C++ tmva::Factory类代码示例

void TMVAClassification()
{
   TMVA::Tools::Instance();

   std::cout << "==> Start TMVAClassification" << std::endl;
   TString outfileName( "TMVA.root" );

   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "AnalysisType=Classification" );
   factory->AddVariable("LifeTime", 'F');
   factory->AddVariable("FlightDistance", 'F');
   factory->AddVariable("pt", 'F');
   
   TString fname = "../tau_data/training.root";
   TFile *input = TFile::Open( fname );
   
   TTree *tree     = (TTree*)input->Get("data");
   
   factory->AddTree(tree, "Signal", 1., "signal == 1", "Training");
   factory->AddTree(tree, "Signal", 1., "signal == 1", "Test");
   factory->AddTree(tree, "Background", 1., "signal == 0", "Training");
   factory->AddTree(tree, "Background", 1., "signal == 0", "Test");
   
   // gradient boosting training
   factory->BookMethod(TMVA::Types::kBDT, "GBDT",
                       "NTrees=40:BoostType=Grad:Shrinkage=0.01:MaxDepth=7:UseNvars=6:nCuts=20:MinNodeSize=10");
   factory->TrainAllMethods();
   input->Close();
   outputFile->Close();

   delete factory;
}

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

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

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

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

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

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

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

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

   // ---- Use BDT: Adaptive Boost
   factory->BookMethod( TMVA::Types::kBDT, "BDT", 
                        "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
 
   // ---- Train MVAs using the set of training events
   factory->TrainAllMethods();

   // ---- Evaluate all MVAs using the set of test events
   factory->TestAllMethods();

   // ----- Evaluate and compare performance of all configured MVAs
   factory->EvaluateAllMethods();    

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

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

   delete factory;
}

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

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

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

  delete TMVAtest ;
  delete outputfile ;
}

void REG::Loop() {

  if (fChain == 0) return;


  Long64_t nentries = fChain->GetEntriesFast();
  
  Long64_t nbytes = 0, nb = 0;
  TMVA::Tools::Instance();
  std::cout << std::endl;
  std::cout << "==> Start TMVARegression" << std::endl;
  TString outfileName( "TMVAReg.root" );
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

  TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile, 
					      "!V:!Silent:Color:DrawProgressBar" );

  factory->AddVariable( "l1Pt", "Variable 1", "units", 'F' );
  factory->AddVariable( "l1Eta", "Variable 2", "units", 'F' );
  factory->AddVariable( "l1Phi", "Variable 1", "units", 'F' );
//  factory->AddVariable( "RhoL1", "Variable 2", "units", 'F' );

  
  factory->AddTarget( "ratio" ); 
  factory->AddRegressionTree(fChain, 1.0 );
  TCut mycut = "";
  factory->PrepareTrainingAndTestTree( mycut, 
				       "nTrain_Regression=10000:nTest_Regression=0:SplitMode=Block:NormMode=NumEvents:!V" );

  factory->BookMethod( TMVA::Types::kBDT, "BDT",
		       "!H:!V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
  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 << "==> TMVARegression is done!" << std::endl;      

   delete factory;
}

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

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

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

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

void classifyBDT(TString inputVariables = "trainingVars.txt",
                 TString signalName = "/mnt/hscratch/dabercro/skims2/BDT_Signal.root",
                 TString backName = "/mnt/hscratch/dabercro/skims2/BDT_Background.root") {
  TMVA::Tools::Instance();
  std::cout << "==> Start TMVAClassification" << std::endl;

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName( "TMVA/TMVA.root" );
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile,
					       "!V:!Silent:Color:DrawProgressBar:Transformations=I;N" );

   // A very simple MVA (feel free to uncomment and comment what you like) => as a rule of thumb 10-20 variables is where people start to get worried about total number

   ifstream configFile;
   configFile.open(inputVariables.Data());
   TString tempFormula;

   configFile >> tempFormula;  // Is the name of the BDT
   while(!configFile.eof()){
     configFile >> tempFormula;
     if(tempFormula != ""){
       factory->AddVariable(tempFormula,'F');
     }
   }

   TString lVars;

   // TCut lCut   = "jet1qg2<2.&&jet1pt>250.&&jet1pullAngle>-5.";// < 10 && jet1mass_m2 > 60 && jet1mass_m2 < 120";
   // TCut lCut = "passZ > 3  && fjet1pt > 250 && fjet1MassPruned < 120 && fatjetid < 2";
   TCut lCut   = "abs(fjet1PartonId)!=24&&abs(fjet1PartonId)!=23";
   // std::string lEventCut = "event % 2 == 1";
   // lCut += lEventCut.c_str();

