///     root -l ./TMVAClassification.C

#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include "TChain.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TROOT.h"

#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/TMVAGui.h"

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

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

   // 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 1;
   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;
   
    // Boosted Decision Trees
   Use["BDT"]             = 1; // uses Adaptive Boost
   Use["BDTG"]            = 0; // uses Gradient Boost
   Use["BDTB"]            = 1; // uses Bagging
   Use["BDTD"]            = 0; // decorrelation + Adaptive Boost
   Use["BDTF"]            = 0; // allow usage of fisher discriminant for node splitting
   
    // 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
   
   // Cut optimisation
   Use["Cuts"]            = 0;
   Use["CutsD"]           = 0;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   
   // 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
   
   #ifdef R__HAS_TMVAGPU
   Use["DNN_GPU"]         = 0; // CUDA-accelerated DNN training.
#else
   Use["DNN_GPU"]         = 0;
#endif

#ifdef R__HAS_TMVACPU
   Use["DNN_CPU"]         = 0; // Multi-core accelerated DNN.
#else
   Use["DNN_CPU"]         = 0;
#endif
   //
   // Support Vector Machine
   Use["SVM"]             = 0;
   
    // 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 1;
         }
         Use[regMethod] = 1;
      }
   }

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

   // Here the preparation phase begins

   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   
                           ///////////////////////////////////////////////    Signal Files   ////////////////////////////////////////////////////////
   TFile *input(0);
   TString fname = "/home/abdul/Software/root/tutorials/tmva/Signal_RL.root";
   if (!gSystem->AccessPathName( fname )) {
      input = TFile::Open( fname ); // check if file in local directory exists
   }
   else {
      TFile::SetCacheFileDir(".");
      input = TFile::Open("/home/abdul/Software/root/tutorials/tmva/Signal_RL.root", "CACHEREAD");
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << std::endl;
      exit(1);
   }
   
   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
   
 
   
 // //////////////////////////////////////////////////         Background  Files       ///////////////////////////////////////////

   
   TFile* data1 = new TFile("/home/abdul/Software/root/tutorials/tmva/Bkg1_ttbar.root");
   TTree* data1Tree = (TTree*)(data1->Get("Tree"));
   
   TFile* data2 = new TFile("/home/abdul/Software/root/tutorials/tmva/bg2_ttz.root");
   TTree* data2Tree = (TTree*)(data2->Get("Tree"));
   
   TFile* data3 = new TFile("/home/abdul/Software/root/tutorials/tmva/Bkg3_ttbarh.root");
   TTree* data3Tree = (TTree*)(data3->Get("Tree"));
   
   //TFile* data4 = new TFile("/home/abdul/Software/root/tutorials/tmva/bg4_wwz.root");
   //TTree* data4Tree = (TTree*)(data4->Get("Tree"));
   
   
   //////////////////////////////////////////////          Register the training and test trees    ////////////////////////////////////////////

   TTree *signalTree     = (TTree*)input->Get("DelphesS");
  // TTree *signalTree     = (TTree*)input->Get("DelphesS2");
  // TTree *signalTree     = (TTree*)input->Get("DelphesS3");
   TTree *background1     = (TTree*)data1->Get("DelphesB1");
   TTree *background2     = (TTree*)data2->Get("DelphesB2");
   TTree *background3     = (TTree*)data3->Get("DelphesB3");
  // TTree *background4     = (TTree*)data4->Get("DelphesB4");
    
   // 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" );

   TMVA::DataLoader *dataloader=new TMVA::DataLoader("dataset");
   
   
   // Add Variable which are given in Tree of root file
   
   dataloader->AddVariable( "Jet.PT", "Jet.PT","GeV", 'F' );
   dataloader->AddVariable( "Jet.Eta", "Jet.Eta", 'F' );
   dataloader->AddVariable( "Jet.Phi", "Jet.Phi", 'F' );
   dataloader->AddVariable( "Jet.Mass", "Jet.Mass", "GeV",'F' );
  dataloader->AddVariable(" MissingET.MET"," MissingET.MET","GeV",'F'); 
  dataloader->AddVariable("@Jet.size()","@Jet.size()","GeV",'F');
   dataloader->AddVariable("Jet.BTag","Jet.BTag",'F');
   dataloader->AddVariable(" ScalarHT.HT"," ScalarHT.HT","GeV",'F');
   dataloader->AddVariable(" Jet.DeltaEta","Jet.DeltaEta",'F'); 
   dataloader->AddVariable(" Jet.DeltaPhi","Jet.DeltaPhi",'F');
   
