--- /dev/null
+/* -*- mode: c; tab-width: 4; c-basic-offset: 4; indent-tabs-mode: nil -*- */
+
+/*********************************************************************
+ * Clustal Omega - Multiple sequence alignment
+ *
+ * Copyright (C) 2010 University College Dublin
+ *
+ * Clustal-Omega is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License as
+ * published by the Free Software Foundation; either version 2 of the
+ * License, or (at your option) any later version.
+ *
+ * This file is part of Clustal-Omega.
+ *
+ ********************************************************************/
+
+/*
+ * RCS $Id: hhhitlist-C.h 243 2011-05-31 13:49:19Z fabian $
+ */
+
+// hhhitlist.C
+
+#ifndef MAIN
+#define MAIN
+#include <iostream> // cin, cout, cerr
+#include <fstream> // ofstream, ifstream
+#include <stdio.h> // printf
+#include <stdlib.h> // exit
+#include <string> // strcmp, strstr
+#include <math.h> // sqrt, pow
+#include <limits.h> // INT_MIN
+#include <float.h> // FLT_MIN
+#include <time.h> // clock
+#include <ctype.h> // islower, isdigit etc
+using std::ios;
+using std::ifstream;
+using std::ofstream;
+using std::cout;
+using std::cerr;
+using std::endl;
+#include "util-C.h" // imax, fmax, iround, iceil, ifloor, strint, strscn, strcut, substr, uprstr, uprchr, Basename etc.
+#include "list.h" // list data structure
+#include "hash.h" // hash data structure
+#include "hhdecl-C.h" // constants, class
+#include "hhutil-C.h" // imax, fmax, iround, iceil, ifloor, strint, strscn, strcut, substr, uprstr, uprchr, Basename etc.
+#include "hhhmm.h" // class HMM
+#include "hhalignment.h" // class Alignment
+#include "hhhit.h"
+#include "hhhalfalignment.h"
+#include "hhfullalignment.h"
+#endif
+
+
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+//// Methods of class HitList
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+
+
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Print summary listing of hits
+ */
+void
+HitList::PrintHitList(HMM& q, char* outfile)
+{
+ Hit hit;
+ int nhits=0;
+ char str[NAMELEN]="";
+
+ FILE* outf=NULL;
+ if (strcmp(outfile,"stdout"))
+ {
+ outf=fopen(outfile,"w");
+ if (!outf) OpenFileError(outfile);
+ }
+ else
+ outf = stdout;
+
+
+ fprintf(outf,"Query %s\n",q.longname);
+// fprintf(outf,"Family %s\n",q.fam);
+ fprintf(outf,"Match_columns %i\n",q.L);
+ fprintf(outf,"No_of_seqs %i out of %i\n",q.N_filtered,q.N_in);
+ fprintf(outf,"Neff %-4.1f\n",q.Neff_HMM);
+ fprintf(outf,"Searched_HMMs %i\n",N_searched);
+
+ // Print date stamp
+ time_t* tp=new(time_t);
+ *tp=time(NULL);
+ fprintf(outf,"Date %s",ctime(tp));
+ delete (tp); (tp) = NULL;
+
+ // Print command line
+ fprintf(outf,"Command ");
+ for (int i=0; i<par.argc; i++)
+ if (strlen(par.argv[i])<=par.maxdbstrlen)
+ fprintf(outf,"%s ",par.argv[i]);
+ else
+ fprintf(outf,"<%i characters> ",(int)strlen(par.argv[i]));
+ fprintf(outf,"\n\n");
+
+#ifdef WINDOWS
+ if (par.trans)
+ fprintf(outf," No Hit Prob E-trans E-value Score SS Cols Query HMM Template HMM\n");
+ else
+ fprintf(outf," No Hit Prob E-value P-value Score SS Cols Query HMM Template HMM\n");
+#else
+ if (par.trans)
+ fprintf(outf," No Hit Prob E-trans E-value Score SS Cols Query HMM Template HMM\n");
+ else
+ fprintf(outf," No Hit Prob E-value P-value Score SS Cols Query HMM Template HMM\n");
+#endif
+
+ Reset();
+ while (!End()) // print hit list
+ {
+ hit = ReadNext();
+ if (nhits>=par.Z) break; //max number of lines reached?
+ if (nhits>=par.z && hit.Probab < par.p) break;
+ if (nhits>=par.z && hit.Eval > par.E) continue;
+// if (hit.matched_cols <=1) continue; // adding this might get to intransparent... analogous statement in PrintAlignments
+ nhits++;
+ sprintf(str,"%3i %-30.30s ",nhits,hit.longname);
+
+
+#ifdef WINDOWS
+ if (par.trans) // Transitive scoring
+ fprintf(outf,"%-34.34s %5.1f %8.2G %8.2G %6.1f %5.1f %4i ",str,hit.Probab,hit.E1val,hit.Eval,hit.score,hit.score_ss,hit.matched_cols);
+ else // Normal scoring
+ fprintf(outf,"%-34.34s %5.1f %8.2G %8.2G %6.1f %5.1f %4i ",str,hit.Probab,hit.Eval,hit.Pval,hit.score,hit.score_ss,hit.matched_cols);
+#else
+ if (par.trans) // Transitive scoring
+ fprintf(outf,"%-34.34s %5.1f %7.2G %7.2G %6.1f %5.1f %4i ",str,hit.Probab,hit.E1val,hit.Eval,hit.score,hit.score_ss,hit.matched_cols);
+ else // Normal scoring
+ fprintf(outf,"%-34.34s %5.1f %7.2G %7.2G %6.1f %5.1f %4i ",str,hit.Probab,hit.Eval,hit.Pval,hit.score,hit.score_ss,hit.matched_cols);
+#endif
+
+ sprintf(str,"%4i-%-4i ",hit.i1,hit.i2);
+ fprintf(outf,"%-10.10s",str);
+ sprintf(str,"%4i-%-4i",hit.j1,hit.j2);
+ fprintf(outf,"%-9.9s(%i)\n",str,hit.L);
+ } //end print hit list
+ fprintf(outf,"\n");
+ if (strcmp(outfile,"stdout")) fclose(outf);
+}
+
+
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Print alignments of query sequences against hit sequences
+ */
+int
+HitList::PrintAlignments(
+
+
+#ifdef CLUSTALO
+ char **ppcFirstProf, char **ppcSecndProf,
+#endif
+ HMM& q, char* outfile, char outformat)
+{
+ Hit hit;
+ FullAlignment qt_ali(par.nseqdis+10); // maximum 10 annotation (pseudo) sequences (ss_dssp, sa_dssp, ss_pred, ss_conf, consens,...)
+ int nhits=0;
+
+#ifndef CLUSTALO_NOFILE
+ FILE* outf=NULL;
+ if (strcmp(outfile,"stdout"))
+ {
+ if (outformat==0)
+ outf=fopen(outfile,"a"); //append to summary hitlist
+ else
+ outf=fopen(outfile,"w"); //open for writing
+ if (!outf) OpenFileError(outfile);
+ }
+ else
+ outf = stdout;
+#endif
+
+ Reset();
+ while (!End()) // print hit list
+ {
+ if (nhits>=par.B) break; //max number of lines reached?
+ hit = ReadNext();
+ if (nhits>=par.b && hit.Probab < par.p) break;
+ if (nhits>=par.b && hit.Eval > par.E) continue;
+ // // adding this might get to intransparent...
+ // // analogous statement in PrintHitlist and hhalign.C
+ // if (hit.matched_cols <=1) continue;
+ nhits++;
+
+ // Build double alignment of query against template sequences
+ int iBuildRet = qt_ali.Build(q,hit);
+ if (iBuildRet != OK){ /* FS, r241 -> r243 */
+ fprintf(stderr, "%s:%s:%d: qt_ali.Build failed\n",
+ __FUNCTION__, __FILE__, __LINE__);
+ return FAILURE;
+ }
+
+#ifndef CLUSTALO
+ // Print out alignment
+ if (outformat==0) // HHR format
+ {
+ fprintf(outf,"No %-3i\n",nhits);
+ qt_ali.PrintHeader(outf,q,hit);
+ qt_ali.PrintHHR(outf,hit);
+ }
+ else if (outformat==1) // FASTA format
+ {
+ fprintf(outf,"# No %-3i\n",nhits);
+ qt_ali.PrintFASTA(outf,hit);
+ }
+ else if(outformat==2) // A2M format
+ {
+ fprintf(outf,"# No %-3i\n",nhits);
+ qt_ali.PrintA2M(outf,hit);
+ }
+ else // A3m format
+ {
+ fprintf(outf,"# No %-3i\n",nhits);
+ qt_ali.PrintA3M(outf,hit);
+ }
+#else
+ qt_ali.OverWriteSeqs(ppcFirstProf, ppcSecndProf);
+#endif
+
+ qt_ali.FreeMemory();
+ }
+#ifndef CLUSTALO_NOFILE
+ if (strcmp(outfile,"stdout")) fclose(outf);
+#endif
+
+ return OK;
+
+} /* this is the end of HitList::PrintAlignments() */
+
+
+
+
+
+////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Return the ROC_5 score for optimization
+ * (changed 28.3.08 by Michael & Johannes)
+ */
+void
+HitList::Optimize(HMM& q, char* buffer)
+{
+ const int NFAM =5; // calculate ROC_5 score
+ const int NSFAM=5; // calculate ROC_5 score
+ int roc=0; // ROC score
+ int fam=0; // number of hits from same family (at current threshold)
+ int not_fam=0; // number of hits not from same family
+ int sfam=0; // number of hits from same suporfamily (at current threshold)
+ int not_sfam=0; // number of hits not from same superfamily
+ Hit hit;
+
+ SortList();
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (!strcmp(hit.fam,q.fam)) fam++; // query and template from same superfamily? => positive
+ else if (not_fam<NFAM) // query and template from different family? => negative
+ {
+ not_fam++;
+ roc += fam;
+ }
+ if (!strcmp(hit.sfam,q.sfam)) sfam++; // query and template from same superfamily? => positive
+ else if (not_sfam<NSFAM) // query and template from different superfamily? => negative
+ {
+ not_sfam++;
+ roc += sfam;
+ }
+// printf("qfam=%s tfam=%s qsfam=%s tsfam=%s fam=%-2i not_fam=%3i sfam=%-3i not_sfam=%-5i roc=%-3i\n",q.fam,hit.fam,q.sfam,hit.sfam,fam,not_fam,sfam,not_sfam,roc);
+ }
+
+ // Write ROC score to file or stdout
+ FILE* buf=NULL;
+ if (strcmp(par.buffer,"stdout"))
+ {
+ buf=fopen(buffer,"w");
+ if (!buf) OpenFileError(par.buffer);
+ }
+ else
+ buf = stdout;
+
+ fprintf(buf,"%f\n",float(roc)/float(fam*NFAM+sfam*NSFAM)); // must be between 0 and 1
+ if (v>=2) printf("ROC=%f\n",float(roc)/float(fam*NFAM+sfam*NSFAM));
+ fclose(buf);
+}
+
+
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Print score distribution into file score_dist
+ */
+void
+HitList::PrintScoreFile(HMM& q)
+{
+ int i=0, n;
+ FILE* scoref=NULL;
+ Hit hit;
+ Hash<int> twice(10000); // make sure only one hit per HMM is listed
+ twice.Null(-1);
+
+ if (strcmp(par.scorefile,"stdout"))
+ {
+ scoref=fopen(par.scorefile,"w");
+ if (!scoref)
+ {cerr<<endl<<"WARNING from "<<par.argv[0]<<": could not open \'"<<par.scorefile<<"\'\n"; return;}
+ }
+ else
+ scoref = stdout;
+ Reset();
+ fprintf(scoref,"NAME %s\n",q.longname);
+ fprintf(scoref,"FAM %s\n",q.fam);
+ fprintf(scoref,"FILE %s\n",q.file);
+ fprintf(scoref,"LENG %i\n",q.L);
+ fprintf(scoref,"\n");
+//fprintf(scoref,"TARGET REL LEN COL LOG-PVA S-TOT MS NALI\n");
+
+//For hhformat, the PROBAB field has to start at position 41 !!
