#include <U2Core/disable-warnings.h>
U2_DISABLE_WARNINGS

/************************************************************
* HMMER - Biological sequence analysis with profile HMMs
* Copyright (C) 1992-2003 Washington University School of Medicine
* All Rights Reserved
* 
*     This source code is distributed under the terms of the
*     GNU General Public License. See the files COPYING and LICENSE
*     for details.
************************************************************/

/* histogram.c
* SRE, Sat Jan 20 16:16:17 1996
* 
* Accumulation, printing, and fitting of score histograms
* from database searches.
*
* CVS $Id: histogram.c,v 1.18 2003/04/14 16:00:16 eddy Exp $
************************************************************
* Basic API:
* 
* struct histogram_s *h;
* 
* h = AllocHistogram(min_hint, max_hint, lumpsize);
* 
* while (getting scores x) AddToHistogram(h, x);
* 
* ExtremeValueFitHistogram(h, high_hint);   
* PrintASCIIHistogram(fp, h);   
* FreeHistogram(h);
*/

#include "funcs.h"

/* Function: AllocHistogram()
* 
* Purpose:  Allocate and return a histogram structure.
*           min and max are your best guess. They need
*           not be absolutely correct; the histogram
*           will expand dynamically to accomodate scores
*           that exceed these suggested bounds. The amount
*           that the histogram grows by is set by "lumpsize".
* 
* Args:     min:      minimum score (integer)
*           max:      maximum score (integer)
 *           lumpsize: when reallocating histogram, pad the reallocation
 *                     by this much (saves excessive reallocation)
*/
struct histogram_s *
    AllocHistogram(int min, int max, int lumpsize)
{
    struct histogram_s *h;
    int            newsize;
    int            i;

    newsize = max - min + 1;

    h = (struct histogram_s *) MallocOrDie(sizeof(struct histogram_s));
    h->min       = min;
    h->max       = max;
    h->total     = 0;
    h->lowscore  = INT_MAX;
    h->highscore = INT_MIN;
    h->lumpsize  = lumpsize;
    h->histogram = (int *) MallocOrDie (sizeof(int) * newsize);
    for (i = 0; i < newsize; i++) h->histogram[i] = 0;

    h->expect    = NULL;
    h->fit_type  = HISTFIT_NONE;

    return h;
}


/* Function: FreeHistogram()
* 
* Purpose:  free a histogram structure.
*/
void
FreeHistogram(struct histogram_s *h)
{
    free(h->histogram);
    if (h->expect != NULL) free(h->expect);
    free(h);
} 

/* Function: UnfitHistogram()
* 
* Purpose:  Free only the theoretical fit part of a histogram.
*/
void
UnfitHistogram(struct histogram_s *h)
{
    if (h->expect != NULL) free(h->expect);
    h->expect   = NULL;
    h->fit_type = HISTFIT_NONE;
}


/* Function: AddToHistogram()
* 
* Purpose:  Bump the appropriate counter in a histogram
*           structure, given a score. The score is
*           rounded off from float precision to the
*           next lower integer.
*/
void
AddToHistogram(struct histogram_s *h, float sc)
{
    int score;
    int moveby;
    int prevsize;
    int newsize;
    int i;

    /* Adding to a histogram conflicts with existing fit:
    * prohibit this.
    */
  if (h->fit_type != HISTFIT_NONE)
    Die("AddToHistogram(): Can't add to a fitted histogram\n");


    /* histogram bins are defined as:  score >= bin value, < bin+1 
    * -1.9 -> -2    -0.4 -> -1    1.9 -> 1
    * -2.1 -> -3     0.4 -> 0     2.1 -> 2
    */
    score = (int) floor(sc);

  /* Check to see if we must reallocate the histogram.
    */
    if (score < h->min)
    {
        prevsize = h->max - h->min + 1;
        moveby   = (h->min - score) + h->lumpsize;
        newsize  = prevsize + moveby;
        h->min  -= moveby;

        h->histogram = (int *) ReallocOrDie(h->histogram, sizeof(int) * newsize);
        memmove(h->histogram+moveby, h->histogram, sizeof(int) * prevsize);
        for (i = 0; i < moveby; i++)
            h->histogram[i] = 0;
    }
    else if (score > h->max)
    {
        prevsize = h->max - h->min + 1;
        h->max   = h->lumpsize + score;
        newsize  = h->max - h->min + 1;

        h->histogram = (int *) ReallocOrDie(h->histogram, sizeof(int) * newsize);
        for (i = prevsize; i < newsize; i++)
            h->histogram[i] = 0;
    }

    /* Bump the correct bin.
    * The bin number is score - h->min
    */
    h->histogram[score - h->min]++;
    h->total++;
    if (score < h->lowscore) h->lowscore   = score;
    if (score > h->highscore) h->highscore = score;

