C++ Factory::AddBackgroundTree方法代码示例

void TMVAClassification(char* trainFile, char* tree, 
                        char* mycuts, char*  mycutb, char* inputVars[], int size) 
{
   // this loads the library
   TMVA::Tools::Instance();

   // Create a new root output file.
   TFile* outputFile = TFile::Open( "TMVA.root", "RECREATE" );

   // Create the factory object.
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
   // ---------- input variables 
   for (int ivar = 0; ivar < size; ++ivar) {
     factory->AddVariable(inputVars[ivar], 'F');
   }

   // read training and test data
   TFile *input = TFile::Open( trainFile);
   TTree *signal     = (TTree*)input->Get(tree);
   TTree *background = (TTree*)input->Get(tree);
   

   // global event weights per tree 
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // ====== register trees ====================================================
   // you can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );

   // tell the factory to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( TCut(mycuts), TCut(mycutb),
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // If no numbers of events are given, half of the events in the tree are used for training, and 
   // the other half for testing:

   // ---- Use BDT: Adaptive Boost
   factory->BookMethod( TMVA::Types::kBDT, "BDT", 
                        "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
 
   // ---- 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();    

   // Save the output
   outputFile->Close();

   std::cout << "==> TMVAClassification is done!" << std::endl;      

   delete factory;
}

void process(const std::vector<std::string>& inputFiles, const std::string& name, const std::string& outputFile) {
  TChain* signal = loadChain(inputFiles, "signal");
  TChain* background = loadChain(inputFiles, "background");

  TFile* output = TFile::Open(outputFile.c_str(), "recreate");

  TMVA::Factory* factory = new TMVA::Factory(name.c_str(), output, "V");
  factory->AddSignalTree(signal, 1.);
  factory->AddBackgroundTree(background, 1.);

  //{
    //factory->AddVariable("lightJet1p2_Pt");
    //factory->AddVariable("leptonic_B_Pt");
    //factory->AddVariable("leptonic_Top_Pt");
    //factory->AddVariable("leptonic_Top_M");
    //factory->AddVariable("hadronic_B_Pt");
    //factory->AddVariable("hadronic_W_M");
    //factory->AddVariable("hadronic_Top_Pt");
    //factory->AddVariable("hadronic_Top_M");
    //factory->AddVariable("delta_R_tops");
    //factory->AddVariable("delta_R_lightjets");
    //factory->AddVariable("leptonic_B_CSV");
    //factory->AddVariable("hadronic_B_CSV");
  //}


  // chi^2 style
  {
    factory->AddVariable("leptonic_Top_M");
    factory->AddVariable("hadronic_W_M");
    factory->AddVariable("hadronic_Top_M");
    factory->AddVariable("ht_fraction");
  }

  factory->SetWeightExpression("weight");

  factory->PrepareTrainingAndTestTree("", "", "V:VerboseLevel=Info:nTrain_Signal=100000:nTrain_Background=100000:nTest_Signal=100000:nTest_Background=100000");

  factory->BookMethod(TMVA::Types::kBDT, "BDT", "V:BoostType=AdaBoost:nCuts=20:VarTransform=D");
  factory->BookMethod(TMVA::Types::kMLP, "NN", "V:VarTransform=D");
  //factory->BookMethod(TMVA::Types::kPDERS, "PDERS", "V");

  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods();

  output->Close();
  delete output;

  delete signal;
  delete background;
}

void test_train(TString signalName = "WW",
		TString bkgName = "DY")
{
  TFile *outFile = new TFile("myAnalysisFile.root","RECREATE");
  
  TMVA::Factory *factory = new TMVA::Factory(signalName, outFile,"");
  
  TString directory = "../rootFiles/SF/MediumIDTighterIP/";
  //signalName = directory + signalName;
  
  //defining WW signal
  TFile *MySignalFile = new TFile("../rootFiles/SF/MediumIDTighterIP/WW.root","READ");
  TTree* sigTree = (TTree*)MySignalFile->Get("nt");
  factory->AddSignalTree(sigTree,1);
  
  //defining DY background
  TFile *MyBkgFile = new TFile("../rootFiles/SF/MediumIDTighterIP/DY.root","READ");
  TTree* bkgTree = (TTree*)MyBkgFile->Get("nt");
  factory->AddBackgroundTree(bkgTree,1);

  factory->SetWeightExpression("baseW");