    // Add spectator vraibles which do not appear in graphs
    
   dataloader->AddSpectator( "spec1 := Jet.PT*3",  "Jet.PT*3", "GeV", 'F' );
   dataloader->AddSpectator( "spec2 := Jet.Eta*3",  "Jet.Eta*3", 'F' );
   dataloader->AddSpectator( "spec3 := Jet.Phi*3",  "Jet.Phi*3", 'F' );
   dataloader->AddSpectator( "spec4 := Jet.Mass*3",  "Jet.Mass*3", "GeV", 'F' );
   dataloader->AddSpectator( "spec5 := MissingET.MET*3",  " MissingET.MET*3", "GeV", 'F' );
   dataloader->AddSpectator( "spec6 := @Jet.size()*3",  "@Jet.size()*3","GeV", 'F' );
   dataloader->AddSpectator( "spec7 := Jet.BTag*3",  "Jet.BTag*3", 'F' );
   dataloader->AddSpectator( "spec8 := ScalarHT.HT*3",  "ScalarHT.HT*3","GeV", 'F' );
   dataloader->AddSpectator( "spec9 := Jet.DeltaEta*3",  "Jet.DeltaEta*3", 'F' );
   dataloader->AddSpectator( "spec10 := Jet.DeltaPhi*3",  "Jet.DeltaPhi*3", 'F' );
 
 
   
  /////////////////////////////////// //           global event weights per tree (see below for setting event-wise weights)   ////////////////////////////////////////////////
   Double_t signalWeight     =  1;
   Double_t background1Weight = 2;
   Double_t background2Weight = 2;
   Double_t background3Weight = 2;
   //Double_t background4Weight = 3;
   
   
   // //////////////////////////////////////////////////////     You can add an arbitrary number of signal or background trees    //////////////////////////////////////////
   dataloader->AddSignalTree    ( signalTree,     signalWeight );
   dataloader->AddBackgroundTree( background1, background1Weight );
   dataloader->AddBackgroundTree( background2, background2Weight );
   dataloader->AddBackgroundTree( background3, background3Weight );
  // dataloader->AddBackgroundTree( background4, background4Weight );

   dataloader->SetBackgroundWeightExpression( "Event.Weight" );

   // Apply additional cuts on the signal and background samples (can be different)
  TCut mycuts = "abs(Jet.PT) > 30 && abs(Jet.Eta) < 2.1 || abs(Jet.Eta) > -2.1 && abs(Jet.Mass) == 500 "; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
  TCut mycutb = "abs(Jet.PT) > 30 && abs(Jet.Eta) < 2.1 || abs(Jet.Eta) > -2.1 && abs(Jet.Mass) == 500 "; // for example: TCut mycutb = "abs(var1)<0.5";
   
  
  
   // Tell the dataloader how to use the training and testing events
   dataloader->PrepareTrainingAndTestTree( mycuts, 
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

                                       
                                       
                                       
                                       //////// Book MVA methods  //////////
   
   
////////////////////////////////////Likelihood Methos/////////////////////////////////////////////////////////// 
   
   
   // Likelihood ("naive Bayes estimator")
   if (Use["Likelihood"])
      factory->BookMethod( dataloader, 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( dataloader, TMVA::Types::kLikelihood, "LikelihoodD",
                           "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=7:NAvEvtPerBin=50:VarTransform=Decorrelate" );

   // PCA-transformed likelihood
   if (Use["LikelihoodPCA"])
      factory->BookMethod( dataloader, TMVA::Types::kLikelihood, "LikelihoodPCA",
                           "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=7:NAvEvtPerBin=50:VarTransform=PCA" );

   // Use a kernel density estimator to approximate the PDFs
   if (Use["LikelihoodKDE"])
      factory->BookMethod( dataloader, 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( dataloader, 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" );



//////////////////////////////////// Multilayer Perceptrons Methods/////////////////////////////////////////////////////



   // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
   if (Use["MLP"])
      factory->BookMethod( dataloader, TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
      
 if (Use["MLPBFGS"])
      factory->BookMethod( dataloader, TMVA::Types::kMLP, "MLPBFGS", "!H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
      // BFGS training with bayesian regulators
      
 if (Use["MLPBNN"])
      factory->BookMethod( dataloader, TMVA::Types::kMLP, "MLPBNN", "!H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); 
  
     
      /////////////////////////////////////Boost Methods//////////////////////////////////////////////////     


// Adaptive Boost
   if (Use["BDT"])  
      factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDT",
                           "!H:!V:NTrees=800:MinNodeSize=2.5%:MaxDepth=3.5:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );


