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();

    // ---- 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 << "==> TMVAClassificationCategory is done!" << std::endl;

    // Clean up
    delete factory;

    // Launch the GUI for the root macros
    if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TMVAClassificationCategory() 
{
   //---------------------------------------------------------------

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

   bool batchMode(false);

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

   // Create the factory object. Later you can choose the methods
   // whose performance you'd like to investigate. The factory will
   // then run the performance analysis for you.
   //
   // The first argument is the base of the name of all the
   // weightfiles in the directory weight/ 
   //
   // The second argument is the output file for the training results
   // All TMVA output can be suppressed by removing the "!" (not) in 
   // front of the "Silent" argument in the option string
   std::string factoryOptions( "!V:!Silent:Transformations=I;D;P;G,D" );
   if (batchMode) factoryOptions += ":!Color:!DrawProgressBar";

   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, factoryOptions );

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

   // Define the input variables that shall be used for the MVA training
   // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
   // [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
   factory->AddVariable( "var1", 'F' );
   factory->AddVariable( "var2", 'F' );
   factory->AddVariable( "var3", 'F' );
   factory->AddVariable( "var4", 'F' );

   // You can add so-called "Spectator variables", which are not used in the MVA training, 
   // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the 
   // input variables, the response values of all trained MVAs, and the spectator variables
   factory->AddSpectator( "eta" );

   // load the signal and background event samples from ROOT trees
   TFile *input(0);
   TString fname( "" );
   if (UseOffsetMethod) fname = "../execs/data/toy_sigbkg_categ_offset.root";
   else                 fname = "../execs/data/toy_sigbkg_categ_varoff.root";
   if (!gSystem->AccessPathName( fname )) {
      // first we try to find tmva_example.root in the local directory
      std::cout << "--- TMVAClassificationCategory: Accessing " << fname << std::endl;
      input = TFile::Open( fname );
   } 

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

   TTree *signal     = (TTree*)input->Get("TreeS");
   TTree *background = (TTree*)input->Get("TreeB");

   /// global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   /// you can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   
   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

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

   // Fisher discriminant   
   factory->BookMethod( TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher" );

   // Likelihood
   factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", 
                        "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); 

   // Categorised classifier
   TMVA::MethodCategory* mcat = 0;
   
   // the variable sets
   TString theCat1Vars = "var1:var2:var3:var4";
   TString theCat2Vars = (UseOffsetMethod ? "var1:var2:var3:var4" : "var1:var2:var3");

   // the Fisher 
   TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
   mcat = dynamic_cast<TMVA::MethodCategory*>(fiCat);
   mcat->AddMethod("abs(eta)<=1.3",theCat1Vars, TMVA::Types::kFisher,"Category_Fisher_1","!H:!V:Fisher");
   mcat->AddMethod("abs(eta)>1.3", theCat2Vars, TMVA::Types::kFisher,"Category_Fisher_2","!H:!V:Fisher");

   // the Likelihood
   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();

   // ---- 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 << "==> TMVAClassificationCategory is done!" << std::endl;      

   // Clean up
   delete factory;

   // Launch the GUI for the root macros
   if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
//void TMVAClassification( TString myMethodList = "" )
void tmvaClassifier( 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 TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
   //
   // if you like to use a method via the plugin mechanism, we recommend using
   //
   // mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
   // (an example is given for using the BDT as plugin (see below),
   // but of course the real application is when you write your own
   // method based)

   //---------------------------------------------------------------
   // 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"]     = 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"]          = 1;
   Use["FisherCat"]          = 0;//added by loic
   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"]             = 0; // 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"]             = 0; // uses Adaptive Boost
   Use["BDTG"]            = 0; // 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;

   // 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.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( "mjjtev:=mjj/1000.",  "mjj","units", 'F' );
   //factory->AddVariable( "dijetf:=-0.19757098+0.833967373*mjj/1000.-0.07935860*detajj",  "dijetf","units", 'F' );

   factory->AddVariable( "mjj",  "mjj","units", 'F' );
   factory->AddVariable( "detajj",             "detajj", "units", 'F' );
   factory->AddVariable( "spt",              "spt", "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' );
   // factory->AddSpectator( "spec2 := var1*3",  "Spectator 2", "units", '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";
   
   //if (gSystem->AccessPathName( fname ))  // file does not exist in local directory
   //   gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
   
  
   //   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
   
   // --- Register the training and test trees

   TFile *inputS = TFile::Open( "MC8TeV_lljj_VBFNLO_summary.root" );
   TTree *signal     = (TTree*)inputS->Get("ewkzp2j");
   TFile *inputB = TFile::Open("MC8TeV_DY2JetsToLL_50toInf_summary.root");
   TTree *background = (TTree*)inputB->Get("ewkzp2j");
   
   // 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" );
   
