/usr/include/libphylo/bestTamura92param.h is in rate4site 3.0.0-2.
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#ifndef ___BEST_TAMURA92_PARAM
#define ___BEST_TAMURA92_PARAM
#include "definitions.h"
#include "likelihoodComputation.h"
#include "sequenceContainer.h"
#include "stochasticProcess.h"
#include "multipleStochasticProcess.h"
#include "gammaDistribution.h"
#include "tree.h"
#include "tamura92.h"
class bestTamura92ParamFixedTree {
public:
explicit bestTamura92ParamFixedTree(const tree& et, // find best TrTv and theta
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights,
const int maxTotalIterations = 5,
const MDOUBLE epsilonLikelihoodImprovment = 0.05,
const MDOUBLE epsilonLoglikelihoodForTrTvOptimization = 0.01,
const MDOUBLE epsilonLoglikelihoodForThetaOptimization = 0.01,
const MDOUBLE upperBoundOnTrTv = 5.0);
MDOUBLE getBestTrTv() {return _bestTrTv;}
MDOUBLE getBestTheta() {return _bestTheta;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestTrTv;
MDOUBLE _bestTheta;
MDOUBLE _bestL;
};
class bestTamura92ParamAndBBL{
public:
explicit bestTamura92ParamAndBBL(tree& et, //find best TrTv, theta and best BBL
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights=NULL,
const int maxTotalIterations=5,
const MDOUBLE epsilonLikelihoodImprovment=0.05,
const MDOUBLE epsilonLoglikelihoodForTrTvOptimization=0.01,
const MDOUBLE epsilonLoglikelihoodForThetaOptimization=0.01,
const MDOUBLE epsilonLoglikelihoodForBBL=0.01,
const MDOUBLE upperBoundOnTrTv=5.0,
const int maxBBLIterations=10);
MDOUBLE getBestTrTv() {return _bestTrTv;}
MDOUBLE getBestTheta(int spIndex) {return _bestTheta;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestTrTv;
MDOUBLE _bestTheta;
MDOUBLE _bestL;
};
class bestTamura92ParamAlphaAndBBL {
public:
explicit bestTamura92ParamAlphaAndBBL( //find best TrTv, theta, Alpha and best branch lengths
tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights=NULL,
const int maxTotalIterations=5,
const MDOUBLE epsilonLikelihoodImprovment= 0.05,
const MDOUBLE epsilonLoglikelihoodForTrTvOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForThetaOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForAlphaOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForBBL= 0.01,
const MDOUBLE upperBoundOnTrTv = 5.0,
const int maxBBLIterations=10,
const MDOUBLE initAlpha = 1.5,
const MDOUBLE upperBoundOnAlpha = 5.0);
MDOUBLE getBestTrTv() {return _bestTrTv;}
MDOUBLE getBestTheta() {return _bestTheta;}
MDOUBLE getBestAlpha() {return _bestAlpha;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestTrTv;
MDOUBLE _bestTheta;
MDOUBLE _bestAlpha;
MDOUBLE _bestL;
};
class bestTamura92ParamAlphaAndBBLProportional {
public:
explicit bestTamura92ParamAlphaAndBBLProportional( //find best TrTv, theta, loca Alpha for each gene, global Alpha and best branch lengths
tree& et,
vector<sequenceContainer>& sc,
multipleStochasticProcess* msp,
gammaDistribution* pProportionDist,
Vdouble initLocalAlphas,
Vdouble initLocalKappas,
Vdouble initLocalThetas,
const MDOUBLE upperBoundOnLocalAlpha,
const MDOUBLE initGlobalAlpha,
const MDOUBLE upperBoundOnGlobalAlpha,
const MDOUBLE upperBoundOnTrTv,
const int maxTotalIterations,
const int maxBBLIterations,
const bool optimizeSelectedBranches=false,
const bool optimizeTree = true,
const string branchLengthOptimizationMethod="bblLS",
const bool optimizeLocalParams = true,
const bool optimizeGlobalAlpha = true,
const Vdouble * weights=NULL,
const MDOUBLE epsilonLikelihoodImprovment= 0.05,
const MDOUBLE epsilonLoglikelihoodForLocalTrTvOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForLocalThetaOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForLocalAlphaOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForGlobalAlphaOptimization= 0.01,
