Katzlab dd76ab1d12 Added PTL2 Scripts
These are PTL2 files from Auden 2/9
2023-02-14 11:20:52 -05:00

153 lines
5.1 KiB
C++

// $Id: likeDist2USSRV.h 962 2006-11-07 15:13:34Z privmane $
#ifndef ___LIKE_DIST_2_USSRV_H
#define ___LIKE_DIST_2_USSRV_H
#include "definitions.h"
#include "countTableComponent.h"
#include "distanceMethod.h"
#include "stochasticProcess.h"
#include "logFile.h"
#include "ussrvModel.h"
#include <cmath>
using namespace std;
class likeDist2USSRV : public distanceMethod {
public:
explicit likeDist2USSRV(const ussrvModel& model,
const MDOUBLE toll =0.0001,
const MDOUBLE maxPairwiseDistance = 5.0) : _model(model) ,_toll(toll),_maxPairwiseDistance(maxPairwiseDistance)
{}
likeDist2USSRV (const likeDist2USSRV& other): _model(other._model) ,_toll(other._toll),_maxPairwiseDistance(other._maxPairwiseDistance) {};
virtual likeDist2USSRV* clone() const {return new likeDist2USSRV(*this);}
// THIS FUNCTION DOES NOT RETURN THE LOG LIKELIHOOD IN RESQ, BUT RATHER "Q", THE CONTRIBUTION of this edge
// TO THE EXPECTED LOG-LIKELIHOOD (SEE SEMPHY PAPER).
// NEVERTHELESS, THE t that optimizes Q is the same t that optimizes log-likelihood.
const MDOUBLE giveDistance( const countTableComponentGam& ctcBase,
const countTableComponentHom& ctcSSRV,
MDOUBLE& resQ,
const MDOUBLE initialGuess= 0.03) const; // initial guess
// returns the estimated ML distance between the 2 sequences.
// if score is given, it will be the log-likelihood.
//!!!!!!!!!!!!!!TO DO @@@@
const MDOUBLE giveDistance(const sequence& s1,
const sequence& s2,
const vector<MDOUBLE> * weights,
MDOUBLE* score=NULL) const {
LOG(4,<<"likeDist2USSRV:giveDistance : This method should never be used" << endl);
return 1;}
const MDOUBLE giveDistanceBrent(const countTableComponentGam& ctcBase,
const countTableComponentHom& ctcSSRV,
MDOUBLE& resL,
MDOUBLE initialGuess) const;
private:
const ussrvModel& _model;
const MDOUBLE _toll;
const MDOUBLE _maxPairwiseDistance;
};
class C_evalLikeDist2USSRV{
private:
const countTableComponentGam& _ctcBase;
const countTableComponentHom& _ctcSSRV;
const ussrvModel& _model;
public:
C_evalLikeDist2USSRV(const countTableComponentGam& ctcBase,
const countTableComponentHom& ctcSSRV,
const ussrvModel& model):_ctcBase(ctcBase),_ctcSSRV(ctcSSRV), _model(model) {};
MDOUBLE operator() (MDOUBLE dist) {
const MDOUBLE epsilonPIJ = 1e-10;
MDOUBLE sumL=0.0;
MDOUBLE pij;
int categor, alph1,alph2;
// base model
const stochasticProcess& baseSp = _model.getBaseModel();
for (alph1=0; alph1 < _ctcBase.alphabetSize(); ++alph1){
for (alph2=0; alph2 < _ctcBase.alphabetSize(); ++alph2){
for (categor = 0; categor < baseSp.categories(); ++categor) {
MDOUBLE rate = baseSp.rates(categor);
pij= baseSp.Pij_t(alph1,alph2,dist*rate);
if (pij<epsilonPIJ) pij = epsilonPIJ;//SEE REMARK (1) FOR EXPLANATION
sumL += _ctcBase.getCounts(alph1,alph2,categor)*(log(pij)-log(baseSp.freq(alph2)));//*_sp.ratesProb(rateCategor);// removed.
}
}
}
// ssrv model
const stochasticProcessSSRV& ssrvSp = _model.getSSRVmodel();
for (alph1=0; alph1 < _ctcSSRV.alphabetSize(); ++alph1){
for (alph2=0; alph2 < _ctcSSRV.alphabetSize(); ++alph2){
pij = ssrvSp.Pij_t(alph1,alph2,dist);
if (pij<epsilonPIJ) pij = epsilonPIJ;
sumL+=_ctcSSRV.getCounts(alph1,alph2)*(log(pij)-log(ssrvSp.freq(alph2)));//*_sp.ratesProb(rateCategor);// removed.
}
}
LOG(12,<<"check bl="<<dist<<" gives "<<sumL<<endl);
return -sumL;
}
};
// REMARK 1: THE LINE if if (pij<epsilonPIJ) pij = epsilonPIJ
// There are cases when i != j, and t!=0, and yet pij =0, because of numerical problems
// For these cases, it is easier to assume pij is very small, so that log-pij don't fly...
// @@@@ doesn't work
class C_evalLikeDist_d_2USSRV{ // derivative.
public:
C_evalLikeDist_d_2USSRV(const countTableComponentGam& ctcBase,
const countTableComponentHom& ctcSSRV,
const ussrvModel& model) : _ctcBase(ctcBase), _ctcSSRV(ctcSSRV),_model(model) {};
private:
const countTableComponentGam& _ctcBase;
const countTableComponentHom& _ctcSSRV;
const ussrvModel& _model;
public:
MDOUBLE operator() (MDOUBLE dist) {
MDOUBLE sumDL=0.0;
MDOUBLE pij, dpij;
int categor, alph1,alph2;
// Base model
const stochasticProcess& spBase = _model.getBaseModel();
for (alph1=0; alph1 < _ctcBase.alphabetSize(); ++alph1){
for (alph2=0; alph2 < _ctcBase.alphabetSize(); ++alph2){
for (categor = 0; categor<_model.noOfCategor(); ++categor) {
MDOUBLE rate = spBase.rates(categor);
MDOUBLE pij= spBase.Pij_t(alph1,alph2,dist);
MDOUBLE dpij= spBase.dPij_dt(alph1,alph2,dist);
sumDL+= _ctcBase.getCounts(alph1,alph2,categor)*dpij
*rate/pij;
}
}
}
// SSRV model
const stochasticProcessSSRV& spSSRV = _model.getSSRVmodel();
for (alph1=0; alph1 < _ctcSSRV.alphabetSize(); ++alph1){
for (alph2=0; alph2 < _ctcSSRV.alphabetSize(); ++alph2){
pij= spSSRV.Pij_t(alph1,alph2,dist);
dpij= spSSRV.dPij_dt(alph1,alph2,dist);
sumDL+= _ctcSSRV.getCounts(alph1,alph2)*dpij/pij; //rate=1;
}
}
LOG(8,<<"derivation = "<<-sumDL<<endl);
return -sumDL;
};
};
#endif // ___LIKE_DIST_2_USSRV_H