C++ Factory::BookMethod方法代码示例

//.........这里部分代码省略.........
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

   // Define the input variables that shall be used for the MVA training
   // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
   // [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
   
   // factory->AddVariable("jet1_pt","jet1_pt","", 'D');	
   // factory->AddVariable("jet2_pt","jet2_pt","", 'D');
   factory->AddVariable("dijet_M","dijet_M","", 'D');
   factory->AddVariable("dijet_deta","dijet_deta","", 'D');
   factory->AddVariable("metnomu_significance","metnomu_significance","",'D');
   factory->AddVariable("alljetsmetnomu_mindphi","alljetsmetnomu_mindphi","",'D');

   //   factory->AddSpectator( "nPhot_presel", "nPhot_presel", "", 'F' );

   std::vector<std::string> backgrounds;

   backgrounds.push_back("WJetsToLNu_HT-100to200_Tune4C_13TeV-madgraph-tauol");
   backgrounds.push_back("WJetsToLNu_HT-200to400_Tune4C_13TeV-madgraph-tauola");
   backgrounds.push_back("WJetsToLNu_HT-400to600_Tune4C_13TeV-madgraph-tauola");
   backgrounds.push_back("WJetsToLNu_HT-600toInf_Tune4C_13TeV-madgraph-tauola");

   backgrounds.push_back("ZJetsToNuNu_HT-100to200_Tune4C_13TeV-madgraph-tauola");
   backgrounds.push_back("ZJetsToNuNu_HT-200to400_Tune4C_13TeV-madgraph-tauola");
   backgrounds.push_back("ZJetsToNuNu_HT-400to600_Tune4C_13TeV-madgraph-tauola");
   backgrounds.push_back("ZJetsToNuNu_HT-600toInf_Tune4C_13TeV-madgraph-tauola");

   std::vector<std::string> signals;
   signals.push_back("VBF_HToInv_M-125_13TeV_powheg-pythia6");
   double lumiData = 10000;//in pb-1

   for (int i=0; i<signals.size(); i++){
     float weight = getNormalisationFactor(lumiData,signals[i]);

     TFile* f=TFile::Open(Form("%s/%s.root",inPath.c_str(),signals[i].c_str()));
     TTree* sig=(TTree*) f->Get("lightTree/LightTree");
     if (!sig)
       {
	 std::cout << "====> ERROR: Sig tree " << signals[i] << " cannot be found" << std::endl;
	 continue;
       }
	 
     factory->AddSignalTree    ( sig, weight);
   } 

   for (int i=0; i<backgrounds.size(); i++){
     float weight = getNormalisationFactor(lumiData,backgrounds[i]);

     TFile* f=TFile::Open(Form("%s/%s.root",inPath.c_str(),backgrounds[i].c_str()));
     TTree* bkg=(TTree*) f->Get("lightTree/LightTree");
     if (!bkg)
       {
	 std::cout << "====> ERROR: Bkg tree " << backgrounds[i] << " cannot be found" << std::endl;
	 continue;
       }
	 
     factory->AddBackgroundTree    ( bkg, weight);
   } 


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

   //Preselection to get rid of QCD  
   mycuts = "passtrigger==1 && nvetomuons==0 && nvetoelectrons==0 && metnomuons>140 && abs(jet1_eta)<4.7 && abs(jet2_eta)<4.7 && dijet_M>700 && jet1_eta*jet2_eta<0 && metnomu_significance>4 && alljetsmetnomu_mindphi>2 && jet1_pt>50 && jet2_pt>40"; 
   mycutb = "passtrigger==1 && nvetomuons==0 && nvetoelectrons==0 && metnomuons>140 && abs(jet1_eta)<4.7 && abs(jet2_eta)<4.7 && dijet_M>700 && jet1_eta*jet2_eta<0 && metnomu_significance>4 && alljetsmetnomu_mindphi>2 && jet1_pt>50 && jet2_pt>40";
 