+// ----+----1----+----2----+----3----+----4----+----
+ fprintf(scoref,"TARGET FAMILY REL LEN COL LOG-PVA S-AASS PROBAB SCORE\n");
+ // d153l__ 5 185 185 287.82 464.22 100.00
+ // d1qsaa2 3 168 124 145.55 239.22 57.36
+ while (!End())
+ {
+ i++;
+ hit = ReadNext();
+ if (twice[hit.name]==1) continue; // better hit with same HMM has been listed already
+ twice.Add(hit.name,1);
+ //if template and query are from the same superfamily
+ if (!strcmp(hit.name,q.name)) n=5;
+ else if (!strcmp(hit.fam,q.fam)) n=4;
+ else if (!strcmp(hit.sfam,q.sfam)) n=3;
+ else if (!strcmp(hit.fold,q.fold)) n=2;
+ else if (!strcmp(hit.cl,q.cl)) n=1;
+ else n=0;
+ fprintf(scoref,"%-10s %-10s %1i %3i %3i %s %7.2f %6.2f %7.2f\n",hit.name,hit.fam,n,hit.L,hit.matched_cols,sprintg(-1.443*hit.logPval,7),-hit.score_aass,hit.Probab,hit.score);
+ }
+ fclose(scoref);
+}
+
+
+inline double
+logPvalue_HHblast(double s, double corr)
+{
+ return -s*(1.0-0.5*corr) + (1.0-corr)*log(1.0+s);
+// return -s*(1.0-0.5*corr) + log( 1.0+(1.0-corr)*s );
+// return -s*(1.0-0.5*corr) + log( 1.0+(1.0-corr)*(1.0-0.5*corr)*s );
+}
+
+inline double
+Pvalue_HHblast(double s, double corr)
+{
+ return exp(-s*(1.0-0.5*corr)) * pow(1.0+s,1.0-corr);
+// return exp(-s*(1.0-0.5*corr)) * ( 1.0+(1.0-corr)*s );
+// return exp(-s*(1.0-0.5*corr)) * ( 1.0+(1.0-corr)*(1.0-0.5*corr)*s );
+}
+
+inline double
+logLikelihood_HHblast(double s, double corr)
+{
+ if (s<0.0) { s=0.0; if (corr<1E-5) corr=1E-5; else if (corr>0.99999) corr=0.99999; }
+ else { if (corr<0.0) corr=0.0; else if (corr>1.0) corr=1.0; }
+ return -s*(1.0-0.5*corr) - corr*log(1.0+s) + log(s*(1.0-0.5*corr)+0.5*corr);
+ // return -s*(1.0-0.5*corr) + log( s*(1.0-corr)*(1.0-0.5*corr)+0.5*corr );
+ // return -s*(1.0-0.5*corr) + log((s*(1.0-corr)*(1.0-0.5*corr)+corr)*(1.0-0.5*corr));
+}
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Evaluate the *negative* log likelihood for the order statistic of the uniform distribution
+ * for the best 10% of hits (vertex v = (corr,offset) )
+ * The k'th order statistic for X~Uniform is p:=X^(k)~Beta(k,n-k+1) = const*p^(k-1)*(1-p)^(n-k)
+ * Needed to fit the correlation and score offset in HHblast
+*/
+double
+HitList::RankOrderFitCorr(double* v)
+{
+ double sum=0.0;
+// printf("%8.2G %8.2G %i\n",v[0],v[1],Nprof);
+ int i1 = imin(Nprof,imax(50,int(0.05*Nprof)));
+ for (int i=0; i<i1; i++)
+ {
+ double p = Pvalue_HHblast(score[i]+v[1],v[0]);
+// sum -= (1.0-double(i)/double(i1)) * weight[i] * ( double(i)*log(p) + (Nprof-i-1.0)*log(1.0-p) );
+ float diff = p-(float(i)+1.0)/(Nprof+1.0);
+ sum += (1.0-double(i)/double(i1)) * weight[i]*diff*diff*(Nprof+1.0)*(Nprof+1.0)*(Nprof+2.0)/(i+10.0)/(Nprof-i);
+// printf("%-3i Pval=%7.5f Preal=%7.5f diff=%7.5f sum=%7.5f\n",i,p,float(i+1)/(1.0+Nprof),diff,sum);
+ }
+ return sum;
+}
+
+/**
+ * @brief Static wrapper-function for calling the nonstatic member function RankOrderFitCorr()
+ * ( see http://www.newty.de/fpt/callback.html#member )
+ */
+double
+HitList::RankOrderFitCorr_static(void* pt2hitlist, double* v)
+{
+ HitList* mySelf = (HitList*) pt2hitlist; // explicitly cast to a pointer to Hitlist
+ return mySelf->RankOrderFitCorr(v); // call member function
+}
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Evaluate the *negative* log likelihood of the data at the vertex v = (corr,offset)
+ * Needed to fit the correlation and score offset in HHblast
+ */
+double
+HitList::LogLikelihoodCorr(double* v)
+{
+ double sum=0.0;
+// printf("%8.2G %8.2G %i\n",v[0],v[1],Nprof);
+ for (int i=0; i<Nprof; i++)
+ {
+ sum -= weight[i]*logLikelihood_HHblast(score[i]+v[1],v[0]);
+// printf("%-3i Pval=%7.5f Preal=%7.5f diff=%7.5f rmsd=%7.5f sum=%7.5f\n",i,Pvalue_HHblast(score[i],v[0]),float(i)/(1.0+Nprof),x,sqrt(sum/sumw),sum);
+ }
+ return sum;
+}
+
+/**
+ * @brief Static wrapper-function for calling the nonstatic member function LogLikelihoodCorr()
+ * ( see http://www.newty.de/fpt/callback.html#member )
+ */
+double
+HitList::LogLikelihoodCorr_static(void* pt2hitlist, double* v)
+{
+ HitList* mySelf = (HitList*) pt2hitlist; // explicitly cast to a pointer to Hitlist
+ return mySelf->LogLikelihoodCorr(v); // call member function
+}
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Evaluate the *negative* log likelihood of the data at the vertex v = (lamda,mu)
+ * p(s) = lamda * exp{ -exp[-lamda*(s-mu)] - lamda*(s-mu) } = lamda * exp( -exp(-x) - x)
+ */
+double
+HitList::LogLikelihoodEVD(double* v)
+{
+ double sum=0.0, sumw=0.0;
+ for (int i=0; i<Nprof; i++)
+ {
+ double x = v[0]*(score[i]-v[1]);
+ sum += weight[i]*(exp(-x)+x);
+ sumw += weight[i];
+ }
+ return sum - sumw*log(v[0]);
+}
+
+/**
+ * @brief Static wrapper-function for calling the nonstatic member function LogLikelihoodEVD()
+ * ( see http://www.newty.de/fpt/callback.html#member )
+ */
+double
+HitList::LogLikelihoodEVD_static(void* pt2hitlist, double* v)
+{
+ HitList* mySelf = (HitList*) pt2hitlist; // explicitly cast to a pointer to Hitlist
+ return mySelf->LogLikelihoodEVD(v); // call member function
+}
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Subroutine to FindMin: try new point given by highest point ihigh and fac and replace ihigh if it is lower
+ */
+double
+HitList::TryPoint(const int ndim, double* p, double* y, double* psum, int ihigh, double fac, double (*Func)(void* pt2hitlist, double* v))
+{
+ // New point p_try = p_c + fac*(p_high-p_c),
+ // where p_c = ( sum_i (p_i) - p_high)/ndim is the center of ndim other points
+ // => p_try = fac1*sum_i(p_i) + fac2*p_high
+ double fac1=(1.-fac)/ndim;
+ double fac2=fac-fac1;
+ double ptry[ndim]; //new point to try out
+ double ytry; //function value of new point
+ int j; //index for the ndim parameters
+
+ for (j=0; j<ndim; j++)
+ ptry[j]=psum[j]*fac1+p[ihigh*ndim+j]*fac2;
+ ytry = (*Func)(this,ptry);
+ if (ytry<=y[ihigh])
+ {
+// if (v>=4) printf("Trying: %-7.3f %-7.3f %-7.3f -> accept\n",ptry[0],ptry[1],ytry);
+ y[ihigh]=ytry;
+ for (j=0; j<ndim; j++)
+ {
+ psum[j] += ptry[j]-p[ihigh*ndim+j]; //update psum[j]
+ p[ihigh*ndim+j]=ptry[j]; //replace p[ihigh] with ptry
+ } //Note: ihigh is now not highest point anymore!
+ }
+// else if (v>=4) printf("Trying: %-7.3f %-7.3f %-7.3f -> reject\n",ptry[0],ptry[1],ytry);
+
+ return ytry;
+}
+
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Find minimum with simplex method of Nelder and Mead (1965)
+ */
+float
+HitList::FindMin(const int ndim, double* p, double* y, double tol, int& nfunc, double (*Func)(void* pt2hitlist, double* v))
+{
+ const int MAXNFUNC=99; //maximum allowed number of function evaluations
+ int ihigh; //index of highest point on simplex
+ int inext; //index of second highest point on simplex
+ int ilow; //index of lowest point on simplex
+ int i; //index for the ndim+1 points
+ int j; //index for the ndim parameters
+ double rtol; //tolerance: difference of function value between highest and lowest point of simplex
+ double temp; //dummy
+ double ytry; //function value of trial point
+ double psum[ndim]; //psum[j] = j'th coordinate of sum vector (sum over all vertex vectors)
+
+ nfunc=0; //number of function evaluations =0
+ //Calculate sum vector psum[j]
+ for (j=0; j<ndim; j++)
+ {
+ psum[j]=p[j];
+ for (i=1; i<ndim+1; i++)
+ psum[j]+=p[i*ndim+j];
+ }
+
+ // Repeat finding better points in simplex until rtol<tol
+ while(1)
+ {
+ // Find indices for highest, next highest and lowest point
+ ilow=0;
+ if (y[0]>y[1]) {inext=1; ihigh=0;} else {inext=0; ihigh=1;}
+ for (i=0; i<ndim+1; i++)
+ {
+ if (y[i]<=y[ilow]) ilow=i;
+ if (y[i]>y[ihigh]) {inext=ihigh; ihigh=i;}
+ else if (y[i]>y[inext] && i!= ihigh) inext=i;
+ }
+
+ // If tolerance in y is smaller than tol swap lowest point to index 0 and break -> return
+ rtol = 2.*fabs(y[ihigh]-y[ilow]) / (fabs(y[ihigh])+fabs(y[ilow])+1E-10);
+ if (rtol<tol)
+ {
+ temp=y[ilow]; y[ilow]=y[0]; y[0]=temp;
+ for (j=0; j<ndim; j++)
+ {
+ temp=p[ilow*ndim+j]; p[ilow*ndim+j]=p[j]; p[j]=temp;
+ }
+ break;
+ }
+
+ // Max number of function evaluations exceeded?
+ if (nfunc>=MAXNFUNC )
+ {
+ if (v) fprintf(stderr,"\nWARNING: maximum likelihood fit of score distribution did not converge.\n");
+ return 1;
+ }
+
+ nfunc+=2;
+ // Point-reflect highest point on the center of gravity p_c of the other ndim points of the simplex
+ if (v>=3) printf("%3i %-7.3f %-7.3f %-12.8f %-9.3E\n",nfunc,p[ilow*ndim],p[ilow*ndim+1],y[ilow],rtol);
+// if (v>=2) printf(" %3i %-9.3E %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f\n",nfunc,rtol,p[ilow*ndim],p[ilow*ndim+1],y[ilow],p[inext*ndim],p[inext*ndim+1],y[inext],p[ihigh*ndim],p[ihigh*ndim+1],y[ihigh]);
+ ytry = TryPoint(ndim,p,y,psum,ihigh,-1.0,Func); //reflect highest point on p_c
+
+ if (ytry<=y[ilow])
+ {
+ ytry = TryPoint(ndim,p,y,psum,ihigh,2.0,Func); //expand: try new point 2x further away from p_c
+// if (v>=2) printf("Expanded: %3i %-9.3E %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f\n",nfunc,rtol,p[ilow*ndim],p[ilow*ndim+1],y[ilow],p[inext*ndim],p[inext*ndim+1],y[inext],p[ihigh*ndim],p[ihigh*ndim+1],y[ihigh]);
+ }
+ else if (ytry>=y[inext])
+ {
+ // The new point is worse than the second worst point
+ temp=y[ihigh];
+ ytry=TryPoint(ndim,p,y,psum,ihigh,0.5,Func); //contract simplex by 0.5 along (p_high-p_c
+// if (v>=2) printf("Compressed:%3i %-9.3E %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f\n",nfunc,rtol,p[ilow*ndim],p[ilow*ndim+1],y[ilow],p[inext*ndim],p[inext*ndim+1],y[inext],p[ihigh*ndim],p[ihigh*ndim+1],y[ihigh]);
+ if (ytry>=temp)
+ {
+ // Trial point is larger than worst point => contract simplex by 0.5 towards lowest point
+ for (i=0; i<ndim+1; i++)
+ {
+ if (i!=ilow)
+ {
+ for (j=0; j<ndim; j++)
+ p[i*ndim+j]=0.5*(p[i*ndim+j]+p[ilow+j]);
+ y[i] = (*Func)(this,p+i*ndim);
+// y[i] = (*Func)(p+i*ndim);
+ }
+ }
+ nfunc+=ndim;
+// if (v>=2) printf("Contracted:%3i %-9.3E %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f %-7.3f\n",nfunc,rtol,p[ilow*ndim],p[ilow*ndim+1],y[ilow],p[inext*ndim],p[inext*ndim+1],y[inext],p[ihigh*ndim],p[ihigh*ndim+1],y[ihigh]);
+
+ //Calculate psum[j]
+ for (j=0; j<ndim; j++)
+ {
+ psum[j]=p[j];
+ for (i=1; i<ndim+1; i++)
+ psum[j]+=p[i*ndim+j];
+ }
+ }
+ }
+ else nfunc--;
+ }
+ return (float)rtol;
+}
+
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Do a maximum likelihod fit of the scores with an EV distribution with parameters lamda and mu
+ */
+void
+HitList::MaxLikelihoodEVD(HMM& q, int nbest)
+{
+ double tol=1E-6; // Maximum relative tolerance when minimizing -log(P)/N (~likelihood)
+ static char first_call=1;
+ static Hash<int> size_fam(MAXPROF/10); // Hash counts number of HMMs in family
+ static Hash<int> size_sfam(MAXPROF/10); // Hash counts number of families in superfamily
+ Hash<int> excluded(50); // Hash containing names of superfamilies to be excluded from fit
+ size_fam.Null(0); // Set int value to return when no data can be retrieved
+ size_sfam.Null(0); // Set int value to return when no data can be retrieved
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+ Hit hit;
+
+ double mu; // EVD[mu,lam](x) = exp(-exp(-(x-mu)/lam)) = P(score<=x)
+ double vertex[2*3]; // three vertices of the simplex in lamda-mu plane
+ double yvertex[3]; // log likelihood values at the three vertices of the simplex
+ int nfunc=0; // number of function calls
+ double sum_weights=0.0;
+ float sum_scores=0.0;
+ float rtol;
+
+ if (first_call==1)
+ {
+ first_call=0;
+ // Count how many HMMs are in each family; set number of multiple hits per template nrep
+ if (v>=4) printf(" count number of profiles in each family and families in each superfamily ...\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (!size_fam.Contains(hit.fam)) (*size_sfam(hit.sfam))++; //Add one to hash element for superfamily
+ (*size_fam(hit.fam))++; //Add one to hash element for family
+ // printf("size(%s)=%i name=%s\n",hit.fam,*size_fam(hit.fam),hit.name)
+ }
+ fams=size_fam.Size();
+ sfams=size_sfam.Size();
+ if (v>=3)
+ printf("%-3i HMMs from %i families and %i superfamilies searched. Found %i hits\n",N_searched,fams,sfams,Size());
+ }
+
+ // Query has SCOP family identifier?
+ if (q.fam && q.fam[0]>='a' && q.fam[0]<='k' && q.fam[1]=='.')
