int main()
{
OmaPino<float> pino(2);
OmaVektori<float> vektori;
vektori.lisaa(10);
vektori.lisaa(20);
vektori.lisaa(30);
vektori.lisaa(40);
vektori.lisaa(50);
vektori.lisaa(60);
vektori.lisaa(27);
vektori.lisaa(11);
pino.push(10);
pino.push(20);
pino.tulosta();
try
{
pino.pop();
pino.pop();
pino.pop();
/*pino.push(30);
pino.push(40);
pino.push(50);
pino.push(60);
pino.push(70);*/
}
catch (const char *s)
{
std::cout << "pookkeus" << s << std::endl;
}
//vektori.tulosta();
//vektori.pop();
//vektori.pop();
//vektori.tulosta();
system("PAUSE");
return 0;
}
void MonteCarlo::Run(const int rank) {
if (checkrun == true) {
if (rank == 0) throw std::runtime_error("ERROR: MonteCarlo::Run() cannot be called after calling MonteCarlo::TestRun().\n");
} else {
checkrun = true;
}
try {
/* set model parameters */
ModelName = myInputParser.ReadParameters(ModelConf, rank, ModPars, Obs, Obs2D, CGO, CGP);
int buffsize = 0;
std::map<std::string, double> DP;
for (std::vector<ModelParameter>::iterator it = ModPars.begin(); it < ModPars.end(); it++) {
if (it->errg > 0. || it->errf > 0.)
buffsize++;
if (it->IsCorrelated()) {
for (int i = 0; i < CGP.size(); i++) {
if (CGP[i].getName().compare(it->getCgp_name()) == 0) {
std::string index = it->name.substr(CGP[i].getName().size());
long int lindex = strtol(index.c_str(), NULL, 10);
if (lindex > 0)
DP[CGP[i].getPar(lindex - 1).name] = CGP[i].getPar(lindex - 1).ave;
else {
std::stringstream out;
out << it->name;
throw std::runtime_error("MonteCarlo::Run(): " + out.str() + "seems to be part of a CorrelatedGaussianParameters object, but I couldn't find the corresponding object");
}
}
}
} else
DP[it->name] = it->ave;
}
if (buffsize == 0)
if (rank == 0) throw std::runtime_error("No parameters being varied. Aborting MCMC run.\n");
buffsize++;
if (!myInputParser.getModel()->Init(DP))
if (rank == 0) throw std::runtime_error("ERROR: Parameter(s) missing in model initialization.\n");
if (rank == 0) std::cout << std::endl << "Running in MonteCarlo mode...\n" << std::endl;
/* create a directory for outputs */
if (rank == 0) {
FileStat_t info;
if (gSystem->GetPathInfo(ObsDirName.c_str(), info) != 0) {
if (gSystem->MakeDirectory(ObsDirName.c_str()) == 0)
std::cout << ObsDirName << " directory has been created." << std::endl;
else
throw std::runtime_error("ERROR: " + ObsDirName + " directory cannot be created.\n");
}
}
MCEngine.SetName(ModelName.c_str());
MCEngine.Initialize(myInputParser.getModel());
#ifdef _MPI
double *recvbuff = new double[buffsize];
#endif
if (rank != 0) {
#ifdef _MPI
double **sendbuff = new double*[1];
sendbuff[0] = new double[1];
std::vector<double> pars;
int obsbuffsize = Obs.size() + 2 * Obs2D.size();
for (std::vector<CorrelatedGaussianObservables>::iterator it1 = CGO.begin(); it1 < CGO.end(); it1++)
obsbuffsize += it1->getObs().size();
while (true) {
MPI::COMM_WORLD.Scatter(sendbuff[0], buffsize, MPI::DOUBLE,
recvbuff, buffsize, MPI::DOUBLE, 0);
if (recvbuff[0] == 0.) { // do nothing and return ll
double ll = log(0.);
MPI::COMM_WORLD.Gather(&ll, 1, MPI::DOUBLE, sendbuff[0], 1, MPI::DOUBLE, 0);
} else if (recvbuff[0] == 1.) { //compute log likelihood
pars.assign(recvbuff + 1, recvbuff + buffsize);
double ll = MCEngine.LogEval(pars);
MPI::COMM_WORLD.Gather(&ll, 1, MPI::DOUBLE, sendbuff[0], 1, MPI::DOUBLE, 0);
} else if (recvbuff[0] == 2.) { // compute observables
double sbuff[obsbuffsize];
std::map<std::string, double> DPars;
pars.assign(recvbuff + 1, recvbuff + buffsize);
MCEngine.setDParsFromParameters(pars,DPars);
myInputParser.getModel()->Update(DPars);
int k = 0;
for (boost::ptr_vector<Observable>::iterator it = Obs.begin(); it < Obs.end(); it++) {
sbuff[k++] = it->computeTheoryValue();
}
for (std::vector<Observable2D>::iterator it = Obs2D.begin(); it < Obs2D.end(); it++) {
sbuff[k++] = it->computeTheoryValue();
sbuff[k++] = it->computeTheoryValue2();
}
for (std::vector<CorrelatedGaussianObservables>::iterator it1 = CGO.begin(); it1 < CGO.end(); it1++) {
std::vector<Observable> pino(it1->getObs());
for (std::vector<Observable>::iterator it = pino.begin(); it != pino.end(); ++it)
sbuff[k++] = it->computeTheoryValue();
}
MPI::COMM_WORLD.Gather(sbuff, obsbuffsize, MPI::DOUBLE, sendbuff[0], obsbuffsize, MPI::DOUBLE, 0);
} else if (recvbuff[0] == 3.) { // do not compute observables, but gather the buffer
double sbuff[obsbuffsize];
MPI::COMM_WORLD.Gather(sbuff, obsbuffsize, MPI::DOUBLE, sendbuff[0], obsbuffsize, MPI::DOUBLE, 0);
} else if (recvbuff[0] == -1.)
