EmpiricalWalker
cern.jet.random

Class EmpiricalWalker

  • All Implemented Interfaces:
    DoubleFunction, IntFunction, Serializable, Cloneable


    public class EmpiricalWalkerextends AbstractDiscreteDistribution
    Discrete Empirical distribution (pdf's can be specified).

    The probability distribution function (pdf) must be provided by the user as an array of positive real numbers. The pdf does not need to be provided in the form of relative probabilities, absolute probabilities are also accepted.

    Instance methods operate on a user supplied uniform random number generator; they are unsynchronized.

    Static methods operate on a default uniform random number generator; they are synchronized.

    Implementation: Walker's algorithm. Generating a random number takes O(1), i.e. constant time, as opposed to commonly used algorithms with logarithmic time complexity. Preprocessing time (on object construction) is O(k) where k is the number of elements of the provided empirical pdf. Space complexity is O(k).

    This is a port of discrete.c which was written by James Theiler and is distributed with GSL 0.4.1. Theiler's implementation in turn is based upon

    Alastair J. Walker, An efficient method for generating discrete random variables with general distributions, ACM Trans Math Soft 3, 253-256 (1977).

    See also: D. E. Knuth, The Art of Computer Programming, Volume 2 (Seminumerical algorithms), 3rd edition, Addison-Wesley (1997), p120.

    See Also:
    Serialized Form
    • Constructor Detail

      • EmpiricalWalker

        public EmpiricalWalker(double[] pdf,               int interpolationType,               RandomEngine randomGenerator)
        Constructs an Empirical distribution. The probability distribution function (pdf) is an array of positive real numbers. It need not be provided in the form of relative probabilities, absolute probabilities are also accepted. The pdf must satisfy both of the following conditions
        • 0.0 <= pdf[i] : 0<=i<=pdf.length-1
        • 0.0 < Sum(pdf[i]) : 0<=i<=pdf.length-1
        Parameters:
        pdf - the probability distribution function.
        interpolationType - can be either Empirical.NO_INTERPOLATION or Empirical.LINEAR_INTERPOLATION.
        randomGenerator - a uniform random number generator.
        Throws:
        IllegalArgumentException - if at least one of the three conditions above is violated.
    • Method Detail

      • cdf

        public double cdf(int k)
        Returns the cumulative distribution function.
      • clone

        public Object clone()
        Returns a deep copy of the receiver; the copy will produce identical sequences. After this call has returned, the copy and the receiver have equal but separate state.
        Overrides:
        clone in class AbstractDistribution
        Returns:
        a copy of the receiver.
      • pdf

        public double pdf(int k)
        Returns the probability distribution function.
      • setState

        public void setState(double[] pdf,            int interpolationType)
        Sets the distribution parameters. The pdf must satisfy all of the following conditions
        • pdf != null && pdf.length > 0
        • 0.0 <= pdf[i] : 0 < =i <= pdf.length-1
        • 0.0 < Sum(pdf[i]) : 0 <=i <= pdf.length-1
        Parameters:
        pdf - probability distribution function.
        Throws:
        IllegalArgumentException - if at least one of the three conditions above is violated.
      • setState2

        public void setState2(double[] pdf)
        Sets the distribution parameters. The pdf must satisfy both of the following conditions
        • 0.0 <= pdf[i] : 0 < =i <= pdf.length-1
        • 0.0 < Sum(pdf[i]) : 0 <=i <= pdf.length-1
        Parameters:
        pdf - probability distribution function.
        Throws:
        IllegalArgumentException - if at least one of the three conditions above is violated.
      • toString

        public String toString()
        Returns a String representation of the receiver.
        Overrides:
        toString in class Object

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