+ {
+ char sfamid[NAMELEN];
+ char* ptr_in_fam=q.fam;
+ while ((ptr_in_fam=strwrd(sfamid,ptr_in_fam,'-')))
+ {
+ char* ptr=strrchr(sfamid,'.');
+ if (ptr) *ptr='\0';
+ excluded.Add(sfamid);
+// fprintf(stderr,"Exclude SCOP superfamily %s ptr_in_fam='%s'\n",sfamid,ptr_in_fam);
+ }
+ }
+ // Exclude best superfamilies from fit
+ else if (nbest>0)
+ {
+ if (sfams<97+nbest) return;
+
+ // Find the nbest best-scoring superfamilies for exclusion from first ML fit
+ if (v>=4) printf(" find %i best-scoring superfamilies to exclude from first fit ...\n",nbest);
+ hit = Smallest();
+ excluded.Add(hit.sfam);
+// printf("Exclude in first round: %s %8.2f %s\n",hit.name,hit.score_aass,hit.sfam);
+ while (excluded.Size()<nbest)
+ {
+ Reset();
+ while (!End() && excluded.Contains(ReadNext().sfam)) ;
+ hit=ReadCurrent();
+ while (!End())
+ {
+ if (ReadNext()<hit && !excluded.Contains(ReadCurrent().sfam))
+ hit=ReadCurrent();
+ }
+ excluded.Add(hit.sfam);
+// printf("Exclude in first round: %s %8.2f %s %i %i\n",hit.name,hit.score_aass,hit.sfam,excluded.Size(),excluded.Contains(hit.sfam));
+ }
+ tol = 0.01/size_sfam.Size(); // tol=1/N would lead to delta(log-likelihood)~1 (where N ~ number of superfamilies) since (1+1/N)^N = e
+ }
+ else
+ {
+ // Find the best-scoring superfamilies from first fit for exclusion from second ML fit
+ if (v>=4) printf(" find best-scoring superfamilies to exclude from second fit ...\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.Eval < 0.05) excluded.Add(hit.sfam); // changed from 0.5 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ }
+ tol = 0.001/size_sfam.Size(); // tol=1/N would lead to delta(log-likelihood)~1 (where N ~ number of superfamilies) since (1+1/N)^N = e
+ }
+
+ // Put scores into score[] and weights into weight[]
+ if (v>=3) printf(" generate scores and weights array for ML fitting ...\n");
+ Nprof=0;
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep > 1) continue; //Use only best hit per template
+ if (Nprof>=MAXPROF) break;
+
+ char sfamid[NAMELEN];
+ char* ptr_in_fam=hit.fam;
+ while ((ptr_in_fam=strwrd(sfamid,ptr_in_fam,'-')))
+ {
+ char* ptr=strrchr(sfamid,'.');
+ if (ptr) *ptr='\0';
+ if (excluded.Contains(sfamid)) break; //HMM is among superfamilies to be excluded
+ }
+ if (excluded.Contains(sfamid)) {
+ if (v>=3) fprintf(stderr,"Exclude hit %s (family %s contains %s)\n",hit.name,hit.fam,sfamid);
+ continue;
+ }
+// ScopID(hit.cl,hit.fold,hit.sfam,hit.fam); //Get scop superfamily code for template
+// if (*hit.sfam=='\0' || excluded.Contains(hit.sfam)) continue; // skip HMM
+
+ score[Nprof] = hit.score;
+ weight[Nprof]=1./size_fam[hit.fam]/size_sfam[hit.sfam];
+ sum_scores +=hit.score*weight[Nprof];
+ sum_weights+=weight[Nprof];
+
+ //DEBUG
+// if (v>=4) printf("%-10.10s %-12.12s %-3i %-12.12s %-3i %6.4f %6.4f %7.1f\n",hit.name,hit.fam,size_fam[hit.fam],hit.sfam,size_sfam[hit.sfam],1./size_fam[hit.fam]/size_sfam[hit.sfam],sum,hit.score);
+ Nprof++;
+ }
+ //DEBUG
+ if (v>=3)
+ printf("%i hits used for score distribution\n",Nprof);
+ // for (int i=0; i<Nprof; i++) printf("%3i score=%8.3f weight=%7.5f\n",i,score[i],weight[i]);
+
+ // Set simplex vertices and function values
+ mu = sum_scores/sum_weights - 0.584/LAMDA;
+ if (par.loc) // fit only in local mode; in global mode use fixed value LAMDA and mu mean score
+ {
+ double (*Func)(void*, double*);
+ Func = HitList::LogLikelihoodEVD_static;
+
+ if (nbest>0) {vertex[0]=LAMDA; vertex[1]=mu;} /////////////////////////////////////////// DEBUG
+ else {vertex[0]=q.lamda; vertex[1]=mu;}
+ vertex[2]=vertex[0]+0.1; vertex[3]=vertex[1];
+ vertex[4]=vertex[0]; vertex[5]=vertex[1]+0.2;
+ yvertex[0]=Func(this,vertex );
+ yvertex[1]=Func(this,vertex+2);
+ yvertex[2]=Func(this,vertex+4);
+
+ // Find lam and mu that minimize negative log likelihood of data
+ if (v>=3) printf("Fitting to EVD by maximum likelihood...\niter lamda mu -log(P)/N tol\n");
+ rtol = FindMin(2,vertex,yvertex,tol,nfunc,Func);
+ if (v>=3) printf("%3i %-7.3f %-7.2f %-7.3f %-7.1E\n\n",nfunc,vertex[0],vertex[1],yvertex[0]-(1.5772-log(vertex[0])),rtol);
+// printf("HHsearch lamda=%-6.3f mu=%-6.3f\n",vertex[0],vertex[1]);
+ }
+ else
+ {
+ vertex[0]=LAMDA_GLOB; vertex[1]=mu;
+ }
+
+ // Set lamda and mu of profile
+ q.lamda = vertex[0];
+ q.mu = vertex[1];
+
+ // Set P-values and E-values
+ // CHECK UPDATE FROM score=-logpval to score=-logpval+SSSCORE2NATLOG*score_ss !!!!
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+
+ // Calculate total score in raw score units: P-value = 1- exp(-exp(-lamda*(Saa-mu)))
+ hit.weight=1./size_fam[hit.fam]/size_sfam[hit.sfam]; // needed for transitive scoring
+ hit.logPval = logPvalue(hit.score,vertex);
+ hit.Pval=Pvalue(hit.score,vertex);
+ hit.Eval=exp(hit.logPval+log(N_searched));
+// hit.score_aass = hit.logPval/0.45-3.0 - hit.score_ss; // median(lamda)~0.45, median(mu)~4.0 in EVDs for scop20.1.63 HMMs
+ hit.score_aass = -q.lamda*(hit.score-q.mu)/0.45-3.0 - fmin(hit.score_ss,fmax(0.0,0.5*hit.score-5.0)); // median(lamda)~0.45, median(mu)~3.0 in EVDs for scop20.1.63 HMMs
+ hit.Probab = Probab(hit);
+ if (nbest>0 && par.loc) // correct length correction (not needed for second round of fitting, since lamda very similar)
+ if (par.idummy==0) ////////////////////////////////////////////
+ hit.score += log(q.L*hit.L)*(1/LAMDA-1/vertex[0]);
+ hit.score_sort = hit.score_aass;
+ Overwrite(hit); // copy hit object into current position of hitlist
+
+ if (nbest==0 && par.trans==1) // if in transitive scoring mode (weights file given)
+ TransitiveScoring();
+ else if (nbest==0 && par.trans==2) // if in transitive scoring mode (weights file given)
+ TransitiveScoring2();
+ else if (nbest==0 && par.trans==3) // if in transitive scoring mode (weights file given)
+ TransitiveScoring3();
+ else if (nbest==0 && par.trans==4) // if in transitive scoring mode (weights file given)
+ TransitiveScoring4();
+ }
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate correlation and score offset for HHblast composite E-values
+ */
+void
+HitList::CalculateHHblastCorrelation(HMM& q)
+{
+ int nfunc=0; // number of function calls
+ double tol; // Maximum relative tolerance when minimizing -log(P)/N (~likelihood)
+ double vertex[2*3]; // three vertices of the simplex in lamda-mu plane
+ double yvertex[3]; // log likelihood values at the three vertices of the simplex
+ Hit hit;
+ Hash<int> excluded(50); // Hash containing names of superfamilies to be excluded from fit
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+
+ // Set sum of HHsearch and PSI-BLAST score for calculating correlation
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ hit.score_sort = hit.logPval + blast_logPvals->Show(hit.name); // if template not in hash, return log Pval = 0, i.e. Pvalue = 1!
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+
+ // Query has SCOP family identifier?
+ if (q.fam && q.fam[0]>='a' && q.fam[0]<='k' && q.fam[1]=='.')
+ {
+ char sfamid[NAMELEN];
+ char* ptr_in_fam=q.fam;
+ while ((ptr_in_fam=strwrd(sfamid,ptr_in_fam,'-')))
+ {
+ char* ptr=strrchr(sfamid,'.');
+ if (ptr) *ptr='\0';
+ excluded.Add(sfamid);
+ fprintf(stderr,"Exclude SCOP superfamily %s ptr_in_fam='%s'\n",sfamid,ptr_in_fam);
+ }
+ }
+
+ // Resort list by sum of log P-values
+ ResortList(); // use InsertSort to resort list according to sum of minus-log-Pvalues
+ Nprof=0;
+ Reset();
+ ReadNext(); // skip best hit
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep>=2) continue; // use only best alignments
+// if (hit.Eval<0.005) {if (v>=3) printf("Fitting HHblast correlation coefficient: skipping %s with Evalue=%9.1g\n",hit.name,hit.Eval); continue;}
+ if (Nprof>=MAXPROF) break;
+
+ char sfamid[NAMELEN];
+ char* ptr_in_fam=hit.fam;
+ while ((ptr_in_fam=strwrd(sfamid,ptr_in_fam,'-')))
+ {
+ char* ptr=strrchr(sfamid,'.');
+ if (ptr) *ptr='\0';
+ if (excluded.Contains(sfamid)) break; //HMM is among superfamilies to be excluded
+ }
+ if (excluded.Contains(sfamid)) {
+ if (v>=1) fprintf(stderr,"Exclude hit %s (family %s contains %s)\n",hit.name,hit.fam,sfamid);
+ continue;
+ }
+ score[Nprof] = -hit.score_sort;
+ weight[Nprof] = 1.0; // = hit.weight;
+// printf("%3i %-12.12s %7.3f + %7.3f = %7.3f \n",Nprof,hit.name,hit.logPval,blast_logPvals->Show(hit.name),-hit.score_sort); //////////////////////
+ printf("%3i %7.3f %7.3f\n",Nprof,hit.Pval,exp(blast_logPvals->Show(hit.name))); //////////////////////
+ Nprof++;
+ }
+
+ // Fit correlation
+ vertex[0]=0.5; vertex[1]=0.2;
+ vertex[2]=vertex[0]+0.2; vertex[3]=vertex[1];
+ vertex[4]=vertex[0]; vertex[5]=vertex[1]+0.2;
+
+ yvertex[0]=RankOrderFitCorr(vertex );
+ yvertex[1]=RankOrderFitCorr(vertex+2);
+ yvertex[2]=RankOrderFitCorr(vertex+4);
+// yvertex[0]=LogLikelihoodCorr(vertex );
+// yvertex[1]=LogLikelihoodCorr(vertex+2);
+// yvertex[2]=LogLikelihoodCorr(vertex+4);
+ tol = 1e-6;
+ v=3;//////////////////////////////////
+ // Find correlation and offset that minimize mean square deviation of reported composite Pvalues from actual
+ if (v>=2) printf("Fitting correlation coefficient for HHblast...\niter corr offset logLikelihood tol\n");
+ float rtol = FindMin(2,vertex,yvertex,tol,nfunc, HitList::RankOrderFitCorr_static);
+ if (v>=2) printf("%3i %-7.3f %-7.2f %-7.3f %-7.1E\n\n",nfunc,vertex[0],vertex[1],yvertex[0],rtol);
+ if (vertex[0]<0) vertex[0]=0.0;
+
+ // Print correlation and offset for profile
+ printf("HHblast correlation=%-6.3f score offset=%-6.3f\n",vertex[0],vertex[1]);
+ v=2;//////////////////////////////////
+}
+
+
+/**
+ * @brief Calculate HHblast composite E-values
+ */
+inline double
+corr_HHblast(float Nq, float Nt)
+{
+ return 0.5;
+}
+
+/**
+ * @brief Calculate HHblast composite E-values
+ */
+inline double
+offset_HHblast(float Nq, float Nt)
+{
+ return 0.0;
+}
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate HHblast composite E-values
+ */
+void
+HitList::CalculateHHblastEvalues(HMM& q)
+{
+ Hit hit;
+ float corr, offset; // correlation coefficient and offset for calculating composite HHblast P-values
+
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ corr = corr_HHblast(q.Neff_HMM,hit.Neff_HMM);
+ offset = offset_HHblast(q.Neff_HMM,hit.Neff_HMM);
+ hit.score_sort = hit.logPval + blast_logPvals->Show(hit.name);
+ hit.logPval = logPvalue_HHblast(-hit.score_sort+offset,corr); // overwrite logPval from HHsearch with composite logPval from HHblast
+ hit.Pval = Pvalue_HHblast(-hit.score_sort+offset,corr); // overwrite P-value from HHsearch with composite P-value from HHblast
+ hit.Eval = exp(hit.logPval+log(N_searched)); // overwrite E-value from HHsearch with composite E-value from HHblast
+ hit.Probab = Probab(hit);
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+ ResortList(); // use InsertSort to resort list according to sum of minus-log-Pvalues
+}
+
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Read file generated by blastpgp (default output) and store P-values in hash
+ */
+void
+HitList::ReadBlastFile(HMM& q)
+{
+ char line[LINELEN]=""; // input line
+ int Ndb; // number of sequences in database
+ int Ldb=0; // size of database in number of amino acids
+ char* templ;
+ int i;
+ if (!blast_logPvals) { blast_logPvals = new(Hash<float>); blast_logPvals->New(16381,0); }
+
+ FILE* blaf = NULL;
+ if (!strcmp(par.blafile,"stdin")) blaf=stdin;
+ else
+ {
+ blaf = fopen(par.blafile,"rb");
+ if (!blaf) OpenFileError(par.blafile);
+ }
+
+ // Read number of sequences and size of database
+ while (fgetline(line,LINELEN-1,blaf) && !strstr(line,"sequences;"));
+ if (!strstr(line,"sequences;")) FormatError(par.blafile,"No 'Database:' string found.");
+ char* ptr=line;
+ Ndb = strint(ptr);
+ if (Ndb==INT_MIN) FormatError(par.blafile,"No integer for number of sequences in database found.");
+ while ((i=strint(ptr))>INT_MIN) Ldb = 1000*Ldb + i;
+ if (Ldb==0) FormatError(par.blafile,"No integer for size of database found.");
+ printf("\nNumber of sequences in database = %i Size of database = %i\n",Ndb,Ldb);
+
+ // Read all E-values and sequence lengths
+ while (fgetline(line,LINELEN-1,blaf))
+ {
+ if (line[0]=='>')
+ {
+ // Read template name
+ templ = new(char[255]);
+ ptr = line+1;
+ strwrd(templ,ptr);
+ if (!blast_logPvals->Contains(templ)) // store logPval only for best HSP with template
+ {
+ // Read length
+ while (fgetline(line,LINELEN-1,blaf) && !strstr(line,"Length ="));
+ ptr = line+18;
+ int length = strint(ptr);
+ // Read E-value
+ fgetline(line,LINELEN-1,blaf);
+ fgetline(line,LINELEN-1,blaf);
+ float EvalDB; // E-value[seq-db] = Evalue for comparison Query vs. database, from PSI-BLAST
+ float EvalQT; // E-value[seq-seq] = Evalue for comparison Query vs. template (seq-seq)
+ double logPval;
+ ptr = strstr(line+20,"Expect =");
+ if (!ptr) FormatError(par.blafile,"No 'Expect =' string found.");
+ if (sscanf(ptr+8,"%g",&EvalDB)<1)
+ {
+ ptr[7]='1';
+ if (sscanf(ptr+7,"%g",&EvalDB)<1)
+ FormatError(par.blafile,"No Evalue found after 'Expect ='.");
+ }
+ // Calculate P-value[seq-seq] = 1 - exp(-E-value[seq-seq]) = 1 - exp(-Lt/Ldb*E-value[seq-db])
+ EvalQT = length/double(Ldb)*double(EvalDB);
+ if (EvalQT>1E-3) logPval = log(1.0-exp(-EvalQT)); else logPval=log(double(EvalQT)+1.0E-99);
