C++ Factory::SetInputTrees方法代码示例

void trainBDT(void)
{
	// Open input file and get tree
	TFile *infile = new TFile("l3bdt.root");
	TTree *l3tree = (TTree*)infile->Get("l3tree");
	if(l3tree == NULL){
		cout << "Couldn't open \"l3bdt.root\"!" << endl;
		return;
	}

	// Open output root file (for TMVA)
	TFile *outfile = new TFile("l3BDT_out.root", "RECREATE");
	TMVA::Factory *fac = new TMVA::Factory("L3",outfile,"");

	// Specify input tree that contains both signal and background 
	TCut signalCut("is_good==1");
	TCut backgroundCut("is_good==0");
	fac->SetInputTrees(l3tree, signalCut, backgroundCut);

	// Add variables
	fac->AddVariable("Nstart_counter",      'I');
	fac->AddVariable("Ntof",                'I');
	fac->AddVariable("Nbcal_points",        'I');
	fac->AddVariable("Nbcal_clusters",      'I');
	fac->AddVariable("Ebcal_points",        'F');
	fac->AddVariable("Ebcal_clusters",      'F');
	fac->AddVariable("Nfcal_clusters",      'I');
	fac->AddVariable("Efcal_clusters",      'F');
	fac->AddVariable("Ntrack_candidates",   'I');
	fac->AddVariable("Ptot_candidates",     'F');

	TCut preSelectCut("");
	fac->PrepareTrainingAndTestTree(preSelectCut,"");
	fac->BookMethod(TMVA::Types::kBDT, "BDT", "");

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

	delete fac;

	outfile->Close();
	delete outfile;
}

void trainBJetIdMVA(TString SELECTION)
{
  // the training is done using a dedicated tree format
  TFile *src = TFile::Open("bjetId_"+SELECTION+".root");
  TTree *tr  = (TTree*)src->Get("jets"); 
  
  TFile *outf    = new TFile("bjetId_"+SELECTION+"_MVA.root","RECREATE");

  TCut signalCut       = "abs(partonId) == 5";
  TCut bkgCut          = "abs(partonId) != 5";
  TCut preselectionCut = "btagIdx<4 && etaIdx<4 && etaIdx>-1 && ptIdx<4";
  
  int N = 100000;
  cout<<"NUMBER OF TRAINING EVENTS = "<<N<<endl;
  
  TMVA::Factory* factory = new TMVA::Factory("factory_"+SELECTION+"_",outf,"!V:!Silent:Color:DrawProgressBar:Transformations=I;G:AnalysisType=Classification" );
  
  factory->SetInputTrees(tr,signalCut,bkgCut);
  
  factory->AddVariable("btagIdx",'I');
  factory->AddVariable("etaIdx" ,'I');
  factory->AddVariable("btag"   ,'F');
  factory->AddVariable("eta"    ,'F');

  char name[1000];
  sprintf(name,"nTrain_Signal=%d:nTrain_Background=%d:nTest_Signal=%d:nTest_Background=%d",N,N,N,N);
  factory->PrepareTrainingAndTestTree(preselectionCut,name);

  // specify the training methods
  factory->BookMethod(TMVA::Types::kLikelihood,"Likelihood");
  //factory->BookMethod(TMVA::Types::kBDT,"BDT_DEF");
  //factory->BookMethod(TMVA::Types::kBDT,"BDT_ADA","NTrees=600:AdaBoostBeta=0.1:nCuts=35"); 
  //factory->BookMethod(TMVA::Types::kBDT,"BDT_GRAD1","NTrees=600:nCuts=40:BoostType=Grad:Shrinkage=0.5");  
  factory->BookMethod(TMVA::Types::kBDT,"BDT_GRAD2","NTrees=600:nCuts=25:BoostType=Grad:Shrinkage=0.2");
  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods(); 
}