  SQD_DPRINTF3(("AddToHistogram(): added %.1f; rounded to %d; in bin %d (%d-%d)\n",
	  sc, score, score-h->min, h->min, h->max));
    return;
}




/* Function: EVDBasicFit()
* Date:     SRE, Wed Nov 12 11:02:27 1997 [St. Louis]
* 
* Purpose:  Fit a score histogram to the extreme value 
*           distribution. Set the parameters lambda
*           and mu in the histogram structure. Fill in the
*           expected values in the histogram. Calculate
*           a chi-square test as a measure of goodness of fit. 
*           
*           This is the basic version of ExtremeValueFitHistogram(),
*           in a nonrobust form: simple linear regression with no
*           outlier pruning.
*           
* Methods:  Uses a linear regression fitting method [Collins88,Lawless82]
*
* Args:     h         - histogram to fit
*           
* Return:   (void)
*/
void
EVDBasicFit(struct histogram_s *h)
{
    float *d;            /* distribution P(S < x)          */
    float *x;            /* x-axis of P(S<x) for Linefit() */
    int    hsize;
    int    sum;
    int    sc, idx;		/* loop indices for score or score-h->min   */
    float  slope, intercept;	/* m,b fit from Linefit()                   */
    float  corr;			/* correlation coeff of line fit, not used  */
    float  lambda, mu;		/* slope, intercept converted to EVD params */

    /* Allocations for x, y axes
    * distribution d runs from min..max with indices 0..max-min
    *     i.e. score - min = index into d, x, histogram, and expect
    */
    hsize = h->highscore - h->lowscore + 1;
    d         = (float *) MallocOrDie(sizeof(float) * hsize);
    x         = (float *) MallocOrDie(sizeof(float) * hsize);
    for (idx = 0; idx < hsize; idx++)
        d[idx] = x[idx] = 0.;

    /* Calculate P(S < x) distribution from histogram.
    * note off-by-one of sc, because histogram bin contains scores between
    * x and x+1. 
    */ 
    sum = 0;
    for (sc = h->lowscore; sc <= h->highscore; sc++)
    {
        sum += h->histogram[sc - h->min];
        d[sc - h->lowscore] = (float) sum / (float) h->total;
        x[sc - h->lowscore] = (float) (sc + 1);
    }

    /* Do a linear regression fit to the log[-log(P(S<x))] "line".
    * we have log[-log(1-P(S>x))]  = -lambda * x + lambda * mu
    * so lambda = -m  and mu = b/lambda
    */
    /* convert y axis to log[-log(P(S<x))]  */
    for (sc = h->lowscore; sc < h->highscore; sc++)
        d[sc - h->lowscore] = log(-1. * log(d[sc - h->lowscore]));

    /* do the linear regression */
    Linefit(x, d, hsize-1, &intercept, &slope, &corr);
    /* calc mu, lambda */
    lambda = -1. * slope;
    mu     = intercept / lambda;

    /* Set the EVD parameters in the histogram;
    * pass 2 for additional lost degrees of freedom because we fit mu, lambda.
    */
    ExtremeValueSetHistogram(h, mu, lambda, h->lowscore, h->highscore, 2);

    free(x);
    free(d);
    return;
}


/* Function: ExtremeValueFitHistogram()
* Date:     SRE, Sat Nov 15 17:16:15 1997 [St. Louis]
* 
* Purpose:  Fit a score histogram to the extreme value 
*           distribution. Set the parameters lambda
*           and mu in the histogram structure. Calculate
*           a chi-square test as a measure of goodness of fit. 
*           
* Methods:  Uses a maximum likelihood method [Lawless82].
*           Lower outliers are removed by censoring the data below the peak.
*           Upper outliers are removed iteratively using method 
*           described by [Mott92].
*           
* Args:     h         - histogram to fit
*           censor    - TRUE to censor data left of the peak
*           high_hint - score cutoff; above this are `real' hits that aren't fit
*           
* Return:   1 if fit is judged to be valid.
*           else 0 if fit is invalid (too few seqs.)
*/
int
ExtremeValueFitHistogram(struct histogram_s *h, int censor, float high_hint) 
{
    float *x;                     /* array of EVD samples to fit */
    int   *y;                     /* histogram counts            */ 
    int    n;			/* number of observed samples  */
    int    z;			/* number of censored samples  */
    int    hsize;			/* size of histogram           */
    float  lambda, mu;		/* new estimates of lambda, mu */
    int    sc;		        /* loop index for score        */
    int    lowbound;		/* lower bound of fitted region*/
    int    highbound;		/* upper bound of fitted region*/
    int    new_highbound;
    int    iteration;