  //************************************ FACTORY  
  
  factory->AddVariable("fullpmet");
  factory->AddVariable("trkpmet");
  factory->AddVariable("ratioMet");
  factory->AddVariable("ptll");
  factory->AddVariable("mth");
  factory->AddVariable("jetpt1");
  factory->AddVariable("ptWW");
  factory->AddVariable("dphilljet");
  factory->AddVariable("dphillmet");
  factory->AddVariable("dphijet1met");
  factory->AddVariable("nvtx");

  factory->PrepareTrainingAndTestTree("",500,500,500,500);
  cout<<"I've prepared trees"<<endl;
  //factory->BookMethod(TMVA::Types::kFisher, "Fisher","");
  factory->BookMethod(TMVA::Types::kBDT, "BDT","");
  
  cout<<"I've booked method"<<endl;
  factory->TrainAllMethods();
  factory->TestAllMethods();
  cout<<"I've tested all methods"<<endl;
  factory->EvaluateAllMethods();
  cout<<"I've evaluated all methods"<<endl;
  
}

int main ()
{
  TFile * outputfile = TFile::Open ("outputTMVA.root","RECREATE");
  TMVA::Factory * TMVAtest = new TMVA::Factory ("TMVAtest", outputfile, "S") ;

  //PG get the signal and deliver it to the TMVA factory
  TFile signalFile ("/Users/govoni/data/ntuplesHWW/H160/NTUPLE_H160_output_0.root") ;
  TTree * signalTree = (TTree *) signalFile.Get ("ntpla/VBFSimpleTree") ;
  std::cout << "READ " << signalTree->GetEntries () << " signal events\n" ;
  TMVAtest->AddSignalTree (signalTree,1) ;  

  //PG get the bkg and deliver it to the TMVA factory
  TFile bkg1File ("/Users/govoni/data/ntuplesHWW/WW_incl/NTUPLE_WW_incl_output_0.root") ;
  TTree * bkg1Tree = (TTree *) bkg1File.Get ("ntpla/VBFSimpleTree") ;
  std::cout << "READ " << bkg1Tree->GetEntries () << " bkg1 events\n" ;
  TMVAtest->AddBackgroundTree (bkg1Tree,1) ;  

  delete TMVAtest ;
  delete outputfile ;
}

void TMVAtest(){
  //gSystem->Load("../lib/slc5_amd64_gcc462/libTAMUWWMEPATNtuple.so");
  gSystem->Load("libPhysics");
  //gSystem->Load("EvtTreeForAlexx_h.so");
  gSystem->Load("libTMVA.1");
  gSystem->Load("AutoDict_vector_TLorentzVector__cxx.so");
  TMVA::Tools::Instance();
  TFile* outputFile = TFile::Open("TMVA1.root", "RECREATE");
  TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification",outputFile,"V=true:Color:DrawProgressBar");// ":Transformations=I;D;P;G,D" );
  TFile* signal = TFile::Open("/uscms_data/d2/aperloff/Spring12ME7TeV/MEResults/microNtuples_oldStructure/microWW_EPDv01.root");
  TFile* bkg = TFile::Open("/uscms_data/d2/aperloff/Spring12ME7TeV/MEResults/microNtuples_oldStructure/microWJets_EPDv01.root");

  TTree* stree = (TTree*)signal->Get("METree");
  TTree* btree = (TTree*)bkg->Get("METree");
  factory->AddSignalTree(stree,1.0);
  factory->AddBackgroundTree(btree,1.0);


  factory->SetSignalWeightExpression("1.0");
  factory->SetBackgroundWeightExpression("1.0");
  factory->AddVariable("tEventProb[0]");
  factory->AddVariable("tEventProb[1]");
  factory->AddVariable("tEventProb[2]");

  //factory->AddVariable("tEventProb0 := tEventProb[0]",'F');
  //factory->AddVariable("tEventProb1 := tEventProb[1]",'F');
  //factory->AddVariable("tEventProb2 := tEventProb[2]",'F');
  TCut test("Entry$>-2 && jLV[1].Pt()>30");
  TCut mycuts (test);
  factory->PrepareTrainingAndTestTree(mycuts,mycuts,"nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=None:V=true:VerboseLevel=DEBUG");
  factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods();
  outputFile->Close(); 

}

//.........这里部分代码省略.........
   
  
  //   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
   
  // --- Register the training and test trees
  TChain *signal     = new TChain("ewkzp2j");
  TChain *background = new TChain("ewkzp2j");
  TSystemDirectory dir(inputDir,inputDir);
  TList *files = dir.GetListOfFiles();
  if (files) {
    TSystemFile *file;
    TString fname;
    TIter next(files);
    while ((file=(TSystemFile*)next())) {
      fname = file->GetName();
      if(!fname.EndsWith("_summary.root")) continue;
      if(fname.Contains("Data")) continue;
      if(!fname.Contains("DY")) continue;
      bool isSignal(false);
      if(fname.Contains("JJ")) { signal->Add(fname); isSignal=true; }
      else if(fname.Contains("50toInf") && fname.Contains("DY")) background->Add(fname);
      cout << fname << " added as " << (isSignal ? "signal" : "background") << endl;
    }
  }else{
    cout << "[Error] no files found in " << inputDir << endl;
  }
  cout << "Signal has " << signal->GetEntries() << " raw events" << endl
       << "Background has " << background->GetEntries() << " raw events"<< endl;

