C++ Reader::AddVariable方法代码示例

int reader_wrapper::bookReader( TString xml_file_name) {
  m_reader = new TMVA::Reader("!Color:Silent");
  for (auto var : m_spectators) {
    m_reader->AddSpectator(var.formula, &var.value);
  }
  for (auto& var : m_variables) {
    m_reader->AddVariable(var.formula, &var.value);
  }
  m_reader->BookMVA( m_methodName, xml_file_name );
  return 0;
}

void makeclassification() {
  
  Float_t *vars = new Float_t[10];
  
  //initialize TMVA Reader (example here is diphoton mva from higgs->gamma gamma mva analysis)
  TMVA::Reader* tmva = new TMVA::Reader();
  tmva->AddVariable("masserrsmeared/mass",            &vars[0]);
  tmva->AddVariable("masserrsmearedwrongvtx/mass",    &vars[1]);
  tmva->AddVariable("vtxprob",                        &vars[2]);
  tmva->AddVariable("ph1.pt/mass",                    &vars[3]);
  tmva->AddVariable("ph2.pt/mass",                    &vars[4]);
  tmva->AddVariable("ph1.eta",                        &vars[5]);
  tmva->AddVariable("ph2.eta",                        &vars[6]);
  tmva->AddVariable("TMath::Cos(ph1.phi-ph2.phi)"   , &vars[7]);
  tmva->AddVariable("ph1.idmva",                      &vars[8]);
  tmva->AddVariable("ph2.idmva",                      &vars[9]);
  
  tmva->BookMVA("BDTG","/afs/cern.ch/user/b/bendavid/cmspublic/diphotonmvaApr1/HggBambu_SMDipho_Jan16_BDTG.weights.xml");
  //tmva->BookMVA("BDTG","/scratch/bendavid/root/HggBambu_SMDipho_Jan16_BDTG.weights.xml");
  
  TMVA::MethodBDT *bdt = dynamic_cast<TMVA::MethodBDT*>(tmva->FindMVA("BDTG"));

 
  //enable root i/o for objects with reflex dictionaries in standalone root mode
  ROOT::Cintex::Cintex::Enable();   

  
  //open output root file
  TFile *fout = new TFile("gbrtest.root","RECREATE");
  
  //create GBRForest from tmva object
  GBRForest *gbr = new GBRForest(bdt);  
  
  //write to file
  fout->WriteObject(gbr,"gbrtest");

  fout->Close();
  
  
}

void TMVAPredict()
{
  std::ofstream outfile ("baseline_c.csv");
  outfile << "id,prediction\n";

  TMVA::Tools::Instance();

  std::cout << "==> Start TMVAPredict" << std::endl;
  TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );  
  string variables_name[3] = {"LifeTime",
                           "FlightDistance",
                           "pt"}
  Float_t variables[3];
  for (int i=0; i < 3; i++){
    reader->AddVariable(variables_name[i].c_str(), &variables[i]);
    variables[i] = 0.0;
  }

  TString dir    = "weights/";
  TString prefix = "TMVAClassification";
  TString method_name = "GBDT";
  TString weightfile = dir + prefix + TString("_") + method_name + TString(".weights.xml");
  reader->BookMVA( method_name, weightfile ); 

  TFile *input(0);
  input = TFile::Open("../tau_data/test.root");
  TTree* tree = (TTree*)input->Get("data");
  
  Int_t ids;
  Float_t prediction;
  tree->SetBranchAddress("id", &ids);

  for (int i=0; i < 3; i++){
    tree->SetBranchAddress(variables_name[i].c_str(), &variables[i]);
  }
 
  for (Long64_t ievt=0; ievt < tree->GetEntries(); ievt++) {
    tree->GetEntry(ievt);
    prediction = reader->EvaluateMVA(method_name);
    outfile << ids << "," << (prediction + 1.) / 2. << "\n";
  }

  outfile.close();
  input->Close();
  delete reader;
}

void TMVAClassificationApplication_tW(TString signal = "data") 
{   
#ifdef __CINT__
  gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
  
  //---------------------------------------------------------------
  // This loads the library
  TMVA::Tools::Instance();
  // --------------------------------------------------------------------------------------------------
  
  // --- Create the Reader object
  
  TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    
  
  // Create a set of variables and declare them to the reader
  // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
  
  Float_t jetpt;
  Float_t jeteta;
  Float_t jetphi;
  Float_t metpt;
  Float_t metpro;
  Float_t lep0pt;
  Float_t lep1pt;
  Float_t lep0eta;
  Float_t lep1eta;
  Float_t lep0phi;
  Float_t lep1phi;
  Float_t ptsys;
  Float_t ht;
  Float_t oblateness;
  Float_t sphericity;
  Float_t aplanarity;
  Float_t njetw;
  Float_t sqrts;
  Float_t deltarleps;
  Float_t deltaphileps;
  Float_t deltaetaleps;
  Float_t philepmetclose;
  Float_t philepmetfar;
  Float_t rlepmetclose;
  Float_t rlepmetfar;
  Float_t philepjetclose;
  Float_t philepjetfar;
  Float_t rlepjetclose;
  Float_t rlepjetfar;
  Float_t phijetmet;
  Float_t rjetmet;
  Float_t mll;
  Float_t htnomet;
  Float_t ptsysnomet;
  Float_t metphi;
  Float_t metminusptsysnomet;
  
