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.;
}
boolean fit_Hscore_to_EVD(Hscore * hs,float guess_of_outliers)
{
int i;
if( hs->his == NULL ) {
warn("Your Hscore has no histogram structure, and so no EVD can be fitted");
return FALSE;
}
if( ExtremeValueFitHistogram(hs->his,TRUE,guess_of_outliers) == 0 ) {
warn("Extreme Value distribution is unable to be fitted. Sorry!");
return FALSE;
}
for(i=0;i<hs->len;i++) {
hs->ds[i]->evalue = ExtremeValueE((*hs->score_to_his)(hs->ds[i]->score),hs->his->param[EVD_MU],hs->his->param[EVD_LAMBDA],hs->his->total);
}
return TRUE;
}
double Evalue_from_Histogram(Histogram * his,double score)
{
return ExtremeValueE(score,his->param[EVD_MU],his->param[EVD_LAMBDA],his->total);
}