+ blast_logPvals->Add(templ,logPval);
+ printf("template=%-10.10s length=%-3i EvalDB=%8.2g EvalQT=%8.2g P-value=%8.2g log Pval=%8.2g\n",templ,length,EvalDB,EvalQT,exp(logPval),logPval);
+ }
+ else {
+ delete[] templ; templ = NULL;
+ }
+ }
+ }
+ fclose(blaf);
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate output of hidden neural network units
+ */
+inline float
+calc_hidden_output(const float* weights, const float* bias, float Lqnorm, float Ltnorm, float Nqnorm, float Ntnorm)
+{
+ float res;
+ // Calculate activation of hidden unit = sum of all inputs * weights + bias
+ res = Lqnorm*weights[0] + Ltnorm*weights[1] + Nqnorm*weights[2] + Ntnorm*weights[3] + *bias;
+ res = 1.0 / (1.0 + exp(-(res ))); // logistic function
+ return res;
+}
+
+////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Neural network regressions of lamda for EVD
+ */
+inline float
+lamda_NN(float Lqnorm, float Ltnorm, float Nqnorm, float Ntnorm)
+{
+ const int inputs = 4;
+ const int hidden = 4;
+ const float biases[] = {-0.73195, -1.43792, -1.18839, -3.01141}; // bias for all hidden units
+ const float weights[] = { // Weights for the neural networks (column = start unit, row = end unit)
+ -0.52356, -3.37650, 1.12984, -0.46796,
+ -4.71361, 0.14166, 1.66807, 0.16383,
+ -0.94895, -1.24358, -1.20293, 0.95434,
+ -0.00318, 0.53022, -0.04914, -0.77046,
+ 2.45630, 3.02905, 2.53803, 2.64379
+ };
+ float lamda=0.0;
+ for (int h = 0; h<hidden; h++) {
+ lamda += calc_hidden_output( weights+inputs*h, biases+h, Lqnorm,Ltnorm,Nqnorm,Ntnorm ) * weights[hidden*inputs+h];
+ }
+ return lamda;
+}
+
+////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Neural network regressions of mu for EVD
+ */
+inline float
+mu_NN(float Lqnorm, float Ltnorm, float Nqnorm, float Ntnorm)
+{
+ const int inputs = 4;
+ const int hidden = 6;
+ const float biases[] = {-4.25264, -3.63484, -5.86653, -4.78472, -2.76356, -2.21580}; // bias for all hidden units
+ const float weights[] = { // Weights for the neural networks (column = start unit, row = end unit)
+ 1.96172, 1.07181, -7.41256, 0.26471,
+ 0.84643, 1.46777, -1.04800, -0.51425,
+ 1.42697, 1.99927, 0.64647, 0.27834,
+ 1.34216, 1.64064, 0.35538, -8.08311,
+ 2.30046, 1.31700, -0.46435, -0.46803,
+ 0.90090, -3.53067, 0.59212, 1.47503,
+ -1.26036, 1.52812, 1.58413, -1.90409, 0.92803, -0.66871
+ };
+ float mu=0.0;
+ for (int h = 0; h<hidden; h++) {
+ mu += calc_hidden_output( weights+inputs*h, biases+h, Lqnorm,Ltnorm,Nqnorm,Ntnorm ) * weights[hidden*inputs+h];
+ }
+ return 20.0*mu;
+}
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate Pvalues as a function of query and template lengths and diversities
+ */
+void
+HitList::CalculatePvalues(HMM& q)
+{
+ Hit hit;
+ float lamda=0.4, mu=3.0;
+ const float log1000=log(1000.0);
+
+ if (par.idummy!=2)
+ {
+ printf("WARNING: idummy should have been ==2 (no length correction)\n");
+ exit(4);
+ }
+
+ if(N_searched==0) N_searched=1;
+ if (v>=2)
+ printf("Calculate Pvalues as a function of query and template lengths and diversities...\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+
+ if (par.loc)
+ {
+ lamda = lamda_NN( log(q.L)/log1000, log(hit.L)/log1000, q.Neff_HMM/10.0, hit.Neff_HMM/10.0 );
+ mu = mu_NN( log(q.L)/log1000, log(hit.L)/log1000, q.Neff_HMM/10.0, hit.Neff_HMM/10.0 );
+// if (v>=3 && nhits++<20)
+// printf("hit=%-10.10s Lq=%-4i Lt=%-4i Nq=%5.2f Nt=%5.2f => lamda=%-6.3f mu=%-6.3f\n",hit.name,q.L,hit.L,q.Neff_HMM,hit.Neff_HMM,lamda,mu);
+ }
+ else
+ {
+ printf("WARNING: global calibration not yet implemented!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
+ }
+ hit.logPval = logPvalue(hit.score,lamda,mu);
+ hit.Pval = Pvalue(hit.score,lamda,mu);
+ hit.Eval=exp(hit.logPval+log(N_searched));
+// hit.score_aass = hit.logPval/LAMDA-3.0 - hit.score_ss; // median(lamda)~0.45, median(mu)~3.0 in EVDs for scop20.1.63 HMMs
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-hit.Pval)) )/0.45 - fmin(lamda*hit.score_ss,fmax(0.0,0.2*(hit.score-8.0)))/0.45 - 3.0;
+ hit.score_sort = hit.score_aass;
+ hit.Probab = Probab(hit);
+ Overwrite(hit);
+ }
+ SortList();
+ Reset();
+ return;
+}
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate Pvalues from calibration of 0: query HMM, 1:template HMMs, 2: both
+ */
+void
+HitList::GetPvalsFromCalibration(HMM& q)
+{
+ Hit hit;
+ char warn=0;
+ if(N_searched==0) N_searched=1;
+ if (v>=2)
+ {
+ switch (par.calm)
+ {
+ case 0:
+ printf("Using lamda=%-5.3f and mu=%-5.2f from calibrated query HMM %s. \n",q.lamda,q.mu,q.name);
+ printf("Note that HMMs need to be recalibrated when changing HMM-HMM alignment options.\n");
+ break;
+ case 1:
+ printf("Using score distribution parameters lamda and mu from database HMMs \n");
+ break;
+ case 2:
+ printf("Combining score distribution parameters lamda and mu from query and database HMMs\n");
+ printf("Note that HMMs need to be recalibrated when changing HMM-HMM alignment options.\n");
+ break;
+ }
+ }
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (par.calm==0 || (hit.logPvalt==0) )
+ {
+ hit.logPval = logPvalue(hit.score,q.lamda,q.mu);
+ hit.Pval = Pvalue(hit.score,q.lamda,q.mu);
+ if (par.calm>0 && warn++<1 && v>=1)
+ printf("Warning: some template HMM (e.g. %s) are not calibrated. Using query calibration.\n",hit.name);
+ }
+ else if (par.calm==1)
+ {
+ hit.logPval = hit.logPvalt;
+ hit.Pval = hit.Pvalt;
+ }
+ else if (par.calm==2)
+ {
+ hit.logPval = 0.5*( logPvalue(hit.score,q.lamda,q.mu) + hit.logPvalt);
+ hit.Pval = sqrt( Pvalue(hit.score,q.lamda,q.mu) * hit.Pvalt);
+ if (v>=5) printf("Score: %7.1f lamda: %7.1f mu: %7.1f P-values: query-calibrated: %8.2G template-calibrated: %8.2G geometric mean: %8.2G\n",hit.score,q.lamda,q.mu,Pvalue(hit.score,q.lamda,q.mu),hit.Pvalt,hit.Pval);
+ }
+
+ hit.Eval=exp(hit.logPval+log(N_searched));
+// hit.score_aass = hit.logPval/LAMDA-3.0 - hit.score_ss; // median(lamda)~0.45, median(mu)~3.0 in EVDs for scop20.1.63 HMMs
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-hit.Pval)) ) / 0.45-3.0 - fmin(hit.score_ss,fmax(0.0,0.5*hit.score-5.0));
+ hit.score_sort = hit.score_aass;
+ hit.Probab = Probab(hit);
+ Overwrite(hit);
+ }
+ SortList();
+ Reset();
+ return;
+}
+
+
+
+
+
+
+
+
+
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+// Transitive scoring
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+//////////////////////////////////////////////////////////////////////////////
+
+
+
+
+
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate P-values and Probabilities from transitive scoring over whole database
+ */
+void
+HitList::TransitiveScoring()
+{
+ void PrintMatrix(float** V, int N);
+ void PrintMatrix(double** V, int N);
+
+ float** Z; // matrix of intra-db Z-scores Z_kl
+ float** C; // covariance matrix for Z_k: C_kl = sum_m=1^N (Z_km * Z_lm)
+ char** fold; // fold name of HMM k
+ char** fam; // family of HMM k
+ float* Prob; // probability of HMM k
+ float* Zq; // Zq[k] = Z-score between query and database HMM k
+ float* Ztq; // Ztq[k] = transitive Z-score from query to database HMM k: Ztq[k] = sum_l[ w_ql * Z_lk] / normalization_q
+ float* Zrq; // Zrq[k] = transitive Z-score from database HMM k to query: Zrq[k] = sum_l[ w_kl * Z_lq] / normalization_k
+ float* w; // unnormalized weight matrix; w[l] is w_ql or w_kl, respectively
+ int* ll; // ll[m] is the m'th index l for which Z_lq, Z_lk > Zmin_trans
+ int N; // dimension of weight matrix is NxN
+ int M; // number of HMMs l with Z_ql>Ztrans_min (or Z_lk>Ztrans_min, respectively)
+ int k,l,m,n; // indices for database HMMs
+ char name[NAMELEN];
+ Hash<int> index(MAXPROF+7); // index{name} = index of HMM name in {1,...,N}
+ index.Null(-1); // Set int value to return when no data can be retrieved
+ Hash<int> excluded(13); // Hash containing names of superfamilies to be excluded from fit
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+ Hit hit;
+ size_t unused; /* disable fread gcc warning */
+
+ // Read weights matrix W with index hash and names array
+ fprintf(stderr,"Reading in weights file\n");
+ FILE* wfile = fopen(par.wfile,"rb");
+ if (v>=1 && wfile==NULL)
+ {
+ fprintf(stderr,"Error: %s could not be opened: (N_searched=%i) ",par.wfile,N_searched);
+ perror("fopen");
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ unused = fread(&N,sizeof(int),1,wfile); // read matrix dimension (i.e. number of HMMs in database)
+ if (v>=1 && N!=N_searched)
+ {
+ fprintf(stderr,"Error: Number %i of HMMs in weight file is different from number %i of HMMs in searched databases. \n",N,N_searched);
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ if (v>=2) fprintf(stderr,"Calculating transitive P-values for %i HMMs\n",N);
+ // Read names of HMMs (to specify mapping of HMM to matrix indices)
+ for (k=0; k<N; k++)
+ {
+ unused = fread(name,sizeof(char),IDLEN,wfile);
+ index.Add(name,k);
+ }
+ // Read symmetric Z-scores matrix
+ Z = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ Z[k] = new(float[N]);
+ for (l=0; l<k; l++) Z[k][l] = Z[l][k];
+ unused = fread(Z[k]+k,sizeof(float),N-k,wfile);
+ }
+ // Read symmetric covariance matrix
+ C = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ C[k] = new(float[N]);
+ for (l=0; l<k; l++) C[k][l] = C[l][k];
+ unused = fread(C[k]+k,sizeof(float),N-k,wfile);
+ }
+ fclose(wfile);
+
+ // Allocate memory
+ Zq = new(float[N]);
+ Ztq = new(float[N]);
+ Zrq = new(float[N]);
+ fold = new(char*[N]);
+ fam = new(char*[N]);
+ Prob = new(float[N]);
+ ll = new(int[N]);
+ w = new(float[N]);
+
+ // Transform P-values to normally distributed Z-scores and store in Zq vector
+ fprintf(stderr,"Transform P-values to Z-scores\n");
+ float Zmax_neg = Score2Z( -log(MINEVALEXCL) + log(N_searched) ); // calculate Z-score corresponding to E-value MINEVALEXCL
+ float Zmin_trans = Score2Z( -log(par.Emax_trans) + log(N_searched) ); // calculate Z-score corresponding to E-value par.Emax_trans
+ printf("Zmax = %6.2f Zmin = %6.2f \n",Zmax_neg,Zmin_trans);
+
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep>1) continue;
+ k = index.Show(hit.name);
+ if (k<0) {fprintf(stderr,"Error: no index found in weights file for domain %s\n",hit.name); exit(1);}
+ if (hit.logPvalt<0)
+ Zq[k] = 0.5*Score2Z(fabs(hit.logPval)) + 0.5*Score2Z(fabs(hit.logPvalt)); // Zq[k] = 0.5*(Zkq + Zqk)
+ else
+ Zq[k] = Score2Z(fabs(hit.logPval)); // Zq[k] = Zqk
+// printf("%4i %-10.10s logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPvalt,Zq[k]);
+// if (isnan(Zq[k])) {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s logPval=%9g logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPval,hit.logPvalt,Zq[k]);
+// par.trans=0;
+// return;
+// }
+ if (Zq[k]>Zmax_neg) excluded.Add(hit.fold);
+ fold[k] = new(char[IDLEN]);
+ fam[k] = new(char[IDLEN]);
+ strcpy(fold[k],hit.fold);
+ strcpy(fam[k],hit.fam);
+ weight[k] = hit.weight;
+ Prob[k] = hit.Probab;
+ }
+
+ if (v>=3)
+ {
+ excluded.Reset();
+ while (!excluded.End())
+ {
+ excluded.ReadNext(name);
+ printf("Excluded fold %s from fitting to Ztq\n",name);
+ }
+ }
+
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate transitive score (query->l) Zt[l]
+
+ // Construct vector ll of indices l for which Z_lq > Zmin_trans
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Zq[l]>=Zmin_trans) ll[m++]=l;
+ M = m; // number of indices l for which Z_lq,Z_lk > Zmin_trans
+
+// for (m=0; m<M; m++)
+// fprintf(stderr,"m=%-4i l=%-4i %-10.10s Zq[l]=%7f\n",m,ll[m],fam[ll[m]],Zq[ll[m]]);
+
+ if (M<=1)
+ for (k=0; k<N; k++) Ztq[k]=0.0;
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ Cinv[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+
+ if (v>=3)
+ {
+ fprintf(stderr,"Covariance matrix\n");
+ PrintMatrix(Csub,M);
+ }
+
+ // Invert Csub
+ fprintf(stderr,"Calculate inverse of covariance submatrix\n");
+ InvertMatrix(Cinv,Csub,M);
+
+ if (v>=3)
+ {
+ fprintf(stderr,"Inverse covariance matrix\n");
+ PrintMatrix(Cinv,M);
+ }
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Cinv[m][n];
+ w[m] = fmax(sum,0.0);
+ }
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+
+ // Calculate Ztq[k] for all HMMs k
+ fprintf(stderr,"Calculate Ztq vector of transitive Z-scores\n");
+ float norm = NormalizationFactor(Csub,w,M);
+ for (k=0; k<N; k++)
+ {
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * Z[ll[m]][k];
+ Ztq[k] = sumZ/norm;
+ }
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate reverse transitive score (l->query-) Zrq[l]
+
+ fprintf(stderr,"Calculate Zrq vector of transitive Z-scores\n");
+ for (k=0; k<N; k++)
+ {
+ // Construct vector ll of indices l for which Z_lk > Zmin_tran
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Z[l][k]+Z[k][l]>=2*Zmin_trans) ll[m++]=l;
+ int M = m; // number of indices l for which Z_lq,Z_lk > Zmin_tran
+
+
+// fprintf(stderr,"\nfam[k]: %s\n",fam[k]);
+// for (m=0; m<M; m++)
+// printf(stderr,"m=%-4i k=%-4i l=%-4i %-10.10s Zq[l]=%7f Z_lk=%7f \n",m,k,ll[m],fold[ll[m]],Zq[ll[m]],Z[k][ll[m]]);
+
+ if (M<=1)
+ {
+ Zrq[k] = Zq[k];
+ }
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+// fprintf(stderr,"Covariance matrix\n");
+// PrintMatrix(Csub,M);
+
+ if (M==2)
+ {
+ for (m=0; m<M; m++) w[m] = 1.0/M;
+ }
+ else
+ {
+
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++) Cinv[m] = new(double[M]);
