Package sim.util.distribution

Class Empirical

java.lang.Object
sim.util.distribution.AbstractDistribution
sim.util.distribution.AbstractContinuousDistribution
sim.util.distribution.Empirical
All Implemented Interfaces:
Serializable
public class Empirical extends AbstractContinuousDistribution
Empirical distribution.

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.

If interpolationType == LINEAR_INTERPOLATION a linear interpolation within the bin is computed, resulting in a constant density within each bin.

If interpolationType == NO_INTERPOLATION no interpolation is performed and the result is a discrete distribution.

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: A uniform random number is generated using a user supplied generator. The uniform number is then transformed to the user's distribution using the cumulative probability distribution constructed from the pdf. The cumulative distribution is inverted using a binary search for the nearest bin boundary.

This is a port of RandGeneral used in CLHEP 1.4.0 (C++).

See Also:

  • Field Details

    • cdf

      protected double[] cdf

    • interpolationType

      protected int interpolationType

    • LINEAR_INTERPOLATION

      public static final int LINEAR_INTERPOLATION
      See Also:

    • NO_INTERPOLATION

      public static final int NO_INTERPOLATION
      See Also:

  • Constructor Details

    • Empirical

      public Empirical(double[] pdf, int interpolationType, MersenneTwisterFast 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 Details

    • cdf

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

    • nextDouble

      public double nextDouble()
      Returns a random number from the distribution.
      Specified by:
      nextDouble in class AbstractDistribution

    • pdf

      public double pdf(double x)
      Returns the probability distribution function.

    • 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 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.
      interpolationType - can be either Empirical.NO_INTERPOLATION or Empirical.LINEAR_INTERPOLATION.
      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