C++ Factory::AddSpectator方法代码示例

void TMVAClassification( TString myMethodList = "" ) 
{

//    TString curDynamicPath( gSystem->GetDynamicPath() );
//    gSystem->SetDynamicPath( "/usr/local/bin/root/bin:" + curDynamicPath );

//    TString curIncludePath(gSystem->GetIncludePath());
//    gSystem->SetIncludePath( " -I /usr/local/bin/root/include " + curIncludePath );

//    // load TMVA shared library created in local release: for MAC OSX
//    if (TString(gSystem->GetBuildArch()).Contains("macosx") ) gSystem->Load( "libTMVA.so" );


   // gSystem->Load( "libTMVA" );
//   TMVA::Tools::Instance();

//    // welcome the user
//    TMVA::gTools().TMVAWelcomeMessage();
   
//    TMVAGlob::SetTMVAStyle();

//    // this loads the library
//    TMVA::Tools::Instance();

   //---------------------------------------------------------------
   // default MVA methods to be trained + tested
   std::map<std::string,int> Use;

   Use["Cuts"]            = 1;
   // Use["Likelihood"]      = 1;
 
   // ---------------------------------------------------------------

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

   if (myMethodList != "") {
      for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;

      std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
      for (UInt_t i=0; i<mlist.size(); i++) {
         std::string regMethod(mlist[i]);

         if (Use.find(regMethod) == Use.end()) {
            std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
            for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
            std::cout << std::endl;
            return;
         }
         Use[regMethod] = 1;
      }
   }

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

   // 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( "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->AddVariable("deltaEta := deta", 'F');
   factory->AddVariable("deltaPhi := dphi", 'F');
   factory->AddVariable("sigmaIetaIeta := sieie", 'F');
   factory->AddVariable("HoverE := hoe", 'F');
   factory->AddVariable("trackIso := trackiso", 'F');
   factory->AddVariable("ecalIso := ecaliso", 'F');
   factory->AddVariable("hcalIso := hcaliso", 'F');
   //factory->AddVariable("nMissingHits := misshits", '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( "et",  'F' );
   factory->AddSpectator( "eta",  'F' );
   factory->AddSpectator( "phi",  'F' );

//.........这里部分代码省略.........

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 TMVARegression( TString myMethodList = "" ) 
{
   // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
   // if you use your private .rootrc, or run from a different directory, please copy the 
   // corresponding lines from .rootrc

   // methods to be processed can be given as an argument; use format:
   //
   // mylinux~> root -l TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
   //

   //---------------------------------------------------------------
   // This loads the library
   TMVA::Tools::Instance();

   // Default MVA methods to be trained + tested
   std::map<std::string,int> Use;

   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 0;
   Use["PDEFoam"]         = 1; 
   Use["KNN"]             = 1;
   // 
   // --- Linear Discriminant Analysis
   Use["LD"]		        = 1;
   // 
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 1;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   // 
   // --- Neural Network
   Use["MLP"]             = 1; 
   // 
   // --- Support Vector Machine 
   Use["SVM"]             = 0;
   // 
   // --- Boosted Decision Trees
   Use["BDT"]             = 0;
   Use["BDTG"]            = 1;
   // ---------------------------------------------------------------

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

   // Select methods (don't look at this code - not of interest)
   if (myMethodList != "") {
      for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;

      std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
      for (UInt_t i=0; i<mlist.size(); i++) {
         std::string regMethod(mlist[i]);

         if (Use.find(regMethod) == Use.end()) {
            std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
            for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
            std::cout << std::endl;
            return;
         }
         Use[regMethod] = 1;
      }
   }

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

   // --- Here the preparation phase begins

   // Create a new root output file
   TString outfileName( "TMVAReg.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
   TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar" );

   // 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", "Variable 1", "units", 'F' );
   factory->AddVariable( "var2", "Variable 2", "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( "spec1:=var1*2",  "Spectator 1", "units", 'F' );
//.........这里部分代码省略.........

