package net.berack.upo.valpre.rand;

/* -------------------------------------------------------------------------- 
 * This is a Java library for generating random variates from six discrete 
 * distributions
 *
 *      Generator         Range (x)     Mean         Variance
 *
 *      bernoulli(p)      x = 0,1       p            p*(1-p)
 *      binomial(n, p)    x = 0,...,n   n*p          n*p*(1-p)
 *      equilikely(a, b)  x = a,...,b   (a+b)/2      ((b-a+1)*(b-a+1)-1)/12
 *      Geometric(p)      x = 0,...     p/(1-p)      p/((1-p)*(1-p))
 *      pascal(n, p)      x = 0,...     n*p/(1-p)    n*p/((1-p)*(1-p))
 *      poisson(m)        x = 0,...     m            m
 * 
 * and seven continuous distributions
 *
 *      uniform(a, b)     a < x < b     (a + b)/2    (b - a)*(b - a)/12 
 *      exponential(m)    x > 0         m            m*m
 *      erlang(n, b)      x > 0         n*b          n*b*b
 *      normal(m, s)      all x         m            s*s
 *      logNormal(a, b)   x > 0            see below
 *      chiSquare(n)      x > 0         n            2*n 
 *      student(n)        all x         0  (n > 1)   n/(n - 2)   (n > 2)
 *
 * For the a Lognormal(a, b) random variable, the mean and variance are
 *
 *                        mean = exp(a + 0.5*b*b)
 *                    variance = (exp(b*b) - 1) * exp(2*a + b*b)
 *
 * Name              : Rvgs.java  (Random Variate GeneratorS)
 * Authors           : Steve Park & Dave Geyer
 * Translated by     : Richard Dutton & Jun Wang
 * Language          : Java
 * Latest Revision   : 7-1-04
 * --------------------------------------------------------------------------
 */
public class Rvgs {

    private final Rng rng;

    // public Rvgs() {
    // this.rngs = new Rngs(Rng.DEFAULT);
    // }

    public Rvgs(Rng rng) {
        if (rng == null)
            throw new NullPointerException();
        this.rng = rng;
    }

    /**
     * Returns 1 with probability p or 0 with probability 1 - p.
     * NOTE: use 0.0 < p < 1.0
     */
    public long bernoulli(double p) {
        return ((this.rng.random() < (1.0 - p)) ? 0 : 1);
    }

    /**
     * Returns a binomial distributed integer between 0 and n inclusive.
     * NOTE: use n > 0 and 0.0 < p < 1.0
     */
    public long binomial(long n, double p) {
        long i, x = 0;

        for (i = 0; i < n; i++)
            x += bernoulli(p);
        return (x);
    }

    /**
     * Returns an equilikely distributed integer between a and b inclusive.
     * NOTE: use a < b
     */
    public long equilikely(long a, long b) {
        return (a + (long) ((b - a + 1) * this.rng.random()));
    }

    /**
     * Returns a geometric distributed non-negative integer.
     * NOTE: use 0.0 < p < 1.0
     */
    public long geometric(double p) {
        return ((long) (Math.log(1.0 - this.rng.random()) / Math.log(p)));
    }

    /**
     * Returns a Pascal distributed non-negative integer.
     * NOTE: use n > 0 and 0.0 < p < 1.0
     */
    public long pascal(long n, double p) {
        long i, x = 0;

        for (i = 0; i < n; i++)
            x += geometric(p);
        return (x);
    }

    /**
     * Returns a Poisson distributed non-negative integer.
     * NOTE: use m > 0
     */
    public long poisson(double m) {
        double t = 0.0;
        long x = 0;

        while (t < m) {
            t += exponential(1.0);
            x++;
        }
        return (x - 1);
    }

    /**
     * Returns a uniformly distributed real number between a and b.
     * NOTE: use a < b
     */
    public double uniform(double a, double b) {
        return (a + (b - a) * this.rng.random());
    }

    /**
     * Returns an exponentially distributed positive real number.
     * NOTE: use m > 0.0
     */
    public double exponential(double m) {
        return (-m * Math.log(1.0 - this.rng.random()));
    }

    /**
     * Returns an Erlang distributed positive real number.
     * NOTE: use n > 0 and b > 0.0
     */
    public double erlang(long n, double b) {
        long i;
        double x = 0.0;

        for (i = 0; i < n; i++)
            x += exponential(b);
        return (x);
    }

    /**
     * Returns a normal (Gaussian) distributed real number.
     * NOTE: use s > 0.0
     *
     * Uses a very accurate approximation of the normal idf due to Odeh & Evans,
     * J. Applied Statistics, 1974, vol 23, pp 96-97.
     */
    public double normal(double m, double s) {
        final double p0 = 0.322232431088;
        final double q0 = 0.099348462606;
        final double p1 = 1.0;
        final double q1 = 0.588581570495;
        final double p2 = 0.342242088547;
        final double q2 = 0.531103462366;
        final double p3 = 0.204231210245e-1;
        final double q3 = 0.103537752850;
        final double p4 = 0.453642210148e-4;
        final double q4 = 0.385607006340e-2;
        double u, t, p, q, z;

        u = this.rng.random();
        if (u < 0.5)
            t = Math.sqrt(-2.0 * Math.log(u));
        else
            t = Math.sqrt(-2.0 * Math.log(1.0 - u));
        p = p0 + t * (p1 + t * (p2 + t * (p3 + t * p4)));
        q = q0 + t * (q1 + t * (q2 + t * (q3 + t * q4)));
        if (u < 0.5)
            z = (p / q) - t;
        else
            z = t - (p / q);
        return (m + s * z);
    }

    /**
     * Returns a lognormal distributed positive real number.
     * NOTE: use b > 0.0
     */
    public double logNormal(double a, double b) {
        return (Math.exp(a + b * normal(0.0, 1.0)));
    }

    /**
     * Returns a chi-square distributed positive real number.
     * NOTE: use n > 0
     */
    public double chiSquare(long n) {
        long i;
        double z, x = 0.0;

        for (i = 0; i < n; i++) {
            z = normal(0.0, 1.0);
            x += z * z;
        }
        return (x);
    }

    /**
     * Returns a student-t distributed real number.
     * NOTE: use n > 0
     */
    public double student(long n) {
        return (normal(0.0, 1.0) / Math.sqrt(chiSquare(n) / n));
    }

}