+
+ // Invert Csub
+ InvertMatrix(Cinv,Csub,M);
+
+ // fprintf(stderr,"Inverse covariance matrix\n");
+ // PrintMatrix(Cinv,M);
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Cinv[m][n];
+ w[m] = fmax(sum,0.0);
+ }
+
+// for (m=0; m<M; m++) fprintf(stderr,"w[%i]=%8.2g\n",m,w[m]);
+
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+ }
+
+ // Calculate Zrq[k] and normalize
+ float norm = NormalizationFactor(Csub,w,M);
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * Zq[ll[m]];
+ Zrq[k] = sumZ/norm;
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+// fprintf(stderr,"\nZq[k]=%8.2g Zq1[k]=%8.2g\n",Zq[k],Zrq[k]);
+ }
+
+ // Total Z-score = weighted sum over original Z-score, forward transitive and reverse transitive Z-score
+ for (k=0; k<N; k++)
+ {
+ float Zqtot = Zq[k] + par.wtrans*(Ztq[k]+Zrq[k]);
+// if (isnan(Zqtot))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+// par.trans=0;
+// return;
+// }
+ if (v>=2 && Zq[k] + Zqtot > 2*Zmin_trans) {
+ printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f -> Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+ }
+ Ztq[k] = Zqtot;
+ }
+
+ // Calculate mean and standard deviation of Z1q
+ fprintf(stderr,"Calculate mean and standard deviation of Ztq\n");
+ double sumw=0.0;
+ double sumZ=0.0;
+ double sumZ2=0.0;
+ for (k=0; k<N; k++)
+ {
+ if (excluded.Contains(fold[k])) continue;
+ sumw += weight[k];
+ sumZ += weight[k]*Ztq[k];
+ sumZ2 += weight[k]*Ztq[k]*Ztq[k];
+// if (isnan(sumZ))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%9f Zrq=%9f Ztq=%9f\n",k,fam[k],Zq[k],Zrq[k],Ztq[k]);
+// par.trans=0;
+// return;
+// }
+ }
+ float mu = sumZ/sumw;
+ float sigma = sqrt(sumZ2/sumw-mu*mu);
+ if (v>=2) printf("mu(Ztq)=%6.3f sigma(Ztq)=%6.2f\n",mu,sigma);
+ sigma *= 1.01;// correct different fitting of EVD and normal variables
+
+ // Normalize Ztq and calculate P1-values
+ fprintf(stderr,"Normalize Ztq and calculate P1-values\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ hit.logPval = -Z2Score((Ztq[index.Show(hit.name)]-mu)/sigma);
+ hit.E1val = N_searched*(hit.logPval<-100.0? 0.0 : exp(hit.logPval));
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-exp(hit.logPval))) ) / 0.45-3.0 - hit.score_ss;
+ hit.Probab = Probab(hit);
+ hit.score_sort = hit.logPval;
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+
+ for (k=0; k<N; k++){
+ delete[](Z[k]); (Z[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](C[k]); (C[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fold[k]); (fold[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fam[k]); (fam[k]) = NULL;
+ }
+ delete[](C); (C) = NULL;
+ delete[](Z); (Z) = NULL;
+ delete[](fold); (fold) = NULL;
+ delete[](fam); (fam) = NULL;
+ delete[](Prob); (Prob) = NULL;
+ delete[](ll); (ll) = NULL;
+ delete[](Zq); (Zq) = NULL;
+ delete[](Ztq); (Ztq) = NULL;
+}
+
+
+
+//////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate P-values and Probabilities from transitive scoring over whole database
+ */
+void
+HitList::TransitiveScoring2()
+{
+ void PrintMatrix(float** V, int N);
+ void PrintMatrix(double** V, int N);
+
+ float** Z; // matrix of intra-db Z-scores Z_kl
+ float** C; // covariance matrix for Z_k: C_kl = sum_m=1^N (Z_km * Z_lm)
+ char** fold; // fold name of HMM k
+ char** fam; // family of HMM k
+ float* Prob; // probability of HMM k
+ float* Zq; // Zq[k] = Z-score between query and database HMM k
+ float* Ztq; // Ztq[k] = transitive Z-score from query to database HMM k: Ztq[k] = sum_l[ w_ql * Z_lk] / normalization_q
+ float* Zrq; // Zrq[k] = transitive Z-score from database HMM k to query: Zrq[k] = sum_l[ w_kl * Z_lq] / normalization_k
+ float* w; // unnormalized weight matrix; w[l] is w_ql or w_kl, respectively
+ int* ll; // ll[m] is the m'th index l for which Z_lq, Z_lk > Zmin_trans
+ int N; // dimension of weight matrix is NxN
+ int M; // number of HMMs l with Z_ql>Ztrans_min (or Z_lk>Ztrans_min, respectively)
+ int k,l,m,n; // indices for database HMMs
+ char name[NAMELEN];
+ Hash<int> index(MAXPROF+7); // index{name} = index of HMM name in {1,...,N}
+ index.Null(-1); // Set int value to return when no data can be retrieved
+ Hash<int> excluded(13); // Hash containing names of superfamilies to be excluded from fit
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+ Hit hit;
+ size_t unused; /* disable fread gcc warning */
+
+ // Read weights matrix W with index hash and names array
+ fprintf(stderr,"Reading in weights file\n");
+ FILE* wfile = fopen(par.wfile,"rb");
+ if (v>=1 && wfile==NULL)
+ {
+ fprintf(stderr,"Error: %s could not be opened: (N_searched=%i) ",par.wfile,N_searched);
+ perror("fopen");
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ unused = fread(&N,sizeof(int),1,wfile); // read matrix dimension (i.e. number of HMMs in database)
+ if (v>=1 && N!=N_searched)
+ {
+ fprintf(stderr,"Error: Number %i of HMMs in weight file is different from number %i of HMMs in searched databases. \n",N,N_searched);
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ if (v>=2) fprintf(stderr,"Calculating transitive P-values for %i HMMs\n",N);
+ // Read names of HMMs (to specify mapping of HMM to matrix indices)
+ for (k=0; k<N; k++)
+ {
+ unused = fread(name,sizeof(char),IDLEN,wfile);
+ index.Add(name,k);
+ }
+ // Read symmetric Z-scores matrix
+ Z = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ Z[k] = new(float[N]);
+ for (l=0; l<k; l++) Z[k][l] = Z[l][k];
+ unused = fread(Z[k]+k,sizeof(float),N-k,wfile);
+ }
+ // Read symmetric covariance matrix
+ C = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ C[k] = new(float[N]);
+ for (l=0; l<k; l++) C[k][l] = C[l][k];
+ unused = fread(C[k]+k,sizeof(float),N-k,wfile);
+ }
+ fclose(wfile);
+
+ // Allocate memory
+ Zq = new(float[N]);
+ Ztq = new(float[N]);
+ Zrq = new(float[N]);
+ fold = new(char*[N]);
+ fam = new(char*[N]);
+ Prob = new(float[N]);
+ ll = new(int[N]);
+ w = new(float[N]);
+
+ // Transform P-values to normally distributed Z-scores and store in Zq vector
+ fprintf(stderr,"Transform P-values to Z-scores\n");
+ float Zmax_neg = Score2Z( -log(MINEVALEXCL) + log(N_searched) ); // calculate Z-score corresponding to E-value MINEVALEXCL
+ float Zmin_trans = Score2Z( -log(par.Emax_trans) + log(N_searched) ); // calculate Z-score corresponding to E-value par.Emax_trans
+ printf("Zmax = %6.2f Zmin = %6.2f \n",Zmax_neg,Zmin_trans);
+
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep>1) continue;
+ k = index.Show(hit.name);
+ if (k<0) {fprintf(stderr,"Error: no index found in weights file for domain %s\n",hit.name); exit(1);}
+ if (hit.logPvalt<0)
+ Zq[k] = 0.5*Score2Z(fabs(hit.logPval)) + 0.5*Score2Z(fabs(hit.logPvalt)); // Zq[k] = 0.5*(Zkq + Zqk)
+ else
+ Zq[k] = Score2Z(fabs(hit.logPval)); // Zq[k] = Zqk
+// printf("%4i %-10.10s logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPvalt,Zq[k]);
+// if (isnan(Zq[k]))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s logPval=%9g logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPval,hit.logPvalt,Zq[k]);
+// par.trans=0;
+// return;
+// }
+ if (Zq[k]>Zmax_neg) excluded.Add(hit.fold);
+ fold[k] = new(char[IDLEN]);
+ fam[k] = new(char[IDLEN]);
+ strcpy(fold[k],hit.fold);
+ strcpy(fam[k],hit.fam);
+ weight[k] = hit.weight;
+ Prob[k] = hit.Probab;
+ }
+
+ if (v>=3)
+ {
+ excluded.Reset();
+ while (!excluded.End())
+ {
+ excluded.ReadNext(name);
+ printf("Excluded fold %s from fitting to Ztq\n",name);
+ }
+ }
+
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate transitive score (query->l) Zt[l]
+
+ // Construct vector ll of indices l for which Z_lq > Zmin_trans
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Zq[l]>=Zmin_trans) ll[m++]=l;
+ M = m; // number of indices l for which Z_lq,Z_lk > Zmin_trans
+
+// for (m=0; m<M; m++)
+// fprintf(stderr,"m=%-4i l=%-4i %-10.10s Zq[l]=%7f\n",m,ll[m],fam[ll[m]],Zq[ll[m]]);
+
+ if (M<=1)
+ for (k=0; k<N; k++) Ztq[k]=0.0;
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ Cinv[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+
+ if (v>=3)
+ {
+ fprintf(stderr,"Covariance matrix\n");
+ PrintMatrix(Csub,M);
+ }
+
+// // Invert Csub
+// fprintf(stderr,"Calculate inverse of covariance submatrix\n");
+// InvertMatrix(Cinv,Csub,M);
+
+// if (v>=3)
+// {
+// fprintf(stderr,"Inverse covariance matrix\n");
+// PrintMatrix(Cinv,M);
+// }
+
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Csub[m][n];
+ printf("w[%4i] = %-8.5f\n",ll[m],1.0/sum);
+ w[m] = (sum>0? Zq[ll[m]] / sum : 0.0);
+ }
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+
+ // Calculate Ztq[k] for all HMMs k
+ fprintf(stderr,"Calculate Ztq vector of transitive Z-scores\n");
+ float norm = NormalizationFactor(Csub,w,M);
+ for (k=0; k<N; k++)
+ {
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * Z[ll[m]][k];
+ Ztq[k] = sumZ/norm;
+ }
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate reverse transitive score (l->query-) Zrq[l]
+
+ fprintf(stderr,"Calculate Zrq vector of transitive Z-scores\n");
+ for (k=0; k<N; k++)
+ {
+ // Construct vector ll of indices l for which Z_lk > Zmin_tran
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Z[l][k]+Z[k][l]>=2*Zmin_trans) ll[m++]=l;
+ int M = m; // number of indices l for which Z_lq,Z_lk > Zmin_tran
+
+
+// fprintf(stderr,"\nfam[k]: %s\n",fam[k]);
+// for (m=0; m<M; m++)
+// printf(stderr,"m=%-4i k=%-4i l=%-4i %-10.10s Zq[l]=%7f Z_lk=%7f \n",m,k,ll[m],fold[ll[m]],Zq[ll[m]],Z[k][ll[m]]);
+
+ if (M<=1)
+ {
+ Zrq[k] = Zq[k];
+ }
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+// fprintf(stderr,"Covariance matrix\n");
+// PrintMatrix(Csub,M);
+
+ if (M<=2)
+ {
+ for (m=0; m<M; m++) w[m] = 1.0/M;
+ }
+ else
+ {
+
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++) Cinv[m] = new(double[M]);
+
+// // Invert Csub
+// InvertMatrix(Cinv,Csub,M);
+
+// // fprintf(stderr,"Inverse covariance matrix\n");
+// // PrintMatrix(Cinv,M);
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Csub[m][n];
+ w[m] = (sum>0? Z[ll[m]][k] / sum : 0.0);
+ }
+
+// for (m=0; m<M; m++) fprintf(stderr,"w[%i]=%8.2g\n",m,w[m]);
+
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+ }
+
+ // Calculate Zrq[k] and normalize
+ float norm = NormalizationFactor(Csub,w,M);
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * Zq[ll[m]];
+ Zrq[k] = sumZ/norm;
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+// fprintf(stderr,"\nZq[k]=%8.2g Zq1[k]=%8.2g\n",Zq[k],Zrq[k]);
+ }
+
+ // Total Z-score = weighted sum over original Z-score, forward transitive and reverse transitive Z-score
+ for (k=0; k<N; k++)
+ {
+ float Zqtot = Zq[k] + par.wtrans*(Ztq[k]+Zrq[k]);
+// if (isnan(Zqtot))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+// par.trans=0;
+// return;
+// }
+ if (v>=2 && Zq[k] + Zqtot > 2*Zmin_trans) {
+ printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f -> Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+ }
+ Ztq[k] = Zqtot;
+ }
+
+ // Calculate mean and standard deviation of Z1q
+ fprintf(stderr,"Calculate mean and standard deviation of Ztq\n");
+ double sumw=0.0;
+ double sumZ=0.0;
+ double sumZ2=0.0;
+ for (k=0; k<N; k++)
+ {
+ if (excluded.Contains(fold[k])) continue;
+ sumw += weight[k];
+ sumZ += weight[k]*Ztq[k];
+ sumZ2 += weight[k]*Ztq[k]*Ztq[k];
+// if (isnan(sumZ))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%9f Zrq=%9f Ztq=%9f\n",k,fam[k],Zq[k],Zrq[k],Ztq[k]);
+// par.trans=0;
+// return;
+// }
+ }
+ float mu = sumZ/sumw;
+ float sigma = sqrt(sumZ2/sumw-mu*mu);
+ if (v>=2) printf("mu(Ztq)=%6.3f sigma(Ztq)=%6.2f\n",mu,sigma);
+ sigma *= 1.01;// correct different fitting of EVD and normal variables
+
+ // Normalize Ztq and calculate P1-values
+ fprintf(stderr,"Normalize Ztq and calculate P1-values\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ hit.logPval = -Z2Score((Ztq[index.Show(hit.name)]-mu)/sigma);
+ hit.E1val = N_searched*(hit.logPval<-100? 0.0 : exp(hit.logPval));
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-exp(hit.logPval))) ) / 0.45-3.0 - hit.score_ss;
+ hit.Probab = Probab(hit);
+ hit.score_sort = hit.logPval;
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+
+ for (k=0; k<N; k++){
+ delete[](Z[k]); (Z[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](C[k]); (C[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fold[k]); (fold[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fam[k]); (fam[k]) = NULL;
+ }
+ delete[](C); (C) = NULL;
+ delete[](Z); (Z) = NULL;
+ delete[](fold); (fold) = NULL;
+ delete[](fam); (fam) = NULL;
+ delete[](Prob); (Prob) = NULL;
+ delete[](ll); (ll) = NULL;
+ delete[](Zq); (Zq) = NULL;
+ delete[](Ztq); (Ztq) = NULL;
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate P-values and Probabilities from transitive scoring over whole database
+ * Like TransitiveScoring(),
+ * but in transitive scoring, Z1_qk = sum_l w_l*Z_lk, use all l:E_ql<=E_qk
+ * and in reverse scoring, Z1_kr = sum_l w_l*Z_lq, use all l:E_kl<=E_kq
+ */
+void
+HitList::TransitiveScoring3()
+{
+ void PrintMatrix(float** V, int N);
+ void PrintMatrix(double** V, int N);
+
+ float** Z; // matrix of intra-db Z-scores Z_kl
+ float** C; // covariance matrix for Z_k: C_kl = sum_m=1^N (Z_km * Z_lm)