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // ---- Book MVA methods
   // factory->BookMethod( TMVA::Types::kCuts, "Cuts",
   // 			//			"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp[0]=FSmart:VarProp[1]=FSmart:VarProp[2]=FSmart:VarProp[3]=FSmart:VarProp[4]=FSmart:VarProp[5]=FSmart" );
    factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
    			"H:!V:FitMethod=GA:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95:VarProp[0]=FSmart:VarProp[1]=FSmart:VarProp[2]=FSmart:VarProp[3]=FSmart" );
   // factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
   // 			"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
    factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=1000:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning");

   // ---- 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 );
}

void Boost(){
   TString outfileName = "boost.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" );
   factory->AddVariable( "var0", 'F' );
   factory->AddVariable( "var1", 'F' );
   TFile *input(0);
   TString fname = "./data.root";
   if (!gSystem->AccessPathName( fname )) {
      // first we try to find tmva_example.root in the local directory
      std::cout << "--- BOOST       : Accessing " << fname << std::endl;
      input = TFile::Open( fname );
   }
   else {
      gROOT->LoadMacro( "./createData.C");
      create_circ(20000);
      cout << " created data.root with data and circle arranged in half circles"<<endl;
      input = TFile::Open( fname );
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << std::endl;
      exit(1);
   }
   TTree *signal     = (TTree*)input->Get("TreeS");
   TTree *background = (TTree*)input->Get("TreeB");
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   gROOT->cd( outfileName+TString(":/") );
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   factory->PrepareTrainingAndTestTree( "", "",
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   TString fisher="H:!V";
   factory->BookMethod( TMVA::Types::kFisher, "Fisher", fisher );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoost", fisher+":Boost_Num=100:Boost_Type=AdaBoost" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog2", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=2.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep2", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.2" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep3", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.5" );

  // 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 );
   
   
}

//.........这里部分代码省略.........
   
   TString outfileName( "bdtTMVA_FCNC_tZ.root" );
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

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


 
   // global event weights per tree (see below for setting event-wise weights)
   //Double_t signalWeight     = 0.003582;
   //Double_t backgroundWeight = 0.0269;
   
   Double_t signalWeight     = 1;
   Double_t backgroundWeight = 1;
   
   TFile *input_sig = TFile::Open( "proof.root" );
   TFile *input_wz = TFile::Open( "proof.root" );
   
   TTree *signal     = (TTree*)input_sig->Get("Ttree_FCNCkut");
   
   
   TTree *background_WZ = (TTree*)input_wz->Get("Ttree_WZ");
   /*TTree *background_ZZ = (TTree*)input_wz->Get("Ttree_ZZ");
   TTree *background_WW = (TTree*)input_wz->Get("Ttree_WW");
   
   TTree *background_TTbar  = (TTree*)input_wz->Get("Ttree_TTbar");
   TTree *background_Zjets  = (TTree*)input_wz->Get("Ttree_Zjets");
   TTree *background_Wjets  = (TTree*)input_wz->Get("Ttree_Wjets");
   TTree *background_TtW    = (TTree*)input_wz->Get("Ttree_TtW");
   TTree *background_TbartW = (TTree*)input_wz->Get("Ttree_TbartW");*/

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,            signalWeight     );
   factory->AddBackgroundTree( background_WZ,     backgroundWeight );
   /*factory->AddBackgroundTree( background_ZZ,     backgroundWeight );
   factory->AddBackgroundTree( background_WW,     backgroundWeight );
   factory->AddBackgroundTree( background_TTbar,  backgroundWeight );
   factory->AddBackgroundTree( background_Zjets,  backgroundWeight );
   factory->AddBackgroundTree( background_Wjets,  backgroundWeight );
   factory->AddBackgroundTree( background_TtW,    backgroundWeight );
   factory->AddBackgroundTree( background_TbartW, backgroundWeight );*/
   
   
   factory->AddVariable("tree_topMass",    'F');
   factory->AddVariable("tree_deltaPhilb", 'F');
   factory->AddVariable("tree_asym",       'F');
   factory->AddVariable("tree_Zpt",        'F');
   
   
   
   
   
   
   
   
   
   // to set weights. The variable must exist in the tree
   //    for signal    : factory->SetSignalWeightExpression    ("weight1*weight2");
   //    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
   
   
   // 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" );
   
   
   
   factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=100:nEventsMin=100:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );




   // 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 );


}

//.........这里部分代码省略.........
      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" );

