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// $Id: likeDistfixRoot.h 4470 2008-07-17 15:37:40Z cohenofi $
#ifndef ___LIKE_DIST_H_GL_FIX_ROOT
#define ___LIKE_DIST_H_GL_FIX_ROOT
#include "definitions.h"
#include "countTableComponent.h"
#include "distanceMethod.h"
#include "stochasticProcess.h"
#include "logFile.h"
#include "jcDistance.h"
#include "sequenceContainer.h"
#include "unObservableData.h"
#include <cmath>
using namespace std;
class likeDistfixRoot : public distanceMethod {
public:
// WARNING: the stochasticProcess is NOT copied. The same object is used
explicit likeDistfixRoot(const stochasticProcess& sp,
const MDOUBLE toll =0.0001,
const MDOUBLE maxPairwiseDistance = 5.0,
const MDOUBLE minPairwiseDistance = 0.0000001,
unObservableData* unObservableData_p=NULL)
: _sp(sp),_nonConstSpPtr(NULL),_toll(toll),_maxPairwiseDistance(maxPairwiseDistance),_minPairwiseDistance(minPairwiseDistance),_unObservableData_p(unObservableData_p) {}
likeDistfixRoot(const likeDistfixRoot& other)
: _sp(other._sp),_nonConstSpPtr(other._nonConstSpPtr),_toll(other._toll),_maxPairwiseDistance(other._maxPairwiseDistance),_minPairwiseDistance(other._minPairwiseDistance),_jcDist(other._jcDist) {}
virtual likeDistfixRoot* clone() const {return new likeDistfixRoot(*this);}
// This constructor allows non-const stochasticProcess so that likeDistfixRoot will be able to change alpha, etc.
explicit likeDistfixRoot(stochasticProcess& sp,
const MDOUBLE toll =0.0001,
const MDOUBLE maxPairwiseDistance = 5.0,
const MDOUBLE minPairwiseDistance = 0.0000001)
: _sp(sp),_nonConstSpPtr(&sp),_toll(toll),_maxPairwiseDistance(maxPairwiseDistance),_minPairwiseDistance(minPairwiseDistance) {}
// 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 vector<countTableComponentGam>& ctc,
MDOUBLE& resQ,
const MDOUBLE initialGuess= 0.03) const; // initial guess
// given two sequences, it evaluates the log likelihood.
MDOUBLE evalLogLikelihoodGivenDistance(const sequence& s1,
const sequence& s2,
const MDOUBLE dis2evaluate);
// returns the estimated ML distance between the 2 sequences.
// if score is given, it will be the log-likelihood.
const MDOUBLE giveDistance(const sequence& s1,
const sequence& s2,
const vector<MDOUBLE> * weights,
MDOUBLE* score=NULL) const;
// this function creates a countTableComponent (ctc) from the two sequences.
// it then computes the distance from this ctc.
// THIS FUNCTION DOES NOT RETURN THE LOG LIKELIHOOD IN score, 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.
//MDOUBLE giveDistanceThroughCTC(const sequence& s1,
// const sequence& s2,
// const vector<MDOUBLE> * weights,
// MDOUBLE* score=NULL) const;
const MDOUBLE giveLikelihood(const sequence& s1,
const sequence& s2,
MDOUBLE distance,
const vector<MDOUBLE> * weights=NULL) const;
// return the stochasticProcess
const stochasticProcess& getStochasticProcess() const {return _sp;}
stochasticProcess& getNonConstStochasticProcess();
bool isTheInternalStochasticProcessConst() const {return !_nonConstSpPtr;}
MDOUBLE getToll() const {return _toll;}
MDOUBLE getMaxPairwiseDistance() const {return _maxPairwiseDistance;}
MDOUBLE getMinPairwiseDistance() const {return _minPairwiseDistance;}
protected:
const stochasticProcess &_sp;
stochasticProcess *_nonConstSpPtr;
const MDOUBLE _toll;
const MDOUBLE _maxPairwiseDistance;
const MDOUBLE _minPairwiseDistance;
jcDistance _jcDist;
unObservableData* _unObservableData_p;
private:
const MDOUBLE giveDistanceBrent( const vector<countTableComponentGam>& ctc,
MDOUBLE& resL,
const MDOUBLE initialGuess= 0.03) const; // initial guess
const MDOUBLE giveDistanceNR( const countTableComponentGam& ctc,
MDOUBLE& resL,
const MDOUBLE initialGuess= 0.03) const; // initial guess
public:
static MDOUBLE evalLikelihoodForDistance(const stochasticProcess& sp,
const sequence& s1,
const sequence& s2,