const MDOUBLE epsilonLoglikelihoodForBBL= 0.01);
MDOUBLE getBestTrTv(int spIndex) {return _bestTrTvVec[spIndex];}
MDOUBLE getBestTheta(int spIndex) {return _bestThetaVec[spIndex];}
MDOUBLE getBestLocalAlpha(int spIndex) {return _bestLocalAlphaVec[spIndex];}
MDOUBLE getBestGlobalAlpha() {return _bestGlobalAlpha;}
Vdouble getBestL() {return _bestLvec;}
private:
Vdouble _bestTrTvVec;
Vdouble _bestThetaVec;
Vdouble _bestLocalAlphaVec;
MDOUBLE _bestGlobalAlpha;
Vdouble _bestLvec;
};
class C_evalTrTvParam{
public:
C_evalTrTvParam( const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights = NULL)
: _et(et),_sc(sc),_weights(weights),_sp(sp){};
private:
const tree& _et;
const sequenceContainer& _sc;
const Vdouble * _weights;
stochasticProcess& _sp;
public:
MDOUBLE operator() (MDOUBLE TrTv) {
(static_cast<tamura92*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTrTv(TrTv);
MDOUBLE res = likelihoodComputation::getTreeLikelihoodAllPosAlphTheSame(_et,_sc,_sp,_weights);
LOG(5,<<" with TrTv = "<<TrTv<<" logL = "<<res<<endl);
return -res;
}
};
class C_evalLocalTrTvParam{
public:
C_evalLocalTrTvParam( const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
gammaDistribution* pProportionDist,
const Vdouble * weights = NULL)
: _et(et),_sc(sc),_weights(weights),_sp(sp),_pProportionDist(pProportionDist){};
private:
const tree& _et;
const sequenceContainer& _sc;
const Vdouble * _weights;
stochasticProcess& _sp;
gammaDistribution* _pProportionDist;
public:
MDOUBLE operator() (MDOUBLE TrTv) {
(static_cast<tamura92*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTrTv(TrTv);
vector<sequenceContainer> tmpScVec;
tmpScVec.push_back(_sc);
vector<stochasticProcess> tmpSpVec;
tmpSpVec.push_back(_sp);
multipleStochasticProcess * tmpMsp = new multipleStochasticProcess();
tmpMsp->setSpVec(tmpSpVec);
Vdouble likeVec = likelihoodComputation::getTreeLikelihoodProportionalAllPosAlphTheSame(_et,tmpScVec,tmpMsp,_pProportionDist);
MDOUBLE res = likeVec[0];
delete(tmpMsp);
LOG(5,<<" with TrTv = "<<TrTv<<" logL = "<<res<<endl);
return -res;
}
};
class C_evalLocalTheta{
public:
C_evalLocalTheta( const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
gammaDistribution* pProportionDist,
const Vdouble * weights = NULL)
: _et(et),_sc(sc),_weights(weights),_sp(sp),_pProportionDist(pProportionDist){};
private:
const tree& _et;
const sequenceContainer& _sc;
const Vdouble * _weights;
stochasticProcess& _sp;
gammaDistribution* _pProportionDist;
public:
MDOUBLE operator() (MDOUBLE theta) {
(static_cast<tamura92*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTheta(theta);
vector<sequenceContainer> tmpScVec;
tmpScVec.push_back(_sc);
vector<stochasticProcess> tmpSpVec;
tmpSpVec.push_back(_sp);
multipleStochasticProcess * tmpMsp = new multipleStochasticProcess();
tmpMsp->setSpVec(tmpSpVec);
Vdouble likeVec = likelihoodComputation::getTreeLikelihoodProportionalAllPosAlphTheSame(_et,tmpScVec,tmpMsp,_pProportionDist);
MDOUBLE res = likeVec[0];
delete(tmpMsp);
LOG(5,<<" with Theta = "<<theta<<" logL = "<<res<<endl);
return -res;
}
};
class C_evalTheta{
public:
C_evalTheta( const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights = NULL)
: _et(et),_sc(sc),_weights(weights),_sp(sp){};
private:
const tree& _et;
const sequenceContainer& _sc;
const Vdouble * _weights;
stochasticProcess& _sp;
public:
MDOUBLE operator() (MDOUBLE theta) {
(static_cast<tamura92*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTheta(theta);
MDOUBLE res = likelihoodComputation::getTreeLikelihoodAllPosAlphTheSame(_et,_sc,_sp,_weights);
LOG(5,<<" with theta = "<<theta<<" logL = "<<res<<endl);
return -res;
}
};
#endif
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