//.........这里部分代码省略.........
   factory->AddVariable( "Eta_qq := Eta_qq", 'F' );
   factory->AddVariable( "Charge_qq := Charge_qq", 'F' );
   factory->AddVariable( "DPhi_ll := DPhi_ll", 'F' );
   factory->AddVariable( "DPt_ll := DPt_ll", 'F' );
   //factory->AddVariable( "MinDPhi_lMET := MinDPhi_lMET", 'F' );
   //factory->AddVariable( "Aplanarity := aplanarity", 'F' );
   //factory->AddVariable( "chargeEta := chargeEta",  'F' );
   //factory->AddVariable( "MET := Met",  'F' );
   //factory->AddVariable( "MtauJet := MtauJet",  'F' );
   //factory->AddVariable( "HT := Ht",  'F' );
   //factory->AddVariable( "Chi2 := kinFitChi2",  'F' );
   //factory->AddVariable( "DeltaPhiTauMET := DeltaPhiTauMet",  'F' );
   //factory->AddVariable( "Mt := Mt",  '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( "spec1:=var1*2",  "Spectator 1", "units", 'F' );
   // factory->AddSpectator( "spec2:=var1*3",  "Spectator 2", "units", 'F' );

   // read training and test data
   if (ReadDataFromAsciiIFormat) {
      // 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" );

void mvaDonut(TString Type = "Dl", int iChannel = 1, TString Sample = "Sig") {

  TString Channels[] = {"D0","Ds0","Dp","Dsp"};
  TString fname = "mva"; if(Sample=="Dss") fname += Sample; 
  fname += Type; fname += Channels[iChannel-1];
  TString outfileName = fname; outfileName += ".root";
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
  int isDss = 0;
  if(Sample=="Dss") isDss=1;
  TMVA::Factory *factory = new TMVA::Factory( fname, outputFile, 
					      Form("!V:!Silent:%sColor", gROOT->IsBatch()?"!":"") );

  TChain c("ntp1");
  c.Add("~/releases/ntuplePID50/workdir/AWG82/ntuples/small/Add_R24MVA_RunAll.root");

  TString sigCuts[] = {"(MCType==1||MCType==3||MCType==5)", "(MCType==2||MCType==4||MCType==6)",
		       "(MCType==7||MCType==9||MCType==11)", "(MCType==8||MCType==10||MCType==12)",
		       "MCType>12"};
  TString bkgCuts[2][2] = {{"MCType>6", "(MCType>0&&MCType<7||MCType>12)"},
			   {"MCType>0&&MCType<13","MCType>0&&MCType<13"}};
  TString sigStr = "candLepTru==1&&"; if(isDss) sigStr += "pmisspi0"; else sigStr += "candPMiss";
  sigStr += ">0.2&&candType=="; sigStr += iChannel; sigStr += "&&";
  if(isDss) sigStr += sigCuts[4]; else sigStr += sigCuts[iChannel-1];
  TString bkgStr = "candType=="; bkgStr += iChannel; bkgStr += "&&";
  if(isDss) bkgStr += "pmisspi0"; else bkgStr += "candPMiss";
  bkgStr += ">0.2&&";
  if(Type=="Dl") bkgStr += bkgCuts[isDss][(iChannel-1)/2];
  else bkgStr += "MCType==0";
  TCut sigCut = "1", bkgCut = "1", mycuts = "", mycutb = "";
  sigCut += sigStr; bkgCut += bkgStr;

//   --- Base ---
//   int nSig = 9, nDpi0 = 10;
//   TString sigVari[] = {"candEExtra","candMES","candDmass","candDeltam","candTagChargedMult","candBTagDeltam",
// 		       "candBTagDmass","candDeltaE","candCosT"};
//   TString Dpi0Vari[] = {"mpi0","candDmass","dmpi0","eextrapi0","ppi0","e1pi0","candCosT","candDeltam",
// 			"candMES","candDeltaE"};

