int DiscreteGamma (double freqK[], double rK[],
double alfa, double beta, int K, int median)
{
/* discretization of gamma distribution with equal proportions in each
category
*/
int i;
double gap05=1.0/(2.0*K), t, factor=alfa/beta*K, lnga1;
if (median) {
for (i=0; i<K; i++) rK[i]=PointGamma((i*2.0+1)*gap05, alfa, beta);
for (i=0,t=0; i<K; i++) t+=rK[i];
for (i=0; i<K; i++) rK[i]*=factor/t;
}
else {
lnga1=LnGamma(alfa+1);
for (i=0; i<K-1; i++)
freqK[i]=PointGamma((i+1.0)/K, alfa, beta);
for (i=0; i<K-1; i++)
freqK[i]=IncompleteGamma(freqK[i]*beta, alfa+1, lnga1);
rK[0] = freqK[0]*factor;
rK[K-1] = (1-freqK[K-2])*factor;
for (i=1; i<K-1; i++) rK[i] = (freqK[i]-freqK[i-1])*factor;
}
for (i=0; i<K; i++) freqK[i]=1.0/K;
return (0);
}
static double PointChi2 (double prob, double v)
{
/* returns z so that Prob{x<z}=prob where x is Chi2 distributed with df=v
returns -1 if in error. 0.000002<prob<0.999998
RATNEST FORTRAN by
Best DJ & Roberts DE (1975) The percentage points of the
Chi2 distribution. Applied Statistics 24: 385-388. (AS91)
Converted into C by Ziheng Yang, Oct. 1993.
*/
double e=.5e-6, aa=.6931471805, p=prob, g;
double xx, c, ch, a=0,q=0,p1=0,p2=0,t=0,x=0,b=0,s1,s2,s3,s4,s5,s6;
if (p<.000002 || p>.999998 || v<=0) return (-1);
g = LnGamma (v/2);
xx=v/2; c=xx-1;
if (v >= -1.24*log(p)) goto l1;
ch=pow((p*xx*exp(g+xx*aa)), 1/xx);
if (ch-e<0) return (ch);
goto l4;
l1:
if (v>.32) goto l3;
ch=0.4; a=log(1-p);
l2:
q=ch; p1=1+ch*(4.67+ch); p2=ch*(6.73+ch*(6.66+ch));
t=-0.5+(4.67+2*ch)/p1 - (6.73+ch*(13.32+3*ch))/p2;
ch-=(1-exp(a+g+.5*ch+c*aa)*p2/p1)/t;
if (fabs(q/ch-1)-.01 <= 0) goto l4;
else goto l2;
l3:
x=PointNormal (p);
p1=0.222222/v; ch=v*pow((x*sqrt(p1)+1-p1), 3.0);
if (ch>2.2*v+6) ch=-2*(log(1-p)-c*log(.5*ch)+g);
l4:
do
{
q=ch; p1=.5*ch;
if ((t=IncompleteGamma (p1, xx, g))<0) {
return (-1);
}
p2=p-t;
t=p2*exp(xx*aa+g+p1-c*log(ch));
b=t/ch; a=0.5*t-b*c;
s1=(210+a*(140+a*(105+a*(84+a*(70+60*a))))) / 420;
s2=(420+a*(735+a*(966+a*(1141+1278*a))))/2520;
s3=(210+a*(462+a*(707+932*a)))/2520;
s4=(252+a*(672+1182*a)+c*(294+a*(889+1740*a)))/5040;
s5=(84+264*a+c*(175+606*a))/2520;
s6=(120+c*(346+127*c))/5040;
ch+=t*(1+0.5*t*s1-b*c*(s1-b*(s2-b*(s3-b*(s4-b*(s5-b*s6))))));
}
while (fabs(q/ch-1) > e);
return (ch);
}
/* Gamma cdf */
double cdfGamma (double x, double shape)
{
double result;
result = IncompleteGamma (shape*x, shape, LnGamma(shape));
return result;
}
void DGamRateProcess::UpdateDiscreteCategories() {
if (withpinv) {
double* x = new double[GetNcat()-1];
double* y = new double[GetNcat()-1];
double lg = rnd::GetRandom().logGamma(alpha+1.0);
for (int i=0; i<GetNcat()-1; i++) {