    /* Determine lower bound on fitted region;
    * if we're censoring the data, choose the peak of the histogram.
    * if we're not, then we take the whole histogram.
    */
    lowbound = h->lowscore;
    if (censor) 
    {
        int max = -1;
        for (sc = h->lowscore; sc <= h->highscore; sc++)
            if (h->histogram[sc - h->min] > max) 
            {
                max      = h->histogram[sc - h->min];
                lowbound = sc;
            }
    }

    /* Determine initial upper bound on fitted region.
    */
    highbound = MIN(high_hint, h->highscore);

    /* Now, iteratively converge on our lambda, mu:
    */
    for (iteration = 0; iteration < 100; iteration++)
    {
        /* Construct x, y vectors.
        */
        x = NULL;
        y = NULL;
        hsize = highbound - lowbound + 1;
        if (hsize < 5) goto FITFAILED; /* require at least 5 bins or we don't fit */

        x = (float*)MallocOrDie(sizeof(float) * hsize);
        y = (int*)MallocOrDie(sizeof(int)   * hsize);
        n = 0;
        for (sc = lowbound; sc <= highbound; sc++)
        {
            x[sc-lowbound] = (float) sc + 0.5; /* crude, but tests OK */
            y[sc-lowbound] = h->histogram[sc - h->min];
            n             += h->histogram[sc - h->min];
        }

        if (n < 100) goto FITFAILED;  /* require fitting to at least 100 points */

        /* If we're censoring, estimate z, the number of censored guys
        * left of the bound. Our initial estimate is crudely that we're
        * missing e^-1 of the total distribution (which would be exact
        * if we censored exactly at mu; but we censored at the observed peak).
        * Subsequent estimates are more exact based on our current estimate of mu.
        */
        if (censor)
        {
            if (iteration == 0)
                z = MIN(h->total-n, (int) (0.58198 * (float) n));
            else
            {
                double psx;
                psx = EVDDistribution((float) lowbound, mu, lambda);
                z = MIN(h->total-n, (int) ((double) n * psx / (1. - psx)));
            }
        }

        /* Do an ML fit
        */
        if (censor) {
            if (! EVDCensoredFit(x, y, hsize, z, (float) lowbound, &mu, &lambda))
                goto FITFAILED;
        } else  
            if (! EVDMaxLikelyFit(x, y, hsize, &mu, &lambda))
                goto FITFAILED;

        /* Find the Eval = 1 point as a new highbound;
        * the total number of samples estimated to "belong" to the EVD is n+z  
        */
        new_highbound = (int)
            (mu - (log (-1. * log((double) (n+z-1) / (double)(n+z))) / lambda));

        free(x);
        free(y);
        if (new_highbound >= highbound) break; 
        highbound = new_highbound;
    }

    /* Set the histogram parameters;
    * - we fit from lowbound to highbound; thus we lose 2 degrees of freedom
    *   for fitting mu, lambda, but we get 1 back because we're unnormalized
    *   in this interval, hence we pass 2-1 = 1 as ndegrees.
    */    
    ExtremeValueSetHistogram(h, mu, lambda, lowbound, highbound, 1); 
    return 1;

FITFAILED:
    UnfitHistogram(h);
    if (x != NULL) free(x);
    if (y != NULL) free(y);
    return 0;
}


/* Function: ExtremeValueSetHistogram()
* 
* Purpose:  Instead of fitting the histogram to an EVD,
*           simply set the EVD parameters from an external source.
*
* Args:     h        - the histogram to set
*           mu       - mu location parameter                
*           lambda   - lambda scale parameter
*           lowbound - low bound of the histogram that was fit
*           highbound- high bound of histogram that was fit
*           ndegrees - extra degrees of freedom to subtract in X^2 test:
*                        typically 0 if mu, lambda are parametric,
*                        else 2 if mu, lambda are estimated from data
*/
void
ExtremeValueSetHistogram(struct histogram_s *h, float mu, float lambda, 
                         float lowbound, float highbound, int ndegrees)
{
    int   sc;
    int   hsize, idx;
    int   nbins;
    float delta;

    UnfitHistogram(h);
    h->fit_type          = HISTFIT_EVD;
    h->param[EVD_LAMBDA] = lambda;
    h->param[EVD_MU]     = mu;

    hsize     = h->max - h->min + 1;
    h->expect = (float *) MallocOrDie(sizeof(float) * hsize);
    for (idx = 0; idx < hsize; idx++)
        h->expect[idx] = 0.;

    /* Calculate the expected values for the histogram.
    */
    for (sc = h->min; sc <= h->max; sc++)
        h->expect[sc - h->min] =
        ExtremeValueE((float)(sc), h->param[EVD_MU], h->param[EVD_LAMBDA], 
        h->total) -
        ExtremeValueE((float)(sc+1), h->param[EVD_MU], h->param[EVD_LAMBDA],
        h->total);

