package org.broadinstitute.sting.utils;

import cern.jet.math.Arithmetic;

import java.math.BigDecimal;

/**
 * MathUtils is a static class (no instantiation allowed!) with some useful math methods.
 *
 * @author Kiran Garimella
 */
public class MathUtils {

    /** Public constants - used for the Lanczos approximation to the factorial function
     *  (for the calculation of the binomial/multinomial probability in logspace)
     * @param LANC_SEQ[] - an array holding the constants which correspond to the product
     * of Chebyshev Polynomial coefficients, and points on the Gamma function (for interpolation)
     * [see A Precision Approximation of the Gamma Function J. SIAM Numer. Anal. Ser. B, Vol. 1 1964. pp. 86-96]
     * @param LANC_G - a value for the Lanczos approximation to the gamma function that works to
     * high precision 
     */


    /** Private constructor.  No instantiating this class! */
    private MathUtils() {}

    public static double sum(double[] values) {
        double s = 0.0;
        for ( double v : values) s += v;
        return s;
    }

    public static double sumLog10(double[] log10values) {
        double s = 0.0;
        for ( double v : log10values) s += Math.pow(10.0, v);
        return s;
    }

    public static boolean wellFormedDouble(double val) {
        return ! Double.isInfinite(val) && ! Double.isNaN(val);
    }

    public static boolean isBounded(double val, double lower, double upper) {
        return val >= lower && val <= upper;
    }

    public static boolean isPositive(double val) {
        return ! isNegativeOrZero(val);
    }

    public static boolean isPositiveOrZero(double val) {
        return isBounded(val, 0.0, Double.POSITIVE_INFINITY);
    }

    public static boolean isNegativeOrZero(double val) {
        return isBounded(val, Double.NEGATIVE_INFINITY, 0.0);
    }

    public static boolean isNegative(double val) {
        return ! isPositiveOrZero(val);
    }

    /**
     * Compares double values for equality (within 1e-6), or inequality.
     *
     * @param a  the first double value
     * @param b  the second double value
     * @return   -1 if a is greater than b, 0 if a is equal to be within 1e-6, 1 if b is greater than a.
     */
    public static byte compareDoubles(double a, double b) { return compareDoubles(a, b, 1e-6); }

    /**
     * Compares double values for equality (within epsilon), or inequality.
     *
     * @param a       the first double value
     * @param b       the second double value
     * @param epsilon the precision within which two double values will be considered equal
     * @return        -1 if a is greater than b, 0 if a is equal to be within epsilon, 1 if b is greater than a.
     */
    public static byte compareDoubles(double a, double b, double epsilon)
    {
        if (Math.abs(a - b) < epsilon) { return 0; }
        if (a > b) { return -1; }
        return 1;
    }

    /**
     * Compares float values for equality (within 1e-6), or inequality.
     *
     * @param a  the first float value
     * @param b  the second float value
     * @return   -1 if a is greater than b, 0 if a is equal to be within 1e-6, 1 if b is greater than a.
     */
    public static byte compareFloats(float a, float b) { return compareFloats(a, b, 1e-6f); }

    /**
     * Compares float values for equality (within epsilon), or inequality.
     *
     * @param a       the first float value
     * @param b       the second float value
     * @param epsilon the precision within which two float values will be considered equal
     * @return        -1 if a is greater than b, 0 if a is equal to be within epsilon, 1 if b is greater than a.
     */
    public static byte compareFloats(float a, float b, float epsilon)
    {
        if (Math.abs(a - b) < epsilon) { return 0; }
        if (a > b) { return -1; }
        return 1;
    }

    public static double NormalDistribution(double mean, double sd, double x)
    {
        double a = 1.0 / (sd*Math.sqrt(2.0 * Math.PI));
        double b = Math.exp(-1.0 * (Math.pow(x - mean,2.0)/(2.0 * sd * sd)));
        return a * b;
    }

    /**
     * Computes a binomial probability.  This is computed using the formula
     *
     *  B(k; n; p) = [ n! / ( k! (n - k)! ) ] (p^k)( (1-p)^k )
     *
     * where n is the number of trials, k is the number of successes, and p is the probability of success
     *
     * @param k  number of successes
     * @param n  number of Bernoulli trials
     * @param p  probability of success
     *
     * @return   the binomial probability of the specified configuration.  Computes values down to about 1e-237.
     */
    public static double binomialProbability(int k, int n, double p) {
        return Arithmetic.binomial(n, k)*Math.pow(p, k)*Math.pow(1.0 - p, n - k);
        //return (new cern.jet.random.Binomial(n, p, cern.jet.random.engine.RandomEngine.makeDefault())).pdf(k);
    }

    /**
     * Performs the calculation for a binomial probability in logspace, preventing the blowups of the binomial coefficient
     * consistent with moderately large n and k.
     *
     * @param k - number of successes/observations
     * @param n - number of Bernoulli trials
     * @param p - probability of success/observations
     *
     * @return    the probability mass associated with a binomial mass function for the above configuration
     */
    public static double binomialProbabilityLog(int k, int n, double p) {

        double log_coef = 0.0;
        int min;
        int max;

        if(k < n - k) {
            min = k;
            max = n-k;
        } else {
            min = n - k;
            max = k;
        }

        for(int i=2; i <= min; i++) {
            log_coef -= Math.log((double)i);
        }

        for(int i = max+1; i <= n; i++) {
            log_coef += Math.log((double)i);
        }

        return Math.exp(log_coef + ((double)k)*Math.log(p) + ((double)(n-k))*Math.log(1-p));

