cern.jet.random.tdouble.NegativeBinomial Maven / Gradle / Ivy
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/*
Copyright (C) 1999 CERN - European Organization for Nuclear Research.
Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose
is hereby granted without fee, provided that the above copyright notice appear in all copies and
that both that copyright notice and this permission notice appear in supporting documentation.
CERN makes no representations about the suitability of this software for any purpose.
It is provided "as is" without expressed or implied warranty.
*/
package cern.jet.random.tdouble;
import cern.jet.random.tdouble.engine.DoubleRandomEngine;
import cern.jet.stat.tdouble.Probability;
/**
* Negative Binomial distribution; See the math definition.
*
* 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: High performance implementation. Compound method.
*
This is a port of nbp.c from the C-RAND /
* WIN-RAND library. C-RAND's implementation, in turn, is based upon
*
* J.H. Ahrens, U. Dieter (1974): Computer methods for sampling from gamma,
* beta, Poisson and binomial distributions, Computing 12, 223--246.
*
* @author [email protected]
* @version 1.0, 09/24/99
*/
public class NegativeBinomial extends AbstractDiscreteDistribution {
/**
*
*/
private static final long serialVersionUID = 1L;
protected int n;
protected double p;
protected Gamma gamma;
protected Poisson poisson;
// The uniform random number generated shared by all static methods.
protected static NegativeBinomial shared = new NegativeBinomial(1, 0.5, makeDefaultGenerator());
/**
* Constructs a Negative Binomial distribution. Example: n=1, p=0.5.
*
* @param n
* the number of trials.
* @param p
* the probability of success.
* @param randomGenerator
* a uniform random number generator.
*/
public NegativeBinomial(int n, double p, DoubleRandomEngine randomGenerator) {
setRandomGenerator(randomGenerator);
setNandP(n, p);
this.gamma = new Gamma(n, 1.0, randomGenerator);
this.poisson = new Poisson(0.0, randomGenerator);
}
/**
* Returns the cumulative distribution function.
*/
public double cdf(int k) {
return Probability.negativeBinomial(k, n, p);
}
/**
* 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.
*
* @return a copy of the receiver.
*/
public Object clone() {
NegativeBinomial copy = (NegativeBinomial) super.clone();
if (this.poisson != null)
copy.poisson = (Poisson) this.poisson.clone();
copy.poisson.setRandomGenerator(copy.getRandomGenerator());
if (this.gamma != null)
copy.gamma = (Gamma) this.gamma.clone();
copy.gamma.setRandomGenerator(copy.getRandomGenerator());
return copy;
}
/**
* Returns a random number from the distribution.
*/
public int nextInt() {
return nextInt(n, p);
}
/**
* Returns a random number from the distribution; bypasses the internal
* state.
*/
public int nextInt(int n, double p) {
/***********************************************************************
* * Negative Binomial Distribution - Compound method * *
* ***************************************************************** *
* FUNCTION: - nbp samples a random number from the Negative * Binomial
* distribution with parameters r (no. of * failures given) and p
* (probability of success) * valid for r > 0, 0 < p < 1. * If G from
* Gamma(r) then K from Poiss(pG/(1-p)) * is NB(r,p)--distributed. *
* REFERENCE: - J.H. Ahrens, U. Dieter (1974): Computer methods * for
* sampling from gamma, beta, Poisson and * binomial distributions,
* Computing 12, 223--246. * SUBPROGRAMS: - drand(seed) ...
* (0,1)-Uniform generator with * unsigned long integer *seed * -
* Gamma(seed,a) ... Gamma generator for a > 0 * unsigned long *seed,
* double a * - Poisson(seed,a) ...Poisson generator for a > 0 *
* unsigned long *seed, double a. * *
**********************************************************************/
double x = p / (1.0 - p);
double p1 = p;
double y = x * this.gamma.nextDouble(n, 1.0);
return this.poisson.nextInt(y);
}
/**
* Returns the probability distribution function.
*/
public double pdf(int k) {
if (k > n)
throw new IllegalArgumentException();
return cern.jet.math.tdouble.DoubleArithmetic.binomial(n, k) * Math.pow(p, k) * Math.pow(1.0 - p, n - k);
}
/**
* Sets the parameters number of trials and the probability of success.
*
* @param n
* the number of trials
* @param p
* the probability of success.
*/
public void setNandP(int n, double p) {
this.n = n;
this.p = p;
}
/**
* Returns a random number from the distribution with the given parameters n
* and p.
*
* @param n
* the number of trials
* @param p
* the probability of success.
*/
public static int staticNextInt(int n, double p) {
synchronized (shared) {
return shared.nextInt(n, p);
}
}
/**
* Returns a String representation of the receiver.
*/
public String toString() {
return this.getClass().getName() + "(" + n + "," + p + ")";
}
/**
* Sets the uniform random number generated shared by all static
* methods.
*
* @param randomGenerator
* the new uniform random number generator to be shared.
*/
private static void xstaticSetRandomGenerator(DoubleRandomEngine randomGenerator) {
synchronized (shared) {
shared.setRandomGenerator(randomGenerator);
}
}
}