    /* Calculate the goodness-of-fit (within whole region)
    */
    h->chisq = 0.;
    nbins    = 0;
    for (sc = lowbound; sc <= highbound; sc++)
        if (h->expect[sc-h->min] >= 5. && h->histogram[sc-h->min] >= 5)
        {
            delta = (float) h->histogram[sc-h->min] - h->expect[sc-h->min];
            h->chisq += delta * delta / h->expect[sc-h->min];
            nbins++;
        }

        /* Since we fit the whole histogram, there is at least 
        * one constraint on chi-square: the normalization to h->total.
        */
        if (nbins > 1 + ndegrees)
            h->chip = (float) IncompleteGamma((double)(nbins-1-ndegrees)/2., 
            (double) h->chisq/2.);
        else
            h->chip = 0.;		
}



/* Function: GaussianFitHistogram()
* 
* Purpose:  Fit a score histogram to a Gaussian distribution.
*           Set the parameters mean and sd in the histogram
*           structure, as well as a chi-squared test for
*           goodness of fit.
*
* Args:     h         - histogram to fit
*           high_hint - score cutoff; above this are `real' hits that aren't fit
*           
* Return:   1 if fit is judged to be valid.
*           else 0 if fit is invalid (too few seqs.)           
*/
int
GaussianFitHistogram(struct histogram_s *h, float high_hint)
{
    float sum;
    float sqsum;
    float delta;
    int   sc;
    int   nbins;
    int   hsize, idx;

    /* Clear any previous fitting from the histogram.
    */
    UnfitHistogram(h);

    /* Determine if we have enough hits to fit the histogram;
    * arbitrarily require 1000.
    */
    if (h->total < 1000) { h->fit_type = HISTFIT_NONE; return 0; }

    /* Simplest algorithm for mean and sd;
    * no outlier detection yet (not even using high_hint)
    * 
    * Magic 0.5 correction is because our histogram is for
    * scores between x and x+1; we estimate the expectation
    * (roughly) as x + 0.5. 
    */
    sum = sqsum = 0.;
    for (sc = h->lowscore; sc <= h->highscore; sc++)
    {
        delta  = (float) sc + 0.5;
        sum   += (float) h->histogram[sc-h->min] * delta;
        sqsum += (float) h->histogram[sc-h->min] * delta * delta;
    }
    h->fit_type          = HISTFIT_GAUSSIAN;
    h->param[GAUSS_MEAN] = sum / (float) h->total;
    h->param[GAUSS_SD]   = sqrt((sqsum - (sum*sum/(float)h->total)) / 
        (float)(h->total-1));

    /* Calculate the expected values for the histogram.
    * Note that the magic 0.5 correction appears again.
    * Calculating difference between distribution functions for Gaussian 
    * would be correct but hard.
    */
    hsize     = h->max - h->min + 1;
    h->expect = (float *) MallocOrDie(sizeof(float) * hsize);
    for (idx = 0; idx < hsize; idx++)
        h->expect[idx] = 0.;

    for (sc = h->min; sc <= h->max; sc++)
    {
        delta = (float) sc + 0.5 - h->param[GAUSS_MEAN];
        h->expect[sc - h->min] =
            (float) h->total * ((1. / (h->param[GAUSS_SD] * sqrt(2.*3.14159))) * 
            (exp(-1.* delta*delta / (2. * h->param[GAUSS_SD] * h->param[GAUSS_SD]))));
    }

    /* Calculate the goodness-of-fit (within region that was fitted)
    */
    h->chisq = 0.;
    nbins    = 0;
    for (sc = h->lowscore; sc <= h->highscore; sc++)
        if (h->expect[sc-h->min] >= 5. && h->histogram[sc-h->min] >= 5)
        {
            delta = (float) h->histogram[sc-h->min] - h->expect[sc-h->min];
            h->chisq += delta * delta / h->expect[sc-h->min];
            nbins++;
        }
        /* -1 d.f. for normalization; -2 d.f. for two free parameters */
        if (nbins > 3)
            h->chip = (float) IncompleteGamma((double)(nbins-3)/2., 
            (double) h->chisq/2.);
        else
            h->chip = 0.;		

        return 1;
}


/* Function: GaussianSetHistogram()
* 
* Purpose:  Instead of fitting the histogram to a Gaussian,
*           simply set the Gaussian parameters from an external source.
*/
void
GaussianSetHistogram(struct histogram_s *h, float mean, float sd)
{
    int   sc;
    int   hsize, idx;
    int   nbins;
    float delta;

