How to use

NegativeBinomial

in

cern.jet.random.tdouble

/**
 * 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);
  }
}

  /**
   * Sets the uniform random number generated shared by all <b>static</b>
   * methods.
   * 
   * @param randomGenerator
   *            the new uniform random number generator to be shared.
   */
  private static void xstaticSetRandomGenerator(DoubleRandomEngine randomGenerator) {
    synchronized (shared) {
      shared.setRandomGenerator(randomGenerator);
    }
  }
}

/**
 * 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 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;
}

/**
 * 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 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 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);
  }
}

  /**
   * Sets the uniform random number generated shared by all <b>static</b>
   * methods.
   * 
   * @param randomGenerator
   *            the new uniform random number generator to be shared.
   */
  private static void xstaticSetRandomGenerator(DoubleRandomEngine randomGenerator) {
    synchronized (shared) {
      shared.setRandomGenerator(randomGenerator);
    }
  }
}

/**
 * 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 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);
}