  reader->AddVariable ("jetpt", &jetpt);
  reader->AddVariable ("jeteta", &jeteta);
  reader->AddVariable ("jetphi", &jetphi);
  reader->AddVariable ("metpt", &metpt);
  reader->AddVariable ("metpro",&metpro);
  reader->AddVariable ("lep0pt",&lep0pt);
  reader->AddVariable ("lep1pt",&lep1pt);
  reader->AddVariable ("lep0eta",&lep0eta);
  reader->AddVariable ("lep1eta",&lep1eta);
  reader->AddVariable ("lep0phi",&lep0phi);
  reader->AddVariable ("lep1phi",&lep1phi);
  reader->AddVariable ("ptsys",&ptsys);
  reader->AddVariable ("ht",&ht);
  reader->AddVariable ("oblateness", &oblateness);
  reader->AddVariable ("sphericity", &sphericity);
  reader->AddVariable ("aplanarity", &aplanarity);
  reader->AddVariable ("njetw", &njetw);
  reader->AddVariable ("sqrts", &sqrts);
  reader->AddVariable ("deltarleps", &deltarleps);
  reader->AddVariable ("deltaphileps", &deltaphileps);
  reader->AddVariable ("deltaetaleps", &deltaetaleps);
  reader->AddVariable ("philepmetclose", &philepmetclose);
  reader->AddVariable ("philepmetfar", &philepmetfar);
  reader->AddVariable ("rlepmetclose", &rlepmetclose);
  reader->AddVariable ("rlepmetfar", &rlepmetfar);
  reader->AddVariable ("philepjetclose", &philepjetclose);
  reader->AddVariable ("philepjetfar", &philepjetfar);
  reader->AddVariable ("rlepjetclose", &rlepjetclose);
  reader->AddVariable ("rlepjetfar", &rlepjetfar);
  reader->AddVariable ("phijetmet", &phijetmet);
  reader->AddVariable ("rjetmet", &rjetmet);
  reader->AddVariable ("mll", &mll);
  reader->AddVariable ("htnomet", &htnomet);
  reader->AddVariable ("ptsysnomet", &ptsysnomet);
  reader->AddVariable ("metphi", &metphi);
  reader->AddVariable ("metminusptsysnomet", &metminusptsysnomet);
  
  // *************************************************
  
  // --- Book the MVA methods
  
  TString dir    = "weights/";
  
  TString prefix = "test_tw_00";
  TString name = "BDT_"+prefix;
//.........这里部分代码省略.........

void apply(std::string iName="train/OutputTmp.root") { 
  TMVA::Tools::Instance();
  TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    

  float lPt        = 0; reader->AddVariable("pt"                 , &lPt);
  //float lEta       = 0; reader->AddVariable("eta"                , &lEta);
  //float lDR        = 0; reader->AddVariable("dR"                 , &lDR);
  //float lPtc       = 0; reader->AddVariable("ptc"                , &lPtc);
  // float lPtdR      = 0; reader->AddVariable("ptdR"               , &lPtdR);
  //float lPuppi     = 0; reader->AddVariable("puppi"              , &lPuppi);
  float lPtODR     = 0; reader->AddVariable("ptodR"              , &lPtODR);
  //float lPtODRS    = 0; reader->AddVariable("ptodRS"             , &lPtODRS);
  float lPtODRSO   = 0; reader->AddVariable("ptodRSO"            , &lPtODRSO);
  //float lDRLV      = 0; reader->AddVariable("dR_lv"              , &lDRLV);
  //float lPtcLV     = 0; reader->AddVariable("ptc_lv"             , &lPtcLV);
  //float lPtdRLV    = 0; reader->AddVariable("ptdR_lv"            , &lPtdRLV);
  //float lPuppiLV   = 0; reader->AddVariable("puppi_lv"           , &lPuppiLV);
  float lPtODRLV   = 0; reader->AddVariable("ptodR_lv"           , &lPtODRLV);
  //float lPtODRSLV  = 0; reader->AddVariable("ptodRS_lv"          , &lPtODRSLV);
  float lPtODRSOLV = 0; reader->AddVariable("ptodRSO_lv"         , &lPtODRSOLV);
  //float lDRPU      = 0; reader->AddVariable("dR_pu"              , &lDRPU);
  //float lPtcPU     = 0; reader->AddVariable("pt_pu"              , &lPtcPU);
  //float lPtdRPU    = 0; reader->AddVariable("ptdR_pu"            , &lPtdRPU);
  //float lPuppiPU   = 0; reader->AddVariable("puppi_pu"           , &lPuppiPU);
  //float lPtODRPU   = 0; reader->AddVariable("ptodR_pu"           , &lPtODRPU);
  //float lPtODRSPU  = 0; reader->AddVariable("ptodRS_pu"          , &lPtODRSPU);
  //float lPtODRSOPU = 0; reader->AddVariable("ptodRSO_pu"         , &lPtODRSOPU);
  
  std::string lJetName = "BDT";
  reader->BookMVA(lJetName .c_str(),(std::string("weights/TMVAClassificationCategory_PUDisc_v1")+std::string(".weights.xml")).c_str());
  
  TFile *lFile = new TFile(iName.c_str());
  TTree *lTree = (TTree*) lFile->Get("tree");
   lTree->SetBranchAddress("pt"                 , &lPt);
  //lTree->SetBranchAddress("eta"                , &lEta);
   //lTree->SetBranchAddress("dR"                 , &lDR);
   //lTree->SetBranchAddress("ptc"                , &lPtc);
   //lTree->SetBranchAddress("ptdR"               , &lPtdR);
  //lTree->SetBranchAddress("puppi"              , &lPuppi);
  lTree->SetBranchAddress("ptodR"              , &lPtODR);
  //lTree->SetBranchAddress("ptodRS"             , &lPtODRS);
  lTree->SetBranchAddress("ptodRSO"            , &lPtODRSO);
  //lTree->SetBranchAddress("dR_lv"              , &lDRLV);
  // lTree->SetBranchAddress("ptc_lv"             , &lPtcLV);
  //lTree->SetBranchAddress("ptdR_lv"            , &lPtdRLV);
  //lTree->SetBranchAddress("puppi_lv"           , &lPuppiLV);
  lTree->SetBranchAddress("ptodR_lv"           , &lPtODRLV);
  //lTree->SetBranchAddress("ptodRS_lv"          , &lPtODRSLV);
  lTree->SetBranchAddress("ptodRSO_lv"         , &lPtODRSOLV);
  //lTree->SetBranchAddress("dR_pu"              , &lDRPU);
  //lTree->SetBranchAddress("pt_pu"              , &lPtcPU);
  //lTree->SetBranchAddress("ptdR_pu"            , &lPtdRPU);
  //lTree->SetBranchAddress("puppi_pu"           , &lPuppiPU);
  //lTree->SetBranchAddress("ptodR_pu"           , &lPtODRPU);
  //lTree->SetBranchAddress("ptodRS_pu"          , &lPtODRSPU);
  //lTree->SetBranchAddress("ptodRSO_pu"         , &lPtODRSOPU);
    