    UnfitHistogram(h);
    h->fit_type          = HISTFIT_GAUSSIAN;
    h->param[GAUSS_MEAN] = mean;
    h->param[GAUSS_SD]   = sd;

    /* Calculate the expected values for the histogram.
    */
    hsize     = h->max - h->min + 1;
    h->expect = (float *) MallocOrDie(sizeof(float) * hsize);
    for (idx = 0; idx < hsize; idx++)
        h->expect[idx] = 0.;

    /* Note: ideally we'd use the Gaussian distribution function
    * to find the histogram occupancy in the window sc..sc+1. 
    * However, the distribution function is hard to calculate.
    * Instead, estimate the histogram by taking the density at sc+0.5.
    */
    for (sc = h->min; sc <= h->max; sc++)
    { 
        delta = ((float)sc + 0.5) - h->param[GAUSS_MEAN];
        h->expect[sc - h->min] =
            (float) h->total * ((1. / (h->param[GAUSS_SD] * sqrt(2.*3.14159))) * 
            (exp(-1.*delta*delta / (2. * h->param[GAUSS_SD] * h->param[GAUSS_SD]))));
    }

    /* Calculate the goodness-of-fit (within whole region)
    */
    h->chisq = 0.;
    nbins    = 0;
    for (sc = h->lowscore; sc <= h->highscore; sc++)
        if (h->expect[sc-h->min] >= 5. && h->histogram[sc-h->min] >= 5)
        {
            delta = (float) h->histogram[sc-h->min] - h->expect[sc-h->min];
            h->chisq += delta * delta / h->expect[sc-h->min];
            nbins++;
        }
        /* -1 d.f. for normalization */
        if (nbins > 1)
            h->chip = (float) IncompleteGamma((double)(nbins-1)/2., 
            (double) h->chisq/2.);
        else
            h->chip = 0.;		
}



/* Function: EVDDensity()
* Date:     SRE, Sat Nov 15 19:37:52 1997 [St. Louis]
* 
* Purpose:  Return the extreme value density P(S=x) at
*           a given point x, for an EVD controlled by
*           parameters mu and lambda.
*/
double
EVDDensity(float x, float mu, float lambda)
{
    return (lambda * exp(-1. * lambda * (x - mu) 
        - exp(-1. * lambda * (x - mu))));
}

/* Function: EVDDistribution()
* Date:     SRE, Tue Nov 18 08:02:22 1997 [St. Louis]
* 
* Purpose:  Returns the extreme value distribution P(S < x)
*           evaluated at x, for an EVD controlled by parameters
*           mu and lambda.
*/
double
EVDDistribution(float x, float mu, float lambda)
{
    return (exp(-1. * exp(-1. * lambda * (x - mu))));
}

/* Function: ExtremeValueP()
* 
* Purpose:  Calculate P(S>x) according to an extreme
*           value distribution, given x and the parameters
*           of the distribution (characteristic
*           value mu, decay constant lambda).
*           
*           This function is exquisitely prone to
*           floating point exceptions if it isn't coded
*           carefully.
*           
* Args:     x      = score
*           mu     = characteristic value of extreme value distribution
*           lambda = decay constant of extreme value distribution
*           
* Return:   P(S>x)
*/                   
double
ExtremeValueP(float x, float mu, float lambda)
{
    double y;
    /* avoid exceptions near P=1.0 */
    /* typical 32-bit sys: if () < -3.6, return 1.0 */
    if ((lambda * (x - mu)) <= -1. * log(-1. * log(DBL_EPSILON))) return 1.0;
    /* avoid underflow fp exceptions near P=0.0*/
    if ((lambda * (x - mu)) >= 2.3 * (double) DBL_MAX_10_EXP)     return 0.0;
    /* a roundoff issue arises; use 1 - e^-x --> x for small x */
    y = exp(-1. * lambda * (x - mu));
    if       (y < 1e-7) return y;
    else     return (1.0 - exp(-1. * y));
}


/* Function: ExtremeValueP2()
* 
* Purpose:  Calculate P(S>x) in a database of size N,
*           using P(S>x) for a single sequence, according
*           to a Poisson distribution.
*
* Args:     x      = score
*           mu     = characteristic value of extreme value distribution
*           lambda = decay constant of extreme value distribution
*           N      = number of trials (number of sequences)
*
* Return:   P(S>x) for database of size N
*/
double
ExtremeValueP2(float x, float mu, float lambda, int N)
{
    double y;
    y = N * ExtremeValueP(x,mu,lambda);
    if (y < 1e-7) return y;
    else          return (1.0 - exp(-1. * y));
}