//.........这里部分代码省略.........
    bkgfiles.push_back("MC_GJets-HT-400ToInf-madgraph");
    bkgfiles.push_back("MC_WGamma");
    bkgfiles.push_back("MC_EWK-Z2j");
    bkgfiles.push_back("MC_EWK-Z2jiglep");
    bkgfiles.push_back("MC_EWK-W2jminus_enu");
    bkgfiles.push_back("MC_EWK-W2jplus_enu");
    bkgfiles.push_back("MC_EWK-W2jminus_munu");
    bkgfiles.push_back("MC_EWK-W2jplus_munu");
    bkgfiles.push_back("MC_EWK-W2jminus_taunu");
    bkgfiles.push_back("MC_EWK-W2jplus_taunu");
  }

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
  TFile *output_tmva = TFile::Open((folder+"/TMVA_QCDrej.root").c_str(),"RECREATE");

  // Create the factory object. Later you can choose the methods
  // whose performance you'd like to investigate. The factory is 
  // the only TMVA object you have to interact with
   //
   // 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
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", output_tmva,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );


  //fill the variables with event weight from the trees
  //const unsigned nVars = 4;

   
   factory->AddSpectator("jet1_pt","Jet 1 p_{T}", "GeV", 'F');
   factory->AddSpectator("jet2_pt","Jet 2 p_{T}", "GeV", 'F');
   factory->AddSpectator("jet1_eta","Jet 1 #eta", "", 'F');
   factory->AddVariable("jet2_eta","Jet 2 #eta", "", 'F');// **
   factory->AddSpectator("jet1_phi","Jet 1 #phi", "", 'F');
   factory->AddSpectator("jet2_phi","Jet 2 #phi", "", 'F');
   factory->AddSpectator("dijet_M","M_{jj}", " GeV", 'F');
   factory->AddSpectator("dijet_deta","#Delta#eta_{jj}", "", 'F');
   factory->AddSpectator("dijet_sumeta","#eta_{j1}+#eta_{j2}", "", 'F');
   factory->AddSpectator("dijet_dphi","#Delta#phi_{jj}", "", 'F');
   factory->AddSpectator("met","MET", "GeV", 'F');// **
   factory->AddSpectator("met_phi","MET #phi", "", 'F');
   factory->AddVariable("met_significance","MET significance", "", 'F');// **
   factory->AddSpectator("sumet","#Sum E_{T}", "GeV", 'F');
   factory->AddSpectator("ht","H_{T}", "GeV", 'F');
   factory->AddVariable("mht","MH_{T}", "GeV", 'F');// **
   factory->AddSpectator("sqrt_ht","#sqrt{H_{T}}", "GeV^{0.5}", 'F');
   factory->AddSpectator("unclustered_et","Unclustered E_{T}", "GeV", 'F');
   factory->AddSpectator("unclustered_phi","Unclustered #phi", "GeV", 'F');
   factory->AddSpectator("jet1met_dphi","#Delta#phi(MET,jet1)", "", 'F');
   factory->AddVariable("jet2met_dphi","#Delta#phi(MET,jet2)", "", 'F');// **
   factory->AddVariable("jetmet_mindphi","minimum #Delta#phi(MET,jet)", "", 'F');// **
   factory->AddVariable("jetunclet_mindphi","minimum #Delta#phi(unclustered,jet)", "",  'F');// **
   factory->AddVariable("metunclet_dphi","#Delta#phi(MET,unclustered)", "",  'F');// **
   factory->AddVariable("dijetmet_scalarSum_pt", "p_{T}^{jet1}+p_{T}^{jet2}+MET", "GeV", 'F');// **
   factory->AddSpectator("dijetmet_vectorialSum_pt","p_{T}(#vec{j1}+#vec{j2}+#vec{MET})", "GeV", 'F');
   factory->AddVariable("dijetmet_ptfraction","p_{T}^{dijet}/(p_{T}^{dijet}+MET)", "", 'F');// **
   //factory->AddVariable("jet1met_scalarprod := (jet1_pt*cos(jet1_phi)*met_x+jet1_pt*sin(jet1_phi)*met_y)/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F');
   //factory->AddVariable("jet2met_scalarprod := (jet2_pt*cos(jet2_phi)*met_x+jet2_pt*sin(jet2_phi)*met_y)/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F');
   factory->AddVariable("jet1met_scalarprod", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F');// **
   factory->AddVariable("jet2met_scalarprod", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F');// **
   factory->AddVariable("jet1met_scalarprod_frac := jet1met_scalarprod/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET^{2}", "" , 'F');// **
   factory->AddVariable("jet2met_scalarprod_frac := jet2met_scalarprod/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET^{2}", "" , 'F');// **

//.........这里部分代码省略.........
                return;
            }
            Use[regMethod] = 1;
        }
    }

    // ---------------------------------------------------------------
    // --- Here the preparation phase begins

    // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
    TString outfileName( "TMVA_cc1pcoh_bdt_ver6noveract.root" );//newchange
    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