break;
else {
std::cout << "recvbuff = " << recvbuff[0] << " rank " << rank << std::endl;
throw "MonteCarlo::Run(): error in MPI message!\n";
}
}
delete sendbuff[0];
delete [] sendbuff;
#endif
} else {
bool writechains = false;
std::cout << ModPars.size() << " parameters defined." << std::endl;
std::cout << Obs.size() << " observables defined." << std::endl;
std::cout << CGO.size() << " correlated gaussian observables defined";
if (CGO.size() == 0)
std::cout << "." << std::endl;
else
std::cout << ":" << std::endl;
for (std::vector<CorrelatedGaussianObservables>::iterator it1 = CGO.begin();
it1 != CGO.end(); ++it1)
std::cout << " " << it1->getName() << " containing "
<< it1->getObs().size() << " observables." << std::endl;
//MonteCarlo configuration parser
std::ifstream ifile(MCMCConf.c_str());
if (!ifile.is_open())
throw std::runtime_error("\nERROR: " + MCMCConf + " does not exist. Make sure to specify a valid Monte Carlo configuration file.\n");
std::string line;
bool IsEOF = false;
do {
IsEOF = getline(ifile, line).eof();
if (*line.rbegin() == '\r') line.erase(line.length() - 1); // for CR+LF
if (line.empty() || line.at(0) == '#')
continue;
boost::char_separator<char> sep(" ");
boost::tokenizer<boost::char_separator<char> > tok(line, sep);
boost::tokenizer<boost::char_separator<char> >::iterator beg = tok.begin();
if (beg->compare("NChains") == 0) {
++beg;
MCEngine.setNChains(atoi((*beg).c_str()));
} else if (beg->compare("PrerunMaxIter") == 0) {
++beg;
MCEngine.MCMCSetNIterationsMax(atoi((*beg).c_str()));
} else if (beg->compare("NIterationsUpdateMax") == 0) {
++beg;
MCEngine.MCMCSetNIterationsUpdateMax(atoi((*beg).c_str()));
} else if (beg->compare("Seed") == 0) {
++beg;
int seed = atoi((*beg).c_str());
if (seed != 0)
MCEngine.MCMCSetRandomSeed(seed);
} else if (beg->compare("Iterations") == 0) {
++beg;
MCEngine.MCMCSetNIterationsRun(atoi((*beg).c_str()));
} else if (beg->compare("MinimumEfficiency") == 0) {
++beg;
MCEngine.MCMCSetMinimumEfficiency(atof((*beg).c_str()));
} else if (beg->compare("WriteChain") == 0) {
++beg;
if (beg->compare("true") == 0)
writechains = true;
} else if (beg->compare("FindModeWithMinuit") == 0) {
++beg;
if (beg->compare("true") == 0) {
FindModeWithMinuit = true;
}
} else if (beg->compare("CalculateNormalization") == 0) {
++beg;
if (beg->compare("true") == 0) {
CalculateNormalization = true;
}
} else if (beg->compare("PrintAllMarginalized") == 0) {
++beg;
if (beg->compare("true") == 0) {
PrintAllMarginalized = true;
}
} else if (beg->compare("PrintCorrelationMatrix") == 0) {
++beg;
if (beg->compare("true") == 0) {
PrintCorrelationMatrix = true;
}
} else if (beg->compare("PrintKnowledgeUpdatePlots") == 0) {
++beg;
if (beg->compare("true") == 0) {
PrintKnowledgeUpdatePlots = true;
}
} else if (beg->compare("PrintParameterPlot") == 0) {
++beg;
if (beg->compare("true") == 0) {
PrintParameterPlot = true;
}
} else if (beg->compare("WritePreRunData") == 0) {
++beg;
if (beg->compare("true") == 0) {
WritePreRunData = true;
}
} else if (beg->compare("ReadPreRunData") == 0) {
++beg;
ReadPreRunData(*beg);
} else if (beg->compare("OrderParameters") == 0) {
++beg;
if (beg->compare("false") == 0) {
MCEngine.MCMCSetFlagOrderParameters(false);
}
} else
throw std::runtime_error("\nERROR: Wrong keyword in MonteCarlo config file: " + *beg + "\n Make sure to specify a valid Monte Carlo configuration file.\n");
} while (!IsEOF);
BCModelOutput out(&MCEngine, OutFile.c_str());
if (writechains) {
out.WriteMarkovChain(true);
MCEngine.AddChains();
}
// set nicer style for drawing than the ROOT default
BCAux::SetStyle();
// open log file
BCLog::OpenLog(("log" + JobTag + ".txt").c_str());
BCLog::SetLogLevel(BCLog::debug);
// run the MCMC and marginalize w.r.t. to all parameters