+ char** fold; // fold name of HMM k
+ char** fam; // family of HMM k
+ float* Prob; // probability of HMM k
+ float* Zq; // Zq[k] = Z-score between query and database HMM k
+ float* Ztq; // Ztq[k] = transitive Z-score from query to database HMM k: Ztq[k] = sum_l[ w_ql * Z_lk] / normalization_q
+ float* Zrq; // Zrq[k] = transitive Z-score from database HMM k to query: Zrq[k] = sum_l[ w_kl * Z_lq] / normalization_k
+ float* w; // unnormalized weight matrix; w[l] is w_ql or w_kl, respectively
+ int* ll; // ll[m] is the m'th index l for which Z_lq, Z_lk > Zmin_trans
+ int N; // dimension of weight matrix is NxN
+ int M; // number of HMMs l with Z_ql>Ztrans_min (or Z_lk>Ztrans_min, respectively)
+ int k,l,m,n; // indices for database HMMs
+ char name[NAMELEN];
+ Hash<int> index(MAXPROF+7); // index{name} = index of HMM name in {1,...,N}
+ index.Null(-1); // Set int value to return when no data can be retrieved
+ Hash<int> excluded(13); // Hash containing names of superfamilies to be excluded from fit
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+ Hit hit;
+ size_t unused; /* disable fread gcc warning */
+
+ // Read weights matrix W with index hash and names array
+ fprintf(stderr,"Reading in weights file\n");
+ FILE* wfile = fopen(par.wfile,"rb");
+ if (v>=1 && wfile==NULL)
+ {
+ fprintf(stderr,"Error: %s could not be opened: (N_searched=%i) ",par.wfile,N_searched);
+ perror("fopen");
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ unused = fread(&N,sizeof(int),1,wfile); // read matrix dimension (i.e. number of HMMs in database)
+ if (v>=1 && N!=N_searched)
+ {
+ fprintf(stderr,"Error: Number %i of HMMs in weight file is different from number %i of HMMs in searched databases. \n",N,N_searched);
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ if (v>=2) fprintf(stderr,"Calculating transitive P-values for %i HMMs\n",N);
+ // Read names of HMMs (to specify mapping of HMM to matrix indices)
+ for (k=0; k<N; k++)
+ {
+ unused = fread(name,sizeof(char),IDLEN,wfile);
+ index.Add(name,k);
+ }
+ // Read symmetric Z-scores matrix
+ Z = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ Z[k] = new(float[N]);
+ for (l=0; l<k; l++) Z[k][l] = Z[l][k];
+ unused = fread(Z[k]+k,sizeof(float),N-k,wfile);
+ }
+ // Read symmetric covariance matrix
+ C = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ C[k] = new(float[N]);
+ for (l=0; l<k; l++) C[k][l] = C[l][k];
+ unused = fread(C[k]+k,sizeof(float),N-k,wfile);
+ }
+ fclose(wfile);
+
+ // Allocate memory
+ Zq = new(float[N]);
+ Ztq = new(float[N]);
+ Zrq = new(float[N]);
+ fold = new(char*[N]);
+ fam = new(char*[N]);
+ Prob = new(float[N]);
+ ll = new(int[N]);
+ w = new(float[N]);
+
+ // Transform P-values to normally distributed Z-scores and store in Zq vector
+ fprintf(stderr,"Transform P-values to Z-scores\n");
+ float Zmax_neg = Score2Z( -log(MINEVALEXCL) + log(N_searched) ); // calculate Z-score corresponding to E-value MINEVALEXCL
+ float Zmin_trans = Score2Z( -log(par.Emax_trans) + log(N_searched) ); // calculate Z-score corresponding to E-value par.Emax_trans
+ printf("Zmax = %6.2f Zmin = %6.2f \n",Zmax_neg,Zmin_trans);
+
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep>1) continue;
+ k = index.Show(hit.name);
+ if (k<0) {fprintf(stderr,"Error: no index found in weights file for domain %s\n",hit.name); exit(1);}
+ if (hit.logPvalt<0)
+ Zq[k] = 0.5*Score2Z(fabs(hit.logPval)) + 0.5*Score2Z(fabs(hit.logPvalt)); // Zq[k] = 0.5*(Zkq + Zqk)
+ else
+ Zq[k] = Score2Z(fabs(hit.logPval)); // Zq[k] = Zqk
+// printf("%4i %-10.10s logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPvalt,Zq[k]);
+// if (isnan(Zq[k]))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s logPval=%9g logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPval,hit.logPvalt,Zq[k]);
+// par.trans=0;
+// return;
+// }
+ if (Zq[k]>Zmax_neg) excluded.Add(hit.fold);
+ fold[k] = new(char[IDLEN]);
+ fam[k] = new(char[IDLEN]);
+ strcpy(fold[k],hit.fold);
+ strcpy(fam[k],hit.fam);
+ weight[k] = hit.weight;
+ Prob[k] = hit.Probab;
+ }
+
+ if (v>=3)
+ {
+ excluded.Reset();
+ while (!excluded.End())
+ {
+ excluded.ReadNext(name);
+ printf("Excluded fold %s from fitting to Ztq\n",name);
+ }
+ }
+
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate transitive score (query->l) Ztq[l]
+
+ fprintf(stderr,"Calculate Ztq vector of transitive Z-scores\n");
+ for (k=0; k<N; k++)
+ {
+ // Construct vector ll of indices l for which Z_lq OR Z_lk >= max(Z_kq,Zmin_trans)
+ float Zmink = fmax(Zq[k],Zmin_trans);
+ for (m=l=0; l<N; l++)
+ if (Zq[l]>=Zmink) ll[m++]=l;
+ M = m; // number of indices l for which Z_lq OR Z_lk >= max(Z_kq,Zmin_trans)
+
+// for (m=0; m<M; m++)
+// fprintf(stderr,"m=%-4i l=%-4i %-10.10s Zq[l]=%7f\n",m,ll[m],fam[ll[m]],Zq[ll[m]]);
+
+ if (M<=1)
+ {
+ Ztq[k]=Zq[k];
+ }
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ Cinv[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+
+// fprintf(stderr,"Covariance matrix\n");
+// PrintMatrix(Csub,M);
+
+ // Invert Csub
+// fprintf(stderr,"Calculate inverse of covariance submatrix\n");
+ InvertMatrix(Cinv,Csub,M);
+
+// fprintf(stderr,"Inverse covariance matrix\n");
+// PrintMatrix(Cinv,M);
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Cinv[m][n]; // signal ~ sum_l w_l*Z_lq !
+ w[m] = fmax(sum,0.0);
+ }
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+
+ // Calculate Ztq[k]
+ float norm = NormalizationFactor(Csub,w,M);
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * fmin(Zq[ll[m]],Z[ll[m]][k]);
+// sumZ += w[m] * Z[ll[m]][k];
+ Ztq[k] = sumZ/norm;
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+ }
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate reverse transitive score (l->query-) Zrq[l]
+
+ fprintf(stderr,"Calculate Zrq vector of transitive Z-scores\n");
+ for (k=0; k<N; k++)
+ {
+ // Construct vector ll of indices l for which Z_lk > Zmin_tran
+ float Zmink = fmax(Zq[k],Zmin_trans);
+ for (m=l=0; l<N; l++)
+ if (Z[l][k]>=Zmink) ll[m++]=l;
+ int M = m; // number of indices l for which Z_lq,Z_lk > Zmin_tran
+
+
+// fprintf(stderr,"\nfam[k]: %s\n",fam[k]);
+// for (m=0; m<M; m++)
+// printf(stderr,"m=%-4i k=%-4i l=%-4i %-10.10s Zq[l]=%7f Z_lk=%7f \n",m,k,ll[m],fold[ll[m]],Zq[ll[m]],Z[k][ll[m]]);
+
+ if (M<=1)
+ {
+ Zrq[k] = Zq[k];
+ }
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+// fprintf(stderr,"Covariance matrix\n");
+// PrintMatrix(Csub,M);
+
+ if (M==2)
+ {
+ for (m=0; m<M; m++) w[m] = 1.0/M;
+ }
+ else
+ {
+
+ double** Cinv = new(double*[M]);
+ for (m=0; m<M; m++) Cinv[m] = new(double[M]);
+
+ // Invert Csub
+ InvertMatrix(Cinv,Csub,M);
+
+ // fprintf(stderr,"Inverse covariance matrix\n");
+ // PrintMatrix(Cinv,M);
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += 1.0 * Cinv[m][n]; // signal ~ sum_l w_l*Z_lq !
+ w[m] = fmax(sum,0.0);
+ }
+// for (m=0; m<M; m++) fprintf(stderr,"w[%i]=%8.2g\n",m,w[m]);
+ for (l=0; l<M; l++){
+ delete[](Cinv[l]); (Cinv[l]) = NULL;
+ }
+ delete[](Cinv); (Cinv) = NULL;
+ }
+
+ // Calculate Zrq[k] and normalize
+ float norm = NormalizationFactor(Csub,w,M);
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * fmin(Zq[ll[m]],Z[ll[m]][k]);
+// sumZ += w[m] * Zq[ll[m]];
+ Zrq[k] = sumZ/norm;
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+// fprintf(stderr,"\nZq[k]=%8.2g Zq1[k]=%8.2g\n",Zq[k],Zrq[k]);
+ }
+
+ // Total Z-score = weighted sum over original Z-score, forward transitive and reverse transitive Z-score
+ for (k=0; k<N; k++)
+ {
+
+ float Zqtot = Zq[k] + par.wtrans*(Ztq[k]+Zrq[k]);
+// if (isnan(Zqtot))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f -> Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+// par.trans=0;
+// return;
+// }
+ if (v>=3 && Zqtot > 2*Zmin_trans) {
+ printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f -> Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+ }
+ Ztq[k] = Zqtot;
+ }
+
+ // Calculate mean and standard deviation of Z1q
+ fprintf(stderr,"Calculate mean and standard deviation of Ztq\n");
+ double sumw=0.0;
+ double sumZ=0.0;
+ double sumZ2=0.0;
+ for (k=0; k<N; k++)
+ {
+ if (excluded.Contains(fold[k])) continue;
+ sumw += weight[k];
+ sumZ += weight[k]*Ztq[k];
+ sumZ2 += weight[k]*Ztq[k]*Ztq[k];
+// if (isnan(sumZ))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%9f Zrq=%9f Ztq=%9f\n",k,fam[k],Zq[k],Zrq[k],Ztq[k]);
+// par.trans=0;
+// return;
+// }
+ }
+ float mu = sumZ/sumw;
+ float sigma = sqrt(sumZ2/sumw-mu*mu);
+ if (v>=2) printf("mu(Ztq)=%6.3f sigma(Ztq)=%6.2f\n",mu,sigma);
+ sigma *= 1.01;// correct different fitting of EVD and normal variables
+
+ // Normalize Ztq and calculate P1-values
+ fprintf(stderr,"Normalize Ztq and calculate P1-values\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ hit.logPval = -Z2Score((Ztq[index.Show(hit.name)]-mu)/sigma);
+ hit.E1val = N_searched*(hit.logPval<-100? 0.0 : exp(hit.logPval));
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-exp(hit.logPval))) ) / 0.45-3.0 - hit.score_ss;
+ hit.Probab = Probab(hit);
+ hit.score_sort = hit.logPval;
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+
+ for (k=0; k<N; k++){
+ delete[](Z[k]); (Z[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](C[k]); (C[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fold[k]); (fold[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fam[k]); (fam[k]) = NULL;
+ }
+ delete[](C); (C) = NULL;
+ delete[](Z); (Z) = NULL;
+ delete[](fold); (fold) = NULL;
+ delete[](fam); (fam) = NULL;
+ delete[](Prob); (Prob) = NULL;
+ delete[](ll); (ll) = NULL;
+ delete[](Zq); (Zq) = NULL;
+ delete[](Ztq); (Ztq) = NULL;
+
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate P-values and Probabilities from transitive scoring over whole database
+ * Best tested scheme. Use fmin(Zq[ll[m]],Z[ll[m]][k])
+ * and fast approximation for weights (not inverse covariance matrix)
+ */
+void
+HitList::TransitiveScoring4()
+{
+ void PrintMatrix(float** V, int N);
+ void PrintMatrix(double** V, int N);
+
+ float** Z; // matrix of intra-db Z-scores Z_kl
+ float** C; // covariance matrix for Z_k: C_kl = sum_m=1^N (Z_km * Z_lm)
+ char** fold; // fold name of HMM k
+ char** fam; // family of HMM k
+ float* Prob; // probability of HMM k
+ float* Zq; // Zq[k] = Z-score between query and database HMM k
+ float* Ztq; // Ztq[k] = transitive Z-score from query to database HMM k: Ztq[k] = sum_l[ w_ql * Z_lk] / normalization_q
+ float* Zrq; // Zrq[k] = transitive Z-score from database HMM k to query: Zrq[k] = sum_l[ w_kl * Z_lq] / normalization_k
+ float* w; // unnormalized weight matrix; w[l] is w_ql or w_kl, respectively
+ int* ll; // ll[m] is the m'th index l for which Z_lq, Z_lk > Zmin_trans
+ int N; // dimension of weight matrix is NxN
+ int M; // number of HMMs l with Z_ql>Ztrans_min (or Z_lk>Ztrans_min, respectively)
+ int k,l,m,n; // indices for database HMMs
+ char name[NAMELEN];
+ Hash<int> index(MAXPROF+7); // index{name} = index of HMM name in {1,...,N}
+ index.Null(-1); // Set int value to return when no data can be retrieved
+ Hash<int> excluded(13); // Hash containing names of superfamilies to be excluded from fit
+ excluded.Null(0); // Set int value to return when no data can be retrieved
+ Hit hit;
+ size_t unused; /* disable fread gcc warning */
+
+ // Read weights matrix W with index hash and names array
+ fprintf(stderr,"Reading in weights file\n");
+ FILE* wfile = fopen(par.wfile,"rb");
+ if (v>=1 && wfile==NULL)
+ {
+ fprintf(stderr,"Error: %s could not be opened: (N_searched=%i) ",par.wfile,N_searched);
+ perror("fopen");
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ unused = fread(&N,sizeof(int),1,wfile); // read matrix dimension (i.e. number of HMMs in database)
+ if (v>=1 && N!=N_searched)
+ {
+ fprintf(stderr,"Error: Number %i of HMMs in weight file is different from number %i of HMMs in searched databases. \n",N,N_searched);
+ fprintf(stderr,"Skipping caclulation of transitive P-values\n");
+ par.trans=0;
+ return;
+ }
+ if (v>=2) fprintf(stderr,"Calculating transitive P-values for %i HMMs\n",N);
+ // Read names of HMMs (to specify mapping of HMM to matrix indices)
+ for (k=0; k<N; k++)
+ {
+ unused = fread(name,sizeof(char),IDLEN,wfile);
+ index.Add(name,k);
+ }
+ // Read symmetric Z-scores matrix
+ Z = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ Z[k] = new(float[N]);
+ for (l=0; l<k; l++) Z[k][l] = Z[l][k];
+ unused = fread(Z[k]+k,sizeof(float),N-k,wfile);
+ }
+ // Read symmetric covariance matrix
+ C = new(float*[N]);
+ for (k=0; k<N; k++)
+ {
+ C[k] = new(float[N]);
+ for (l=0; l<k; l++) C[k][l] = C[l][k];
+ unused = fread(C[k]+k,sizeof(float),N-k,wfile);
+ }
+ fclose(wfile);
+
+ // Allocate memory
+ Zq = new(float[N]);
+ Ztq = new(float[N]);
+ Zrq = new(float[N]);
+ fold = new(char*[N]);
+ fam = new(char*[N]);
+ Prob = new(float[N]);
+ ll = new(int[N]);
+ w = new(float[N]);
+
+ // Transform P-values to normally distributed Z-scores and store in Zq vector
+ fprintf(stderr,"Transform P-values to Z-scores\n");
+ float Zmax_neg = Score2Z( -log(MINEVALEXCL) + log(N_searched) ); // calculate Z-score corresponding to E-value MINEVALEXCL
+ float Zmin_trans = Score2Z( -log(par.Emax_trans) + log(N_searched) ); // calculate Z-score corresponding to E-value par.Emax_trans