  int lNEvents = lTree->GetEntries();
  TFile *lOFile = new TFile("Output.root","RECREATE");
  TTree *lOTree = lTree->CloneTree(0);
  float lMVA    = 0; lOTree->Branch("bdt"     ,&lMVA ,"lMVA/F");
  for (Long64_t i0=0; i0<lNEvents;i0++) {
    if (i0 % 10000 == 0) std::cout << "--- ... Processing event: " << double(i0)/double(lNEvents) << std::endl;
    lTree->GetEntry(i0);
    lMVA      = float(reader->EvaluateMVA(lJetName.c_str()));
    lOTree->Fill();
  }
  lOTree->Write();
  lOFile->Close();
  delete reader;
}

//.........这里部分代码省略.........
    float SubJet2_JP;
    float SubJet2_JBP;
    float SubJet2_CSV;
    float SubJet2_CSVIVF;

    // CSV TaggingVariables
    // per jet
    float TagVarCSV2_jetNTracks;                           // tracks associated to jet
    float TagVarCSV2_jetNTracksEtaRel;                     // tracks associated to jet for which trackEtaRel is calculated
    float TagVarCSV2_trackSumJetEtRatio;                   // ratio of track sum transverse energy over jet energy
    float TagVarCSV2_trackSumJetDeltaR;                    // pseudoangular distance between jet axis and track fourvector sum
    float TagVarCSV2_trackSip2dValAboveCharm;              // track 2D signed impact parameter of first track lifting mass above charm
    float TagVarCSV2_trackSip2dSigAboveCharm;              // track 2D signed impact parameter significance of first track lifting mass above charm
    float TagVarCSV2_trackSip3dValAboveCharm;              // track 3D signed impact parameter of first track lifting mass above charm
    float TagVarCSV2_trackSip3dSigAboveCharm;              // track 3D signed impact parameter significance of first track lifting mass above charm
    float TagVarCSV2_vertexCategory;                       // category of secondary vertex (Reco, Pseudo, No)
    float TagVarCSV2_jetNSecondaryVertices;                // number of reconstructed possible secondary vertices in jet
    float TagVarCSV2_vertexMass;                           // mass of track sum at secondary vertex
    float TagVarCSV2_vertexNTracks;                        // number of tracks at secondary vertex
    float TagVarCSV2_vertexEnergyRatio;                    // ratio of energy at secondary vertex over total energy
    float TagVarCSV2_vertexJetDeltaR;                      // pseudoangular distance between jet axis and secondary vertex direction
    float TagVarCSV2_flightDistance2dVal;                  // transverse distance between primary and secondary vertex
    float TagVarCSV2_flightDistance2dSig;                  // transverse distance significance between primary and secondary vertex
    float TagVarCSV2_flightDistance3dVal;                  // distance between primary and secondary vertex
    float TagVarCSV2_flightDistance3dSig;                  // distance significance between primary and secondary vertex
    // per jet per etaRel track
    float TagVarCSV2_trackEtaRel_0;                        // lowest track eta relative to jet axis
    float TagVarCSV2_trackEtaRel_1;                        // second lowest track eta relative to jet axis
    float TagVarCSV2_trackEtaRel_2;                        // third lowest track eta relative to jet axis


    TMVA::Reader *reader = new TMVA::Reader( "!Color" );
    
    reader->AddVariable("TagVarCSV_vertexCategory",&TagVarCSV_vertexCategory);
    reader->AddVariable("TagVarCSV_jetNTracks",&TagVarCSV_jetNTracks);
    //reader->AddVariable("TagVarCSV_trackSip2dSig_0",&TagVarCSV_trackSip2dSig_0);
    //reader->AddVariable("TagVarCSV_trackSip2dSig_1",&TagVarCSV_trackSip2dSig_1);
    //reader->AddVariable("TagVarCSV_trackSip2dSig_2",&TagVarCSV_trackSip2dSig_2);
    //reader->AddVariable("TagVarCSV_trackSip2dSig_3",&TagVarCSV_trackSip2dSig_3);
    reader->AddVariable("TagVarCSV_trackSip3dSig_0",&TagVarCSV_trackSip3dSig_0);
    reader->AddVariable("TagVarCSV_trackSip3dSig_1",&TagVarCSV_trackSip3dSig_1);
    reader->AddVariable("TagVarCSV_trackSip3dSig_2",&TagVarCSV_trackSip3dSig_2);
    reader->AddVariable("TagVarCSV_trackSip3dSig_3",&TagVarCSV_trackSip3dSig_3);
    //reader->AddVariable("TagVarCSV_trackPtRel_0",&TagVarCSV_trackPtRel_0);
    //reader->AddVariable("TagVarCSV_trackPtRel_1",&TagVarCSV_trackPtRel_1);
    //reader->AddVariable("TagVarCSV_trackPtRel_2",&TagVarCSV_trackPtRel_2);
    //reader->AddVariable("TagVarCSV_trackPtRel_3",&TagVarCSV_trackPtRel_3);
    reader->AddVariable("TagVarCSV_trackSip2dSigAboveCharm",&TagVarCSV_trackSip2dSigAboveCharm);
    //reader->AddVariable("TagVarCSV_trackSip3dSigAboveCharm",&TagVarCSV_trackSip3dSigAboveCharm);
    //reader->AddVariable("TagVarCSV_trackSumJetEtRatio",&TagVarCSV_trackSumJetEtRatio);
    //reader->AddVariable("TagVarCSV_trackSumJetDeltaR",&TagVarCSV_trackSumJetDeltaR);
    reader->AddVariable("TagVarCSV_jetNTracksEtaRel",&TagVarCSV_jetNTracksEtaRel);
    reader->AddVariable("TagVarCSV_trackEtaRel_0",&TagVarCSV_trackEtaRel_0);
    reader->AddVariable("TagVarCSV_trackEtaRel_1",&TagVarCSV_trackEtaRel_1);
    reader->AddVariable("TagVarCSV_trackEtaRel_2",&TagVarCSV_trackEtaRel_2);
    reader->AddVariable("TagVarCSV_jetNSecondaryVertices",&TagVarCSV_jetNSecondaryVertices);
    reader->AddVariable("TagVarCSV_vertexMass",&TagVarCSV_vertexMass);
    reader->AddVariable("TagVarCSV_vertexNTracks",&TagVarCSV_vertexNTracks);
    reader->AddVariable("TagVarCSV_vertexEnergyRatio",&TagVarCSV_vertexEnergyRatio);
    reader->AddVariable("TagVarCSV_vertexJetDeltaR",&TagVarCSV_vertexJetDeltaR);
    reader->AddVariable("TagVarCSV_flightDistance2dSig",&TagVarCSV_flightDistance2dSig);
    //reader->AddVariable("TagVarCSV_flightDistance3dSig",&TagVarCSV_flightDistance3dSig);
    