    // Create the factory object.
    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification_ver6noveract", outputFile,//newchange
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

    
    // Add variable
    //sprintf(select,  "Ntrack==2&&mumucl>0.6&&pmucl>0.25&&pang<90&&muang_t<15 && veract*7.66339869e-2<34");
    //factory->AddVariable( "Ntrack", 'F' );
    factory->AddVariable( "mumucl", 'F' );
    factory->AddVariable( "pmucl", 'F' );
    factory->AddVariable( "pang_t", 'F' );//use pang instead of pang_t
    factory->AddVariable( "muang_t", 'F' );
    //factory->AddVariable( "veract", 'F' );
    factory->AddVariable( "ppe", 'F');
    factory->AddVariable( "mupe", 'F');
    factory->AddVariable( "range", 'F');
    factory->AddVariable( "coplanarity", 'F');
    factory->AddVariable( "opening", 'F');//newadd

    // Add spectator
    factory->AddSpectator( "fileIndex", 'I' );
    factory->AddSpectator( "nuE", 'F' );
    factory->AddSpectator( "inttype", 'I' );
    factory->AddSpectator( "norm", 'F' );
    factory->AddSpectator( "totcrsne", 'F' );
    factory->AddSpectator( "veract", 'F' );
    factory->AddSpectator( "pang", 'F' );
    factory->AddSpectator( "mupdg", 'I' );
    factory->AddSpectator( "ppdg", 'I' );

    // ---------------------------------------------------------------
    // --- Get weight
    TString fratioStr="/home/kikawa/macros/nd34_tuned_11bv3.1_250ka.root";
    
    
    
    // ---------------------------------------------------------------
    // --- Add sample
    TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot.root";
    TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe.root";
    TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot.root";
    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);

void TMVAClassification( ) 
{
   // this loads the library
   TMVA::Tools::Instance();
   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;

   // Create a new root output file.
   TString outfileName( "TMVA.root" );
   TFile* outputFile = TFile::Open( outfileName, "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 
   factory->AddVariable("iso12x12", 'F');
   factory->AddVariable("iso4x4", 'F');
   factory->AddVariable("isoLshaped", 'F');
   factory->AddVariable("PUM0", 'F');

   // ---------- spectators ----------------
   factory->AddSpectator("pt", "F");
   factory->AddSpectator("eta", "F");
   factory->AddSpectator("phi", "F");
   factory->AddSpectator("e", "F");

   // read training and test data
   TString fSig = "DrellYan_Zee.root";
   TString fBkg = "MinBias.root";

   TFile *fileSig = TFile::Open( fSig );
   TFile *fileBkg = TFile::Open( fBkg );
   TTree *signal     = (TTree*)fileSig->Get("tree");
   TTree *background = (TTree*)fileBkg->Get("tree");
   

   // global event weights per tree (see below for setting event-wise weights)
   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 );
   TCut mycuts = "pt > 10"; 
   TCut mycutb = "pt > 10"; 


   // 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" );

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

   // ---- Book MVA methods
   // Boosted Decision Trees
   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 << "==> 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 TMVAClassificationElecTau(std::string ordering_ = "Pt", std::string bkg_ = "qqH115vsWZttQCD") {

    TMVA::Tools::Instance();

    TString outfileName( "TMVAElecTau"+ordering_+"Ord_"+bkg_+".root" );
    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationElecTau"+ordering_+"Ord_"+bkg_, outputFile,
            "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
    factory->AddVariable( "pt1", "pT-tag1", "GeV/c"         , 'F'  );
    factory->AddVariable( "pt2", "pT-tag2", "GeV/c"         , 'F'  );
    factory->AddVariable( "Deta","|y-tag1 - y-tag2|",""     , 'F'  );
    //factory->AddVariable( "opposite:=abs(eta1*eta2)/eta1/eta2","sign1*sign2",""             , 'F'  );
    //factory->AddVariable( "Dphi", "#Delta#phi" ,""             , 'F'  );
    factory->AddVariable( "Mjj", "M(tag1,tag2)", "GeV/c^{2}"  , 'F'  );

    factory->AddSpectator( "eta1",  "#eta_{tag1}" , 'F' );
    factory->AddSpectator( "eta2",  "#eta_{tag2}" , 'F' );

    factory->SetWeightExpression( "sampleWeight" );

