+++ /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
- */