    reader->AddSpectator("Jet_pt", &Jet_pt);
    reader->AddSpectator("Jet_eta", &Jet_eta);
    reader->AddSpectator("Jet_phi", &Jet_phi);

//.........这里部分代码省略.........
      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;
      }
   }

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

   // --- Create the Reader object

   TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    

   // Create a set of variables and declare them to the reader
   // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
   //Float_t var1, var2;
   //Float_t var3, var4;

   Float_t la_agl, la_dlos, la_dau1_dzos, la_dau1_dxyos, la_dau2_dzos, la_dau2_dxyos;
   Float_t la_vtxChi2, la_dau1_nhit, la_dau2_nhit;

   reader->AddVariable( "la_agl", &la_agl );
   reader->AddVariable( "la_dlos", &la_dlos );
   reader->AddVariable( "la_dau1_dzos", &la_dau1_dzos );
   reader->AddVariable( "la_dau2_dzos",&la_dau2_dzos);
   reader->AddVariable( "la_dau1_dxyos",                &la_dau1_dxyos );
   
   reader->AddVariable( "la_dau2_dxyos",&la_dau2_dxyos);
   reader->AddVariable( "la_vtxChi2",&la_vtxChi2);
   reader->AddVariable( "la_dau1_nhit",&la_dau1_nhit);
   reader->AddVariable( "la_dau2_nhit",&la_dau2_nhit);


   // Spectator variables declared in the training have to be added to the reader, too
   Float_t la_mass;
   reader->AddSpectator( "la_mass",   &la_mass );
   //reader->AddSpectator( "spec2 := var1*3",   &spec2 );
/*
   Float_t Category_cat1, Category_cat2, Category_cat3;
   if (Use["Category"]){
      // Add artificial spectators for distinguishing categories
      reader->AddSpectator( "Category_cat1 := var3<=0",             &Category_cat1 );
      reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)",  &Category_cat2 );
      reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
   }
*/
   // --- Book the MVA methods

   TString dir    = "weights/";
   TString prefix = "TMVAClassification";

   // Book method(s)
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      if (it->second) {

void TMVAClassificationApplication_new(TString myMethodList = "" , TString iFileName = "", TString bkgSample = "", TString sampleLocation = "", TString massPoint = "", TString oFileLocation = "") 
{   
#ifdef __CINT__
   gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif

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

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

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

   // --- Cut optimisation
   Use["Cuts"]            = 0;
   Use["CutsD"]           = 0;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   // 
   // --- Boosted Decision Trees
   Use["BDT"]             = 1; // uses Adaptive Boost
   std::cout << std::endl;
   std::cout << "==> Start TMVAClassificationApplication" << 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;
      }
   }

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

   // --- Create the Reader object

   TMVA::Reader *reader = new TMVA::Reader("!Color:!Silent" );   
    TString weightTail = "_";
    weightTail = weightTail + massPoint;
   // Create a set of variables and declare them to the reader
   // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
   Float_t var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16, var17, var18;
   reader->AddVariable( "svMass", &var1);
   reader->AddVariable( "dRTauTau", &var3 );
   reader->AddVariable( "dRJJ", &var4 );
//    reader->AddVariable( "svPt", &var5 );
//    reader->AddVariable( "dRhh", &var6 );
   reader->AddVariable( "met", &var7 );
   reader->AddVariable( "mJJ", &var8 );
//    reader->AddVariable( "metTau1DPhi", &var9 );
//    reader->AddVariable( "metTau2DPhi", &var10);
//    reader->AddVariable( "metJ1DPhi", &var11);
//    reader->AddVariable( "metJ2DPhi", &var12 );
//    reader->AddVariable( "metTauPairDPhi", &var13 );
//    reader->AddVariable( "metSvTauPairDPhi", &var14 );
//    reader->AddVariable( "metJetPairDPhi", &var15 );
//    reader->AddVariable( "CSVJ1", &var16 );
//    reader->AddVariable( "CSVJ2", &var17 );
   reader->AddVariable( "fMassKinFit", &var2 );
   reader->AddVariable( "chi2KinFit2", &var18 );


   // Spectator variables declared in the training have to be added to the reader, too
//    Float_t spec1,spec2;
//    reader->AddSpectator( "spec1 := var1*2",   &spec1 );
//    reader->AddSpectator( "spec2 := var1*3",   &spec2 );

