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111 lines
3.8 KiB
C++
111 lines
3.8 KiB
C++
// $Id: likeDist2Codon.h 4699 2008-08-14 14:19:46Z privmane $
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#ifndef ___LIKE_DIST_2_CODON_H
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#define ___LIKE_DIST_2_CODON_H
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#include "definitions.h"
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#include "countTableComponent.h"
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#include "distanceMethod.h"
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#include "stochasticProcess.h"
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#include "logFile.h"
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#include "wYangModel.h"
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#include <cmath>
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using namespace std;
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class likeDist2Codon : public distanceMethod {
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public:
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explicit likeDist2Codon(const vector<stochasticProcess>& spVec,
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const MDOUBLE toll =0.0001,
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const MDOUBLE maxPairwiseDistance = 2.0) : _spVec(spVec) ,_toll(toll),_maxPairwiseDistance(maxPairwiseDistance) {
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}
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likeDist2Codon (const likeDist2Codon& other): _spVec(other._spVec) ,_toll(other._toll),_maxPairwiseDistance(other._maxPairwiseDistance) {};
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virtual likeDist2Codon* clone() const {return new likeDist2Codon(*this);}
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// THIS FUNCTION DOES NOT RETURN THE LOG LIKELIHOOD IN RESQ, BUT RATHER "Q", THE CONTRIBUTION of this edge
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// TO THE EXPECTED LOG-LIKELIHOOD (SEE SEMPHY PAPER).
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// NEVERTHELESS, THE t that optimizes Q is the same t that optimizes log-likelihood.
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const MDOUBLE giveDistance( const countTableComponentGam& ctc,
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MDOUBLE& resQ,
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const MDOUBLE initialGuess= 0.03) const; // initial guess
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// returns the estimated ML distance between the 2 sequences.
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// if score is given, it will be the log-likelihood.
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//!!!!!!!!!!!!!!TO DO
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const MDOUBLE giveDistance(const sequence& s1,
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const sequence& s2,
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const vector<MDOUBLE> * weights,
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MDOUBLE* score=NULL) const { return 1;}
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const MDOUBLE giveDistanceBrent( const countTableComponentGam& ctc,
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MDOUBLE& resL,
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const MDOUBLE initialGuess) const;
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private:
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const vector<stochasticProcess>& _spVec;
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const MDOUBLE _toll;
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const MDOUBLE _maxPairwiseDistance;
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};
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class C_evalLikeDist2Codon{
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private:
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const countTableComponentGam& _ctc;
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const vector<stochasticProcess>& _spVec;
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public:
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C_evalLikeDist2Codon(const countTableComponentGam& ctc,
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const vector<stochasticProcess>& inS1):_ctc(ctc), _spVec(inS1) {};
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MDOUBLE operator() (MDOUBLE dist) {
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const MDOUBLE epsilonPIJ = 1e-10;
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MDOUBLE sumL=0.0;
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for (int alph1=0; alph1 < _ctc.alphabetSize(); ++alph1){
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for (int alph2=0; alph2 < _ctc.alphabetSize(); ++alph2){
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for (int categor = 0; categor<_spVec.size(); ++categor) {
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MDOUBLE pij= _spVec[categor].Pij_t(alph1,alph2,dist);
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if (pij<epsilonPIJ) pij = epsilonPIJ;//SEE REMARK (1) FOR EXPLANATION
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sumL += _ctc.getCounts(alph1,alph2,categor)*(log(pij)-log(_spVec[categor].freq(alph2)));//*_sp.ratesProb(rateCategor);// removed.
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}
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}
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}
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// LOG(5,<<"check bl="<<dist<<" gives "<<sumL<<endl);
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return -sumL;
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};
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};
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// REMARK 1: THE LINE if if (pij<epsilonPIJ) pij = epsilonPIJ
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// There are cases when i != j, and t!=0, and yet pij =0, because of numerical problems
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// For these cases, it is easier to assume pij is very small, so that log-pij don't fly...
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class C_evalLikeDist_d_2Codon{ // derivative.
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public:
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C_evalLikeDist_d_2Codon(const countTableComponentGam& ctc,
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const vector<stochasticProcess>& inS1) : _ctc(ctc), _spVec(inS1) {};
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private:
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const countTableComponentGam& _ctc;
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const vector<stochasticProcess>& _spVec;
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public:
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MDOUBLE operator() (MDOUBLE dist) {
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MDOUBLE sumDL=0.0;
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for (int alph1=0; alph1 < _ctc.alphabetSize(); ++alph1){
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for (int alph2=0; alph2 < _ctc.alphabetSize(); ++alph2){
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for (int categor = 0; categor<_spVec.size(); ++categor) {
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MDOUBLE selection = static_cast<wYangModel*>(_spVec[categor].getPijAccelerator()->getReplacementModel())->getW();
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MDOUBLE pij= _spVec[categor].Pij_t(alph1,alph2,dist);
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MDOUBLE dpij = _spVec[categor].dPij_dt(alph1,alph2,dist);
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sumDL+= _ctc.getCounts(alph1,alph2,categor)*dpij //*_sp.ratesProb(rateCategor) : removed CODE_RED
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*selection/pij;
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}
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}
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}
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//LOG(5,<<"derivation = "<<-sumDL<<endl);
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return -sumDL;
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};
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};
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#endif
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