  // global event weights per tree
  Double_t signalWeight     = 1.0;
  Double_t backgroundWeight = 1.0;
  factory->AddSignalTree    ( signal,     signalWeight     );
  factory->AddBackgroundTree( background, backgroundWeight );
  // event-per-event weights per tree
  factory->SetBackgroundWeightExpression( "weight/cnorm" );
  factory->SetSignalWeightExpression( "weight/cnorm" );

  //define variables for the training
  if(minimalTrain)
    {
      factory->AddVariable( "mjj",     "M_{jj}"              "GeV", 'F' );
      factory->AddVariable( "detajj",  "#Delta#eta_{jj}",     "",    'F' );
      factory->AddVariable( "spt",     "#Delta_{rel}",        "GeV", 'F' );
    }
  else
    {
      factory->AddVariable( "mjj",     "M_{jj}"              "GeV",  'F' );
      factory->AddVariable( "detajj",  "#Delta#eta_{jj}",     "",    'F' );
      factory->AddVariable( "setajj",  "#Sigma#eta_{j}",      "",    'F' );
      factory->AddVariable( "eta1",    "#eta(1)",             "",    'F' );
      factory->AddVariable( "eta2",    "#eta(2)",             "",    'F' );
      factory->AddVariable( "pt1",     "p_{T}(1)",            "GeV", 'F' );
      factory->AddVariable( "pt2",     "p_{T}(2)",            "GeV", 'F' );
      factory->AddVariable( "spt",     "#Delta_{rel}",        "GeV", 'F' );
      if(useQG) factory->AddVariable( "qg1",   "q/g(1)",      "",    'F' );
      if(useQG) factory->AddVariable( "qg2",   "q/g(2)",      "",    'F' );
    }
  

  // Apply additional cuts on the signal and background samples (can be different)
  TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
  TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
  factory->PrepareTrainingAndTestTree( mycuts, mycutb,
				       "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

void TMVAClassificationCategory()
{
    //---------------------------------------------------------------
    // Example for usage of different event categories with classifiers

    std::cout << std::endl << "==> Start TMVAClassificationCategory" << std::endl;

    bool batchMode = false;

    // Create a new root output file.
    TString outfileName( "TMVA.root" );
    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

    // Create the factory object (see TMVAClassification.C for more information)

    std::string factoryOptions( "!V:!Silent:Transformations=I;D;P;G,D" );
    if (batchMode) factoryOptions += ":!Color:!DrawProgressBar";

    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, factoryOptions );

    // Define the input variables used for the MVA training
    factory->AddVariable( "var1", 'F' );
    factory->AddVariable( "var2", 'F' );
    factory->AddVariable( "var3", 'F' );
    factory->AddVariable( "var4", 'F' );

    // You can add so-called "Spectator variables", which are not used in the MVA training,
    // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
    // input variables, the response values of all trained MVAs, and the spectator variables
    factory->AddSpectator( "eta" );

    // Load the signal and background event samples from ROOT trees
    TFile *input(0);
    TString fname( "" );
    if (UseOffsetMethod) fname = "data/toy_sigbkg_categ_offset.root";
    else                 fname = "data/toy_sigbkg_categ_varoff.root";
    if (!gSystem->AccessPathName( fname )) {
        // first we try to find tmva_example.root in the local directory
        std::cout << "--- TMVAClassificationCategory: Accessing " << fname << std::endl;
        input = TFile::Open( fname );
    }

    if (!input) {
        std::cout << "ERROR: could not open data file: " << fname << std::endl;
        exit(1);
    }

    TTree *signal     = (TTree*)input->Get("TreeS");
    TTree *background = (TTree*)input->Get("TreeB");

    /// Global event weights per tree (see below for setting event-wise weights)
    Double_t signalWeight     = 1.0;
    Double_t backgroundWeight = 1.0;

    /// You can add an arbitrary number of signal or background trees
    factory->AddSignalTree    ( signal,     signalWeight     );
    factory->AddBackgroundTree( background, backgroundWeight );

    // Apply additional cuts on the signal and background samples (can be different)
    TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
    TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

    // Tell the factory how to use the training and testing events
    factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                         "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

    // ---- Book MVA methods

    // Fisher discriminant
    factory->BookMethod( TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher" );

    // Likelihood
    factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
                         "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

    // --- Categorised classifier
    TMVA::MethodCategory* mcat = 0;

    // The variable sets
    TString theCat1Vars = "var1:var2:var3:var4";
    TString theCat2Vars = (UseOffsetMethod ? "var1:var2:var3:var4" : "var1:var2:var3");

    // Fisher with categories
    TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
    mcat = dynamic_cast<TMVA::MethodCategory*>(fiCat);
    mcat->AddMethod( "abs(eta)<=1.3", theCat1Vars, TMVA::Types::kFisher, "Category_Fisher_1","!H:!V:Fisher" );
    mcat->AddMethod( "abs(eta)>1.3",  theCat2Vars, TMVA::Types::kFisher, "Category_Fisher_2","!H:!V:Fisher" );