//    Float_t Category_cat1, Category_cat2, Category_cat3;
//    if (Use["Category"]){
//       // Add artificial spectators for distinguishing categories
//       reader->AddSpectator( "Category_cat1 := var3<=0",             &Category_cat1 );
//       reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)",  &Category_cat2 );
//       reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
//    }

   // --- Book the MVA methods
   // Book method(s)
  TString weightFileName = "/nfs_scratch/zmao/test/CMSSW_5_3_15/src/TMVA-v4.2.0/test/weights/TMVAClassification_BDT.weights_";
  weightFileName += bkgSample;
  weightFileName += weightTail;
  reader->BookMVA("BDT method", weightFileName+".xml" ); 

   
   // Book output histograms
//.........这里部分代码省略.........

void TMVARegressionApplication( int wMs,int wM, string st,string st2,string option="",TString myMethodList = "" ) 
{
   //---------------------------------------------------------------
   // 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"]         = 0; 
   Use["KNN"]             = 0;
   // 
   // --- Linear Discriminant Analysis
   Use["LD"]		        = 0;
   // 
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   // 
   // --- Neural Network
   Use["MLP"]             = 0; 
   // 
   // --- Support Vector Machine 
   Use["SVM"]             = 0;
   // 
   // --- Boosted Decision Trees
   Use["BDT"]             = 0;
   Use["BDTG"]            = 1;
   // ---------------------------------------------------------------

   std::cout << std::endl;
   std::cout << "==> Start TMVARegressionApplication" << 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;
      }
   }

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

   // --- Create the Reader object

   TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    

   // Create a set of variables and declare them to the reader
   // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
   //Float_t var1, var2;
   //reader->AddVariable( "var1", &var1 );
   //reader->AddVariable( "var2", &var2 );
   Float_t pt_AK8MatchedToHbb,eta_AK8MatchedToHbb,nsv_AK8MatchedToHbb,sv0mass_AK8MatchedToHbb,sv1mass_AK8MatchedToHbb,
   nch_AK8MatchedToHbb,nmu_AK8MatchedToHbb,nel_AK8MatchedToHbb,muenfr_AK8MatchedToHbb,emenfr_AK8MatchedToHbb;
   reader->AddVariable( "pt_AK8MatchedToHbb", &pt_AK8MatchedToHbb );
   reader->AddVariable( "eta_AK8MatchedToHbb", &eta_AK8MatchedToHbb );
   reader->AddVariable( "nsv_AK8MatchedToHbb", &nsv_AK8MatchedToHbb );
   reader->AddVariable( "sv0mass_AK8MatchedToHbb", &sv0mass_AK8MatchedToHbb );
   reader->AddVariable( "sv1mass_AK8MatchedToHbb", &sv1mass_AK8MatchedToHbb );
   reader->AddVariable( "nch_AK8MatchedToHbb", &nch_AK8MatchedToHbb );
   reader->AddVariable( "nmu_AK8MatchedToHbb", &nmu_AK8MatchedToHbb );
   reader->AddVariable( "nel_AK8MatchedToHbb", &nel_AK8MatchedToHbb );
   reader->AddVariable( "muenfr_AK8MatchedToHbb", &muenfr_AK8MatchedToHbb );
   reader->AddVariable( "emenfr_AK8MatchedToHbb", &emenfr_AK8MatchedToHbb );

   
   // Spectator variables declared in the training have to be added to the reader, too
   Float_t spec1,spec2;
    reader->AddSpectator( "spec1:=n_pv",  &spec1 );
   reader->AddSpectator( "spec2:=msoftdrop_AK8MatchedToHbb",  &spec2 );

   // --- Book the MVA methods

   TString dir    = "weights/";
   TString prefix = "TMVARegression";

   // Book method(s)
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      if (it->second) {
         TString methodName = it->first + " method";
         TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
         reader->BookMVA( methodName, weightfile ); 
      }
   }
   
     TH1* hists[100];
//.........这里部分代码省略.........

//.........这里部分代码省略.........
   std::cout << " vectorMyMethodMassList[" << iMVAMass << "] = " << vectorMyMethodMassList.at(iMVAMass) << std::endl;
   
   TMVA::Reader *TMVAreader = new TMVA::Reader( "!Color:!Silent" );
   
//    TMVAreader->AddVariable("jetpt1",       &jetpt1);
//    TMVAreader->AddVariable("jetpt2",       &jetpt2);
//    TMVAreader->AddVariable("mjj",          &mjj);
//    TMVAreader->AddVariable("detajj",       &detajj);
//    TMVAreader->AddVariable("dphilljetjet", &dphilljetjet);
//    TMVAreader->AddVariable("pt1",          &pt1);
//    TMVAreader->AddVariable("pt2",          &pt2);
//    TMVAreader->AddVariable("mll",          &mll);
//    TMVAreader->AddVariable("dphill",       &dphill);
//    TMVAreader->AddVariable("mth",          &mth);
//    TMVAreader->AddVariable("dphillmet",    &dphillmet);
//    TMVAreader->AddVariable("mpmet",        &mpmet);

   Float_t input_variables[1000];
//    float input_variables[1000];
   
//    TMVAreader->AddVariable("jetpt1",       &(input_variables[0]));
//    TMVAreader->AddVariable("jetpt2",       &(input_variables[1]));
//    TMVAreader->AddVariable("mjj",          &(input_variables[2]));
//    TMVAreader->AddVariable("detajj",       &(input_variables[3]));
//    TMVAreader->AddVariable("dphilljetjet", &(input_variables[4]));
//    TMVAreader->AddVariable("pt1",          &(input_variables[5]));
//    TMVAreader->AddVariable("pt2",          &(input_variables[6]));
//    TMVAreader->AddVariable("mll",          &(input_variables[7]));
//    TMVAreader->AddVariable("dphill",       &(input_variables[8]));
//    TMVAreader->AddVariable("mth",          &(input_variables[9]));
//    TMVAreader->AddVariable("dphillmet",    &(input_variables[10]));
//    TMVAreader->AddVariable("mpmet",        &(input_variables[11]));
   
