/usr/include/libphylo/bestHKYparam.h is in rate4site 3.0.0-2.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | // $Id: bestHKYparam.h 9992 2011-11-08 03:57:29Z rubi $
#ifndef ___BEST_HKY_PARAM
#define ___BEST_HKY_PARAM
#include "definitions.h"
#include "likelihoodComputation.h"
#include "sequenceContainer.h"
#include "stochasticProcess.h"
#include "gammaDistribution.h"
#include "tree.h"
#include "hky.h"
#include "multipleStochasticProcess.h"
class bestHkyParamFixedTree {
public:
explicit bestHkyParamFixedTree(const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights=NULL,
const MDOUBLE upperBoundOnHkyParam = 0.5,
const MDOUBLE epsilonHkyParamOptimization = 0.01);
MDOUBLE getBestHkyParam() {return _bestHkyParam;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestHkyParam;
MDOUBLE _bestL;
};
class bestHkyParamAndBBL {
public:
explicit bestHkyParamAndBBL(tree& et, //find Best HkyParam and best BBL
const sequenceContainer& sc,
stochasticProcess& sp,
const Vdouble * weights=NULL,
const MDOUBLE upperBoundOnHkyParam = 5.0,
const MDOUBLE epsilonHkyParamOptimization= 0.01,
const MDOUBLE epsilonLikelihoodImprovment= 0.05,
const int maxBBLIterations=10,
const int maxTotalIterations=5);
MDOUBLE getBestHkyParam() {return _bestHkyParam;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestHkyParam;
MDOUBLE _bestL;
};
class C_evalHkyParam{
public:
C_evalHkyParam( 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 HkyParam) {
(static_cast<hky*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTrTv(HkyParam);
MDOUBLE res = likelihoodComputation::getTreeLikelihoodAllPosAlphTheSame(_et,_sc,_sp,_weights);
//LOG(5,<<" with HkyParam = "<<HkyParam<<" logL = "<<res<<endl);
return -res;
}
};
class C_evalLocalHkyParam{
public:
C_evalLocalHkyParam( const tree& et,
const sequenceContainer& sc,
stochasticProcess& sp,
const 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;
const gammaDistribution* _pProportionDist;
public:
MDOUBLE operator() (MDOUBLE HkyParam) {
(static_cast<hky*>(_sp.getPijAccelerator()->getReplacementModel()))->changeTrTv(HkyParam);
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 HkyParam = "<<HkyParam<<" logL = "<<res<<endl);
return -res;
}
};
class bestHkyParamAlphaAndBBL {
public:
explicit bestHkyParamAlphaAndBBL( //find best TrTv (=HkyParam), 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 epsilonHkyParamOptimization= 0.01,
const MDOUBLE epsilonAlphaOptimization= 0.01,
const MDOUBLE epsilonBBL= 0.01,
const MDOUBLE upperBoundOnHkyParam = 5.0,
const int maxBBLIterations=10,
const MDOUBLE initAlpha = 1.5,
const MDOUBLE upperBoundOnAlpha = 5.0);
MDOUBLE getBestHkyParam() {return _bestHkyParam;}
MDOUBLE getBestAlpha() {return _bestAlpha;}
MDOUBLE getBestL() {return _bestL;}
private:
MDOUBLE _bestHkyParam;
MDOUBLE _bestAlpha;
MDOUBLE _bestL;
};
class bestHkyParamAlphaAndBBLProportional {
public:
explicit bestHkyParamAlphaAndBBLProportional( //find best Kappa (=HkyParam), global Alpha, local Alpha, and best branch lengths
tree& et,
vector<sequenceContainer>& sc,
multipleStochasticProcess* msp,
gammaDistribution* pProportionDist,
Vdouble initLocalAlphas,
Vdouble initLocalKappas,
const MDOUBLE upperBoundOnLocalAlpha,
const MDOUBLE initGlobalAlpha,
const MDOUBLE upperBoundOnGlobalAlpha,
const MDOUBLE upperBoundOnHkyParam,
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 epsilonHkyParamOptimization= 0.01,
const MDOUBLE epsilonLocalAlphaOptimization= 0.01,
const MDOUBLE epsilonGlobalAlphaOptimization= 0.01,
const MDOUBLE epsilonBBL= 0.01);
MDOUBLE getBestHkyParam(int spIndex) {return _bestHkyParamVec[spIndex];}
MDOUBLE getBestLocalAlpha(int spIndex) {return _bestLocalAlphaVec[spIndex];}
MDOUBLE getBestGlobalAlpha(){return _bestGlobalAlpha;}
Vdouble getBestL() {return _bestLvec;}
private:
Vdouble _bestHkyParamVec;
Vdouble _bestLocalAlphaVec;
MDOUBLE _bestGlobalAlpha;
Vdouble _bestLvec;
};
#endif
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