    // Likelihood with categories
    TMVA::MethodBase* liCat = factory->BookMethod( TMVA::Types::kCategory, "LikelihoodCat","" );
    mcat = dynamic_cast<TMVA::MethodCategory*>(liCat);
    mcat->AddMethod( "abs(eta)<=1.3",theCat1Vars, TMVA::Types::kLikelihood,
                     "Category_Likelihood_1","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
    mcat->AddMethod( "abs(eta)>1.3", theCat2Vars, TMVA::Types::kLikelihood,
                     "Category_Likelihood_2","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

    // ---- Now you can tell the factory to train, test, and evaluate the MVAs

    // Train MVAs using the set of training events
    factory->TrainAllMethods();
//.........这里部分代码省略.........

void Classification()
{
   TMVA::Tools::Instance();
   TMVA::PyMethodBase::PyInitialize();

   TString outfileName("TMVA.root");
   TFile *outputFile = TFile::Open(outfileName, "RECREATE");

   TMVA::Factory *factory = new TMVA::Factory("TMVAClassification", outputFile,
         "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");


   factory->AddVariable("myvar1 := var1+var2", 'F');
   factory->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
   factory->AddVariable("var3",                "Variable 3", "units", 'F');
   factory->AddVariable("var4",                "Variable 4", "units", 'F');


   factory->AddSpectator("spec1 := var1*2",  "Spectator 1", "units", 'F');
   factory->AddSpectator("spec2 := var1*3",  "Spectator 2", "units", 'F');


   TString fname = "./tmva_class_example.root";

   if (gSystem->AccessPathName(fname))    // file does not exist in local directory
      gSystem->Exec("curl -O http://root.cern.ch/files/tmva_class_example.root");

   TFile *input = TFile::Open(fname);

   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;

   // --- Register the training and test trees

   TTree *tsignal     = (TTree *)input->Get("TreeS");
   TTree *tbackground = (TTree *)input->Get("TreeB");

   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree(tsignal,     signalWeight);
   factory->AddBackgroundTree(tbackground, backgroundWeight);


   // Set individual event weights (the variables must exist in the original TTree)
   factory->SetBackgroundWeightExpression("weight");


   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // Tell the factory how to use the training and testing events
   factory->PrepareTrainingAndTestTree(mycuts, mycutb,
                                       "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V");


   ///////////////////
   //Booking         //
   ///////////////////
   // Boosted Decision Trees

   //PyMVA methods
   factory->BookMethod(TMVA::Types::kPyRandomForest, "PyRandomForest",
                       "!V:NEstimators=150:Criterion=gini:MaxFeatures=auto:MaxDepth=3:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE");
   factory->BookMethod(TMVA::Types::kPyAdaBoost, "PyAdaBoost",
                       "!V:BaseEstimator=None:NEstimators=100:LearningRate=1:Algorithm=SAMME.R:RandomState=None");
   factory->BookMethod(TMVA::Types::kPyGTB, "PyGTB",
                       "!V:NEstimators=150:Loss=deviance:LearningRate=0.1:Subsample=1:MaxDepth=6:MaxFeatures='auto'");


   // 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();
   // --------------------------------------------------------------

   // Save the output
   outputFile->Close();

   std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
   std::cout << "==> TMVAClassification is done!" << std::endl;

}

//.........这里部分代码省略.........
      // load the signal and background event samples from ascii files
      // format in file must be:
      // var1/F:var2/F:var3/F:var4/F
      // 0.04551   0.59923   0.32400   -0.19170
      // ...

      TString datFileS = "tmva_example_sig.dat";
      TString datFileB = "tmva_example_bkg.dat";

      factory->SetInputTrees( datFileS, datFileB );
   }
   else {

  
    //TFile* f0 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_sig_newLumi.root");
    //TFile* f1 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_bkg_newLumi.root");
    TFile* f0 = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_ttW.root");
    TFile* f1 = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_tt-dilepton.root");
  
    TTree *signal     = (TTree*)f0->Get("theTree2");
    TTree *background = (TTree*)f1->Get("theTree2");
    cout<<"trees: "<<signal<<" "<<background<<endl;

    //Double_t backgroundWeight = 1.0;
    //Double_t signalWeight     = 1.0;
    Double_t signalWeight     = 0.30*20/185338;
    Double_t backgroundWeight = 222.*0.1*20/9982625;
    // ====== register trees ====================================================
    //
    // the following method is the prefered one:
    // you can add an arbitrary number of signal or background trees

    factory->AddSignalTree    ( signal,     signalWeight     );
    factory->AddBackgroundTree( background, backgroundWeight );

     //   factory->AddSignalTree    ( signal );
     //factory->AddBackgroundTree( background );