//   --- NoDmNoMp0 ---
//   int nSig = 8, nDpi0 = 9;
//   TString sigVari[] = {"candEExtra","candMES","candDmass","candDeltam","candTagChargedMult",
// 		       "candBTagDmass","candDeltaE","candCosT"};
//   TString Dpi0Vari[] = {"candDmass","dmpi0","eextrapi0","ppi0","e1pi0","candCosT","candDeltam",
// 			"candMES","candDeltaE"};
//   sigCuts[4] = "MCType>12&&mpi0>.125&&mpi0<.145";

//  ---  NoMes ---
//   int nSig = 8, nDpi0 = 9;
//   TString sigVari[] = {"candEExtra","candDmass","candDeltam","candTagChargedMult","candBTagDeltam",
// 		       "candBTagDmass","candDeltaE","candCosT"};
//   TString Dpi0Vari[] = {"mpi0","candDmass","dmpi0","eextrapi0","ppi0","e1pi0","candCosT","candDeltam",
// 			"candDeltaE"};

//   --- NoMulYesDm ---
  int nSig = 8, nDpi0 = 11;
  TString sigVari[] = {"candEExtra","candMES","candDmass","candDeltam","candBTagDeltam",
		       "candBTagDmass","candDeltaE","candCosT"};
  TString Dpi0Vari[] = {"mpi0","candDmass","dmpi0","eextrapi0","ppi0","e1pi0","candCosT","candDeltam",
			"candMES","candDeltaE","candBTagDeltam"};

  factory->SetInputTrees(&c, sigCut, bkgCut);
  if(isDss==0){
    for(int vari = 0; vari < nSig; vari++){
      if(sigVari[vari]=="candDeltam" && iChannel%2==1) continue;
      char variChar = 'F';
      if(sigVari[vari]=="candTagChargedMult") variChar = 'I';
      factory->AddVariable(sigVari[vari], variChar);
    }
  } else {
    for(int vari = 0; vari < nDpi0; vari++){
      if(Dpi0Vari[vari]=="candDeltam" && iChannel%2==1) continue;
      factory->AddVariable(Dpi0Vari[vari], 'F');
    }
  }

  factory->PrepareTrainingAndTestTree( mycuts, mycutb,
				       "NSigTest=100:NBkgTest=100:SplitMode=Random:NormMode=NumEvents:!V" );

  factory->BookMethod( TMVA::Types::kBDT, "BDT", 
		       "!H:!V:NTrees=500:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=2.5" );

  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods();    
   
  // Save the output
  outputFile->Close();
  std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
  delete factory;

  // Launch the GUI for the root macros
  //if (!gROOT->IsBatch()) TMVAGui( outfileName );
  gROOT->ProcessLine(".q");
}

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 ;
}

//.........这里部分代码省略.........
   //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 *SB_Chain = new TChain("latino");
   SB_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root");
   SB_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntShoulder_8TeV.root");
   SB_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntTail_8TeV.root");
//   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
   
   // --- Register the training and test trees

   //TTree *signal     = (TTree*)input->Get("ggTree");
   //TTree *background = (TTree*)input->Get("ggTree");
   
   // 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 );
   // Classification training and test data in ROOT tree format with signal and background events being located in the same tree
   TCut sigCut = "mWW>160 && (channel == 2 || channel == 3)"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut bkgCut = "mWW<160 && (channel == 2 || channel == 3)"; // for example: TCut mycutb = "abs(var1)<0.5";
   factory->SetInputTrees(SB_Chain, sigCut, bkgCut);
   
   // 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], weight;
   //     
   //     // Signal
   //     for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   //     for (UInt_t i=0; i<signal->GetEntries(); i++) {
   //        signal->GetEntry(i);
   //        for (UInt_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()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight );
   //        else                              factory->AddSignalTestEvent    ( vars, signalWeight );
   //     }
   //   
   //     // Background (has event weights)
   //     background->SetBranchAddress( "weight", &weight );
   //     for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   //     for (UInt_t i=0; i<background->GetEntries(); i++) {
   //        background->GetEntry(i);
   //        for (UInt_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.........
   // (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( "MET"      , "ME_{T}", "GeV", 'F');
   //factory->AddVariable( "TMath::Max(pT1,pT2)"      , "Lead Lepton p_{T}", "GeV", 'F');
   factory->AddVariable( "HT"       , "H_{T}", "GeV", 'F');
   //factory->AddVariable( "M3"       , "M_{3}", "GeV", 'F');
   factory->AddVariable( "TMath::Min(pT1,pT2)"      , "Sublead Lepton p_{T}", "GeV", 'F');
   //factory->AddVariable( "NbJ"      , "N B Jets", "", 'I');
   //factory->AddVariable( "NbJmed"   , "N B Jets (medium)", "", 'I');
   //factory->AddVariable( "NJ"       , "N Jets", "", 'I');