   // TCut lSCut = "passT > 0   && fjet1pt > 250 && fjet1MassPruned < 120 && abs(fjet1PartonId) == 24&& fatjetid < 2";
   TCut lSCut   = "abs(fjet1PartonId)==24||abs(fjet1PartonId)==23";
   // lSCut += lEventCut.c_str();

   TCut cleanCut = "fjet1QGtagSub2 > -10 && fjet1PullAngle > -4 && abs(fjet1pt/fjet1MassTrimmed)<200 && abs(fjet1pt/fjet1MassPruned)<200";

   TFile *lSAInput = TFile::Open(signalName);
   TTree   *lSASignal    = (TTree*)lSAInput    ->Get("DMSTree"); 
   TFile *lSBInput = TFile::Open(backName);
   TTree   *lSBSignal    = (TTree*)lSBInput    ->Get("DMSTree"); 
   
   Double_t lSWeight = 1.0;
   Double_t lBWeight = 1.0;
   gROOT->cd( outfileName+TString(":/") );   
   factory->AddSignalTree    ( lSASignal, lSWeight );
   
   gROOT->cd( outfileName+TString(":/") );   
   factory->AddBackgroundTree( lSBSignal, lBWeight );
   
   factory->SetWeightExpression("weight");
   std::stringstream pSignal,pBackground;
   pSignal << "nTrain_Signal="<< lSASignal->GetEntries() << ":nTrain_Background=" << lSBSignal->GetEntries();
   // factory->PrepareTrainingAndTestTree( lSCut, lCut,(pSignal.str()+":SplitMode=Block:NormMode=NumEvents:!V").c_str() );
   factory->PrepareTrainingAndTestTree(lSCut&&cleanCut,lCut&&cleanCut,"nTrain_Signal=0:nTrain_Background=0:SplitMode=Alternate:NormMode=NumEvents:!V");
   std::string lName = "alpha_VBF";
   TString lBDTDef   = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=5:UseYesNoLeaf=F:nEventsMin=200";
//    TString lBDTDef   = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:MaxDepth=5:UseYesNoLeaf=F:MinNodeSize=0.086:NegWeightTreatment=IgnoreNegWeightsInTraining";
   factory->BookMethod(TMVA::Types::kBDT,"BDT_simple_alpha",lBDTDef);   
   factory->TrainAllMethods();
   factory->TestAllMethods();
   factory->EvaluateAllMethods();
   outputFile->Close();

   std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
   std::cout << "==> TMVAClassification is done!" << std::endl;
   delete factory;
   //if (!gROOT->IsBatch()) TMVAGui( outfileName );
   //TString lBDTDef   = "!H:!V:NTrees=100:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=3:SeparationType=GiniIndex";
}

//.........这里部分代码省略.........
     else if( index == "2" ) indexTest = "0";
     fnameTestS += "_"+indexTest;
     fnameTestB += "_"+indexTest;
     outputFileName += "_"+index;
     weightDirName += index;
   }

   fnameTrainS     = rootDir + fnameTrainS    + ".root";
   fnameTrainB     = rootDir + fnameTrainB    + ".root";
   fnameTestS      = rootDir + fnameTestS     + ".root";
   fnameTestB      = rootDir + fnameTestB     + ".root";
   outputFileName  = rootDir + outputFileName + ".root";
   weightDirName   = weightsDir + weightDirName + "Weights";


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

   // --- Here the preparation phase begins

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName(outputFileName);
   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";
   // (TMVA::gConfig().GetIONames()).fWeightFileDir = outputFileName;
   (TMVA::gConfig().GetIONames()).fWeightFileDir = weightDirName;

   // 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" )]

   bool useNewMuonID = false;

   factory->AddVariable( "fls3d",      "fls3d", "", 'F' );
   factory->AddVariable( "alpha",      "alpha", "", 'F' );
   factory->AddVariable( "pvips",      "pvips", "", 'F' );
   factory->AddVariable( "iso",        "iso", "", 'F' );
   factory->AddVariable( "m1iso",      "m1iso", "", 'F' );
   factory->AddVariable( "m2iso",      "m2iso", "", 'F' );
   factory->AddVariable( "chi2dof",    "chi2/dof", "", 'F' );
   if( region == "barrel" ) {
     factory->AddVariable( "eta",      "eta", "", 'F' );
     factory->AddVariable( "maxdoca",  "maxdoca", "cm", 'F' );
   }
   else {
     factory->AddVariable( "pt",       "pt", "GeV/c", 'F' );
     factory->AddVariable( "pvip",     "pvip", "cm", 'F' );
   }
   factory->AddVariable( "docatrk",    "docatrk", "cm", 'F' );
   // factory->AddVariable( "pt",         "pt", "GeV/c", 'F' );

int main(int argc, char**argv){
 
 if(argc != 2){
 std::cerr << " >>>>> analysis.cpp::usage: " << argv[0] << " configFileName" << std::endl ;
 return 1;
 }

 parseConfigFile (argv[1]) ;