    TString fSignalName              = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleVBFH115-powheg-PUS1_Open_ElecTauStream.root";
    TString fBackgroundNameDYJets    = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleZjets-alpgen-PUS1_Open_ElecTauStream.root";
    TString fBackgroundNameWJets     = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleWJets-madgraph-PUS1_Open_ElecTauStream.root";
    TString fBackgroundNameQCD       = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleQCD_Open_ElecTauStream.root";
    TString fBackgroundNameTTbar     = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleTTJets-madgraph-PUS1_Open_ElecTauStream.root";


    TFile *fSignal(0);
    TFile *fBackgroundDYJets(0);
    TFile *fBackgroundWJets(0);
    TFile *fBackgroundQCD(0);
    TFile *fBackgroundTTbar(0);

    fSignal           = TFile::Open( fSignalName );
    fBackgroundDYJets = TFile::Open( fBackgroundNameDYJets );
    fBackgroundWJets  = TFile::Open( fBackgroundNameWJets );
    fBackgroundQCD    = TFile::Open( fBackgroundNameQCD );
    fBackgroundTTbar  = TFile::Open( fBackgroundNameTTbar );

    if(!fSignal || !fBackgroundDYJets || !fBackgroundWJets || !fBackgroundQCD || !fBackgroundTTbar) {
        std::cout << "ERROR: could not open files" << std::endl;
        exit(1);
    }

    TString tree = "outTree"+ordering_+"Ord";

    TCut mycuts = "";
    TCut mycutb = "";

    TCut cutA  = "pt1>0 && tightestHPSWP>0";
    TCut cutB  = "pt1>0 && combRelIsoLeg1<0.1";
    TCut cutBl = "pt1>0 && combRelIsoLeg1<0.3";
    TCut cutC  = "pt1>0 && diTauCharge==0";
    TCut cutD  = "pt1>0 && MtLeg1<40";

    // select events for training
    TFile* dummy = new TFile("dummy.root","RECREATE");
    TH1F* allEvents = new TH1F("allEvents","",1,-10,10);
    float totalEvents, cutEvents;

    // signal: all
    TTree *signal           = ((TTree*)(fSignal->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD);
    cout << "Copied signal tree with full selection: " << ((TTree*)(fSignal->Get(tree)))->GetEntries() << " --> "  << signal->GetEntries()  << endl;
    allEvents->Reset();
    signal->Draw("eta1>>allEvents","sampleWeight");
    cutEvents  = allEvents->Integral();
    Double_t signalWeight =   1.0;
    cout << "Signal: expected yield " << cutEvents << " -- weight " << signalWeight << endl;

    // Z+jets: all
    TTree *backgroundDYJets = ((TTree*)(fBackgroundDYJets->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD);
    cout << "Copied DYJets tree with full selection: " << ((TTree*)(fBackgroundDYJets->Get(tree)))->GetEntries() << " --> "  << backgroundDYJets->GetEntries()  << endl;
    allEvents->Reset();
    backgroundDYJets->Draw("eta1>>allEvents","sampleWeight");
    cutEvents  = allEvents->Integral();
    Double_t backgroundDYJetsWeight = 1.0;
    cout << "ZJets: expected yield " << cutEvents << " -- weight " << backgroundDYJetsWeight << endl;

    // W+jets: iso+Mt
    TTree *backgroundWJets  = ((TTree*)(fBackgroundWJets->Get(tree)))->CopyTree(cutB&&cutD);
    cout << "Copied WJets tree with iso+Mt selection: " << ((TTree*)(fBackgroundWJets->Get(tree)))->GetEntries() << " --> "  << backgroundWJets->GetEntries()  << endl;
    allEvents->Reset();
    backgroundWJets->Draw("eta1>>allEvents","sampleWeight");
    totalEvents  = allEvents->Integral();
    allEvents->Reset();
    backgroundWJets->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0)");
    cutEvents  = allEvents->Integral();
    Double_t backgroundWJetsWeight  =  cutEvents / totalEvents;
    cout << "WJets: expected yield " << cutEvents  << " -- weight " << backgroundWJetsWeight << endl;

    // QCD: Mt+loose iso
    TTree *backgroundQCD    = ((TTree*)(fBackgroundQCD->Get(tree)))->CopyTree(cutD&&cutBl);
    cout << "Copied QCD tree with Mt selection: " << ((TTree*)(fBackgroundQCD->Get(tree)))->GetEntries() << " --> "  << backgroundQCD->GetEntries()  << endl;
    allEvents->Reset();
    backgroundQCD->Draw("eta1>>allEvents","sampleWeight");
    totalEvents  = allEvents->Integral();
    allEvents->Reset();
    backgroundQCD->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0 && combRelIsoLeg1<0.1)");
    cutEvents  = allEvents->Integral();
//.........这里部分代码省略.........