  // ---- 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",
			 "H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );

  if (Use["CutsSA"])
    factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
			 "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );

  // Likelihood ("naive Bayes estimator")
  if (Use["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" );

  // Decorrelated likelihood
  if (Use["LikelihoodD"])
    factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
			 "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );

  // PCA-transformed likelihood
  if (Use["LikelihoodPCA"])
    factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
			 "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); 

void Reg(){
  
  TMVA::Tools::Instance();
  std::cout << "==> Start TMVARegression" << std::endl;
    
  ifstream myfile; 
  myfile.open("99per.txt");


  ostringstream xcS,xcH,xcP,xcC,xcN;  
  double xS,xH,xC,xN,xP;

  if(myfile.is_open()){
    while(!myfile.eof()){
      myfile>>xS>>xH>>xC>>xN>>xP;
    }
  }

  xcS<<xS;
  xcH<<xH;
  xcC<<xC;
  xcN<<xN;
  xcP<<xP;

  //Output file 
  TString outfileName( "Ex1out_FullW_def.root" );
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
  
  //Declaring the factory
  TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, 
					      "!V:!Silent:Color:DrawProgressBar" );
  //Declaring Input Varibles 
  factory->AddVariable( "Sieie",'F');
  factory->AddVariable( "ToE", 'F' );
  factory->AddVariable( "isoC",'F' );
  factory->AddVariable( "isoN",'F' );
  factory->AddVariable( "isoP",'F' );
  
  TString fname = "../../CutTMVATrees_Barrel.root";
  input = TFile::Open( fname );
  
  // --- Register the regression tree
  TTree *signal = (TTree*)input->Get("t_S");
  TTree *background = (TTree*)input->Get("t_B");
  
  //Just Some more settings
   Double_t signalWeight      = 1.0; 
   Double_t backgroundWeight  = 1.0; 

   // You can add an arbitrary number of regression trees
   factory->AddSignalTree( signal, signalWeight );
   factory->AddBackgroundTree( background , backgroundWeight );
 
   TCut mycuts ="";
   TCut mycutb ="";

   // factory->PrepareTrainingAndTestTree(mycuts,mycutb,"nTrain_Signal=9000:nTrain_Background=9000:nTest_Signal=10000:nTest_Background=10000");

   factory->SetBackgroundWeightExpression("weightPT*weightXS");
   factory->SetSignalWeightExpression("weightPT*weightXS");

   TString methodName = "Cuts_FullsampleW_def";
   TString methodOptions ="!H:!V:FitMethod=GA:EffMethod=EffSEl"; 
   methodOptions +=":VarProp[0]=FMin:VarProp[1]=FMin:VarProp[2]=FMin:VarProp[3]=FMin:VarProp[4]=FMin";
  
   methodOptions +=":CutRangeMax[0]="+xcS.str(); 
   methodOptions +=":CutRangeMax[1]="+xcH.str();
   methodOptions +=":CutRangeMax[2]="+xcC.str();
   methodOptions +=":CutRangeMax[3]="+xcN.str();
   methodOptions +=":CutRangeMax[4]="+xcP.str();

   //************
   factory->BookMethod(TMVA::Types::kCuts,methodName,methodOptions);
   factory->TrainAllMethods();
   factory->TestAllMethods();
   factory->EvaluateAllMethods();    
   
   // --------------------------------------------------------------
   // Save the output
   outputFile->Close();

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

}

int TestBDT_forreal_test(TString sig) {

   // This loads the library
   TMVA::Tools::Instance();
   // to get access to the GUI and all tmva macros
   //TString thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName());//was not commented, but does not work anymore
   //gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath());//was not commented, but cannot not work anymore
   //gROOT->ProcessLine(".L TMVAGui.C");
   TString outfileName( "rootfiles/TMVA/resultTMVA_"+sig+"VsTTbar2l.root" );
   TString weightname( "weightsTMVA_"+sig+"VsTTbar2l" );
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
   TMVA::Factory *factory = new TMVA::Factory( weightname, outputFile,"!V:!Silent:Color:DrawProgressBar");

  vector<TString> histonames; histonames.clear();
  map<string, float> value;
  