        // in the future, we may want to precompile a table of exact values, where the entry (a,b) indicates
        // the sum of log(i) from i = (1+(50*a)) to i = 50+(50*b) to increase performance on binomial coefficients
        // which may require many sums.
    }

    /**
     * Performs the cumulative sum of binomial probabilities, where the probability calculation is done in log space.
     * @param start - start of the cumulant sum (over hits)
     * @param end - end of the cumulant sum (over hits)
     * @param total - number of attempts for the number of hits
     * @param probHit - probability of a successful hit
     * @return - returns the cumulative probability
     */
    public static double cumBinomialProbLog(int start, int end, int total, double probHit) {
        double cumProb = 0.0;
        double prevProb;
        BigDecimal probCache = BigDecimal.ZERO;

        for(int hits = start; hits < end; hits++) {
            prevProb = cumProb;
            double probability = binomialProbabilityLog(hits,total,probHit);
            cumProb += probability;
            if ( probability > 0 && cumProb - prevProb < probability/2 ) { // loss of precision
                probCache = probCache.add(new BigDecimal(prevProb));
                cumProb = 0.0;
                hits--; // repeat loop
                // prevProb changes at start of loop
            }
        }

        return probCache.add(new BigDecimal(cumProb)).doubleValue();
    }
  
    /**
     * Computes a multinomial.  This is computed using the formula
     *
     *   M(x1,x2,...,xk; n) = [ n! / (x1! x2! ... xk!) ]
     *
     * where xi represents the number of times outcome i was observed, n is the number of total observations.
     * In this implementation, the value of n is inferred as the sum over i of xi.
     *
     * @param x  an int[] of counts, where each element represents the number of times a certain outcome was observed
     * @return   the multinomial of the specified configuration.
     */
    public static double multinomial(int[] x) {
        // In order to avoid overflow in computing large factorials in the multinomial
        // coefficient, we split the calculation up into the product of a bunch of
        // binomial coefficients.

        double multinomialCoefficient = 1.0;

        for (int i = 0; i < x.length; i++) {
            int n = 0;
            for (int j = 0; j <= i; j++) { n += x[j]; }

            double multinomialTerm = Arithmetic.binomial(n, x[i]);
            multinomialCoefficient *= multinomialTerm;
        }

        return multinomialCoefficient;
    }

    /**
     * Computes a multinomial probability.  This is computed using the formula
     *
     *   M(x1,x2,...,xk; n; p1,p2,...,pk) = [ n! / (x1! x2! ... xk!) ] (p1^x1)(p2^x2)(...)(pk^xk)
     *
     * where xi represents the number of times outcome i was observed, n is the number of total observations, and
     * pi represents the probability of the i-th outcome to occur.  In this implementation, the value of n is
     * inferred as the sum over i of xi.
     *
     * @param x  an int[] of counts, where each element represents the number of times a certain outcome was observed
     * @param p  a double[] of probabilities, where each element represents the probability a given outcome can occur
     * @return   the multinomial probability of the specified configuration.
     */
    public static double multinomialProbability(int[] x, double[] p) {
        // In order to avoid overflow in computing large factorials in the multinomial
        // coefficient, we split the calculation up into the product of a bunch of
        // binomial coefficients.
        double multinomialCoefficient = multinomial(x);

        double probs = 1.0, totalprob = 0.0;
        for (int obsCountsIndex = 0; obsCountsIndex < x.length; obsCountsIndex++) {
            probs *= Math.pow(p[obsCountsIndex], x[obsCountsIndex]);
            totalprob += p[obsCountsIndex];
        }

        assert(MathUtils.compareDoubles(totalprob, 1.0, 0.01) == 0);

        return multinomialCoefficient*probs;
    }

    /**
     * calculate the Root Mean Square of an array of integers
     * @param x  an int[] of numbers
     * @return   the RMS of the specified numbers.
    */
    public static double rms(int[] x) {
        if ( x.length == 0 )
            return 0.0;

        double rms = 0.0;
        for (int i : x)
            rms += i * i;
        rms /= x.length;
        return Math.sqrt(rms);
    }

    /**
     * normalizes the log10-based array
     * @param array  the array to be normalized
    */
    public static double[] normalizeFromLog10(double[] array) {
        double[] normalized = new double[array.length];

        // for precision purposes, we need to add (or really subtract, since they're
        // all negative) the largest value; also, we need to convert to normal-space.
        double maxValue = Utils.findMaxEntry(array);
        for (int i = 0; i < array.length; i++)
            normalized[i] = Math.pow(10, array[i] - maxValue);

        // normalize
        double sum = 0.0;
        for (int i = 0; i < array.length; i++)
            sum += normalized[i];
        for (int i = 0; i < array.length; i++)
            normalized[i] /= sum;

        return normalized;
    }
}