   TMVAreader->AddVariable("jetpt1",       &input_variables[0]);
   TMVAreader->AddVariable("jetpt2",       &input_variables[1]);
   TMVAreader->AddVariable("mjj",          &input_variables[2]);
   TMVAreader->AddVariable("detajj",       &input_variables[3]);
   TMVAreader->AddVariable("dphilljetjet", &input_variables[4]);
   TMVAreader->AddVariable("pt1",          &input_variables[5]);
   TMVAreader->AddVariable("pt2",          &input_variables[6]);
   TMVAreader->AddVariable("mll",          &input_variables[7]);
   TMVAreader->AddVariable("dphill",       &input_variables[8]);
   TMVAreader->AddVariable("mth",          &input_variables[9]);
   TMVAreader->AddVariable("dphillmet",    &input_variables[10]);
   TMVAreader->AddVariable("mpmet",        &input_variables[11]);
   TMVAreader->AddSpectator("channel",     &input_variables[12]);
   
 
   TString myMethodMassList = Form ("%s",vectorMyMethodMassList.at(iMVAMass).c_str());
   TString weightfile = Form ("%s/weights_%s_testVariables/TMVAMulticlass_%s.weights.xml",MVADirectory.c_str(),myMethodMassList.Data(),myMethodList.Data());
   
   std::cout << " myMethodList = " << myMethodList.Data() << std::endl;
   std::cout << " weightfile   = " << weightfile.Data()   << std::endl;
   
//    TString myMethodListBook = Form ("%s",vectorMyMethodList.at(iMVA).c_str());
   
//    TMVAreader->BookMVA( myMethodListBook, weightfile );
   TMVAreader->BookMVA( myMethodList, weightfile );
   
   
   for (int iSample=0; iSample<numberOfSamples; iSample++){ 
    std::cout << " iSample = " << iSample << " :: " << numberOfSamples << std::endl;
    file[iSample] -> cd();
    Double_t MVA_Value;
    TBranch *newBranch;
    

//.........这里部分代码省略.........
  Use["SVM_Poly"]        = 0;
  Use["SVM_Lin"]         = 0;

  std::cout << std::endl;
  std::cout << "==> Start TMVAClassificationApplication" << 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;
    }
  }

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

  // --- Create the Reader object

  TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    

  Float_t B_s0_ln_FDCHI2; reader->AddVariable("B_s0_ln_FDCHI2", &B_s0_ln_FDCHI2 );
  Float_t B_s0_ln_IPCHI2; reader->AddVariable("B_s0_ln_IPCHI2", &B_s0_ln_IPCHI2 );
  Float_t B_s0_ln_EVCHI2; reader->AddVariable("B_s0_ln_EVCHI2", &B_s0_ln_EVCHI2 );
  Float_t B_s0_PT_fiveGeV;reader->AddVariable("B_s0_PT_fiveGeV",&B_s0_PT_fiveGeV);
  Float_t B_s0_Eta;       reader->AddVariable("B_s0_Eta",       &B_s0_Eta       );
  Float_t minK_PT_GeV;    reader->AddVariable("minK_PT_GeV",    &minK_PT_GeV    );
  Float_t minK_ln_IPCHI2; reader->AddVariable("minK_ln_IPCHI2", &minK_ln_IPCHI2 );
  
  Float_t Category_cat1, Category_cat2, Category_cat3;
  if (Use["Category"]){
    // Add artificial spectators for distinguishing categories
    reader->AddSpectator( "Category_cat1 := var3<=0",             &Category_cat1 );
    reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)",  &Category_cat2 );
    reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
  }

  // --- Book the MVA methods

  TString dir    = "weights/";
  TString prefix = "TMVAClassification";

  // Book method(s)
  for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
    if (it->second) {
       TString methodName = TString(it->first) + TString(" method");
       TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
       reader->BookMVA( methodName, weightfile ); 
    }
  }
  
  // Book output histograms
  UInt_t nbin = 100;
  TH1F   *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);

//.........这里部分代码省略.........
  outputRootFile[iSample] = new TFile ( nameFile, "RECREATE") ;
  outputRootFile[iSample] -> cd () ;
  cloneTreeJetLepVect[iSample] = treeJetLepVect[iSample] -> CloneTree (0) ;
 }
 
 
 
 for (int iSample=0; iSample<numberOfSamples; iSample++){ 
  file[iSample] -> cd();
  ///==== loop ====
  Long64_t nentries = treeJetLepVect[iSample]->GetEntries();
  for (Long64_t iEntry = 0; iEntry < nentries; iEntry++){
   treeJetLepVect[iSample]->GetEntry(iEntry);
   cloneTreeJetLepVect[iSample] -> Fill () ; 
  }
 }
 
 
 /**
  * cycle on MVA (method-mass)
  * * cycle on samples
  * * * cycle on events
  */
 
 for (int iMVA = 0; iMVA < vectorMyMethodList.size(); iMVA++) {
  std::cout << " vectorMyMethodList[" << iMVA << "] = " << vectorMyMethodList.at(iMVA) << std::endl;
  TString myMethodList = Form ("%s",vectorMyMethodList.at(iMVA).c_str());
  for (int iMVAMass = 0; iMVAMass < vectorMyMethodMassList.size(); iMVAMass++) {
   std::cout << " vectorMyMethodMassList[" << iMVAMass << "] = " << vectorMyMethodMassList.at(iMVAMass) << std::endl;
   
   TMVA::Reader *TMVAreader = new TMVA::Reader( "!Color:!Silent" );
   Float_t input_variables[1000];
      