  //histonames.push_back("MT2W");             
  //histonames.push_back("MT2_lb_b");         
  //histonames.push_back("MT2_lb_bqq");       
  //histonames.push_back("MT2_lb_b_mass");   
  //histonames.push_back("MT2_lb_bqq_mass"); 
  histonames.push_back("Mlb_lead_bdiscr");              
  //histonames.push_back("Mjjj");              
  //histonames.push_back("topness");          
  //histonames.push_back("topnessMod");          
  histonames.push_back("pfmet");              
  //histonames.push_back("ak4_HT");               
  //histonames.push_back("MET_over_sqrtHT");    
  //histonames.push_back("ak4_htratiom");          
  histonames.push_back("dR_lep_leadb");        
  //histonames.push_back("hadronic_top_chi2");             
  //histonames.push_back("ngoodbtags");           
  histonames.push_back("ngoodjets");            
  //histonames.push_back("mindphi_met_j1_j2");        
  //histonames.push_back("lep1_pt");
  histonames.push_back("ak4pfjets_leadMEDbjet_p4_Pt"); 

  for(unsigned int b = 0; b<histonames.size(); ++b){
    factory->AddVariable(histonames[b], 'F' );
  }

  TString signame = "/nfs-7/userdata/stopRun2/testMVA/"+sig+".root";
  TString bkgname1 = "/nfs-7/userdata/stopRun2/testMVA/TTJets_DiLept_madgraph_25ns_1.root";
  TString bkgname2 = "/nfs-7/userdata/stopRun2/testMVA/TTJets_DiLept_madgraph_25ns_2.root";
  /*
  TString signame = "/hadoop/cms/store/user/haweber/forBDT/"+sig+".root";
  TString bkgname1 = "/hadoop/cms/store/user/haweber/forBDT/TTJets_DiLept_madgraph_25ns_1.root";
  TString bkgname2 = "/hadoop/cms/store/user/haweber/forBDT/TTJets_DiLept_madgraph_25ns_2.root";
  */
  cout << "signame " << signame << endl;
  TFile *inputSig = TFile::Open( signame );
  TFile *inputBkg1 = TFile::Open( bkgname1 );
  TFile *inputBkg2 = TFile::Open( bkgname2 );
  TTree *signal     = (TTree*)inputSig->Get("t");
  TTree *background1 = (TTree*)inputBkg1->Get("t");
  TTree *background2 = (TTree*)inputBkg2->Get("t");

   // 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( background1, backgroundWeight );
   factory->AddBackgroundTree( background2, backgroundWeight );

   //factory->SetBackgroundWeightExpression( "weight" );
   // Apply additional cuts on the signal and background samples (can be different)
   //TCut mycuts = "MT2W>200&&mindphi_met_j1_j2>0.8"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   //TCut mycutb = "MT2W>200&&mindphi_met_j1_j2>0.8"; // for example: TCut mycutb = "abs(var1)<0.5";
   //if(sig.Contains("T2tt_425_325")||sig.Contains("T2tt_500_325")){ mycuts = "mindphi_met_j1_j2>0.8"; mycutb = "mindphi_met_j1_j2>0.8"; }
   TCut mycuts = "";
   TCut mycutb = "";
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
   factory->BookMethod( 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" );
   // Train MVAs using the set of training events
   cout << "Train methods" << endl;
   factory->TrainAllMethods();

   // ---- Evaluate all MVAs using the set of test events
   cout << "Test methods" << endl;
   factory->TestAllMethods();

   // ----- Evaluate and compare performance of all configured MVAs
   cout << "Evaluate methods" << endl;
   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
//.........这里部分代码省略.........

//.........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 );


}

void TMVATraining_ch4( )
{

  TFile* outputFile = TFile::Open( "TMVA_ch4.root", "RECREATE" );
  TMVA::Factory *factory = new TMVA::Factory( "MVAnalysis", outputFile,"!V");
  TFile *signal = TFile::Open("../production/BGx0/Prod2_iptubeK0/B0_etapr-eta-3pi2pi_KS-pi+pi-_output_signal_iptubeK0.root");
  TFile *background = TFile::Open("../production/BGx0/Prod2_iptubeK0/B0_etapr-eta-3pi2pi_KS-pi+pi-_output_ccbar_iptubeK0.root");
  factory->AddSignalTree ( (TTree*)signal->Get("B0"), 1.0 );
  factory->AddBackgroundTree ( (TTree*)background->Get("B0"), 1.0 );
  sigCut = TCut("B0__isContinuumEvent==0");
  bgCut = TCut("B0__isContinuumEvent==1");