+ printf("Zmax = %6.2f Zmin = %6.2f \n",Zmax_neg,Zmin_trans);
+
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ if (hit.irep>1) continue;
+ k = index.Show(hit.name);
+ if (k<0) {fprintf(stderr,"Error: no index found in weights file for domain %s\n",hit.name); exit(1);}
+ if (hit.logPvalt<0)
+ Zq[k] = 0.5*Score2Z(fabs(hit.logPval)) + 0.5*Score2Z(fabs(hit.logPvalt)); // Zq[k] = 0.5*(Zkq + Zqk)
+ else
+ Zq[k] = Score2Z(fabs(hit.logPval)); // Zq[k] = Zqk
+// printf("%4i %-10.10s logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPvalt,Zq[k]);
+// if (isnan(Zq[k])) {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s logPval=%9g logPvalt=%9g Zq=%9f\n",k,hit.name,hit.logPval,hit.logPvalt,Zq[k]);
+// par.trans=0;
+// return;
+// }
+ if (Zq[k]>Zmax_neg) excluded.Add(hit.fold);
+ fold[k] = new(char[IDLEN]);
+ fam[k] = new(char[IDLEN]);
+ strcpy(fold[k],hit.fold);
+ strcpy(fam[k],hit.fam);
+ weight[k] = hit.weight;
+ Prob[k] = hit.Probab;
+ }
+
+ if (v>=3)
+ {
+ excluded.Reset();
+ while (!excluded.End())
+ {
+ excluded.ReadNext(name);
+ printf("Excluded fold %s from fitting to Ztq\n",name);
+ }
+ }
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate transitive score (query->l) Zt[l]
+
+ // Construct vector ll of indices l for which Z_lq > Zmin_trans
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Zq[l]>=Zmin_trans) ll[m++]=l;
+ M = m; // number of indices l for which Z_lq,Z_lk > Zmin_trans
+
+// for (m=0; m<M; m++)
+// fprintf(stderr,"m=%-4i l=%-4i %-10.10s Zq[l]=%7f\n",m,ll[m],fam[ll[m]],Zq[ll[m]]);
+
+ if (M<=1)
+ for (k=0; k<N; k++) Ztq[k]=0.0;
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+
+ if (v>=3)
+ {
+ fprintf(stderr,"Covariance matrix\n");
+ PrintMatrix(Csub,M);
+ }
+
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += fmax(0.0,Csub[m][n]);
+ printf("w[%4i] = %-8.5f\n",ll[m],1.0/sum);
+ w[m] = 1.0/sum;
+ }
+
+ // Calculate Ztq[k] for all HMMs k
+ fprintf(stderr,"Calculate Ztq vector of transitive Z-scores\n");
+ float norm = NormalizationFactor(Csub,w,M);
+ for (k=0; k<N; k++)
+ {
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * fmin(Zq[ll[m]],Z[ll[m]][k]);
+ Ztq[k] = sumZ/norm;
+ }
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+ ////////////////////////////////////////////////////////////////
+ // Calculate reverse transitive score (l->query-) Zrq[l]
+
+ fprintf(stderr,"Calculate Zrq vector of transitive Z-scores\n");
+ for (k=0; k<N; k++)
+ {
+ // Construct vector ll of indices l for which Z_lk > Zmin_tran
+ m = 0;
+ for (l=0; l<N; l++)
+ if (Z[k][l]>=Zmin_trans) ll[m++]=l;
+ int M = m; // number of indices l for which Z_lq,Z_lk > Zmin_tran
+
+
+// fprintf(stderr,"\nfam[k]: %s\n",fam[k]);
+// for (m=0; m<M; m++)
+// printf(stderr,"m=%-4i k=%-4i l=%-4i %-10.10s Zq[l]=%7f Z_lk=%7f \n",m,k,ll[m],fold[ll[m]],Zq[ll[m]],Z[k][ll[m]]);
+
+ if (M<=1)
+ {
+ Zrq[k] = Zq[k];
+ }
+ else
+ {
+ // Generate submatrix of C for indices l for which Z_lq,Z_lk > Zmin_trans
+ double** Csub = new(double*[M]);
+ for (m=0; m<M; m++)
+ {
+ Csub[m] = new(double[M]);
+ for (n=0; n<M; n++)
+ Csub[m][n] = double(C[ll[m]][ll[n]]);
+ }
+// fprintf(stderr,"Covariance matrix\n");
+// PrintMatrix(Csub,M);
+
+ // Calculate weights w[l]
+ for (m=0; m<M; m++)
+ {
+ double sum = 0.0;
+ for (n=0; n<M; n++)
+ sum += fmax(0.0,Csub[m][n]);
+ w[m] = 1.0/sum;
+ }
+
+// for (m=0; m<M; m++) fprintf(stderr,"w[%i]=%8.2g\n",m,w[m]);
+
+
+ // Calculate Zrq[k] and normalize
+ float norm = NormalizationFactor(Csub,w,M);
+ double sumZ = 0.0;
+ for (m=0; m<M; m++)
+ sumZ += w[m] * fmin(Zq[ll[m]],Z[ll[m]][k]);
+ Zrq[k] = sumZ/norm;
+
+ for (l=0; l<M; l++){
+ delete[](Csub[l]); (Csub[l]) = NULL;
+ }
+ delete[](Csub); (Csub) = NULL;
+ }
+
+// fprintf(stderr,"\nZq[k]=%8.2g Zq1[k]=%8.2g\n",Zq[k],Zrq[k]);
+ }
+
+ // Total Z-score = weighted sum over original Z-score, forward transitive and reverse transitive Z-score
+ for (k=0; k<N; k++)
+ {
+ float Zqtot = Zq[k] + par.wtrans*(Ztq[k]+Zrq[k]);
+// if (isnan(Zqtot))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+// par.trans=0;
+// return;
+// }
+ if (v>=3 && Zq[k] + Zqtot > 2*Zmin_trans) {
+ printf("%4i %-10.10s Zq=%6.2f Ztq=%6.2f Zrq=%6.2f -> Zqtot=%6.2f\n",k,fam[k],Zq[k],Ztq[k],Zrq[k],Zqtot);
+ }
+ Ztq[k] = Zqtot;
+ }
+
+ // Calculate mean and standard deviation of Z1q
+ fprintf(stderr,"Calculate mean and standard deviation of Ztq\n");
+ double sumw=0.0;
+ double sumZ=0.0;
+ double sumZ2=0.0;
+ for (k=0; k<N; k++)
+ {
+ if (excluded.Contains(fold[k])) continue;
+ sumw += weight[k];
+ sumZ += weight[k]*Ztq[k];
+ sumZ2 += weight[k]*Ztq[k]*Ztq[k];
+// if (isnan(sumZ))
+// {
+// fprintf(stderr,"Error: a floating point exception occurred. Skipping transitive scoring\n");
+// printf("%4i %-10.10s Zq=%9f Zrq=%9f Ztq=%9f\n",k,fam[k],Zq[k],Zrq[k],Ztq[k]);
+// par.trans=0;
+// return;
+// }
+ }
+ float mu = sumZ/sumw;
+ float sigma = sqrt(sumZ2/sumw-mu*mu);
+ if (v>=2) printf("mu(Ztq)=%6.3f sigma(Ztq)=%6.2f\n",mu,sigma);
+ sigma *= 1.01;// correct different fitting of EVD and normal variables
+
+ // Normalize Ztq and calculate P1-values
+ fprintf(stderr,"Normalize Ztq and calculate P1-values\n");
+ Reset();
+ while (!End())
+ {
+ hit = ReadNext();
+ hit.logPval = -Z2Score((Ztq[index.Show(hit.name)]-mu)/sigma);
+ hit.E1val = N_searched*(hit.logPval<-100? 0.0 : exp(hit.logPval));
+ // P-value = 1- exp(-exp(-lamda*(Saa-mu))) => -lamda*(Saa-mu) = log(-log(1-Pvalue))
+ hit.score_aass = (hit.logPval<-10.0? hit.logPval : log(-log(1-exp(hit.logPval))) ) / 0.45-3.0 - hit.score_ss;
+ hit.Probab = Probab(hit);
+ hit.score_sort = hit.logPval;
+ Overwrite(hit); // copy hit object into current position of hitlist
+ }
+
+ for (k=0; k<N; k++){
+ delete[](Z[k]); (Z[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](C[k]); (C[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fold[k]); (fold[k]) = NULL;
+ }
+ for (k=0; k<N; k++){
+ delete[](fam[k]); (fam[k]) = NULL;
+ }
+ delete[](C); (C) = NULL;
+ delete[](Z); (Z) = NULL;
+ delete[](fold); (fold) = NULL;
+ delete[](fam); (fam) = NULL;
+ delete[](Prob); (Prob) = NULL;
+ delete[](ll); (ll) = NULL;
+ delete[](Zq); (Zq) = NULL;
+ delete[](Ztq); (Ztq) = NULL;
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Score2Z transforms the -log(P-value) score into a Z-score for 0 < S
+ * Score2Z(S) = sqrt(2)*dierfc(2*e^(-S)), where dierfc is the inverse of the complementary error function
+ */
+double
+HitList::Score2Z(double S)
+{
+ double s, t, u, w, x, y, z;
+ if (S<=0) return double(-100000);
+ y = ( S>200 ? 0.0 : 2.0*exp(-S) );
+ if (y > 1)
+ {
+ z = (S<1e-6? 2*S : 2-y);
+ w = 0.916461398268964 - log(z);
+ }
+ else
+ {
+ z = y;
+ w = 0.916461398268964 - (0.69314718056-S);
+ }
+
+ u = sqrt(w);
+ s = (log(u) + 0.488826640273108) / w;
+ t = 1 / (u + 0.231729200323405);
+
+ x = u * (1 - s * (s * 0.124610454613712 + 0.5)) -
+ ((((-0.0728846765585675 * t + 0.269999308670029) * t +
+ 0.150689047360223) * t + 0.116065025341614) * t +
+ 0.499999303439796) * t;
+ t = 3.97886080735226 / (x + 3.97886080735226);
+ u = t - 0.5;
+ s = (((((((((0.00112648096188977922 * u +
+ 1.05739299623423047e-4) * u - 0.00351287146129100025) * u -
+ 7.71708358954120939e-4) * u + 0.00685649426074558612) * u +
+ 0.00339721910367775861) * u - 0.011274916933250487) * u -
+ 0.0118598117047771104) * u + 0.0142961988697898018) * u +
+ 0.0346494207789099922) * u + 0.00220995927012179067;
+ s = ((((((((((((s * u - 0.0743424357241784861) * u -
+ 0.105872177941595488) * u + 0.0147297938331485121) * u +
+ 0.316847638520135944) * u + 0.713657635868730364) * u +
+ 1.05375024970847138) * u + 1.21448730779995237) * u +
+ 1.16374581931560831) * u + 0.956464974744799006) * u +
+ 0.686265948274097816) * u + 0.434397492331430115) * u +
+ 0.244044510593190935) * t -
+ (z==0? 0: z * exp(x * x - 0.120782237635245222));
+ x += s * (x * s + 1);
+ if (y > 1) {
+ x = -x;
+ }
+ return double (1.41421356237*x);
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Z2Score transforms the Z-score into a -log(P-value) value
+ * Z2Score(Z) = log(2) - log( erfc(Z/sqrt(2)) ) , where derfc is the complementary error function
+ */
+double
+HitList::Z2Score(double Z)
+{
+ double t, u, x, y;
+ x = 0.707106781188*Z;
+ if (x>10) return 0.69314718056 - (-x*x - log( (1-0.5/x/x)/x/1.772453851) );
+ t = 3.97886080735226 / (fabs(x) + 3.97886080735226);
+ u = t - 0.5;
+ y = (((((((((0.00127109764952614092 * u + 1.19314022838340944e-4) * u -
+ 0.003963850973605135) * u - 8.70779635317295828e-4) * u +
+ 0.00773672528313526668) * u + 0.00383335126264887303) * u -
+ 0.0127223813782122755) * u - 0.0133823644533460069) * u +
+ 0.0161315329733252248) * u + 0.0390976845588484035) * u +
+ 0.00249367200053503304;
+ y = ((((((((((((y * u - 0.0838864557023001992) * u -
+ 0.119463959964325415) * u + 0.0166207924969367356) * u +
+ 0.357524274449531043) * u + 0.805276408752910567) * u +
+ 1.18902982909273333) * u + 1.37040217682338167) * u +
+ 1.31314653831023098) * u + 1.07925515155856677) * u +
+ 0.774368199119538609) * u + 0.490165080585318424) * u +
+ 0.275374741597376782) * t * (x>10? 0.0 : exp(-x * x));
+ return 0.69314718056 - log( x < 0 ? 2 - y : y );
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief
+ */
+void
+PrintMatrix(float** V, int N)
+{
+ int k,l;
+ for (k=0; k<N; k++)
+ {
+ fprintf(stderr,"k=%4i \n",k);
+ for (l=0; l<N; l++)
+ {
+ fprintf(stderr,"%4i:%6.3f ",l,V[k][l]);
+ if ((l+1)%10==0) fprintf(stderr,"\n");
+ }
+ fprintf(stderr,"\n");
+ }
+ fprintf(stderr,"\n");
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief
+ */
+void
+PrintMatrix(double** V, int N)
+{
+ int k,l;
+ for (k=0; k<N; k++)
+ {
+ fprintf(stderr,"k=%4i \n",k);
+ for (l=0; l<N; l++)
+ {
+ fprintf(stderr,"%4i:%6.3f ",l,V[k][l]);
+ if ((l+1)%10==0) fprintf(stderr,"\n");
+ }
+ fprintf(stderr,"\n");
+ }
+ fprintf(stderr,"\n");
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief
+ */
+void
+HitList::Normalize(float* Ztq, char** fold, Hash<int>& excluded)
+{
+ double sumw=0.0;
+ double sumZ=0.0;
+ double sumZ2=0.0;
+ for (int k=0; k<N_searched; k++)
+ {
+ if (excluded.Contains(fold[k])) continue;
+ sumw += weight[k];
+ sumZ += weight[k]*Ztq[k];
+ sumZ2 += weight[k]*Ztq[k]*Ztq[k];
+ }
+ float mu = sumZ/sumw;
+ float sigma = sqrt(sumZ2/sumw-mu*mu);
+ printf("Transitive score Ztq: mu=%8.3g sigma=%8.3g\n",mu,sigma);
+ for (int k=0; k<N_searched; k++) Ztq[k] = (Ztq[k]-mu)/sigma;
+ return;
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate standard deviation of Z1 = sum_m [ w_m * Z_m ], where Csub_mn = cov(Z_m,Z_n)
+ */
+float
+HitList::NormalizationFactor(double** Csub, float* w, int M)
+ {
+ double sum=0.0;
+ for (int m=0; m<M; m++)
+ {
+ double summ=0.0;
+ for (int n=0; n<M; n++) summ += Csub[m][n]*w[n];
+ sum += w[m]*summ;
+ }
+ return sqrt(sum);
+ }
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief Calculate inverse of matrix A and store result in B
+ */
+void
+HitList::InvertMatrix(double** B, double** A, int N)
+{
+ if (N==0)
+ {
+ printf("Error: InvertMatrix called with matrix of dimension 0\n");
+ exit(6);
+ }
+ if (N==1)
+ {
+ B[0][0] = (A[0][0]==0.0? 0 :1.0/A[0][0]);
+ return;
+ }
+
+ int k,l,m;
+ double** V = new(double*[N]);
+ double* s = new(double[N]);
+ for (k=0; k<N; k++) V[k] = new(double[N]);
+
+ // Copy original matrix A into B since B will be overwritten by SVD()
+ for (k=0; k<N; k++)
+ for (l=0; l<N; l++)
+ B[k][l] = A[k][l];
+
+ SVD(B, N, s, V); // U replaces B on output; s[] contains singluar values
+
+ // Calculate inverse of A: A^-1 = V * diag(1/s) * U^t
+ double** U = B;
+ // Calculate V[k][m] -> V[k][m] *diag(1/s)
+ for (k=0; k<N; k++)
+ for (m=0; m<N; m++)
+ if (s[m]!=0.0) V[k][m] /= s[m]; else V[k][m] = 0.0;
+ // Calculate V[k][l] -> (V * U^t)_kl
+ for (k=0; k<N; k++)
+ {
+ if (v>=4 && k%100==0) printf("%i\n",k);
+ for (l=0; l<N; l++)
+ {
+ s[l] = 0.0; // use s[] as temporary memory to avoid overwriting B[k][] as long as it is needed
+ for (m=0; m<N; m++)
+ s[l] += V[k][m]*U[l][m];
+ }
+ for (l=0; l<N; l++) V[k][l]=s[l];
+ }
+ for (k=0; k<N; k++)
+ for (l=0; l<N; l++)
+ B[k][l] = V[k][l];
+
+ for (k=0; k<N; k++){
+ delete[](V[k]); (V[k]) = NULL;
+ }
+ delete[](V); (V) = NULL;
+ return;
+}
+
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+/**
+ * @brief
+ */
+void
+HitList::TransposeMatrix(double** V, int N)
+{
+ int k,l;
+ for (k=0; k<N; k++) // transpose Z for efficiency of ensuing matrix multiplication
+ for (l=0; l<k; l++)
+ {
+ double buf = V[k][l];
+ V[k][l] = V[l][k];
+ V[l][k] = buf;
+ }
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////////////
+static double sqrarg;
+#define SQR(a) ((sqrarg=(a)) == 0.0 ? 0.0 : sqrarg*sqrarg)
+static double maxarg1,maxarg2;
+#define FMAX(a,b) (maxarg1=(a),maxarg2=(b),(maxarg1) > (maxarg2) ? (maxarg1) : (maxarg2))
+static int iminarg1,iminarg2;
+#define IMIN(a,b) (iminarg1=(a),iminarg2=(b),(iminarg1) < (iminarg2) ? (iminarg1) : (iminarg2))
+#define SIGN(a,b) ((b) >= 0.0 ? fabs(a) : -fabs(a))
+
+/**
+ * @brief This is a version of the Golub and Reinsch algorithm for singular value decomposition for a quadratic
+ * (n x n) matrix A. It is sped up by transposing A amd V matrices at various places in the algorithm.