//.........这里部分代码省略.........
   //factory->AddVariable( "minpioneta", "minpioneta", "", 'F' );
   factory->AddVariable( "nTT", "nTT", "", 'F' );
   // factory->AddVariable( "pidpimin", "pidpimin", "", 'F' );
   // factory->AddVariable( "pidpimax", "pidpimax", "", 'F' );
   factory->AddVariable( "normxpt", "normxpt", "", 'F' );
   factory->AddVariable( "eta", "eta", "", 'F' );
   //factory->AddVariable( "phi", "phi", "", 'F' );
   //  factory->AddVariable( "normptsum", "normptsum", "", 'F' );
    //factory->AddVariable( "ptAsym", "ptAsym", "", 'F' );
   //factory->AddVariable( "dphimax", "dphimax", "", 'F' );
 //factory->AddVariable( "dphimin", "dphimin", "", 'F' );
   //factory->AddVariable( "drmax", "drmax", "", 'F' );
   // factory->AddVariable( "drmin", "drmin", "", 'F' );
    //    factory->AddVariable( "normpionp", "normpionp", "", 'F' );
    factory->AddVariable( "normminpionpt", "normminpionpt", "", 'F' );
    //factory->AddVariable( "normminpionp", "normminpionp", "", 'F' );
    factory->AddVariable( "normmaxpionpt", "normmaxpionpt", "", 'F' );
    //  factory->AddVariable( "normptj", "normptj", "", 'F' );
    //factory->AddVariable( "jmasspull", "jmasspull", "", 'F' );
    //factory->AddVariable( "vchi2dof", "vchi2dof", "", 'F' );
    //    factory->AddVariable("maxchi2","maxchi2","", 'F');  
    // 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

//.........这里部分代码省略.........
   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   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 is 
   // the only TMVA object you have to interact with
   //
   // 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
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

   // 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( "myvar1 := var1+var2", 'F' );
   //factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
   factory->AddVariable( "Lambdab_ETA",                "Lambdab_ETA", "", 'F' );
   factory->AddVariable( "Lambdab_P",                "Lambdab_P", "", '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( "Lambdab_PT",  "Lambdab_PT", "units", 'F' );


   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   TString fname_signal = "/exp/LHCb/amhis/LeptonU/tuples/montecarlo/spring16/mc-15154001-leptonU.root";
   TString fname_background = "/exp/LHCb/amhis/LeptonU/tuples/data/LeptonU-total-electrons-11122015.root";
      
   
   TFile *input_signal = TFile::Open( fname_signal );
   TFile *input_background = TFile::Open( fname_background );
   
   std::cout << "--- TMVAClassification       : Using input file for signal: " << input_signal->GetName() << std::endl;
   std::cout << "--- TMVAClassification       : Using input file for background: " << input_background->GetName() << std::endl;

   // --- Register the training and test trees

   TTree *signal     = (TTree*)input_signal->Get("Tuple_Bu2LLK_eeLine2/DecayTree");
   TTree *background = (TTree*)input_background->Get("TupleFromData_Bu2LLK_eeLine2/DecayTree");
   std::cout << signal << std::endl;
   std::cout << background << std::endl;
   // 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 );
   
   // To give different trees for training and testing, do as follows:
   //    factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
   //    factory->AddSignalTree( signalTestTree,     signalTestWeight,  "Test" );
   

void TMVAClassificationHwwNtuple( TString myMethodList = "" )
{
   // This loads the library
   TMVA::Tools::Instance();

    gROOT->ProcessLine(".L TMVAGui.C");


   // Default MVA methods to be trained + tested
   std::map<std::string,int> Use;

   // --- Cut optimisation
   Use["Cuts"]            = 1;
   Use["CutsD"]           = 0;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   Use["BDT"]             = 1; // uses Adaptive Boost
   Use["BDTG"]            = 0; // uses Gradient Boost
   Use["BDTB"]            = 0; // uses Bagging
   Use["BDTD"]            = 0; // decorrelation + Adaptive Boost
   Use["BDTF"]            = 0; // allow usage of fisher discriminant for node splitting 
   // 
   // --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
   Use["RuleFit"]         = 0;
   // ---------------------------------------------------------------