   TMVAreader->AddVariable("lep1pt",    &input_variables[0]);
   TMVAreader->AddVariable("lep2pt",    &input_variables[1]);
   TMVAreader->AddVariable("dPhi",      &input_variables[2]);
   TMVAreader->AddVariable("dR",        &input_variables[3]);
   TMVAreader->AddVariable("dilmass",   &input_variables[4]);
   TMVAreader->AddVariable("type",      &input_variables[5]);
   TMVAreader->AddVariable("mt",        &input_variables[6]);
   TMVAreader->AddVariable("mjj",       &input_variables[7]);
   TMVAreader->AddVariable("detajj",    &input_variables[8]);   
   TMVAreader->AddVariable("jet1eta",   &input_variables[9]);
   
   TString myMethodMassList = Form ("%s",vectorMyMethodMassList.at(iMVAMass).c_str());
   TString weightfile = Form ("%s/weights_%s_testVariables/TMVAClassification_%s.weights.xml",MVADirectory.c_str(),myMethodMassList.Data(),myMethodList.Data());
   
   std::cout << " myMethodList = " << myMethodList.Data() << std::endl;
   std::cout << " weightfile   = " << weightfile.Data()   << std::endl;
   TMVAreader->BookMVA( myMethodList, weightfile );
   
   
   for (int iSample=0; iSample<numberOfSamples; iSample++){ 
    std::cout << " iSample = " << iSample << " :: " << numberOfSamples << std::endl;
    file[iSample] -> cd();
    Double_t MVA_Value;
    TBranch *newBranch;
    
    TString methodName4Tree = Form ("%s_%s_MVAHiggs",myMethodList.Data(),myMethodMassList.Data());
    TString methodName4Tree2 =  Form ("%s_%s_MVAHiggs_2JVBF/D",myMethodList.Data(),myMethodMassList.Data());
    newBranch = cloneTreeJetLepVect[iSample]->Branch(methodName4Tree,&MVA_Value,methodName4Tree2);
    
    
    ///==== loop ====
    Long64_t nentries = treeJetLepVect[iSample]->GetEntries();
    

void TMVAClassificationApplication( TString SampleName = "" ) 
{   
#ifdef __CINT__
   gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif

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

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

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

   // --- Cut optimisation
   Use["Cuts"]            = 0;
   Use["CutsD"]           = 1;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   // --- 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 0;
   Use["LikelihoodD"]     = 1; // the "D" extension indicates decorrelated input variables (see option strings)
   Use["LikelihoodPCA"]   = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
   Use["LikelihoodKDE"]   = 0;
   Use["LikelihoodMIX"]   = 0;
   //
   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 0;
   Use["PDERSD"]          = 0;
   Use["PDERSPCA"]        = 0;
   Use["PDEFoam"]         = 0;
   Use["PDEFoamBoost"]    = 0; // uses generalised MVA method boosting
   Use["KNN"]             = 0; // k-nearest neighbour method
   //
   // --- Linear Discriminant Analysis
   Use["LD"]              = 0; // Linear Discriminant identical to Fisher
   Use["Fisher"]          = 0;
   Use["FisherG"]         = 0;
   Use["BoostedFisher"]   = 0; // uses generalised MVA method boosting
   Use["HMatrix"]         = 0;
   //
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 0; // minimisation of user-defined function using Genetics Algorithm
   Use["FDA_SA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   Use["FDA_MCMT"]        = 0;
   //
   // --- Neural Networks (all are feed-forward Multilayer Perceptrons)
   Use["MLP"]             = 1; // Recommended ANN
   Use["MLPBFGS"]         = 1; // Recommended ANN with optional training method
   Use["MLPBNN"]          = 1; // Recommended ANN with BFGS training method and bayesian regulator
   Use["CFMlpANN"]        = 0; // Depreciated ANN from ALEPH
   Use["TMlpANN"]         = 0; // ROOT's own ANN
   //
   // --- Support Vector Machine 
   Use["SVM"]             = 0;
   // 
   // --- Boosted Decision Trees
   Use["BDT"]             = 1; // uses Adaptive Boost
   Use["BDTG"]            = 1; // uses Gradient Boost
   Use["BDTB"]            = 0; // uses Bagging
   Use["BDTD"]            = 1; // decorrelation + Adaptive Boost
   // 
   // --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
   Use["RuleFit"]         = 0;
   // ---------------------------------------------------------------
   Use["Plugin"]          = 0;
   Use["Category"]        = 0;
   Use["SVM_Gauss"]       = 0;
   Use["SVM_Poly"]        = 0;
   Use["SVM_Lin"]         = 0;

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

   // --- Create the Reader object

   TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    

   // Create a set of variables and declare them to the reader
   // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
   Float_t var1, var2;
   Float_t var3, var4;
   reader->AddVariable( "jet_csv[0]", &var1 );
   reader->AddVariable( "jet_csv[1]", &var2 );
   reader->AddVariable( "jet_csv[2]", &var3 );
   reader->AddVariable( "jet_csv[3]", &var4 );
   // --- Book the MVA methods

   TString dir    = "weights/";
   TString prefix = "TMVAClassification";

   // Book method(s)
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      if (it->second) {
         TString methodName = TString(it->first) + TString(" method");
//.........这里部分代码省略.........

//.........这里部分代码省略.........
  TEvent->Branch("run",&run,"run/I");
  TEvent->Branch("evtn",&evtn,"evtn/I");

  TEvent->Branch("b0f",&b0f,"b0f/I");
  TEvent->Branch("d0f",&d0f,"d0f/I");

  TEvent->Branch("mbc",&mbc,"mbc/D");
  TEvent->Branch("de",&de,"de/D");
  TEvent->Branch("bdt",&bdt,"bdt/D");
  TEvent->Branch("bdtg",&bdtg,"bdtg/D");
  TEvent->Branch("bdts",&bdts,"bdts/D");
  TEvent->Branch("bdtgs",&bdtgs,"bdtgs/D");
  TEvent->Branch("bdtlh",&bdtlh,"bdtlh/D");
  TEvent->Branch("bdtglh",&bdtglh,"bdtglh/D");
  TEvent->Branch("bdtlhs",&bdtlhs,"bdtlhs/D");
  TEvent->Branch("bdtglhs",&bdtglhs,"bdtglhs/D");
  TEvent->Branch("lh",&lh,"lh/D");
  