 // 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();

 std::vector<std::string> UseMethodName;
 UseMethodName = gConfigParser -> readStringListOption("Input::UseMethodName");

 std::cout << " >>>>> Input::UseMethodName size = " << UseMethodName.size() << std::endl;  
 std::cout << " >>>>> >>>>>  "; 
 for (unsigned int iCat = 0; iCat < UseMethodName.size(); iCat++){
  std::cout << " " << UseMethodName.at(iCat) << ", ";
 }
 std::cout << std::endl; 

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

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

 std::map<std::string,int> Use;

 for(std::vector<std::string>::iterator it=UseMethodName.begin(); it!=UseMethodName.end(); ++it) Use[*it]=0;

 std::string UseMethodFlag;
 try{ UseMethodFlag = gConfigParser -> readStringOption("Input::UseMethodFlag");
      std::cout<< UseMethodFlag<<std::endl;
      std::vector<TString> mlist = gTools().SplitString( UseMethodFlag, '/' );
      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 -1;
         }
         Use[regMethod] = 1;
         std::cout << "regMethod= " << regMethod<< " 1 "<<std::endl;

      }
     }
 catch (char * exception){
   
   std::cerr << " exception =  Use All method " << std::endl;
   for(std::vector<std::string>::iterator it=UseMethodName.begin() ;it!=UseMethodName.end(); it++) Use[*it]=1;

   }
 

 // --------------------------------------------------------------------------------------------------
 // --- Here the preparation phase begins
 // Create a new root output file
 
 std::string outputFileName =  gConfigParser -> readStringOption("Output::outputFileName");
 std::cout<<" Output Data File = "<<outputFileName<<std::endl;
 
 TFile* outputFile = TFile::Open( outputFileName.c_str(), "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/ 
 // All TMVA output can be suppressed by removing the "!" (not) in 
 // front of the "Silent" argument in the option string

 // Read training and test data (see TMVAClassification for reading ASCII files)
 // load the signal and background event samples from ROOT trees
 std::string inputFileList =  gConfigParser -> readStringOption("Input::inputFileList");
 std::string treeNameDATA =  gConfigParser -> readStringOption("Input::treeNameDATA");

 std::cout<<" Input Data List = "<<inputFileList<<std::endl;

 TChain* treeDATA = new TChain(treeNameDATA.c_str());

 FillChain(*treeDATA,inputFileList.c_str());


 TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar" );

 // If you wish to modify default settings 
//.........这里部分代码省略.........

void testBDT(){


   //---------------------------------------------------------------
   // This loads the library
   TMVA::Tools::Instance();
/*
  TString outfileName( "TMVA.root" );
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

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


 
   // global event weights per tree (see below for setting event-wise weights)
   //Double_t signalWeight     = 0.003582;
   //Double_t backgroundWeight = 0.0269;
   
   Double_t signalWeight     = 1;
   Double_t backgroundWeight = 1;
   
   TFile *input_sig = TFile::Open( "signal_exclusif.root" );
   TFile *input_wz = TFile::Open( "bruit_w_z.root" );
   
   TTree *signal     = (TTree*)input_sig->Get("tree");
   TTree *background = (TTree*)input_wz->Get("tree");

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   
   
   factory->AddVariable("PT_z"   , 'F');
   factory->AddVariable("ASYM"    , 'F');
   factory->AddVariable("PHI_lw_b", 'F');
   factory->AddVariable("M_top", 'F');
   */
   
   
   
   TString outfileName( "bdtTMVA_FCNC_tZ.root" );
  TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

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


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

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,            signalWeight     );
   factory->AddBackgroundTree( background_WZ,     backgroundWeight );
   /*factory->AddBackgroundTree( background_ZZ,     backgroundWeight );
   factory->AddBackgroundTree( background_WW,     backgroundWeight );
   factory->AddBackgroundTree( background_TTbar,  backgroundWeight );
   factory->AddBackgroundTree( background_Zjets,  backgroundWeight );
   factory->AddBackgroundTree( background_Wjets,  backgroundWeight );
   factory->AddBackgroundTree( background_TtW,    backgroundWeight );
   factory->AddBackgroundTree( background_TbartW, backgroundWeight );*/
   
   
   factory->AddVariable("tree_topMass",    'F');
   factory->AddVariable("tree_deltaPhilb", 'F');
   factory->AddVariable("tree_asym",       'F');
   factory->AddVariable("tree_Zpt",        'F');
   
   
   
   
   
   
   
   
   
   // to set weights. The variable must exist in the tree
   //    for signal    : factory->SetSignalWeightExpression    ("weight1*weight2");
   //    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
//.........这里部分代码省略.........