/* Function: ExtremeValueE()
* 
* Purpose:  Calculate E(S>x) in a database of size N,
*           using P(S>x) for a single sequence: simply np.
*
* Args:     x      = score
*           mu     = characteristic value of extreme value distribution
*           lambda = decay constant of extreme value distribution
*           N      = number of trials (number of sequences)
*
* Return:   E(S>x) for database of size N
*/
double
ExtremeValueE(float x, float mu, float lambda, int N)
{
    return (double)N * ExtremeValueP(x,mu,lambda);
}


/* Function: EVDrandom()
* 
* Purpose:  Randomly sample an x from an EVD.
*           Trivially done by the transformation method, since
*           the distribution is analytical:
*              x = \mu - \frac{\log \left[ -\log P(S<x) \right]}{\lambda}
*           where P(S<x) is sampled uniformly on 0 < P(S<x) < 1.
*/
float
EVDrandom(float mu, float lambda)
{
    float p = 0.0;

    /* Very unlikely, but possible,
    * that sre_random() would give us exactly 0 or 1 
    */
    while (p == 0. || p == 1.) p = sre_random(); 
    return mu - log(-1. * log(p)) / lambda;
} 


/* Function: Lawless416()
* Date:     SRE, Thu Nov 13 11:48:50 1997 [St. Louis]
* 
* Purpose:  Equation 4.1.6 from [Lawless82], pg. 143, and
*           its first derivative with respect to lambda,
*           for finding the ML fit to EVD lambda parameter.
*           This equation gives a result of zero for the maximum
*           likelihood lambda.
*           
*           Can either deal with a histogram or an array.
*           
*           Warning: beware overflow/underflow issues! not bulletproof.
*           
* Args:     x      - array of sample values (or x-axis of a histogram)
*           y      - NULL (or y-axis of a histogram)
*           n      - number of samples (or number of histogram bins)
*           lambda - a lambda to test
*           ret_f  - RETURN: 4.1.6 evaluated at lambda
*           ret_df - RETURN: first derivative of 4.1.6 evaluated at lambda
*           
* Return:   (void)
*/ 
void
Lawless416(float *x, int *y, int n, float lambda, float *ret_f, float *ret_df)
{

    double esum;			/* \sum e^(-lambda xi)      */
    double xesum;			/* \sum xi e^(-lambda xi)   */
    double xxesum;		/* \sum xi^2 e^(-lambda xi) */
    double xsum;			/* \sum xi                  */
    double mult;			/* histogram count multiplier */
    double total;			/* total samples            */
    int i;


    esum = xesum = xsum  = xxesum = total = 0.;
    for (i = 0; i < n; i++)
    {
        mult = (y == NULL) ? 1. : (double) y[i];
        xsum   += mult * x[i];
        xesum  += mult * x[i] * exp(-1. * lambda * x[i]);
        xxesum += mult * x[i] * x[i] * exp(-1. * lambda * x[i]);
        esum   += mult * exp(-1. * lambda * x[i]);
        total  += mult;
    }
    *ret_f  = 1./lambda - xsum / total + xesum / esum;
    *ret_df = ((xesum / esum) * (xesum / esum))
        - (xxesum / esum)
        - (1. / (lambda * lambda));

    return;
}


/* Function: Lawless422()
* Date:     SRE, Mon Nov 17 09:42:48 1997 [St. Louis]
* 
* Purpose:  Equation 4.2.2 from [Lawless82], pg. 169, and
*           its first derivative with respect to lambda,
*           for finding the ML fit to EVD lambda parameter
*           for Type I censored data. 
*           This equation gives a result of zero for the maximum
*           likelihood lambda.
*           
*           Can either deal with a histogram or an array.
*           
*           Warning: beware overflow/underflow issues! not bulletproof.
*           
* Args:     x      - array of sample values (or x-axis of a histogram)
*           y      - NULL (or y-axis of a histogram)
*           n      - number of observed samples (or number of histogram bins)
*           z      - number of censored samples 
*           c      - censoring value; all observed x_i >= c         
*           lambda - a lambda to test
*           ret_f  - RETURN: 4.2.2 evaluated at lambda
*           ret_df - RETURN: first derivative of 4.2.2 evaluated at lambda
*           
* Return:   (void)
*/ 
void
Lawless422(float *x, int *y, int n, int z, float c,
           float lambda, float *ret_f, float *ret_df)
{
    double esum;			/* \sum e^(-lambda xi)      + z term    */
    double xesum;			/* \sum xi e^(-lambda xi)   + z term    */
    double xxesum;		/* \sum xi^2 e^(-lambda xi) + z term    */
    double xsum;			/* \sum xi                  (no z term) */
    double mult;			/* histogram count multiplier */
    double total;			/* total samples            */
    int i;

    esum = xesum = xsum  = xxesum = total = 0.;
    for (i = 0; i < n; i++)
    {
        mult = (y == NULL) ? 1. : (double) y[i];
        xsum   += mult * x[i];
        esum   += mult *               exp(-1. * lambda * x[i]);
        xesum  += mult * x[i] *        exp(-1. * lambda * x[i]);
        xxesum += mult * x[i] * x[i] * exp(-1. * lambda * x[i]);
        total  += mult;
    }