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

   // read training and test data
   if (ReadDataFromAsciiIFormat) {
      // 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 {
      // load the signal and background event samples from ROOT trees
      TFile *input(0);
      //TString fname = "../macros/tmva_example.root";
      //TString fname = "opt_ttW_Apr10_Iso005_NoZVeto_jet20.root";
      TString fname = "opt_ttW_Nov20_muDetIso0p05_elDetIso0p05_jet20_withZveto_optimization.root";
      //TString fname = "opt_ttW_" + selectionName + ".root";
      if (!gSystem->AccessPathName( fname )) {
         input = TFile::Open( fname ); // check if file in local directory exists
      } 
      else { 
         input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
      }

      if (!input) {
         std::cout << "ERROR: could not open data file" << std::endl;
         exit(1);
      }
      std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;

      
      TTree* opt_tree = (TTree*)input->Get("tree_opt");

      TFile* signalFile = TFile::Open("/shome/mdunser/workspace/CMSSW_5_2_5/src/DiLeptonAnalysis/NTupleProducer/macros/plots/Nov26_muPFIso0p05_elPFIso0p05_jet20_withZveto/TTbarW_Yields.root");      
      TTree *signal     = (TTree*)signalFile->Get("SigEvents");

      TChain* background = new TChain("SigEvents");
      background->Add("/shome/mdunser/workspace/CMSSW_5_2_5/src/DiLeptonAnalysis/NTupleProducer/macros/plots/Nov26_muPFIso0p05_elPFIso0p05_jet20_withZveto/TTJets_Yields.root");
      background->Add("/shome/mdunser/workspace/CMSSW_5_2_5/src/DiLeptonAnalysis/NTupleProducer/macros/plots/Nov26_muPFIso0p05_elPFIso0p05_jet20_withZveto/WZTo3LNu_Yields.root");

      //TTree *background = (TTree*)opt_tree->CopyTree("SName==\"TTJets\" || SName==\"DYJets\" || SName==\"WZTo3LNu\"");

//.........这里部分代码省略.........
  TFile *inputWZ= TFile::Open(WZFileTrain );
  TFile *inputZZ= TFile::Open(ZZFileTrain );
  TFile *inputZGAMMA= TFile::Open( ZGAMMAFileTrain);

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

 
  std::cout << "--- TMVAnalysis    : Accessing Signal Train: " << sigFileTrain << std::endl;
  std::cout << "--- TMVAnalysis    : Accessing Background Train: " << bkgFileTrain << std::endl;
  
  TTree *signalTrain      = (TTree*)inputSTrain->FindObjectAny("atq");
  TTree *backgroundTrain = (TTree*)inputBTrain->FindObjectAny("atq");
  TTree *ttbarbackground1 = (TTree*)inputBttbar1->FindObjectAny("atq");
  TTree *ttbarbackground2 = (TTree*)inputBttbar2->FindObjectAny("atq");
  TTree *ttbarbackground3 = (TTree*)inputBttbar3->FindObjectAny("atq");
  TTree *TTGbackground = (TTree*)inputTTG->FindObjectAny("atq");
  TTree *WWbackground = (TTree*)inputWW->FindObjectAny("atq");
  TTree *WZbackground = (TTree*)inputWZ->FindObjectAny("atq");
  TTree *ZZbackground = (TTree*)inputZZ->FindObjectAny("atq");
  TTree *ZGAMMAbackground = (TTree*)inputZGAMMA->FindObjectAny("atq");

