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84 lines
2.3 KiB
C++
84 lines
2.3 KiB
C++
// $Id: allTreesSeparateModel.cpp 962 2006-11-07 15:13:34Z privmane $
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#include "definitions.h"
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#include "treeIt.h"
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#include "allTreesSeparateModel.h"
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#include "bblEMSeperate.h"
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#include <algorithm>
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#include <iostream>
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#include "someUtil.h"
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using namespace std;
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#ifndef VERBOS
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#define VERBOS
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#endif
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allTreesSeparateModel::allTreesSeparateModel(){
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_bestScore = VERYSMALL;
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}
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void allTreesSeparateModel::recursiveFind( const vector<sequenceContainer>* sc,
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const vector<stochasticProcess>* sp,
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const vector<Vdouble* > * weights,
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const int maxIterations,
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const MDOUBLE epsilon){
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tree starT;
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vector<int> ids;
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get3seqTreeAndIdLeftVec(&(*sc)[0],starT,ids);
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recursiveFind(starT,*sp,*sc,ids,weights,maxIterations,epsilon);
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}
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void allTreesSeparateModel::recursiveFind(tree et,
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const vector<stochasticProcess>& sp,
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const vector<sequenceContainer>& sc,
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vector<int> idLeft,
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const vector<Vdouble* > * weights,
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const int maxIterations,
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const MDOUBLE epsilon) {
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if (idLeft.empty()) {
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//static int k=1; k++;
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MDOUBLE treeScore = evalTree(et,sp,sc,maxIterations,epsilon,weights);
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//LOG(5,<<"tree: "<<k<<" l= "<<treeScore<<endl);
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LOG(5,<<".");
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if (treeScore > _bestScore) {
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//LOG(5,<<"new Best score!"<<endl);
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_bestTree = et;
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_bestScore = treeScore;
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_treeVecBest = _treeVecTmp; // keep the seperate trees too.
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}
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} else {
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et.create_names_to_internal_nodes();
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treeIterTopDown tIt(et);
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tree::nodeP mynode = tIt.first();
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mynode = tIt.next(); // skipping the root
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for (; mynode != tIt.end(); mynode = tIt.next()) {
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int NameToAdd = idLeft[idLeft.size()-1];
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tree newT = getAnewTreeFrom(et,mynode,idLeft,sc[0][NameToAdd].name());
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recursiveFind(newT,sp,sc,idLeft,weights,maxIterations,epsilon);
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idLeft.push_back(NameToAdd);
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}
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}
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}
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MDOUBLE allTreesSeparateModel::evalTree( tree& et,
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const vector<stochasticProcess>& sp,
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const vector<sequenceContainer>& sc,
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const int maxIterations,
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const MDOUBLE epsilon,
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const vector<Vdouble* > * weights) {
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MDOUBLE res = 0;
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vector<tree> tVec;
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for (int k=0; k < sc.size(); ++k ) tVec.push_back(et);
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bblEMSeperate bblemsep1(tVec,sc,sp,weights,maxIterations,epsilon);
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res = bblemsep1.getTreeLikelihood();
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_treeVecTmp = tVec;
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return res;
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}
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