    /* Add z terms for censored data
    */
    esum   += (double) z *         exp(-1. * lambda * c);
    xesum  += (double) z * c *     exp(-1. * lambda * c);
    xxesum += (double) z * c * c * exp(-1. * lambda * c);

    *ret_f  = 1./lambda - xsum / total + xesum / esum;
    *ret_df = ((xesum / esum) * (xesum / esum))
        - (xxesum / esum)
        - (1. / (lambda * lambda));

    return;
}



/* Function: EVDMaxLikelyFit()
* Date:     SRE, Fri Nov 14 07:56:29 1997 [St. Louis]
* 
* Purpose:  Given a list or a histogram of EVD-distributed samples,
*           find maximum likelihood parameters lambda and
*           mu. 
*           
* Algorithm: Uses approach described in [Lawless82]. Solves
*           for lambda using Newton/Raphson iterations;
*           then substitutes lambda into Lawless' equation 4.1.5
*           to get mu. 
*           
*           Newton/Raphson algorithm developed from description in
*           Numerical Recipes in C [Press88]. 
*           
* Args:     x          - list of EVD distributed samples or x-axis of histogram
*           c          - NULL, or y-axis of histogram
*           n          - number of samples, or number of histogram bins 
*           ret_mu     : RETURN: ML estimate of mu
*           ret_lambda : RETURN: ML estimate of lambda
*           
* Return:   1 on success; 0 on any failure
*/
int
EVDMaxLikelyFit(float *x, int *c, int n, float *ret_mu, float *ret_lambda)
{
    float  lambda, mu;
    float  fx;			/* f(x)  */
    float  dfx;			/* f'(x) */
    double esum;                  /* \sum e^(-lambda xi) */ 
    double mult;
    double total;
    float  tol = 1e-5;
    int    i;

    /* 1. Find an initial guess at lambda: linear regression here?
    */
    lambda = 0.2;

    /* 2. Use Newton/Raphson to solve Lawless 4.1.6 and find ML lambda
    */
    for (i = 0; i < 100; i++)
    {
        Lawless416(x, c, n, lambda, &fx, &dfx);
        if (fabs(fx) < tol) break;             /* success */
        lambda = lambda - fx / dfx;	     /* Newton/Raphson is simple */
        if (lambda <= 0.) lambda = 0.001;      /* but be a little careful  */
    }

    /* 2.5: If we did 100 iterations but didn't converge, Newton/Raphson failed.
    *      Resort to a bisection search. Worse convergence speed
    *      but guaranteed to converge (unlike Newton/Raphson).
    *      We assume (!?) that fx is a monotonically decreasing function of x;
    *      i.e. fx > 0 if we are left of the root, fx < 0 if we
    *      are right of the root.
    */ 
    if (i == 100)
    {
        float left, right, mid;
      SQD_DPRINTF2(("EVDMaxLikelyFit(): Newton/Raphson failed; switchover to bisection"));

        /* First we need to bracket the root */
        lambda = right = left = 0.2;
        Lawless416(x, c, n, lambda, &fx, &dfx);
        if (fx < 0.) 
        {			/* fix right; search left. */
            do {
                left -= 0.1;
                if (left < 0.) { 
	      SQD_DPRINTF2(("EVDMaxLikelyFit(): failed to bracket root")); 
                    return 0; 
                }
                Lawless416(x, c, n, left, &fx, &dfx);
            } while (fx < 0.);
        }
        else
        {			/* fix left; search right. */
            do {
                right += 0.1;
                Lawless416(x, c, n, right, &fx, &dfx);
                if (right > 100.) {
	      SQD_DPRINTF2(("EVDMaxLikelyFit(): failed to bracket root")); 
                    return 0; 
                }
            } while (fx > 0.);
        }
        /* now we bisection search in left/right interval */
        for (i = 0; i < 100; i++)
        {
            mid = (left + right) / 2.; 
            Lawless416(x, c, n, mid, &fx, &dfx);
            if (fabs(fx) < tol) break;             /* success */
            if (fx > 0.)	left = mid;
            else          right = mid;
        }
        if (i == 100) { 
	SQD_DPRINTF2(("EVDMaxLikelyFit(): even the bisection search failed")); 
            return 0; 
        }
        lambda = mid;
    }

    /* 3. Substitute into Lawless 4.1.5 to find mu
    */
    esum = 0.;
    total = 0.;
    for (i = 0; i < n; i++)
    {
        mult   = (c == NULL) ? 1. : (double) c[i];
        esum  += mult * exp(-1 * lambda * x[i]);
        total += mult;
    }
    mu = -1. * log(esum / total) / lambda;