   // Use the following code instead of the above two or four lines to add signal and background
   // training and test events "by hand"
   // NOTE that in this case one should not give expressions (such as "var1+var2") in the input
   //      variable definition, but simply compute the expression before adding the event
   //
   //     // --- begin ----------------------------------------------------------
   //     std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
   //     Float_t  treevars[4], weight;
   //     
   //     // Signal
   //     for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   //     for (UInt_t i=0; i<signal->GetEntries(); i++) {
   //        signal->GetEntry(i);
   //        for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
   //        // add training and test events; here: first half is training, second is testing
   //        // note that the weight can also be event-wise
   //        if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight );
   //        else                              factory->AddSignalTestEvent    ( vars, signalWeight );
   //     }
   //   
   //     // Background (has event weights)
   //     background->SetBranchAddress( "weight", &weight );
   //     for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   //     for (UInt_t i=0; i<background->GetEntries(); i++) {
   //        background->GetEntry(i);
   //        for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
   //        // add training and test events; here: first half is training, second is testing
   //        // note that the weight can also be event-wise
   //        if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
   //        else                                factory->AddBackgroundTestEvent    ( vars, backgroundWeight*weight );
   //     }
         // --- end ------------------------------------------------------------
   //
   // --- end of tree registration 

   // Set individual event weights (the variables must exist in the original TTree)
   //    for signal    : factory->SetSignalWeightExpression    ("weight1*weight2");
   //    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
   factory->SetBackgroundWeightExpression( "weight" );

   // 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
   //
   // If no numbers of events are given, half of the events in the tree are used 
   // for training, and the other half for testing:
   //    factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
   // To also specify the number of testing events, use:
   //    factory->PrepareTrainingAndTestTree( mycut,
   //                                         "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // ---- Book MVA methods
   //
   // Please lookup the various method configuration options in the corresponding cxx files, eg:
   // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
   // it is possible to preset ranges in the option string in which the cut optimisation should be done:
   // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable

   // Cut optimisation
   if (Use["Cuts"])
      factory->BookMethod( TMVA::Types::kCuts, "Cuts",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );

   if (Use["CutsD"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsD",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );

   if (Use["CutsPCA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );

   if (Use["CutsGA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
                           "H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );

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

   // Likelihood ("naive Bayes estimator")
   if (Use["Likelihood"])
      factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
                           "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

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

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

   // Use a kernel density estimator to approximate the PDFs
   if (Use["LikelihoodKDE"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
                           "!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" ); 

   // Use a variable-dependent mix of splines and kernel density estimator
   if (Use["LikelihoodMIX"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
                           "!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" ); 

   // Test the multi-dimensional probability density estimator
   // here are the options strings for the MinMax and RMS methods, respectively:
   //      "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
   //      "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
   if (Use["PDERS"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
                           "!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );

   if (Use["PDERSD"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );

   if (Use["PDERSPCA"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );

   // Multi-dimensional likelihood estimator using self-adapting phase-space binning
   if (Use["PDEFoam"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
                           "!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );

   if (Use["PDEFoamBoost"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );

   // K-Nearest Neighbour classifier (KNN)
   if (Use["KNN"])
      factory->BookMethod( TMVA::Types::kKNN, "KNN",
                           "H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );

   // H-Matrix (chi2-squared) method
   if (Use["HMatrix"])
      factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );

   // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
   
   if (Use["FisherCat"]){
      TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
      TMVA::MethodCategory* mcategory = dynamic_cast<TMVA::MethodCategory*>(fiCat);
      mcategory->AddMethod( "mjj<250", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=250&&mjj<350" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0000", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=350&&mjj<450" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0350", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=450&&mjj<550" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0450", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=550&&mjj<750" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0550", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=750&&mjj<1000", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0750", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      mcategory->AddMethod( "mjj>=1000"         , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1000", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
   }


   // Fisher with Gauss-transformed input variables
   if (Use["FisherG"])
      factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );

   // Composite classifier: ensemble (tree) of boosted Fisher classifiers
   if (Use["BoostedFisher"])
      factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", 
                           "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" );

   // Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
   if (Use["FDA_MC"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );

   if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );

   if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );

   if (Use["FDA_MT"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );

   if (Use["FDA_GAMT"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );

   if (Use["FDA_MCMT"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );

   // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
   if (Use["MLP"])
      factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );

   if (Use["MLPBFGS"])
      factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );

   if (Use["MLPBNN"])
      factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators

   // CF(Clermont-Ferrand)ANN
   if (Use["CFMlpANN"])
      factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N"  ); // n_cycles:#nodes:#nodes:...  

   // Tmlp(Root)ANN
   if (Use["TMlpANN"])
      factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3"  ); // n_cycles:#nodes:#nodes:...

   // Support Vector Machine
   if (Use["SVM"])
      factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );

   // Boosted Decision Trees
   if (Use["BDTG"]) // Gradient Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTG",
                           "!H:!V:NTrees=1000:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:MaxDepth=2" );

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

   if (Use["BDTB"]) // Bagging
      factory->BookMethod( TMVA::Types::kBDT, "BDTB",
                           "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );

   if (Use["BDTD"]) // Decorrelation + Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTD",
			   "!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=25:PruneMethod=CostComplexity:PruneStrength=25.0:VarTransform=Decorrelate");
                         //"!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

   if (Use["BDTF"])  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
      factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
                           "!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );

   // RuleFit -- TMVA implementation of Friedman's method
   if (Use["RuleFit"])
      factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
                           "H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );

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




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

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

   // factory->OptimizeAllMethods("SigEffAt001","Scan");
   // factory->OptimizeAllMethods("ROCIntegral","FitGA");

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

   // ---- Now you can tell the factory to train, test, and evaluate the MVAs

   // Train MVAs using the set of training events
   factory->TrainAllMethods();

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

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

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

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

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

   delete factory;

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