+ * On a 400x400 matrix it runs in 1.6 s or 2.3 times faster than the original (n x m) version.
+ * On a 4993x4993 matrix it runs in 2h03 or 4.5 times faster than the original (n x m) version.
+ *
+ * Given a matrix a[0..n-1][0..n-1], this routine computes its singular value decomposition, A = U · W · V^t .
+ * The matrix U replaces a on output. The diagonal matrix of singular values W is out-put as a vector w[0..n-1].
+ * The matrix V (not the transpose V^t) is output as V[0..n-1][0..n-1] ./
+ */
+void
+HitList::SVD(double **A, int n, double w[], double **V)
+{
+ int m=n; // in general algorithm A is an (m x n) matrix instead of (n x n)
+
+ double pythag(double a, double b);
+ int flag,i,its,j,jj,k,l=1,nm=1;
+ double anorm,c,f,g,h,s,scale,x,y,z,*rv1;
+ rv1=new(double[n]);
+ g=scale=anorm=0.0;
+
+ // Householder reduction to bidiagonal form.
+ if (v>=5) printf("\nHouseholder reduction to bidiagonal form\n");
+ for (i=0;i<n;i++) {
+ if (v>=4 && i%100==0) printf("i=%i\n",i);
+ if (v>=4) fprintf(stderr,".");
+ l=i+1;
+ rv1[i]=scale*g;
+ g=s=scale=0.0;
+ if (i < m) {
+ for (k=i;k<m;k++) scale += fabs(A[k][i]);
+ if (scale) {
+ for (k=i;k<m;k++) {
+ A[k][i] /= scale;
+ s += A[k][i]*A[k][i];
+ }
+ f=A[i][i];
+ g = -SIGN(sqrt(s),f);
+ h=f*g-s;
+ A[i][i]=f-g;
+ for (j=l;j<n;j++) {
+ for (s=0.0,k=i;k<m;k++) s += A[k][i]*A[k][j];
+ f=s/h;
+ for (k=i;k<m;k++) A[k][j] += f*A[k][i];
+ }
+ for (k=i;k<m;k++) A[k][i] *= scale;
+ }
+ }
+ w[i]=scale *g;
+ g=s=scale=0.0;
+ if (i < m && i != n-1) {
+ for (k=l;k<n;k++) scale += fabs(A[i][k]);
+ if (scale) {
+ for (k=l;k<n;k++) {
+ A[i][k] /= scale;
+ s += A[i][k]*A[i][k];
+ }
+ f=A[i][l];
+ g = -SIGN(sqrt(s),f);
+ h=f*g-s;
+ A[i][l]=f-g;
+ for (k=l;k<n;k++) rv1[k]=A[i][k]/h;
+ for (j=l;j<m;j++) {
+ for (s=0.0,k=l;k<n;k++) s += A[j][k]*A[i][k];
+ for (k=l;k<n;k++) A[j][k] += s*rv1[k];
+ }
+ for (k=l;k<n;k++) A[i][k] *= scale;
+ }
+ }
+ anorm=FMAX(anorm,(fabs(w[i])+fabs(rv1[i])));
+ }
+ // Accumulation of right-hand transformations.
+ if (v>=5) printf("\nAccumulation of right-hand transformations\n");
+ TransposeMatrix(V,n);
+ for (i=n-1;i>=0;i--) {
+ if (v>=4 && i%100==0) printf("i=%i\n",i);
+ if (v>=4) fprintf(stderr,".");
+ if (i < n-1) {
+ if (g) {
+ // Double division to avoid possible underflow.
+ for (j=l;j<n;j++)
+ V[i][j]=(A[i][j]/A[i][l])/g;
+ for (j=l;j<n;j++) {
+ for (s=0.0,k=l;k<n;k++) s += A[i][k]*V[j][k];
+ for (k=l;k<n;k++) V[j][k] += s*V[i][k];
+ }
+ }
+ for (j=l;j<n;j++) V[j][i]=V[i][j]=0.0;
+ }
+ V[i][i]=1.0;
+ g=rv1[i];
+ l=i;
+ }
+ // Accumulation of left-hand transformations.
+ if (v>=5) printf("\nAccumulation of left-hand transformations\n");
+ TransposeMatrix(A,n);
+ for (i=IMIN(m,n)-1;i>=0;i--) {
+ if (v>=4 && i%100==0) printf("i=%i\n",i);
+ if (v>=4) fprintf(stderr,".");
+ l=i+1;
+ g=w[i];
+ for (j=l;j<n;j++) A[j][i]=0.0;
+ if (g) {
+ g=1.0/g;
+ for (j=l;j<n;j++) {
+ for (s=0.0,k=l;k<m;k++) s += A[i][k]*A[j][k];
+ f=(s/A[i][i])*g;
+ for (k=i;k<m;k++) A[j][k] += f*A[i][k];
+ }
+ for (j=i;j<m;j++) A[i][j] *= g;
+ } else for (j=i;j<m;j++) A[i][j]=0.0;
+ ++A[i][i];
+ }
+
+ // Diagonalization of the bidiagonal form: Loop over singular values, and over allowed iterations.
+ if (v>=5) printf("\nDiagonalization of the bidiagonal form\n");
+ for (k=n-1;k>=0;k--) {
+ if (v>=4 && k%100==0) printf("k=%i\n",k);
+ if (v>=4) fprintf(stderr,".");
+ for (its=1;its<=30;its++) {
+ flag=1;
+ // Test for splitting. Note that rv1[1] is always zero.
+ for (l=k;l>=0;l--) {
+ nm=l-1;
+ if ((double)(fabs(rv1[l])+anorm) == anorm) {
+ flag=0;
+ break;
+ }
+ if ((double)(fabs(w[nm])+anorm) == anorm) break;
+ }
+ if (flag) {
+ // Cancellation of rv1[l], if l > 1.
+ c=0.0;
+ s=1.0;
+ for (i=l;i<=k;i++) {
+ f=s*rv1[i];
+ rv1[i]=c*rv1[i];
+ if ((double)(fabs(f)+anorm) == anorm) break;
+ g=w[i];
+ h=pythag(f,g);
+ w[i]=h;
+ h=1.0/h;
+ c=g*h;
+ s = -f*h;
+ for (j=0;j<m;j++) {
+ y=A[nm][j];
+ z=A[i][j];
+ A[nm][j]=y*c+z*s;
+ A[i][j]=z*c-y*s;
+ }
+ }
+ }
+ z=w[k];
+ // Convergence.
+ if (l == k) {
+ // Singular value is made nonnegative.
+ if (z < 0.0) {
+ w[k] = -z;
+ for (j=0;j<n;j++) V[k][j] = -V[k][j];
+ }
+ break;
+ }
+ if (its == 30) {printf("Error in SVD: no convergence in 30 iterations\n"); exit(7);}
+ // Shift from bottom 2-by-2 minor.
+ x=w[l];
+ nm=k-1;
+ y=w[nm];
+ g=rv1[nm];
+ h=rv1[k];
+ f=((y-z)*(y+z)+(g-h)*(g+h))/(2.0*h*y);
+ g=pythag(f,1.0);
+ f=((x-z)*(x+z)+h*((y/(f+SIGN(g,f)))-h))/x;
+ // Next QR transformation:
+ c=s=1.0;
+ for (j=l;j<=nm;j++) {
+ i=j+1;
+ g=rv1[i];
+ y=w[i];
+ h=s*g;
+ g=c*g;
+ z=pythag(f,h);
+ rv1[j]=z;
+ c=f/z;
+ s=h/z;
+ f=x*c+g*s;
+ g = g*c-x*s;
+ h=y*s;
+ y *= c;
+ for (jj=0;jj<n;jj++) {
+ x=V[j][jj];
+ z=V[i][jj];
+ V[j][jj]=x*c+z*s;
+ V[i][jj]=z*c-x*s;
+ }
+ z=pythag(f,h);
+ // Rotation can be arbitrary if z = 0.
+ w[j]=z;
+ if (z) {
+ z=1.0/z;
+ c=f*z;
+ s=h*z;
+ }
+ f=c*g+s*y;
+ x=c*y-s*g;
+
+ for (jj=0;jj<m;jj++) {
+ y=A[j][jj];
+ z=A[i][jj];
+ A[j][jj]=y*c+z*s;
+ A[i][jj]=z*c-y*s;
+ }
+ }
+ rv1[l]=0.0;
+ rv1[k]=f;
+ w[k]=x;
+ }
+ }
+ TransposeMatrix(V,n);
+ TransposeMatrix(A,n);
+ delete[](rv1); (rv1) = NULL;
+}
+
+/**
+ * @brief Computes (a2 + b2 )^1/2 without destructive underflow or overflow.
+ */
+double
+pythag(double a, double b)
+{
+ double absa,absb;
+ absa=fabs(a);
+ absb=fabs(b);
+ if (absa > absb)
+ return absa*sqrt(1.0+SQR(absb/absa));
+ else
+ return (absb == 0.0 ? 0.0 : absb*sqrt(1.0+SQR(absa/absb)));
+}
+
+
+/* @* HitList::ClobberGlobal(void)
+ */
+void
+HitList::ClobberGlobal(void){
+
+
+ /* @<variables local to HitList::ClobberGlobal@> */
+ class List<Hit>::ListEl<Hit> *pvIter = head;
+
+ /* NOTE: no free/delete-ing of data to be done here
+ hitlist only holds a shallow copy of hit;
+ hit is being cleared off properly.
+ just reset everything to 0/0.0/NULL.
+ The only important thing to do at this stage
+ is to attach head and tail and set size = 0
+ (FS, 2010-02-18)
+
+ NOTE: I only ever saw 1 (one) in-between element,
+ but there may ctually be a real linked list
+ of more than 1 element (FS, 2010-02-18)
+ */
+
+ // printf("POINTER:\t%p\t=HEAD\n", head);
+ while (pvIter->next != tail){
+
+ // printf("POINTER:\t%p->\t%p\n", pvIter, pvIter->next);
+ pvIter = pvIter->next;
+
+#if 1
+ pvIter->data.longname = pvIter->data.name =
+ pvIter->data.file = pvIter->data.dbfile = NULL;
+ pvIter->data.sname = NULL;
+ pvIter->data.seq = NULL;
+ pvIter->data.self = 0;
+ pvIter->data.i = pvIter->data.j = NULL;
+ pvIter->data.states = NULL;
+ pvIter->data.S = pvIter->data.S_ss = pvIter->data.P_posterior = NULL;
+ pvIter->data.Xcons = NULL;
+ pvIter->data.sum_of_probs = 0.0;
+ pvIter->data.Neff_HMM = 0.0;
+ pvIter->data.score_ss = pvIter->data.Pval = pvIter->data.logPval =
+ pvIter->data.Eval = pvIter->data.Probab = pvIter->data.Pforward = 0.0;
+ pvIter->data.nss_conf = pvIter->data.nfirst =
+ pvIter->data.i1 = pvIter->data.i2 = pvIter->data.j1 = pvIter->data.j2 =
+ pvIter->data.matched_cols = pvIter->data.ssm1 = pvIter->data.ssm2 = 0;
+#endif
+ }
+ // printf("POINTER:\t\t\t%p=TAIL\n", tail);
+
+
+ head->next = tail;
+ tail->prev = head;
+ size = 0;
+
+ /* @= */
+ return;
+
+} /* this is the end of HitList::ClobberGlobal() */
+
+
+/*
+ * EOF hhhitlist-C.h
+ */
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