      // To give different trees for training and testing, do as follows:
      //    factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
      //    factory->AddSignalTree( signalTestTree,     signalTestWeight,  "Test" );

      // Use the following code instead of the above two or four lines to add signal and background 
      // training and test events "by hand"
      // NOTE that in this case one should not give expressions (such as "var1+var2") in the input 
      //      variable definition, but simply compute the expression before adding the event
      // 
      //    // --- begin ----------------------------------------------------------
      //    std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
      //    Float_t  treevars[4];
      //    for (Int_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
      //    for (Int_t i=0; i<signal->GetEntries(); i++) {
      //       signal->GetEntry(i);
      //       for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
      //       // add training and test events; here: first half is training, second is testing
      //       // note that the weight can also be event-wise	
      //       if (i < signal->GetEntries()/2) factory->AddSignalTrainingEvent( vars, signalWeight ); 
      //       else                            factory->AddSignalTestEvent    ( vars, signalWeight ); 
      //    }
      //
      //    for (Int_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
      //    for (Int_t i=0; i<background->GetEntries(); i++) {
      //       background->GetEntry(i); 
      //       for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
      //       // add training and test events; here: first half is training, second is testing

//.........这里部分代码省略.........
    // factory->AddVariable("normr","normr","", 'F');    
    //    factory->AddVariable("normq","normq","", 'F'); 
    //factory->AddVariable("normminm","normminm","", 'F'); 
    factory->AddVariable("logipmax","logipmax","", 'F'); 
    factory->AddVariable("logipmin","logipmin","", 'F'); 
    factory->AddVariable("logfd","logfd",'F');
    factory->AddVariable("logvd","logvd",'F');
    //factory->AddVariable("pointAngle","pointingAngle",'F');
    factory->AddVariable("logvpi","",'F');
    //factory->AddVariable("logmaxprob","",'F');
    //factory->AddVariable("logminprob","",'F');
 
    factory->AddSpectator( "mReFit", "mReFit", "", 'D' );
    //    factory->AddSpectator( "Qdecay", "Qdecay", "",'F' );
    //  factory->AddSpectator( "m23", "m23", "",'F' );
    
    //   TFile * input_Background = new TFile("../back.root");
   TFile * input_Signal = new TFile("../cmx12.root");
   TFile * input_Background = new TFile("../background12.root");
   std::cout << "--- TMVAClassification       : Using input file for signal    : " << input_Signal->GetName() << std::endl;
   std::cout << "--- TMVAClassification       : Using input file for backgound : " << input_Background->GetName() << std::endl;
   
   // --- Register the training and test trees

   TTree *signal     = (TTree*)input_Signal->Get("psiCand");
   TTree *background = (TTree*)input_Background->Get("psiCand");
   
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "QDecay < 300&&fdchi2 > 300"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = "QDecay < 300&&fdchi2> 300"; // for example: TCut mycutb = "abs(var1)<0.5";

   
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // ---- Book MVA methods
   //
   // Please lookup the various method configuration options in the corresponding cxx files, eg:
   // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
   // it is possible to preset ranges in the option string in which the cut optimisation should be done:
   // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable

   // Cut optimisation
   if (Use["Cuts"])
      factory->BookMethod( TMVA::Types::kCuts, "Cuts",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );

   if (Use["CutsD"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsD",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );

   if (Use["CutsPCA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );

   if (Use["CutsGA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsGA",

//.........这里部分代码省略.........
    TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot.root";
    /*TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pm_merged_ccqe_tot.root";
    TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pmbar_merged_ccqe.root";
    TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/wall_merged_ccqe_tot.root";
    TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/ingrid_merged_nd3_ccqe_tot.root";*/

    
    TFile *pfileSignal = new TFile(fsignalStr);
    TFile *pfileBar = new TFile(fbarStr);
    TFile *pfileBkg = new TFile(fbkgStr);
    TFile *pfileBkg2 = new TFile(fbkg2Str);
    TFile *pfileRatio = new TFile(fratioStr);
    
    TTree *ptree_sig  = (TTree*)pfileSignal->Get("tree");
    TTree *ptree_bar  = (TTree*)pfileBar->Get("tree");
    TTree *ptree_bkg   = (TTree*)pfileBkg->Get("tree");
    TTree *ptree_bkg2  = (TTree*)pfileBkg2->Get("tree");
    
    // POT normalization
    const int   nmcFile  = 3950;
    const int   nbarFile  = 986;
    const int   nbkgFile  = 55546;//(31085+24461);
    const int   nbkg2File  = 7882;//(3941+3941);
    

   
    // global event weights per tree (see below for setting event-wise weights)
    // adding for signal sample
    // using this as standard and add other later
    Double_t signalWeight_sig     = 1.0;
    Double_t backgroundWeight_sig = 1.0;
   
    factory->AddSignalTree    ( ptree_sig,     signalWeight_sig );
    factory->AddBackgroundTree( ptree_sig, backgroundWeight_sig );
    