//.........这里部分代码省略.........
    Use["RuleFit"]         = 1;
    
    // ---------------------------------------------------------------
    // Choose method
    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;
        }
    }

    // ---------------------------------------------------------------
    // --- 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";
    

void ZTMVAClassification( TString myMethodList = "" )
{
   

   //---------------------------------------------------------------
   // 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;
   // 
   // --- 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 0;
   Use["LikelihoodD"]     = 0; // 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"]         = 0; // Recommended ANN with optional training method
   Use["MLPBNN"]          = 0; // 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
   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;



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

   // --- 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" );


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

   //factory->AddVariable( "maxpioneta", "maxpioneta", "", 'F' );
   //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' );
//.........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.........

//.........这里部分代码省略.........

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

   // --- Here the preparation phase begins

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName = "TMVA_" + signal_name + "_" + s_train_region;
   if (signal_name == "T2bw") outfileName = outfileName +"_" + s_x_parameter;
   outfileName += ".root";
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

   TString classification_name = "classification_" + signal_name + "_" + s_train_region;
   if (signal_name == "T2bw") classification_name = classification_name +"_" + s_x_parameter;

   /*
   TString multioutfileName( "TMVA_HWW_multi.root" );
   TFile* multioutputFile;

   if( doMultipleOutputs )
     multioutputFile = TFile::Open( multioutfileName, "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( classification_name, outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
   /*
   TMVA::Factory *multifactory;
   if( doMultipleOutputs )
     multifactory= new TMVA::Factory( "TMVAMulticlass", multioutputFile,
                                      "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=multiclass" );
   */
   
   // 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' );

   //--------------------------------------------------------
   // choose which variables to include in training
   //--------------------------------------------------------

   if( mvaVar[ "met"           ]  == 1 ) factory->AddVariable( "mini_met"                    ,  "E_{T}^{miss}"               ,       "GeV", 'F' );
   if( mvaVar[ "mt"            ]  == 1 ) factory->AddVariable( "mini_mt"                     ,  "M_{T}"                      ,       "GeV", 'F' );
   if( mvaVar[ "mt2w"          ]  == 1 ) factory->AddVariable( "mini_mt2w"                   ,  "MT2W"                       ,       "GeV", 'F' );
   if( mvaVar[ "mt2bl"         ]  == 1 ) factory->AddVariable( "mini_mt2bl"                  ,  "MT2bl"                      ,       "GeV", 'F' );
   if( mvaVar[ "mt2b"          ]  == 1 ) factory->AddVariable( "mini_mt2b"                   ,  "MT2b"                       ,       "GeV", 'F' );
   if( mvaVar[ "chi2"          ]  == 1 ) factory->AddVariable( "mini_chi2"                   ,  "chi2"                       ,       ""   , 'F' );
   if( mvaVar[ "lep1pt"        ]  == 1 ) factory->AddVariable( "mini_lep1pt"                 ,  "lepton pt"                  ,       ""   , 'F' );

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 TMVAMulticlass(){
   TString outfileName = "TMVAMulticlass.root";
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=multiclass" );
   factory->AddVariable( "var0", 'F' );
   factory->AddVariable( "var1", 'F' );
   TFile *input(0);
   TString fname = "./data.root";
   if (!gSystem->AccessPathName( fname )) {
      // first we try to find data.root in the local directory
      std::cout << "--- TMVAMulticlass   : Accessing " << fname << std::endl;
      input = TFile::Open( fname );
   }
   else {
      gROOT->LoadMacro( "./createData.C");
      create_multiclassdata(20000);
      cout << " created data.root for tests of the multiclass features"<<endl;
      input = TFile::Open( fname );
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << std::endl;
      exit(1);
   }
   TTree *tree     = (TTree*)input->Get("TreeR");
   
   gROOT->cd( outfileName+TString(":/") );
   factory->AddTree    ( tree, "Signal1",    1. , "cls==0"   );
   factory->AddTree    ( tree, "Signal2",    1. , "cls==1"   );
   factory->AddTree    ( tree, "Background",    1., "cls==2" );
   factory->PrepareTrainingAndTestTree( "", "SplitMode=Random:NormMode=NumEvents:!V" );

   factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5");
   factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:VarTransform=N:NCycles=100:HiddenLayers=N+5,3:TestRate=5"); // testing vartransforms
   factory->BookMethod( TMVA::Types::kMLP, "MLP2", "!H:!V:NeuronType=tanh:NCycles=100:HiddenLayers=N+5,3:TestRate=5");
   factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
                        "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x0*x1:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
   
  // 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 );
   
   
}