    *ret_lambda = lambda;
    *ret_mu     = mu;   
    return 1;
}


/* Function: EVDCensoredFit()
* Date:     SRE, Mon Nov 17 10:01:05 1997 [St. Louis]
* 
* Purpose:  Given a /left-censored/ list or histogram of EVD-distributed 
*           samples, as well as the number of censored samples z and the
*           censoring value c,              
*           find maximum likelihood parameters lambda and
*           mu. 
*           
* Algorithm: Uses approach described in [Lawless82]. Solves
*           for lambda using Newton/Raphson iterations;
*           then substitutes lambda into Lawless' equation 4.2.3
*           to get mu. 
*           
*           Newton/Raphson algorithm developed from description in
*           Numerical Recipes in C [Press88]. 
*           
* Args:     x          - list of EVD distributed samples or x-axis of histogram
*           y          - NULL, or y-axis of histogram
*           n          - number of observed samples,or number of histogram bins 
*           z          - number of censored samples
*           c          - censoring value (all x_i >= c)
*           ret_mu     : RETURN: ML estimate of mu
*           ret_lambda : RETURN: ML estimate of lambda
*           
* Return:   (void)
*/
int
EVDCensoredFit(float *x, int *y, int n, int z, float c, 
               float *ret_mu, float *ret_lambda)
{
    float  lambda, mu;
    float  fx;			/* f(x)  */
    float  dfx;			/* f'(x) */
    double esum;                  /* \sum e^(-lambda xi) */ 
    double mult;
    double total;
    float  tol = 1e-5;
    int    i;

    /* 1. Find an initial guess at lambda: linear regression here?
    */
    lambda = 0.2;

    /* 2. Use Newton/Raphson to solve Lawless 4.2.2 and find ML lambda
    */
    for (i = 0; i < 100; i++)
    {
        Lawless422(x, y, n, z, c, lambda, &fx, &dfx);
        if (fabs(fx) < tol) break;             /* success */
        lambda = lambda - fx / dfx;	     /* Newton/Raphson is simple */
        if (lambda <= 0.) lambda = 0.001;      /* but be a little careful  */
    }

    /* 2.5: If we did 100 iterations but didn't converge, Newton/Raphson failed.
    *      Resort to a bisection search. Worse convergence speed
    *      but guaranteed to converge (unlike Newton/Raphson).
    *      We assume (!?) that fx is a monotonically decreasing function of x;
    *      i.e. fx > 0 if we are left of the root, fx < 0 if we
    *      are right of the root.
    */ 
    if (i == 100)
    {
        float left, right, mid;
        /* First we need to bracket the root */
      SQD_DPRINTF2(("EVDCensoredFit(): Newton/Raphson failed; switched to bisection"));
        lambda = right = left = 0.2;
        Lawless422(x, y, n, z, c, lambda, &fx, &dfx);
        if (fx < 0.) 
        {			/* fix right; search left. */
            do {
                left -= 0.03;
                if (left < 0.) { 
	      SQD_DPRINTF2(("EVDCensoredFit(): failed to bracket root")); 
                    return 0;
                }
                Lawless422(x, y, n, z, c, left, &fx, &dfx);
            } while (fx < 0.);
        }
        else
        {			/* fix left; search right. */
            do {
                right += 0.1;
                Lawless422(x, y, n, z, c, left, &fx, &dfx);
                if (right > 100.) {
	      SQD_DPRINTF2(("EVDCensoredFit(): failed to bracket root"));
                    return 0;
                }
            } while (fx > 0.);
        }
        /* now we bisection search in left/right interval */
        for (i = 0; i < 100; i++)
        {
            mid = (left + right) / 2.; 
            Lawless422(x, y, n, z, c, left, &fx, &dfx);
            if (fabs(fx) < tol) break;             /* success */
            if (fx > 0.)	left = mid;
            else          right = mid;
        }
        if (i == 100) {
	SQD_DPRINTF2(("EVDCensoredFit(): even the bisection search failed"));
            return 0;
        }
        lambda = mid;
    }

    /* 3. Substitute into Lawless 4.2.3 to find mu
    */
    esum =  total = 0.;
    for (i = 0; i < n; i++)
    {
        mult   = (y == NULL) ? 1. : (double) y[i];
        esum  += mult * exp(-1. * lambda * x[i]);
        total += mult;
    }
    esum += (double) z * exp(-1. * lambda * c);    /* term from censored data */
    mu = -1. * log(esum / total) / lambda;        

    *ret_lambda = lambda;
    *ret_mu     = mu;   
    return 1;
}