    // Add Numubar sample
    //Double_t signalWeight_bar     = nmcFile/float(nbarFile);
    Double_t backgroundWeight_bar = nmcFile/float(nbarFile);
    
    //factory->AddSignalTree    ( ptree_bar,     signalWeight_bar );
    factory->AddBackgroundTree( ptree_bar, backgroundWeight_bar );
    
    // Add wall background
    //Double_t signalWeight_bkg     = nmcFile/float(nbkgFile);
    Double_t backgroundWeight_bkg = nmcFile/float(nbkgFile);
    
    //factory->AddSignalTree    ( ptree_bkg,     signalWeight_bkg );
    factory->AddBackgroundTree( ptree_bkg, backgroundWeight_bkg );
    
    // Add INGRID background
    //Double_t signalWeight_bkg2     = nmcFile/float(nbkg2File);
    Double_t backgroundWeight_bkg2 = nmcFile/float(nbkg2File);
    
    //factory->AddSignalTree    ( ptree_bkg2,     signalWeight_bkg2 );
    factory->AddBackgroundTree( ptree_bkg2, backgroundWeight_bkg2 );
    
   
   
    //factory->SetSignalWeightExpression    ("norm*totcrsne*2.8647e-13");
    //factory->SetBackgroundWeightExpression( "norm*totcrsne*2.8647e-13" );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "Ntrack==2 && abs(inttype)==16 && fileIndex==1 && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = "Ntrack==2 && (abs(inttype)!=16 || fileIndex>1) && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // for example: TCut mycutb = "abs(var1)<0.5";

   // Tell the factory how to use the training and testing events

int main(){
  TMVA::Tools::Instance();
  std::cout<<"Hello world"<<std::endl;

  TFile* OutputFile = TFile::Open("Outputfile.root","RECREATE");

  TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", OutputFile,
					      "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

  std::vector<VMVariable*> Variables;
  MVariable* Var3= new MVariable("var3",F,none);
  MVariable* Var4 = new MVariable("var4",F,none);
  Variables.push_back(Var3);
  Variables.push_back(Var4);
  MVariable* Var1 = new MVariable("var1",F,none);
  MVariable* Var2 = new MVariable("var2",F,none);

  MultiVariable* MyVar1 = new MultiVariable("Var1+Var2",sum);
  MyVar1->AddVariable(Var1);
  MyVar1->AddVariable(Var2);
  Variables.push_back(MyVar1);

  MultiVariable* MyVar2 = new MultiVariable("Minus",subtract);
  MyVar2->AddVariable(Var1);
  MyVar2->AddVariable(Var2);
  Variables.push_back(MyVar2);
  std::string InputName= "./tmva_class_exampleD.root";
  
  TFile *input = TFile::Open("./tmva_class_exampleD.root" );
  
  TTree *signal = (TTree*)input->Get("TreeS");
  TTree *background=(TTree*)input->Get("TreeB");

  Double_t signalWeight     = 1.0;
  Double_t backgroundWeight = 1.0;

  factory->AddSignalTree    ( signal,     signalWeight     );
  factory->AddBackgroundTree( background, backgroundWeight );

  for(auto v:Variables){
    factory->AddVariable(v->GetFactoryName(),v->GetType());
  }
  
  factory->SetBackgroundWeightExpression( "weight" );
  
  TCut mycuts = "";
  TCut mycutb = "";
  
  factory->PrepareTrainingAndTestTree( mycuts, mycutb,
				       "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
  
  std::vector<MClassifier*> Classifiers;
  
  Classifiers.push_back(new MClassifier(TMVA::Types::kBDT, "BDT",
					"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20"));
  
  for(auto C:Classifiers){
    if(!(C->AddMethodToFactory(factory))){
      std::cout<<"Booking classifier failed"<<std::endl;
      return 1;
    }
  }

  factory->TrainAllMethods();
  
  factory->TestAllMethods();
  
  factory->EvaluateAllMethods();
  
  OutputFile->Close();
  
  delete factory;
  
  TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    
  
  for(auto v: Variables){
    reader->AddVariable(v->GetFactoryName(),v->GetReaderAddress());
  }
  
  for(auto C:Classifiers){
    if(!(C->AddMethodToReader(reader,"./weights/","TMVAClassification"))){
      std::cout<<"Failed adding classifer to reader"<<std::endl;
      return 1;
    }
  }

  TFile* Input =  TFile::Open("./tmva_class_exampleD.root");
  TTree* TreeToEvaluate= (TTree*)Input->Get("TreeS");
  
  TFile* AppliedFile =  new TFile("AppliedFile.root","RECREATE");
  TTree* AppliedTree=TreeToEvaluate->CloneTree(0);
  
  for(auto C:Classifiers){
    if(!(C->MakeBranch(AppliedTree)))return 1;
  }
  
  for(auto Var:Variables){
    if(!(Var->SetBA(TreeToEvaluate))){
      std::cout<<"Problem Setting Branch addresses"<<std::endl;
      return 1;
//.........这里部分代码省略.........