  factory->AddVariable("B0_ThrustB",'F');
  factory->AddVariable("B0_ThrustO",'F');
  factory->AddVariable("B0_CosTBTO",'F');
  factory->AddVariable("B0_CosTBz",'F');
  factory->AddVariable("B0_R2",'F');
  factory->AddVariable("B0_cc1",'F');
  factory->AddVariable("B0_cc2",'F');
  factory->AddVariable("B0_cc3",'F');
  factory->AddVariable("B0_cc4",'F');
  factory->AddVariable("B0_cc5",'F');
  factory->AddVariable("B0_cc6",'F');
  factory->AddVariable("B0_cc7",'F');
  factory->AddVariable("B0_cc8",'F');
  factory->AddVariable("B0_cc9",'F');
  factory->AddVariable("B0_mm2",'F');
  factory->AddVariable("B0_et",'F');
  factory->AddVariable("B0_hso00",'F');
  // factory->AddVariable("B0_hso01",'F');
  factory->AddVariable("B0_hso02",'F');
  //factory->AddVariable("B0_hso03",'F');
  factory->AddVariable("B0_hso04",'F');
  factory->AddVariable("B0_hso10",'F');
  factory->AddVariable("B0_hso12",'F');
  factory->AddVariable("B0_hso14",'F');
  factory->AddVariable("B0_hso20",'F');
  factory->AddVariable("B0_hso22",'F');
  factory->AddVariable("B0_hso24",'F');
  factory->AddVariable("B0_hoo0",'F');
  factory->AddVariable("B0_hoo1",'F');
  factory->AddVariable("B0_hoo2",'F');
  factory->AddVariable("B0_hoo3",'F');
  factory->AddVariable("B0_hoo4",'F');

  factory->PrepareTrainingAndTestTree(sigCut, bgCut, "!V:nTrain_Signal=10000:nTest_Signal=10000:nTrain_Background=10000:nTest_Background=10000:SplitMode=Random:NormMode=NumEvents" );

  //factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", "H:V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA");
  //factory->BookMethod( TMVA::Types::kMLP, "MLP", "!V:NCycles=200:HiddenLayers=N+1,N:TestRate=5" );
  factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" );
  factory->BookMethod( 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" );
  factory->BookMethod( TMVA::Types::kSVM, "SVM", "!H:!V:Gamma=0.25:Tol=0.001:VarTransform=Norm" );

  //factory->BookMethod( TMVA::Types::kBDT, "FastBDT", "!H:!V:CreateMVAPdfs:NbinsMVAPdf=40:NTrees=100:Shrinkage=0.10"); //:RandRatio=0.5:NCutLevel=8:NTreeLayers=3");

  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods();
  outputFile->Close();
  delete factory;

  // Launch the GUI for the root macros
  if (!gROOT->IsBatch()) TMVA::TMVAGui( "TMVA_ch4.root" );
}

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
  
  TChain signalTree ("sample") ;
  signalTree.Add ("data/sig_0.root") ;
  std::cout << "READ " << signalTree.GetEntries () << " signal events\n" ;
  TMVAtest->AddSignalTree (&signalTree, 1) ;  

  //PG get the bkg and deliver it to the TMVA factory
  
  TChain bkgTree ("sample") ;
  bkgTree.Add ("data/bkg_0.root") ;
  std::cout << "READ " << bkgTree.GetEntries () << " bkg events\n" ;
  TMVAtest->AddBackgroundTree (&bkgTree, 1) ;  

  //PG get the training and test samples and deliver them to the TMVA factory

  TChain signalTrainTree ("sample") ;
  signalTrainTree.Add ("data/sig_1.root") ;
  std::cout << "READ " << signalTrainTree.GetEntries () << " signal train events\n" ;
  
  TChain bkgTrainTree ("sample") ;
  bkgTrainTree.Add ("data/bkg_1.root") ;
  std::cout << "READ " << bkgTrainTree.GetEntries () << " bkg train events\n" ;
  
