/*
  Copyright � 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 sim.util.distribution;
import ec.util.MersenneTwisterFast;

/**
 * Negative Binomial distribution; See the <A HREF="http://www.statlets.com/usermanual/glossary2.htm"> math definition</A>.
 * <p>
 * Instance methods operate on a user supplied uniform random number generator; they are unsynchronized.
 * <dt>
 * Static methods operate on a default uniform random number generator; they are synchronized.
 * <p>
 * <b>Implementation:</b> High performance implementation. Compound method. 
 * <dt>
 * This is a port of <tt>nbp.c</tt> from the <A HREF="http://www.cis.tu-graz.ac.at/stat/stadl/random.html">C-RAND / WIN-RAND</A> library.
 * C-RAND's implementation, in turn, is based upon
 * <p>
 * J.H. Ahrens, U. Dieter (1974): Computer methods for sampling from gamma, beta, Poisson and binomial distributions, Computing 12, 223--246.
 *
 * @author wolfgang.hoschek@cern.ch
 * @version 1.0, 09/24/99
 */
public class NegativeBinomial extends AbstractDiscreteDistribution {
    protected int n;
    protected double p;

    protected Gamma gamma;
    protected Poisson poisson;
        
/**
 * 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, MersenneTwisterFast 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 Arithmetic.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 String representation of the receiver.
 */
    public String toString() {
        return this.getClass().getName()+"("+n+","+p+")";
        }
    }