x[i] = PointGamma((i+1.0)/(GetNcat()-1),alpha,alpha);
}
for (int i=0; i<GetNcat()-2; i++) {
y[i] = IncompleteGamma(alpha*x[i],alpha+1,lg);
}
y[GetNcat()-2] = 1.0;
rate[0] = 0;
rate[1] = (GetNcat()-1) * y[0];
for (int i=1; i<(GetNcat()-1); i++) {
rate[i+1] = (GetNcat()-1) * (y[i] - y[i-1]);
}
delete[] x;
delete[] y;
}
else {
double* x = new double[GetNcat()];
double* y = new double[GetNcat()];
double lg = rnd::GetRandom().logGamma(alpha+1.0);
for (int i=0; i<GetNcat(); i++) {
x[i] = PointGamma((i+1.0)/GetNcat(),alpha,alpha);
}
for (int i=0; i<GetNcat()-1; i++) {
y[i] = IncompleteGamma(alpha*x[i],alpha+1,lg);
}
y[GetNcat()-1] = 1.0;
rate[0] = GetNcat() * y[0];
for (int i=1; i<GetNcat(); i++) {
rate[i] = GetNcat() * (y[i] - y[i-1]);
}
delete[] x;
delete[] y;
}
}
void GaussianSetHistogram(Histogram * 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 *) ckalloc(sizeof(float) * hsize);
if( h->expect == NULL ) {
fatal("Unable to allocate expect size in expected histogram...");
}
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.;
}
void ExtremeValueSetHistogram(Histogram * h, float mu, float lambda, float lowbound, float highbound, float wonka, 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;
h->param[EVD_WONKA] = wonka;
hsize = h->max - h->min + 1;
h->expect = (float *) ckalloc(sizeof(float) * hsize);
if( h->expect == NULL ) {
fatal("Cannot make memory for expect thing... ");
}
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.;
}
int DiscreteGamma(double freqK[], double rK[], double alpha, double beta, int K,
int UseMedian) {
/* discretization of G(alpha, beta) with equal proportions in each category.
*/
int i;
double t, mean = alpha / beta, lnga1;
if (UseMedian) { /* median */
for (i = 0; i < K; i++) {
rK[i] = QuantileGamma((i * 2. + 1) / (2. * K), alpha, beta);
}
for (i = 0, t = 0; i < K; i++) {
t += rK[i];
}
for (i = 0; i < K; i++) {
rK[i] *= mean * K / t; /* rescale so that the mean is alpha/beta. */
}
} else { /* mean */
lnga1 = LnGamma(alpha + 1);
for (i = 0; i < K - 1; i++) { /* cutting points, Eq. 9 */
freqK[i] = QuantileGamma((i + 1.0) / K, alpha, beta);
}
for (i = 0; i < K - 1; i++) { /* Eq. 10 */
freqK[i] = IncompleteGamma(freqK[i] * beta, alpha + 1, lnga1);
}
rK[0] = freqK[0] * mean * K;
for (i = 1; i < K - 1; i++) {
rK[i] = (freqK[i] - freqK[i - 1]) * mean * K;
}
rK[K - 1] = (1 - freqK[K - 2]) * mean * K;
}
for (i = 0; i < K; i++)
freqK[i] = 1.0 / K;
return (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;
}
/* Incomplete Gamma function Q(a,x)
- this is a cleanroom implementation of NRs gammq(a,x)
*/
double IncompleteGammaQ (double a, double x)
{
return 1.0-IncompleteGamma (x, a, LnGamma(a));
}