  TMVAtest->SetInputTrees (signalTrainTree.GetTree (), bkgTrainTree.GetTree (), 1., 1.) ;  

  //PG variables to be used for the selection
  //PG must be defined in the TTrees
  
  TMVAtest->AddVariable ("vars.x", 'F') ;
  TMVAtest->AddVariable ("vars.y" , 'F') ;

  int signalNumTrain = signalTrainTree.GetEntries () * 4 / 5 ;
  int bkgNumTrain = bkgTrainTree.GetEntries () * 4 / 5 ;
  int signalNumTest = signalTrainTree.GetEntries () - signalNumTrain ;
  int bkgNumTest = bkgTrainTree.GetEntries () - bkgNumTrain ;
  char trainOptions[120] ;
  sprintf (trainOptions,"NSigTrain=%d:NBkgTrain=%d:NSigTest=%d:NBkgTest=%d",
           signalNumTrain, bkgNumTrain,
           signalNumTest, bkgNumTest) ;
  sprintf (trainOptions,"NSigTrain=%d:NBkgTrain=%d:NSigTest=%d:NBkgTest=%d",
           0,0,0,0) ;
  std::cout << "TRAINING CONFIGURATION : " << trainOptions << "\n" ;
  TMVAtest->PrepareTrainingAndTestTree ("",trainOptions) ;
  
  //PG prepare the classifier
  
  //PG cut-based, default params
  TMVAtest->BookMethod (TMVA::Types::kCuts, "Cuts") ;
  
  TMVAtest->TrainAllMethods () ;
  TMVAtest->TestAllMethods () ;
  TMVAtest->EvaluateAllMethods () ;
 
  delete TMVAtest ;
  delete outputfile ;
}

int main(int argc, char * argv[])
{
    //Processing input options
    int c;
    std::string outFname;
    outFname = std::string("QualityNaF.root");

    // Open  input files, get the trees
    TChain *mc = InputFileReader("FileListNtuples_ext.txt","parametri_geo");
    // Preparing options for the TMVA::Factory
    std::string options( 
        "!V:" 
        "!Silent:"
        "Color:"
        "DrawProgressBar:"
        "Transformations=I;D;P;G,D:"
        "AnalysisType=Classification"
    );

    //Creating the factory
    TFile *   ldFile = new TFile(outFname.c_str(),"RECREATE");
    TMVA::Factory * factory = new TMVA::Factory("QualityNaF", ldFile, options.c_str());

    //Preparing variables 
    //general
    /*factory->AddVariable("Chisquare", 'F');
    factory->AddVariable("Layernonusati", 'I');
    factory->AddVariable("NTofUsed", 'I');
    factory->AddVariable("diffR", 'F');
    factory->AddVariable("TOF_Up_Down", 'F');*/
    //Tof	
    //factory->AddVariable("TOFchisq_s", 'F');
    //factory->AddVariable("TOFchisq_t", 'F');

    //RICH	
    factory->AddVariable("Richtotused", 'F');	
    factory->AddVariable("RichPhEl", 'F');
    factory->AddVariable("RICHprob", 'F');
    factory->AddVariable("RICHcollovertotal");
    factory->AddVariable("RICHLipBetaConsistency");  
    factory->AddVariable("RICHTOFBetaConsistency");  
    factory->AddVariable("RICHChargeConsistency");
    
    factory->AddVariable("RICHPmts");
    factory->AddVariable("RICHgetExpected");		
    factory->AddVariable("tot_hyp_p_uncorr");
    factory->AddVariable("Bad_ClusteringRICH");
    factory->AddVariable("NSecondariesRICHrich");

    //factory->AddVariable("HitHValldir"); 
    //factory->AddVariable("HitHVallrefl");  	
    
    //factory->AddVariable("HVBranchCheck:= (HitHValldir - HitHVoutdir) - (HitHVallrefl - HitHVoutrefl)");    

    factory->AddVariable("HitHVoutdir"); 
    factory->AddVariable("HitHVoutrefl");

    //Spectator Variables
    factory->AddSpectator("R", 'F');
    factory->AddSpectator("BetaRICH_new", 'F');	