const MDOUBLE dist,
const vector<MDOUBLE> * weights=NULL);
};
class C_evallikeDistfixRoot{
private:
const vector<countTableComponentGam>& _ctc;
const stochasticProcess& _sp;
unObservableData* _unObservableData_p;
public:
C_evallikeDistfixRoot(const vector<countTableComponentGam>& ctc, // ctc[letterAtRoot][rate][alph][alph]
const stochasticProcess& inS1, unObservableData* unObservableData_p=NULL)
:_ctc(ctc), _sp(inS1),_unObservableData_p(unObservableData_p) {};
MDOUBLE operator() (MDOUBLE dist)
{
//if(_plogLforMissingData){
// sequenceContainer scZero;
// gainLossAlphabet alph;
// scZero.startZeroSequenceContainerGL(_sc, alph);
// *_plogLforMissingData = likelihoodComputation::getTreeLikelihoodAllPosAlphTheSame(_et,scZero,*_sp);
//}
const MDOUBLE epsilonPIJ = 1e-10;
MDOUBLE sumL=0.0;
for (int letterAtRoot = 0; letterAtRoot < _sp.alphabetSize(); ++letterAtRoot){
for (int alph1=0; alph1 < _sp.alphabetSize(); ++alph1){
for (int alph2=0; alph2 < _sp.alphabetSize(); ++alph2){
for (int rateCategor = 0; rateCategor<_sp.categories(); ++rateCategor) {
MDOUBLE rate = _sp.rates(rateCategor);
MDOUBLE pij= _sp.Pij_t(alph1,alph2,dist*rate);
if (pij<epsilonPIJ) pij = epsilonPIJ;//SEE REMARK (1) FOR EXPLANATION
sumL += _ctc[letterAtRoot].getCounts(alph1,alph2,rateCategor)
//*_sp.freq(letterAtRoot)
//*(log(pij)-log(_sp.freq(letterAtRoot))) ;
//*_sp.freq(letterAtRoot)
*(log(pij)-log(_sp.freq(alph2))) ;
}
}
}
}
//if(_unObservableData_p)
// sumL = sumL/(1- exp(_unObservableData_p->getlogLforMissingData())); // need to find an efficient way to update LofMissingData with dist
LOG(8,<<"check bl="<<dist<<" gives sumL "<<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...
class C_evalLikeDist_dfixRoot{ // derivative.
public:
C_evalLikeDist_dfixRoot(const vector<countTableComponentGam>& ctc,
const stochasticProcess& inS1) : _ctc(ctc), _sp(inS1) {};
private:
const vector<countTableComponentGam>& _ctc;
const stochasticProcess& _sp;
public:
MDOUBLE operator() (MDOUBLE dist) {
MDOUBLE sumDL=0.0;
for (int letterAtRoot = 0; letterAtRoot < _sp.alphabetSize(); ++letterAtRoot){
for (int alph1=0; alph1 < _ctc[letterAtRoot].alphabetSize(); ++alph1){
for (int alph2=0; alph2 < _ctc[letterAtRoot][alph1].alphabetSize(); ++alph2){
for (int rateCategor = 0; rateCategor<_sp.categories(); ++rateCategor) {
MDOUBLE rate = _sp.rates(rateCategor);
MDOUBLE pij= _sp.Pij_t(alph1,alph2,dist*rate);
MDOUBLE dpij = _sp.dPij_dt(alph1,alph2,dist*rate);
//cout<<letterAtRoot<<"\n";
//cout<<alph1<<"\n";
//cout<<alph2<<"\n";
//cout<<rateCategor<<"\n";
//cout<<rate<<"\n";
//cout<<_ctc[letterAtRoot].getCounts(alph1,alph2,rateCategor)<<"\n";
sumDL+= _ctc[letterAtRoot].getCounts(alph1,alph2,rateCategor)*dpij
//*_sp.freq(letterAtRoot)
*rate/pij ;
}
}
}//cerr<<"derivation = "<<-sumDL<<endl;
}
LOG(8,<<"check bl="<<dist<<" gives sumDL "<<sumDL<<endl);
return -sumDL;
};
};
//////////////////////////////////////////////////////////////////////////
class C_evalLikeDist_d2GLfixRoot{ // second derivative.
public:
C_evalLikeDist_d2GLfixRoot(const countTableComponentGam& ctc,
const stochasticProcess& inS1) : _ctc(ctc), _sp(inS1) {};
private:
const countTableComponentGam& _ctc;
const stochasticProcess& _sp;
public:
MDOUBLE operator() (MDOUBLE dist) {
MDOUBLE sumDL=0.0;
for (int alph1=0; alph1 < _ctc.alphabetSize(); ++alph1){
for (int alph2=0; alph2 < _ctc.alphabetSize(); ++alph2){
for (int rateCategor = 0; rateCategor<_sp.categories(); ++rateCategor) {
MDOUBLE rate = _sp.rates(rateCategor);
MDOUBLE pij= _sp.Pij_t(alph1,alph2,dist*rate);
MDOUBLE dpij = _sp.dPij_dt(alph1,alph2,dist*rate);
MDOUBLE d2pij = _sp.d2Pij_dt2(alph1,alph2,dist*rate);
sumDL+= rate*_ctc.getCounts(alph1,alph2,rateCategor)*
(pij*d2pij - dpij *dpij )/(pij*pij);
}
}
}
return -sumDL;
};
};
#endif
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