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 * 
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 * the documentation and/or other materials provided with the
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 * contributors may be used to endorse or promote products derived
 * from this software without specific prior written permission.
 * 
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
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 */
package jhplot.math.num.random;


/**
 * 

 * A random variable generator for the Negative Binomial distribution.
 * 
 * 

 * References:
 * 

 * 
Wikipedia contributors, "Negative Binomial Distribution," Wikipedia, The
 * Free Encyclopedia, 
 * http://en.wikipedia.org/wiki/Negative_binomial_distribution
 * 
 * 

 * 
 * @since 1.3
 * @version $Revision: 1.2 $ $Date: 2007/11/08 17:37:57 $
 */
public class NegativeBinomialRandomVariable extends
    AbstractDiscreteRandomVariable {

    /** the number of successes to observe. */
    private int numberOfSuccesses;

    /** the probability of success for each trial. */
    private double probabilityOfSuccess;

    /**
     * Default constructor. Number of successes is set to one and probability of
     * success is set to 0.5.
     */
    public NegativeBinomialRandomVariable() {
        this(1, 0.5);
    }

    /**
     * Create a random variable with the given number of successes and
     * probability of success.
     * 
     * @param r the number of successes.
     * @param p the probability of success.
     */
    public NegativeBinomialRandomVariable(int r, double p) {
        this(r, p, new RandomRNG());
    }

    /**
     * Create a random variable with the given parameters.
     * 
     * @param r the number of successes.
     * @param p the probability of success.
     * @param source the source generator.
     */
    public NegativeBinomialRandomVariable(int r, double p, RNG source) {
        super(source);
        setNumberOfSuccesses(r);
        setProbabilityOfSuccess(p);
    }

    /**
     * Access the next random variable using the given generator.
     * 
     * @param r the number of successes.
     * @param p the probability of success.
     * @param source the source generator.
     * @return the next random variable.
     */
    public static int nextRandomVariable(int r, double p, RNG source) {
        double y = GammaRandomVariable.nextRandomVariable(r, (1.0 - p) / p,
            source);
        return PoissonRandomVariable.nextRandomVariable(y, source);
    }

    /**
     * Access the number of successes.
     * 
     * @return the number of successes.
     */
    private int getNumberOfSuccesses() {
        return numberOfSuccesses;
    }

    /**
     * Access probability of success.
     * 
     * @return the probability of success.
     */
    private double getProbabilityOfSuccess() {
        return probabilityOfSuccess;
    }

    /**
     * Access the next random variable from this generator.
     * 
     * @return the next random variable.
     */
    public int nextRandomVariable() {
        return nextRandomVariable(getNumberOfSuccesses(),
            getProbabilityOfSuccess(), getSource());
    }

    /**
     * Modify the number of successes.
     * 
     * @param r the new number of successes value.
     */
    private void setNumberOfSuccesses(int r) {
        if (r <= 0) {
            throw new IllegalArgumentException(
                "number of successes must be positive.");
        }
        numberOfSuccesses = r;
    }

    /**
     * Modify probability of success.
     * 
     * @param p the new probability of success value.
     */
    private void setProbabilityOfSuccess(double p) {
        if (Double.isNaN(p) || p <= 0.0 || p >= 1.0) {
            throw new IllegalArgumentException("probability of success must"
                + "be between 0.0 and 1.0, exclusive.");
        }
        probabilityOfSuccess = p;
    }
}