//.........这里部分代码省略.........
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)

   if (gSystem->AccessPathName( fnameTrainS )) {  // file does not exist in local directory
     std::cout << "Did not access " << fnameTrainS << " exiting." << std::endl;
     std::exit(4);
   }

     //gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
   
   TFile *inputTrainS = TFile::Open( fnameTrainS );
   TFile *inputTrainB = TFile::Open( fnameTrainB );
   TFile *inputTestS  = TFile::Open( fnameTestS  );
   TFile *inputTestB  = TFile::Open( fnameTestB  );
   // --- Register the training and test trees
   TTree *signalTrainTree     = (TTree*)inputTrainS->Get("probe_tree");
   TTree *backgroundTrainTree = (TTree*)inputTrainB->Get("probe_tree");
   TTree *signalTestTree     = (TTree*)inputTestS->Get("probe_tree");
   TTree *backgroundTestTree = (TTree*)inputTestB->Get("probe_tree");
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalTrainWeight     = 1.0;
   Double_t backgroundTrainWeight = 1.0;
   Double_t signalTestWeight     = 1.0;
   Double_t backgroundTestWeight = 1.0;
   // Decide if using the split and mixing or the full trees
   if( fnameTrainS == fnameTestS ) {
     if( fnameTrainB != fnameTestB ) {
       std::cout << "This macro cannot handle cases where the same signal sample is used for training and testing, but different background samples are used.";
       exit(1);
     }
     std::cout << "--- TMVAClassification       : Using input file: " << inputTrainS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTrainB->GetName() << std::endl;
     // You can add an arbitrary number of signal or background trees
     factory->AddSignalTree    ( signalTrainTree,     signalTrainWeight     );
     factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight );
   }
   else {
     if( fnameTrainB == fnameTestB ) {
       std::cout << "This macro cannot handle cases where the same background sample is used for training and testing, but different signal samples are used.";
       exit(1);
     }
     std::cout << "--- TMVAClassification       : Using input file: " << inputTrainS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTrainB->GetName() << " for training and" << std::endl;
     std::cout << "--- input file: " << inputTestS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTestB->GetName() << " for testing." << std::endl;
     // To give different trees for training and testing, do as follows:
     factory->AddSignalTree( signalTrainTree,     signalTrainWeight, "Training" );
     factory->AddSignalTree( signalTestTree,      signalTestWeight,  "Test" );
     factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight, "Training" );
     factory->AddBackgroundTree( backgroundTestTree,  backgroundTestWeight,  "Test" );
   }

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "";
   TCut mycutb = "";

   // Tell the factory how to use the training and testing events
   //
   // If no numbers of events are given, half of the events in the tree are used 
   // for training, and the other half for testing:
   //    factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
   // To also specify the number of testing events, use:
   //    factory->PrepareTrainingAndTestTree( mycut,
   //                                         "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );

//.........这里部分代码省略.........
   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   //
	TString fname_signal ="/afs/cern.ch/work/n/nchernya/Hbb/qgl_mva/qgl_tmva_tree_VBFHToBB_M-125_13TeV_powheg_v14"+type[set_type]+"_2jets.root";
	TString fname_bg ="/afs/cern.ch/work/n/nchernya/Hbb/qgl_mva/qgl_tmva_tree_BTagCSV_v14"+type[set_type]+"_2jets.root";

   if (gSystem->AccessPathName( fname_signal )) { // file does not exist in local directory
		cout<<"input file "<< fname_signal<<" doesn't exist!"<<endl;
		//break;
	}
   if (gSystem->AccessPathName( fname_bg)) { // file does not exist in local directory
		cout<<"input file "<< fname_bg<<" doesn't exist!"<<endl;
		//break;
	}
    //  gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
   
   TFile *input_signal = TFile::Open( fname_signal );
   TFile *input_bg = TFile::Open( fname_bg );
   
   std::cout << "--- TMVAClassification       : Using input signal file: " << input_signal->GetName() << std::endl;
   std::cout << "--- TMVAClassification       : Using input bg file: " << input_bg->GetName() << std::endl;
   
   // --- Register the training and test trees

   TTree *signal     = (TTree*)input_signal->Get("QGL_1");
   TTree *bg = (TTree*)input_bg->Get("QGL_1");
   
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t bgWeight = 1.0;
   
   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight  );
   factory->AddBackgroundTree( bg, bgWeight );

