cern.jet.random

Class EmpiricalWalker

java.lang.Object

cern.colt.PersistentObject

cern.jet.random.AbstractDistribution

cern.jet.random.AbstractDiscreteDistribution

cern.jet.random.EmpiricalWalker

All Implemented Interfaces:

DoubleFunction, IntFunction, Serializable, 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.

Version:

1.0, 09/24/99

Author:

wolfgang.hoschek@cern.ch

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
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

Constructors

Constructor and Description
EmpiricalWalker(double[] pdf, int interpolationType, RandomEngine randomGenerator) Constructs an Empirical distribution.

Method Summary

Methods

Modifier and Type	Method and Description
double	cdf(int k) Returns the cumulative distribution function.
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.
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:

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.

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:

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