//  TTree *signalTrain      = (TTree*)inputSTrain->FindObjectAny("insidetopmass");
//  TTree *backgroundTrain = (TTree*)inputBTrain->FindObjectAny("insidetopmass");
  
 // factory->AddSignalTree( signalTrain, 1);
//cout<<"reza1";
  
 // factory->AddBackgroundTree( backgroundTrain, 1, );
//cout<<"reza2";

 factory->SetInputTrees(signalTrain,backgroundTrain,1,1);
 factory->AddBackgroundTree( ttbarbackground1, 1.1);
 factory->AddBackgroundTree( ttbarbackground2, 1.1);
// factory->AddBackgroundTree( ttbarbackground3, 0.3);
 factory->AddBackgroundTree( TTGbackground, 1);
 factory->AddBackgroundTree( WWbackground, 1);
 factory->AddBackgroundTree(WZbackground , 1);
 factory->AddBackgroundTree(ZZbackground , 1);
 factory->AddBackgroundTree(ZGAMMAbackground , 1);

  // Set xs-weight
//  factory->SetSignalWeightExpression    ("weight");
  //factory->SetBackgroundWeightExpression("weight");
  
  // Apply additional cuts on the signal and background samples (can be different)
  TCut mycut = "";
 // TCut mycutb = ""; 
  
  Int_t nSignalTrain = signalTrain->GetEntries();
  
  Int_t nBackTrain  = backgroundTrain->GetEntries();
  factory->PrepareTrainingAndTestTree( "","", "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:SplitSeed=88!V" );

  
//factory->PrepareTrainingAndTestTree( "", "",
//				       "nTrain_Signal=100:nTrain_Background=100:nTest_Signal=100:nTest_Background=100:!V" );

//  factory->PrepareTrainingAndTestTree( mycuts, mycutb,
				       //":NSigTrain=:NBkgTrain=:NSigTest=:NBkgTest=:SplitMode=Alternate:!V" );  
				       //":nTrain_Signal=10000:nTest_Signal=2260:nTrain_Background=100000:nTest_Background=100000:SplitMode=Alternate:!V" );  
//				       ":nTrain_Signal=nSignalTrain:nTrain_Background=nBackTrain:!V");
  //":nTrain_Signal=nSignalTrain:nTest_Signal=nSignalTest:nTrain_Background=nBackTrain:nTest_Background=nBackTest:SplitMode=Block:!V");
  //":SplitMode=Alternate:!V");

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 ("shrinked") ;
  signalTree.Add ("data/H160_VBF.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 ("shrinked") ;
  bkgTree.Add ("data/H160_NOVBF.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 ("shrinked") ;
  signalTrainTree.Add ("data/H150_VBF.root") ;
  std::cout << "READ " << signalTrainTree.GetEntries () << " signal train events\n" ;
  
  TChain bkgTrainTree ("shrinked") ;
  bkgTrainTree.Add ("data/H150_NOVBF.root") ;
  std::cout << "READ " << bkgTrainTree.GetEntries () << " bkg train events\n" ;

  // PG this is useless!!  
  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.Deta", 'F') ;
  TMVAtest->AddVariable ("vars.Dphi" , 'F') ;
  TMVAtest->AddVariable ("vars.Minv" , '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 classifiers
  //PG ------------------------

  //PG cut-based, default params
  TMVAtest->BookMethod (TMVA::Types::kCuts, "Cuts") ;
  
  //PG multi layer perceptron
  TMVAtest->BookMethod (TMVA::Types::kMLP, "MLP", 
                        "H:!V:!Normalise:NeuronType=tanh:NCycles=200:HiddenLayers=N+1,N:TestRate=5");
  
  TMVAtest->TrainAllMethods () ;
  TMVAtest->TestAllMethods () ;
  TMVAtest->EvaluateAllMethods () ;
 
  delete TMVAtest ;
  delete outputfile ;
}