本文整理汇总了C++中tmva::Factory::DefaultDataSetInfo方法的典型用法代码示例。如果您正苦于以下问题：C++ Factory::DefaultDataSetInfo方法的具体用法？C++ Factory::DefaultDataSetInfo怎么用？C++ Factory::DefaultDataSetInfo使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tmva::Factory的用法示例。
在下文中一共展示了Factory::DefaultDataSetInfo方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: TMVAClassification
//.........这里部分代码省略.........
// plugin mechanism
if (Use["Plugin"]) {
//
// first the plugin has to be defined, which can happen either through the following line in the local or global .rootrc:
//
// # plugin handler plugin name(regexp) class to be instanciated library constructor format
// [email protected]@MethodBase: ^BDT TMVA::MethodBDT TMVA.1 "MethodBDT(TString,TString,DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("[email protected]@MethodBase", "BDT", "TMVA::MethodBDT", "TMVA.1", "MethodBDT(TString,TString,DataSet&,TString)");
factory->BookMethod( TMVA::Types::kPlugins, "BDT",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=50" );
}
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
if (Use["Cuts"]) {
for( unsigned iEff=1; iEff<11; ++iEff ) {
TMVA::IMethod* method = (TMVA::IMethod*)factory->GetMethod("Cuts");
TMVA::MethodCuts* cuts = dynamic_cast<TMVA::MethodCuts*>(method);
std::string optcutsdir = "optcuts_" + selectionName + "_" + charge;
std::string mkdir_command = "mkdir -p " + optcutsdir;
system(mkdir_command.c_str());
char cutsFileName[500];
sprintf( cutsFileName, "%s/cuts_Seff%d.txt", optcutsdir.c_str(), 10*iEff );
ofstream ofs(cutsFileName);
std::vector<Double_t> cutsMin, cutsMax;
cuts->GetCuts((float)iEff*0.10, cutsMin, cutsMax);
bool found_pT1 = false;
bool found_pT2 = false;
bool found_NJ = false;
bool found_NbJ = false;
bool found_NbJmed = false;
for( unsigned iCut=0; iCut<cutsMin.size(); ++iCut) {
TString varName = factory->DefaultDataSetInfo().GetVariableInfo(iCut).GetInternalName();
if( varName=="TMath_Min_pT1,pT2_") {
ofs << "pT1 " << cutsMin[iCut] << " " << cutsMax[iCut] << std::endl;
ofs << "pT2 " << cutsMin[iCut] << " " << cutsMax[iCut] << std::endl;
found_pT1 = true;
found_pT2 = true;
} else {
ofs << varName << " " << cutsMin[iCut] << " " << cutsMax[iCut] << std::endl;
}
if( varName=="pT1" ) found_pT1 = true;
if( varName=="pT2" ) found_pT2 = true;
if( varName=="NJ" ) found_NJ = true;
if( varName=="NbJ" ) found_NbJ = true;
if( varName=="NbJmed" ) found_NbJmed = true;
}
// preselection cuts (if not optimized):
if( !found_pT1 ) ofs << "pT1 20. 100000." << std::endl;
if( !found_pT2 ) ofs << "pT2 20. 100000." << std::endl;
if( !found_NJ ) ofs << "NJ 3 100000." << std::endl;
if( !found_NbJ ) ofs << "NbJ 1 100000." << std::endl;
if( !found_NbJmed && btagMed_presel_ ) ofs << "NbJmed 1 100000." << std::endl;
if( charge=="plus" )
ofs << "Charge 1 10" << std::endl;
else if( charge=="minus" )
ofs << "Charge -10 0" << std::endl;
ofs.close();
} // for eff
} // if cuts
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}