   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;
   // Select methods (don't look at this code - not of interest)
   if (myMethodList != "") {
      for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;

      std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
      for (UInt_t i=0; i<mlist.size(); i++) {
         std::string regMethod(mlist[i]);

         if (Use.find(regMethod) == Use.end()) {
            std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
            for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
            std::cout << std::endl;
            return;
         }
         Use[regMethod] = 1;
	 cout<<regMethod<<" is on"<<endl;
      }
   }
   // -------------------------------------------------------------------------

   // --- Here the preparation phase begins

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName( "TMVA.root" );
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

   // For one variable
   //TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
   //                                            "!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" );
   // For Multiple Variables
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
   //factory->AddVariable( "pt1",                "LeadLepton pt", "", 'F' );
   //factory->AddVariable( "pt2",                "TailLepton pt", "", 'F' );
   factory->AddVariable( "pfmet",                "MissingEt", "", 'F' );
   factory->AddVariable( "mpmet",              "Minimum Proj. Met", "", 'F' );
   factory->AddVariable( "dphill",             "DeltPhiOfLepLep", "", 'F' );
   //factory->AddVariable( "mll",                "DiLepton Mass", "", 'F' );
   factory->AddVariable( "ptll",               "DiLepton pt", "", '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' );
   //
   //factory->AddSpectator( "mWW",                "Higgs Mass", "", 'F' );
   factory->AddSpectator( "pt1",                "LeadLepton pt", "", 'F' );
   factory->AddSpectator( "pt2",                "TailLepton pt", "", 'F' );
   factory->AddSpectator( "pfmet",                "MissingEt", "", 'F' );
   factory->AddSpectator( "mpmet",              "Minimum Proj. Met", "", 'F' );
   factory->AddSpectator( "dphill",             "DeltPhiOfLepLep", "", 'F' );
   factory->AddSpectator( "mll",                "DiLepton Mass", "", 'F' );
   factory->AddSpectator( "ptll",               "DiLepton pt", "", 'F' );
   // 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");
//.........这里部分代码省略.........

//.........这里部分代码省略.........
	std::vector<TFile*> SignalSamples;
	for(unsigned int sigIter=0;sigIter<signallist.size();++sigIter){
		SignalSamples.push_back(TFile::Open((folder+signallist.at(sigIter)+"_mt_2012.root").c_str()));
	}

	std::vector<TTree*> backgroundTrees;
	for(unsigned int iter2=0;iter2<BackgroundSamples.size();++iter2){
		backgroundTrees.push_back(dynamic_cast<TTree*>(BackgroundSamples.at(iter2)->Get("ntuple")));
	}

	std::vector<TTree*> signalTrees;
	for(unsigned int sigIter2=0;sigIter2<SignalSamples.size();++sigIter2){
		signalTrees.push_back(dynamic_cast<TTree*>(SignalSamples.at(sigIter2)->Get("ntuple")));
	}

	TFile *outfile = new TFile((output_folder+output_name).c_str(),"RECREATE");

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


	std::vector<std::string> vars;
	std::ifstream parafile(paramfile2.c_str());
	std::cout<<paramfile2.c_str()<<std::endl;
	string line;
	while(getline(parafile,line)){
		vars.push_back(line);
	}
	parafile.close();

	std::cout<<(vars.at(0)).c_str()<<std::endl;

	std::vector<float> var2;
	for(unsigned int variter=0;variter<vars.size();++variter){
		var2.push_back(::atof((vars.at(variter)).c_str()));
	}


	for(unsigned int variter=0;variter<vars.size();++variter){
		factory->AddVariable((vars.at(variter)).c_str(),(vars.at(variter)).c_str(),"",'F');
	}

	factory->AddSpectator("mt_1","mt_1","",'F');
	factory->AddSpectator("n_prebjets","n_prebjets","",'I');
	factory->AddSpectator("prebjetbcsv_1","prebjetbcsv_1","",'F');
	factory->AddSpectator("prebjetbcsv_2","prebjetbcsv_2","",'F');

	double weightval_=0;