  TEvent->Branch("mp",&mp,"mp/D");
  TEvent->Branch("mm",&mm,"mm/D");
  TEvent->Branch("bin",&bin,"bin/I");
  
  TEvent->Branch("dz",&dz,"dz/D");
  TEvent->Branch("dt",&dt,"dt/D");
  
  TEvent->Branch("tag_LH",&tag_LH,"tag_LH/D");
  TEvent->Branch("tag_LH_err",&tag_LH_err,"tag_LH_err/D");

  TEvent->Branch("atckpi_max",&atckpi_max,"atckpi_max/D");
  TEvent->Branch("mpi0",&mpi0,"mpi0/D");
  TEvent->Branch("mk",&mk,"mk/D");
  TEvent->Branch("md",&md,"md/D");

  reader->AddVariable("abs(cos_b0)",&m_cos_b0);
//  reader->AddVariable("p_ks",&m_p_ks);
  reader->AddVariable("log(chi2_ndf_D0)",&m_chi2_ndf_D0);
//  reader->AddVariable("log(chi2_ndf_B0)",&m_chi2_ndf_B0);
//  reader->AddVariable("log(chi2_tag_vtx/ndf_tag_vtx)",&m_chi2_ndf_tag_vtx);
  reader->AddVariable("abs(cos_thr)",&m_cos_thr);
  reader->AddVariable("abs(thr_sig-0.885)",&m_thr_sig);
  reader->AddVariable("thr_oth",&m_thr_oth);
  reader->AddVariable("log(tag_LH_err)",&m_tag_LH_err);
  reader->AddVariable("log(dzerr)",&m_dzerr);
  reader->AddVariable("log(pi0_chi2)",&m_pi0_chi2);
  reader->AddVariable("log(egamma)",&m_egamma);

  reader->AddVariable("k1mm2",&m_k1mm2);
  reader->AddVariable("k1et",&m_k1et);
  reader->AddVariable("k1hso00",&m_k1hso00);
  reader->AddVariable("k1hso02",&m_k1hso02);
  reader->AddVariable("k1hso04",&m_k1hso04);
  reader->AddVariable("k1hso10",&m_k1hso10);
  reader->AddVariable("k1hso12",&m_k1hso12);
  reader->AddVariable("k1hso14",&m_k1hso14);
  reader->AddVariable("k1hso20",&m_k1hso20);
  reader->AddVariable("k1hso22",&m_k1hso22);
  reader->AddVariable("k1hso24",&m_k1hso24);
  reader->AddVariable("k1hoo0",&m_k1hoo0);
  reader->AddVariable("k1hoo1",&m_k1hoo1);
  reader->AddVariable("k1hoo2",&m_k1hoo2);
  reader->AddVariable("k1hoo3",&m_k1hoo3);
  reader->AddVariable("k1hoo4",&m_k1hoo4);

  reader->BookMVA("BDT","weights/MVAnalysis_BDT.weights.xml");
  reader->BookMVA("BDTG","weights/MVAnalysis_BDTG.weights.xml");
  reader->BookMVA("BDTs","weights/MVAnalysis_sig_BDT.weights.xml");

void TMVAClassificationApplication_cc1pcoh_bdt_ver3noveractFFFSI( TString myMethodList = "", TString fname)
{
#ifdef __CINT__
    gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
    
    //---------------------------------------------------------------
    
    // This loads the library
    TMVA::Tools::Instance();
    
    // Default MVA methods to be trained + tested
    std::map<std::string,int> Use;
    //
    // --- Boosted Decision Trees
    Use["BDT"]             = 1; // uses Adaptive Boost
    
    
    std::cout << std::endl;
    std::cout << "==> Start TMVAClassificationApplication" << 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;
        }
    }
    
    // --------------------------------------------------------------------------------------------------
    
    // --- Create the Reader object
    
    TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
    
    // Create a set of variables and declare them to the reader
    // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
    Float_t mumucl, pmucl;
    Float_t pang_t, muang_t;
    Float_t veract;
    Float_t ppe, mupe;
    Float_t range, coplanarity;
    Float_t opening;//newadd
    
    reader->AddVariable( "mumucl", &mumucl );
    reader->AddVariable( "pmucl", &pmucl );
    reader->AddVariable( "pang_t", &pang_t );
    reader->AddVariable( "muang_t", &muang_t );
    //reader->AddVariable( "veract", &veract );
    reader->AddVariable( "ppe", &ppe);
    reader->AddVariable( "mupe", &mupe);
    reader->AddVariable( "range", &range);
    reader->AddVariable( "coplanarity", &coplanarity);
    reader->AddVariable( "opening", &opening);//newadd
    
    // Spectator variables declared in the training have to be added to the reader, too
    Int_t fileIndex, inttype;
    Float_t nuE, norm, totcrsne;
    reader->AddSpectator( "fileIndex", &fileIndex );
    reader->AddSpectator( "nuE", &nuE );
    reader->AddSpectator( "inttype", &inttype );
    reader->AddSpectator( "norm", &norm );
    reader->AddSpectator( "totcrsne", &totcrsne );
    reader->AddSpectator( "veract", &veract );
    
    // --- Book the MVA methods
    
    TString dir    = "weights/";
    TString prefix = "TMVAClassification_ver3noveractFFFSI";//newchange
    
    // Book method(s)
    for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
        if (it->second) {
            TString methodName = TString(it->first) + TString(" method");
            TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
            reader->BookMVA( methodName, weightfile );
        }
    }
    
    // Prepare input tree (this must be replaced by your data source)
    // in this example, there is a toy tree with signal and one with background events
    // we'll later on use only the "signal" events for the test in this example.
    //
    

    
    
//.........这里部分代码省略.........