MCEngine.BCIntegrate::SetNbins(NBINSMODELPARS);
MCEngine.SetMarginalizationMethod(BCIntegrate::kMargMetropolis);
std::time_t ti = std::time(NULL);
char mbstr[100];
if (std::strftime(mbstr, sizeof(mbstr), "%H:%M:%S %d/%m/%y ", std::localtime(&ti)))
std::cout << "MCMC Run started at " << mbstr << std::endl;
MCEngine.MarginalizeAll();
std::time_t tf = std::time(NULL);
if (std::strftime(mbstr, sizeof(mbstr), "%H:%M:%S %d/%m/%y ", std::localtime(&tf)))
std::cout << "MCMC Run ended at " << mbstr << std::endl;
// find mode using the best fit parameters as start values
if (FindModeWithMinuit)
MCEngine.FindMode(MCEngine.GetBestFitParameters());
if (CalculateNormalization) normalization = MCEngine.computeNormalization();
// draw all marginalized distributions into a pdf file
if (PrintAllMarginalized)
MCEngine.PrintAllMarginalized(("MonteCarlo_plots" + JobTag + ".pdf").c_str());
// print results of the analysis into a text file
MCEngine.PrintResults(("MonteCarlo_results" + JobTag + ".txt").c_str());
// print histograms
MCEngine.PrintHistogram(out, ObsDirName);
BCSummaryTool myBCSummaryTool(&MCEngine);
// draw the correlation matrix into a pdf file
if (PrintCorrelationMatrix)
myBCSummaryTool.PrintCorrelationMatrix(("ParamCorrelations" + JobTag + ".pdf").c_str());
// print the correlation matrix into a tex file
if (PrintCorrelationMatrix)
MCEngine.PrintCorrelationMatrix(("ParamCorrelations" + JobTag + ".tex").c_str());
// print comparisons of the prior knowledge to the posterior knowledge
// for all parameters into a pdf file
if (PrintKnowledgeUpdatePlots)
myBCSummaryTool.PrintKnowledgeUpdatePlots(("ParamUpdate" + JobTag + ".pdf").c_str());
// draw an overview plot of the parameters into a pdf file
if (PrintParameterPlot)
myBCSummaryTool.PrintParameterPlot(("ParamSummary" + JobTag + ".pdf").c_str());
// print a LaTeX table of the parameters into a tex file
//myBCSummaryTool.PrintParameterLatex(("ParamSummary" + JobTag + ".tex").c_str());
out.WriteMarginalizedDistributions();
out.FillAnalysisTree();
out.Close();
// print logs for the histograms of the observables into a text file
std::ofstream outHistoLog;
outHistoLog.open((ObsDirName + "/HistoLog" + JobTag + ".txt").c_str(), std::ios::out);
outHistoLog << MCEngine.getHistoLog();
outHistoLog.close();
// print statistics for the theory values of the observables into a text file
std::ofstream outStatLog;
outStatLog.open((ObsDirName + "/Statistics" + JobTag + ".txt").c_str(), std::ios::out);
if (CalculateNormalization) outStatLog << "Normalization for " << ModelName.c_str() << ": " << normalization << "\n" << std::endl;
outStatLog << MCEngine.computeStatistics();
outStatLog.close();
// print global mode and scale factors for the 1st chain into a text file
if (WritePreRunData) {
std::ofstream outPreRun;
outPreRun.open(("PreRun" + JobTag + ".txt").c_str(), std::ios::out);
outPreRun << MCEngine.writePreRunData();
outPreRun.close();
}
/* Number of events */
std::stringstream ss;
ss << "Number of used events: " << MCEngine.getNumOfUsedEvents();
BCLog::OutSummary(ss.str().c_str());
ss.str("");
ss << "Number of discarded events: " << MCEngine.getNumOfDiscardedEvents();
BCLog::OutSummary(ss.str().c_str());
// close log file
BCLog::CloseLog();
#ifdef _MPI
double ** sendbuff = new double *[MCEngine.procnum];
sendbuff[0] = new double[MCEngine.procnum * buffsize];
for (int il = 1; il < MCEngine.procnum; il++) {
sendbuff[il] = sendbuff[il - 1] + buffsize;
sendbuff[il][0] = -1.; //Exit command
}
MPI::COMM_WORLD.Scatter(sendbuff[0], buffsize, MPI::DOUBLE,
recvbuff, buffsize, MPI::DOUBLE, 0);
delete sendbuff[0];
delete [] sendbuff;
#endif
}
#ifdef _MPI
delete [] recvbuff;
#endif
} catch (std::string message) {
std::cerr << message << std::endl;
exit(EXIT_FAILURE);
}
}