    //Preselection cuts
    std::string PreSelection    = "qL1>0&&(joinCutmask&187)==187&&qL1<1.75&&R>0";
    std::string ChargeCut 	= "qUtof>0.8&&qUtof<1.3&&qLtof>0.8&&qLtof<1.3";
    std::string VelocityCut 	= /*"Beta<0.8";*/"((joinCutmask>>11))==1024&&BetaRICH_new>0&&BetaRICH_new<0.975";
    std::string signalCut 	= /*"(R/Beta)*(1-Beta^2)^0.5>1.65&&GenMass>1&&GenMass<2";*/"(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5>0.5&&(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5<1.5";	
    std::string bkgndCut 	= /*"(R/Beta)*(1-Beta^2)^0.5>1.65&&GenMass>0&&GenMass<1";*/"(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5>3";		 

    factory->AddTree(mc,"Signal"    ,1,(PreSelection +"&&"+ ChargeCut + "&&" + VelocityCut + "&&"+ signalCut).c_str());
    factory->AddTree(mc,"Background",1,(PreSelection +"&&"+ ChargeCut + "&&" + VelocityCut + "&&"+ bkgndCut).c_str());

    // Preparing
    std::string preselection = "";
    std::string inputparams(
        "SplitMode=Random:"
        "NormMode=NumEvents:"
        "!V"
    );
    factory->PrepareTrainingAndTestTree(preselection.c_str(),inputparams.c_str());

    // Training
    std::string trainparams ="!H:!V:MaxDepth=3";
    factory->BookMethod(TMVA::Types::kBDT, "BDT", trainparams.c_str());

    trainparams ="!H:!V";
    factory->BookMethod(TMVA::Types::kLikelihood, "Likelihood", trainparams.c_str());

    trainparams ="!H:!V:VarTransform=Decorrelate";
    //factory->BookMethod(TMVA::Types::kLikelihood, "LikelihoodD", trainparams.c_str());

    trainparams ="!H:!V";
    //factory->BookMethod(TMVA::Types::kCuts, "Cuts", trainparams.c_str());



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

void TMVAClassificationCategory() 
{
   //---------------------------------------------------------------

   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. Later you can choose the methods
   // whose performance you'd like to investigate. The factory will
   // then run the performance analysis for you.
   //
   // The first argument is the base of the name of all the
   // weightfiles in the directory weight/ 
   //
   // The second argument is the output file for the training results
   // All TMVA output can be suppressed by removing the "!" (not) in 
   // front of the "Silent" argument in the option string
   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 );

   // If you wish to modify default settings 
   // (please check "src/Config.h" to see all available global options)
   //    (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
   //    (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";

   // Define the input variables that shall be used for the MVA training
   // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
   // [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
   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 = "../execs/data/toy_sigbkg_categ_offset.root";
   else                 fname = "../execs/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 to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // 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");

   // the Fisher 
   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");

//.........这里部分代码省略.........

//.........这里部分代码省略.........
	if(mH==450.) mass4bodycut = "(fit_mlvjj>332 && fit_mlvjj<518)"; // 3j450el
	if(mH==500.) mass4bodycut = "(fit_mlvjj>362 && fit_mlvjj<569)"; // 3j500el
	if(mH==550.) mass4bodycut = "(fit_mlvjj>398 && fit_mlvjj<616)"; // 3j550el
	if(mH==600.) mass4bodycut = "(fit_mlvjj>419 && fit_mlvjj<660)";  // 3j600el
      }
    }

    char mycutschar[1000];
    sprintf(mycutschar,"ggdevt == %i &&(Mass2j_PFCor>65 && Mass2j_PFCor<95) && %s", njets, mass4bodycut);
    TCut mycuts (mycutschar);
    


    // tell the factory to use all remaining events in the trees after training for testing:
    factory->PrepareTrainingAndTestTree( mycuts, mycuts,
                                        "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:
    //    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" );  
    
    // ---- 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",
                            "H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
    
    if (Use["CutsSA"])
        factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
                            "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
    
    // Likelihood
    if (Use["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" ); 
    
    // test the decorrelated likelihood
    if (Use["LikelihoodD"])
        factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD", 
                            "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" ); 
    
    if (Use["LikelihoodPCA"])
        factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA", 
                            "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); 
    

