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116 lines
3.5 KiB
C++
116 lines
3.5 KiB
C++
// $Id: njGammaMain.cpp 674 2006-04-09 17:06:42Z ninio $
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#include "tree.h"
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#include "sequenceContainer.h"
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#include "mainSemphy.h"
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#include "njGamma.h"
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#include "logFile.h"
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#include "treeUtil.h"
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#include "likeDist.h"
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#include "someUtil.h"
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#include <iostream>
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#include <cassert>
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using namespace std;
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int main(int argc,char* argv[]) {
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bool doPairwiseGammaMLDis = false; // do posterior.
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// bool doPairwiseGammaMLDis = true;
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bool withoutBBL = false;
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if (argc < 2) errorMsg::reportError("The program must get some parameters in the command line, use -h for help");
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semphy_args_info args_info;
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if (semphy_cmdline_parser(argc, argv, &args_info) != 0) {
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errorMsg::reportError("error reading command line",1);
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}
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// we need a starting tree to get starting posterior probs.
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doPairwiseGammaMLDis=args_info.ADVNoPost_flag;
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args_info.optimizeAlpha_flag = 1; // true
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args_info.alpha_given = 1;
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mainSemphy ms(args_info);
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assert(ms.getStochasticProcess().categories() >1);
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ms.computeNJtree();
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if (withoutBBL == false) {
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ms.optimizeBranchLengths(); // this will do both bbl and alpha.
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} else {
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ms.optimizeAlphaOnly();
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}
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// ms._args_info.optimizeGamma_flag = 0; // stop doing alpha optimization.
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VVdouble posteriorProbVV; // pos * rate
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// now we need a distance table:
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MDOUBLE newL = VERYSMALL;
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MDOUBLE oldL = VERYSMALL;
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tree oldT = ms.getTree();
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tree newT = ms.getTree();
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const int maxNumIter = 50;
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bool converged = false;
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int iterNum=0;
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distanceMethod * disMethod;
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while ( !converged && iterNum<maxNumIter) {
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stochasticProcess tmpSP = ms.getStochasticProcess();
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if (doPairwiseGammaMLDis==false) {
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LOG(5,<<"Gamma-NJ-POSTERIOR"<<endl);
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ms.extractPosteriorProb(posteriorProbVV);
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LOG(10,<<posteriorProbVV<<endl);
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disMethod = new gammaMLDistances(tmpSP,posteriorProbVV);
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} else {
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LOG(5,<<"Gamma-NJ-PAIRWISE"<<endl);
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disMethod = new likeDist(tmpSP,0.001);
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}
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VVdouble disTab;
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vector<string> vNames;
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ms.getDistanceTableAndNames(disTab,vNames,disMethod);
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LOG(10,<<"Distance table"<<endl<<disTab<<endl<<endl);
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for (int vNames_I=0;vNames_I<vNames.size();++vNames_I){
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LOG(10,<<vNames[vNames_I]<<endl);
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}
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LOG(10,<<endl<<endl);
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ms.computeNJtreeFromDisTableAndNames(disTab,vNames);
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newT = ms.getTree();
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if (sameTreeTolopogy(newT,oldT)) {
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converged = true;
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} else {
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ms.computeLikelihoodAndLikelihoodPerPosition();
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newL = ms.getLikelihood();
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LOG(5,<<"Iteration = "<<iterNum<<" LL = "<<newL<<endl);
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LOG(5,<<"Found a new tree topology!"<<endl);
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LOGDO(5,newT.output(myLog::LogFile()));
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if (withoutBBL==false) {
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LOG(10,<<"optimize gamma? "<<ms._args_info.optimizeGamma_flag<<endl);
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ms.optimizeBranchLengths(); // this will do bbl
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LOG(5,<<"Iteration = "<<iterNum<<" LL after bbl = "<<newL<<endl);
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newL = ms.getLikelihood();
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if (newL > oldL) {
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oldL = newL;
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oldT = newT;
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} else {
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LOG(5,<<"Stoping GAMMA-NJ Because LL decreased, though topology has changed!");
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converged = true;
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ms.setTree(oldT);
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}
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} else {
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oldT = newT;
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}
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}
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iterNum++;
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delete disMethod;
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}
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if (iterNum==maxNumIter) {
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LOG(3,<<"Stoped iterating not because of convergence, but because max num of iterations");
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}
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ms.optimizeBranchLengths();
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newL = ms.getLikelihood();
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LOG(5,<<"Iteration = "<<iterNum<<" FINAL LOG LIKELIHOOD = "<<newL<<endl);
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// print the likelihood and the resulting tree and alpha.
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ms.output();
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return 0;
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}
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