cern.jet.random
Class EmpiricalWalker

java.lang.Object
  extended by cern.colt.PersistentObject
      extended by cern.jet.random.AbstractDistribution
          extended by cern.jet.random.AbstractDiscreteDistribution
              extended by cern.jet.random.EmpiricalWalker
All Implemented Interfaces:
DoubleFunction, IntFunction, java.io.Serializable, java.lang.Cloneable

public class EmpiricalWalker
extends 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.

Author:
wolfgang.hoschek@cern.ch
See Also:
Serialized Form

Field Summary
protected  int[] A
           
protected  double[] cdf
           
protected  double[] F
           
protected  int K
           
 
Fields inherited from class cern.jet.random.AbstractDistribution
randomGenerator
 
Fields inherited from class cern.colt.PersistentObject
serialVersionUID
 
Constructor Summary
EmpiricalWalker(double[] pdf, int interpolationType, RandomEngine randomGenerator)
          Constructs an Empirical distribution.
 
Method Summary
 double cdf(int k)
          Returns the cumulative distribution function.
 java.lang.Object clone()
          Returns a deep copy of the receiver; the copy will produce identical sequences.
 int nextInt()
          Returns a random integer k with probability pdf(k).
 double pdf(int k)
          Returns the probability distribution function.
 void setState(double[] pdf, int interpolationType)
          Sets the distribution parameters.
 void setState2(double[] pdf)
          Sets the distribution parameters.
 java.lang.String toString()
          Returns a String representation of the receiver.
 
Methods inherited from class cern.jet.random.AbstractDiscreteDistribution
nextDouble
 
Methods inherited from class cern.jet.random.AbstractDistribution
apply, apply, getRandomGenerator, makeDefaultGenerator, setRandomGenerator
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

K

protected int K

A

protected int[] A

F

protected double[] F

cdf

protected double[] cdf
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:
java.lang.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 java.lang.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.

nextInt

public int nextInt()
Returns a random integer k with probability pdf(k).

Specified by:
nextInt in class AbstractDiscreteDistribution

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:
java.lang.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:
java.lang.IllegalArgumentException - if at least one of the three conditions above is violated.

toString

public java.lang.String toString()
Returns a String representation of the receiver.

Overrides:
toString in class java.lang.Object