//.........这里部分代码省略.........
      factory->AddVariable(name, 'F');
   }

   // This would set individual event weights (the variables defined in the
   // expression need to exist in the original TTree)
   //    for signal    : factory->SetSignalWeightExpression("weight1*weight2");
   //    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
   // commented JB : 04/26 ??
   //factory->dSetBackgroundWeightExpression("weight");

   // 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( mycuts,mycutb,"NSigTrain=9000:NBkgTrain=50000:NSigTest=9000:NBkgTest=50000:SplitMode=Random:!V" );
   factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=4900:nTrain_Background=49000:nTest_Signal=4900:nTest_Background=49000:SplitMode=Random:!V"); // for KFVertex
   //   factory->PrepareTrainingAndTestTree( mycuts, mycutb,"nTrain_Signal=20000:nTrain_Background=40000:nTest_Signal=20000:nTest_Background=40000:SplitMode=Random:!V"); // for PPV

   // ---- 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",
                           "H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );

   if (Use["CutsSA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
                           "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );

   // Likelihood ("naive Bayes estimator")
   if (Use["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" );

   // Decorrelated likelihood
   if (Use["LikelihoodD"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
                           "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );

   // PCA-transformed likelihood
   if (Use["LikelihoodPCA"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
                           "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );

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.........

//.........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 = "./tmva_class_example.root";
   //TString fname = "/afs/cern.ch/work/s/salee/private/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root";
   //TString fname = "/terranova_0/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root";
   
   //if (gSystem->AccessPathName( fname ))  // file does not exist in local directory
    // exit(-1);
      //gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
   
   //TFile *input = TFile::Open( fname );
   //TFile *SB_OnPeak = TFile::Open("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root");
   //TTree *SB_OnPeak_Tree = (TTree*)SB_OnPeak->Get("latino");
   
   TChain *S_Chain = new TChain("latino");
   TChain *C_Chain = new TChain("latino");
   TChain *SCI_Chain = new TChain("latino");
   TChain *qqWW_Chain = new TChain("latino");

   S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigOnPeak_8TeV.root");
   S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigShoulder_8TeV.root");
   S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigTail_8TeV.root");
   SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root");
   SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntShoulder_8TeV.root");
   SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntTail_8TeV.root");
   C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotHead_8TeV.root");
   C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotTail_8TeV.root");

   qqWW_Chain->Add("/afs/cern.ch/user/m/maiko/work/public/Tree/tree_skim_wwmin/nominals/latino_000_WWJets2LMad.root");
   
   // --- Register the training and test trees

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( S_Chain  );
   factory->AddBackgroundTree( qqWW_Chain );
   factory->AddBackgroundTree( C_Chain );
   // Classification training and test data in ROOT tree format with signal and background events being located in the same tree
   //factory->SetInputTrees(SCI_Chain, GenOffCut, GenOnCut);
   
   // To give different trees for training and testing, do as follows:
   //    factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
   //    factory->AddSignalTree( signalTestTree,     signalTestWeight,  "Test" );
   
   factory->SetWeightExpression          ("2.1*puW*baseW*effW*triggW*19.468");
   //factory->SetSignalWeightExpression    ("2.1*puW*baseW*effW*triggW*19.468");
   //factory->SetBackgroundWeightExpression("puW*baseW*effW*triggW*19.468");

   //factory->PrepareTrainingAndTestTree( ChanCommOff,
   //                                     "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" );
                                        //"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V";
   factory->PrepareTrainingAndTestTree( ChanCommOff0J,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" );
   // ---- Book MVA methods
   //
   // Cut optimisation
   if (Use["Cuts"])
      factory->BookMethod( TMVA::Types::kCuts, "Cuts",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );

   if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT",
                           "!H:V:NTrees=850:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
                           //"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );

   // For an example of the category classifier usage, see: TMVAClassificationCategory

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

   // ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events

   // ---- STILL EXPERIMENTAL and only implemented for BDT's ! 
   // factory->OptimizeAllMethods("SigEffAt001","Scan");
   // factory->OptimizeAllMethods("ROCIntegral","FitGA");

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

   // ---- 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 );
}