 ParseParamFile(paramfile);	

	for(unsigned int bckgit=0;bckgit<backgroundTrees.size();++bckgit){
		auto it = sample_info_.find(bckglist.at(bckgit).c_str());
		if(it!=sample_info_.end()){
			double evt = it->second.first;
			double xs = it->second.second;
			weightval_=(double) xs/evt;
			std::cout<<weightval_<<std::endl;
		}
		factory->AddBackgroundTree(backgroundTrees.at(bckgit),weightval_);
	}
	for(unsigned int sgit=0;sgit<signalTrees.size();++sgit){
		auto it = sample_info_.find(signallist.at(sgit).c_str());
		if(it!=sample_info_.end()){
			double evt = it->second.first;
			double xs=it->second.second;
			weightval_=(Double_t) xs/evt;
		}
		std::cout<<weightval_<<std::endl;
		factory->AddSignalTree(signalTrees.at(sgit),weightval_);
	}
	factory->SetBackgroundWeightExpression("wt");
	factory->SetSignalWeightExpression("wt");
	TCut mycutb, mycuts;
	if(twotag){
	mycutb="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2>0.679";
	mycuts="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2>0.679";
	}
	else if(onetag){
	mycutb="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2<0.679";
	mycuts="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2<0.679";
	}
	else{
	mycutb="n_prebjets>1&&mt_1<30";
	mycuts="n_prebjets>1&&mt_1<30";
	}
//TCut mycutb="";
//TCut mycuts="";
	factory->PrepareTrainingAndTestTree( mycuts, mycutb,"SplitMode=Random:!V");

	factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

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

	outfile->Close();
	delete factory;

	return 0;
}

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();
}

//.........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
    
    // leptonic W
    factory->AddVariable("WWpt := ptlvjj", 'F');
    factory->AddVariable("WWy := ylvjj", 'F');
    //factory->AddVariable("Wpt := W_pt", 'F');
    //factory->AddVariable("MET := event_met_pfmet", 'F');
    if (chan = "mu"){
        factory->AddVariable("LepCharge := W_muon_charge", 'F');
    }
    else if (chan = "el"){
        factory->AddVariable("LepCharge := W_electron_charge", 'F');
    }
    else{
        std::cout << "Invalid channel!" << std::endl;
        return;
    }
    // factory->AddVariable("J1QGL := JetPFCor_QGLikelihood[0]", 'F');
    // factory->AddVariable("J2QGL := JetPFCor_QGLikelihood[1]", 'F');
    
    factory->AddVariable("costheta1 := ang_ha", 'F');
    factory->AddVariable("costheta2 := ang_hb", 'F');
    factory->AddVariable("costhetaS := ang_hs", 'F');
    factory->AddVariable("Phi := ang_phi", 'F');
    factory->AddVariable("Phi2 := ang_phib", '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("run := event_runNo", "I");
    factory->AddSpectator("lumi := event_lumi", "I");
    factory->AddSpectator("event := event_evtNo", "I");
    factory->AddSpectator("mjj := Mass2j_PFCor", "F");
    factory->AddSpectator("mlvjj := MassV2j_PFCor", "F");
    factory->AddSpectator("masslvjj := masslvjj", "F");
    //factory->AddSpectator("ggdevt := ggdevt", "F");
    //factory->AddSpectator("fit_mlvjj := fit_mlvjj", "F");
    
    
    
    // read training and test data
    char signalOutputName[192];
    sprintf(signalOutputName,"/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_%s_HWWMH%3.0f_CMSSW428.root",chan.Data(),mH);
    TFile *input1 = TFile::Open( signalOutputName );
    //TFile *input1 = TFile::Open( "/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_mu_HWWMH400_CMSSW428.root");
    char backgroundOutputName[192];
    sprintf(backgroundOutputName,"/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_%s_WpJ_CMSSW428.root",chan.Data());
    TFile *input2 = TFile::Open( backgroundOutputName );
    
    std::cout << "--- TMVAClassification : Using input file: " << input1->GetName() << std::endl;
    
    TTree *signal     = (TTree*)input1->Get("WJet");
    TTree *background = (TTree*)input2->Get("WJet");
    
    
    // global event weights per tree (see below for setting event-wise weights)
    Double_t signalWeight     = 1.0;
    Double_t backgroundWeight = 1.0;
    
    // ====== register trees ====================================================
    //
    // the following method is the prefered one:

//.........这里部分代码省略.........
   // factory->AddVariable( "mu1_nMuSegsCln",                   "mu1_nMuSegsCln", "", 'F' );
   // factory->AddVariable( "mu1_nPixHits",                     "mu1_nPixHits", "", 'F' );
   // factory->AddVariable( "mu1_nTrHits",                      "mu1_nTrHits", "", 'F' );
   // factory->AddVariable( "mu1_segComp",                      "mu1_segComp", "", 'F' );
   // factory->AddVariable( "mu1_trkEHitsOut",                  "mu1_trkEHitsOut", "", 'F' );
   // factory->AddVariable( "mu1_trkVHits",                     "mu1_trkVHits", "", 'F' );
   // factory->AddVariable( "mu1_validFrac",                    "mu1_validFrac", "", 'F' );
   // factory->AddVariable( "mu1_chi2LocMom",                   "mu1_chi2LocMom", "", 'F' );
   // factory->AddVariable( "mu1_chi2LocPos",                   "mu1_chi2LocPos", "", 'F' );