   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // Tell the factory how to use the training and testing events
   //
   // If no numbers of events are given, half of the events in the tree are used 
   // for training, and the other half for testing:
   //    factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
   // To also specify the number of testing events, use:
   //    factory->PrepareTrainingAndTestTree( mycut,
   //                                         "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // ---- Book MVA methods
   //
   // Please lookup the various method configuration options in the corresponding cxx files, eg:
   // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
   // it is possible to preset ranges in the option string in which the cut optimisation should be done:
   // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   if (Use["MLPBNN"])
      factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators


   if (Use["BDTD"]) // Decorrelation + Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTD",

//.........这里部分代码省略.........
   factory->AddVariable("TagVarCSV_vertexMass","TagVarCSV_vertexMass","units",'F');
   factory->AddVariable("TagVarCSV_vertexNTracks","TagVarCSV_vertexNTracks","units",'F');
   factory->AddVariable("TagVarCSV_vertexEnergyRatio","TagVarCSV_vertexEnergyRatio","units",'F');
   factory->AddVariable("TagVarCSV_vertexJetDeltaR","TagVarCSV_vertexJetDeltaR","units",'F');
   factory->AddVariable("TagVarCSV_flightDistance2dSig","TagVarCSV_flightDistance2dSig","units",'F');
   //factory->AddVariable("TagVarCSV_flightDistance3dSig","TagVarCSV_flightDistance3dSig","units",'F');
   
   // You can add so-called "Spectator variables", which are not used in the MVA training,
   // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
   // input variables, the response values of all trained MVAs, and the spectator variables
   factory->AddSpectator("Jet_pt","Jet_pt","units",'F');
   factory->AddSpectator("Jet_eta","Jet_eta","units",'F');
   factory->AddSpectator("Jet_phi","Jet_phi","units",'F');
   factory->AddSpectator("Jet_mass","Jet_mass","units",'F');
   factory->AddSpectator("Jet_massGroomed","Jet_massGroomed","units",'F');
   factory->AddSpectator("Jet_flavour","Jet_flavour","units",'F');
   factory->AddSpectator("Jet_nbHadrons","Jet_nbHadrons","units",'F');
   factory->AddSpectator("Jet_JP","Jet_JP","units",'F');
   factory->AddSpectator("Jet_JBP","Jet_JBP","units",'F');
   factory->AddSpectator("Jet_CSV","Jet_CSV","units",'F');
   factory->AddSpectator("Jet_CSVIVF","Jet_CSVIVF","units",'F');
   factory->AddSpectator("Jet_tau1","Jet_tau1","units",'F');
   factory->AddSpectator("Jet_tau2","Jet_tau2","units",'F');

   factory->AddSpectator("SubJet1_CSVIVF","SubJet1_CSVIVF","units",'F');
   factory->AddSpectator("SubJet2_CSVIVF","SubJet2_CSVIVF","units",'F');

   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   TString fnameSig = "RadionToHH_4b_M-800_TuneZ2star_8TeV-Madgraph_pythia6_JetTaggingVariables_training.root";
   TString fnameBkg = "QCD_Pt-300to470_TuneZ2star_8TeV_pythia6_JetTaggingVariables_training.root";
   TFile *inputSig = TFile::Open( fnameSig );
   TFile *inputBkg = TFile::Open( fnameBkg );
   
   std::cout << "--- TMVAClassification       : Using input files: " << inputSig->GetName() << std::endl
                                                                     << inputBkg->GetName() << std::endl;
   
   // --- Register the training and test trees
   TTree *sigTree = (TTree*)inputSig->Get("tagVars/ttree");
   TTree *bkgTree = (TTree*)inputBkg->Get("tagVars/ttree");
   
   // // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // factory->SetInputTrees( tree,signalCut,backgroundCut );
   factory->AddSignalTree    ( sigTree, signalWeight     );
   factory->AddBackgroundTree( bkgTree, backgroundWeight );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut signalCut = "Jet_massGroomed>80 && Jet_massGroomed<150";
   TCut backgroundCut = "abs(Jet_flavour)==5 && Jet_nbHadrons>1 && Jet_massGroomed>80 && Jet_massGroomed<150";

   // Tell the factory how to use the training and testing events
   factory->PrepareTrainingAndTestTree( signalCut, backgroundCut,
                                        "nTrain_Signal=22000:nTest_Signal=20000:nTrain_Background=22000:nTest_Background=2730:SplitMode=Random:!V" );

   // Gradient Boost
   factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D3_fat_BBvsGSP",
                          "!H:!V:NTrees=1000:MaxDepth=3:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );

   //factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D5_fat_BBvsGSP",
   //                       "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20" );

//    // Adaptive Boost
//    factory->BookMethod( TMVA::Types::kBDT, "BDT",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );
//    // Bagging
//    factory->BookMethod( TMVA::Types::kBDT, "BDTB",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
//    // Decorrelation + Adaptive Boost
//    factory->BookMethod( TMVA::Types::kBDT, "BDTD",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

   // ---- 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();

   // --------------------------------------------------------------

   // 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 );


}