate

    • nextErlang

      public double nextErlang(int a) throws Exception
      Generate a value of a variate following standard Erlang distribution. It can be used when the shape parameter is an integer and not too large, say, <100. When the parameter is not an integer (which is often named Gamma distribution) or is large, use nextGamma(double a) instead.
      Parameters:
      a - the shape parameter, must be no less than 1
      Returns:
      a value of the variate
      Throws:
      Exception - if parameter less than 1

    • nextGamma

      public double nextGamma(double a) throws Exception
      Generate a value of a variate following standard Gamma distribution with shape parameter a.

      If a>1, it uses a rejection method developed by Minh(1988)"Generating Gamma Variates", ACM Trans. on Math. Software, Vol.14, No.3, pp261-266.
      If a<1, it uses the algorithm "GS" developed by Ahrens and Dieter(1974) "COMPUTER METHODS FOR SAMPLING FROM GAMMA, BETA, POISSON AND BINOMIAL DISTRIBUTIONS", COMPUTING, 12 (1974), pp223-246, and further implemented in Fortran by Ahrens, Kohrt and Dieter(1983) "Algorithm 599: sampling from Gamma and Poisson distributions", ACM Trans. on Math. Software, Vol.9 No.2, pp255-257.

      Variates related to standard Gamma can be generated using simple transformations.

      Parameters:
      a - the shape parameter, must be greater than 1
      Returns:
      a value of the variate
      Throws:
      Exception - if parameter not greater than 1

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision

    • main

      public static void main(String[] ops)
      Main method for testing this class.
      Parameters:
      ops - # of variates/seed, default is 10/45