   // factory->AddVariable( "mu2_glbTrackProb",                 "mu2_glbTrackProb", "", 'F' );
   // factory->AddVariable( "mu2_nChi2",                        "mu2_nChi2", "", 'F' );
   // factory->AddVariable( "mu2_nMuSegs",                      "mu2_nMuSegs", "", 'F' );
   // factory->AddVariable( "mu2_nMuSegsCln",                   "mu2_nMuSegsCln", "", 'F' );
   // factory->AddVariable( "mu2_nPixHits",                     "mu2_nPixHits", "", 'F' );
   // factory->AddVariable( "mu2_nTrHits",                      "mu2_nTrHits", "", 'F' );
   // factory->AddVariable( "mu2_segComp",                      "mu2_segComp", "", 'F' );
   // factory->AddVariable( "mu2_trkEHitsOut",                  "mu2_trkEHitsOut", "", 'F' );
   // factory->AddVariable( "mu2_trkVHits",                     "mu2_trkVHits", "", 'F' );
   // factory->AddVariable( "mu2_validFrac",                    "mu2_validFrac", "", 'F' );
   // factory->AddVariable( "mu2_chi2LocMom",                   "mu2_chi2LocMom", "", 'F' );
   // factory->AddVariable( "mu2_chi2LocPos",                   "mu2_chi2LocPos", "", 'F' );



   // factory->AddVariable( "l3d := ctauPV*pt/mass",            "l3d", "cm", 'F' );
   // factory->AddVariable( "l3dSig := ctauPV/ctauErrPV",       "l3dSig", "", '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 := mass*2",  "Spectator 1", "units", 'F' );
   // factory->AddSpectator( "spec2 := mass*3",  "Spectator 2", "units", 'F' );
   factory->AddSpectator( "mass",                            "mass", "GeV/c^{2}", 'F' );


   // Read training and test data
   // (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);
     }

void TMVATrainer(){
   // This loads the library
   TMVA::Tools::Instance();

   // --- Here the preparation phase begins
   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName = "TMVATrainingResults_fat_BBvsGSP.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 is 
   // the only TMVA object you have to interact with
   //
   // 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
   TMVA::Factory *factory = new TMVA::Factory( "TMVATrainer", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

   // 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("TagVarCSV_vertexCategory","TagVarCSV_vertexCategory","units",'F');
   factory->AddVariable("TagVarCSV_jetNTracks","TagVarCSV_jetNTracks","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_0","TagVarCSV_trackSip2dSig_0","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_1","TagVarCSV_trackSip2dSig_1","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_2","TagVarCSV_trackSip2dSig_2","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_3","TagVarCSV_trackSip2dSig_3","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_0","TagVarCSV_trackSip3dSig_0","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_1","TagVarCSV_trackSip3dSig_1","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_2","TagVarCSV_trackSip3dSig_2","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_3","TagVarCSV_trackSip3dSig_3","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_0","TagVarCSV_trackPtRel_0","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_1","TagVarCSV_trackPtRel_1","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_2","TagVarCSV_trackPtRel_2","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_3","TagVarCSV_trackPtRel_3","units",'F');
   factory->AddVariable("TagVarCSV_trackSip2dSigAboveCharm","TagVarCSV_trackSip2dSigAboveCharm","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip3dSigAboveCharm","TagVarCSV_trackSip3dSigAboveCharm","units",'F');
   //factory->AddVariable("TagVarCSV_trackSumJetEtRatio","TagVarCSV_trackSumJetEtRatio","units",'F');
   //factory->AddVariable("TagVarCSV_trackSumJetDeltaR","TagVarCSV_trackSumJetDeltaR","units",'F');
   factory->AddVariable("TagVarCSV_jetNTracksEtaRel","TagVarCSV_jetNTracksEtaRel","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_0","TagVarCSV_trackEtaRel_0","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_1","TagVarCSV_trackEtaRel_1","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_2","TagVarCSV_trackEtaRel_2","units",'F');
   factory->AddVariable("TagVarCSV_jetNSecondaryVertices","TagVarCSV_jetNSecondaryVertices","units",'F');
   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 );
//.........这里部分代码省略.........