 // Boosted Decision Trees // Gradient Boost
   if (Use["BDTG"])
      factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTG",
                           "!H:!V:NTrees=900:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" );
      
      // Bagging                     
   if (Use["BDTB"]) 
      factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTB",
                           "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
      
     // Decorrelation + Adaptive Boost
   if (Use["BDTD"]) 
      factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTD",
                           "!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3.5:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );
// Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
   if (Use["BDTF"])  
      factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTF",
                           "!H:!V:NTrees=500:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3.5:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );
     
     
         
      //////////////////////////////////////PDERD Methods//////////////////////////////////////////                     
 
 
  //PDERSD
  
  if (Use["PDERSD"])
      factory->BookMethod( dataloader, TMVA::Types::kPDERS, "PDERSD",
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.5:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );    
  
  //PDERSPCA
                           
   if (Use["PDERSPCA"])
      factory->BookMethod( dataloader, TMVA::Types::kPDERS, "PDERSPCA",
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.5:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
        
   // Multi-dimensional likelihood estimator using self-adapting phase-space binning
   if (Use["PDEFoam"])
      factory->BookMethod( dataloader, 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" );
   
   //PDEFoamBoost
                           
   if (Use["PDEFoamBoost"])
      factory->BookMethod( dataloader, TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=3.5: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( dataloader, 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( dataloader, TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );
      
      // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( dataloader, TMVA::Types::kLD, "LD", "!H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );                        
       
       //////////////////////////////// Fisher discriminant Methods//////////////////////////////////                    
      
       // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( dataloader, TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
      
      
      // Fisher with Gauss-transformed input variables
   if (Use["FisherG"])
      factory->BookMethod( dataloader, TMVA::Types::kFisher, "FisherG", "!H:!V:VarTransform=Gauss" );                                           

// Composite classifier: ensemble (tree) of boosted Fisher classifiers
   if (Use["BoostedFisher"])
      factory->BookMethod( dataloader, 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( dataloader, 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=30000:Sigma=0.1" );

// can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
   if (Use["FDA_GA"]) 
      factory->BookMethod( dataloader, 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=100:Cycles=2:Steps=5:Trim=True:SaveBestGen=1" );

// can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
   if (Use["FDA_SA"]) 
      factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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" );
   
   
   
 
 ////////////////////////////////////////// ANN Methods /////////////////////////////////////////////////////////////
 
 // CF(Clermont-Ferrand)ANN
   if (Use["CFMlpANN"])
      factory->BookMethod( dataloader, TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N"  ); // n_cycles:#nodes:#nodes:...

   // Tmlp(Root)ANN
   if (Use["TMlpANN"])
      factory->BookMethod( dataloader, TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3"  ); // n_cycles:#nodes:#nodes:...
      
   // RuleFit -- TMVA implementation of Friedman's method
   if (Use["RuleFit"])
      factory->BookMethod( dataloader, 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" );   
 
  // Support Vector Machine
   if (Use["SVM"])
      factory->BookMethod( dataloader, TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );


      // General layout.
      TString layoutString ("Layout=TANH|128,TANH|128,TANH|128,LINEAR");

      // Training strategies.
      TString training0("LearningRate=1e-2,Momentum=0.9,Repetitions=1,"
                        "ConvergenceSteps=30,BatchSize=256,TestRepetitions=10,"
                        "WeightDecay=1e-4,Regularization=L2,"
                        "DropConfig=0.0+0.5+0.5+0.5, Multithreading=True");
      TString training1("LearningRate=1e-2,Momentum=0.9,Repetitions=1,"
                        "ConvergenceSteps=20,BatchSize=256,TestRepetitions=10,"
                        "WeightDecay=1e-4,Regularization=L2,"
                        "DropConfig=0.0+0.0+0.0+0.0, Multithreading=True");
      TString training2("LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
                        "ConvergenceSteps=20,BatchSize=256,TestRepetitions=10,"
                        "WeightDecay=1e-4,Regularization=L2,"
                        "DropConfig=0.0+0.0+0.0+0.0, Multithreading=True");
      TString trainingStrategyString ("TrainingStrategy=");
      trainingStrategyString += training0 + "|" + training1 + "|" + training2;

      // General Options.
      TString dnnOptions ("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
                          "WeightInitialization=XAVIERUNIFORM");
      dnnOptions.Append (":"); dnnOptions.Append (layoutString);
      dnnOptions.Append (":"); dnnOptions.Append (trainingStrategyString);



//-------------------------------------------------------------------------------------------------------------------
   // 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;
   delete dataloader;
   // Launch the GUI for the root macros
   if (!gROOT->IsBatch()) TMVA::TMVAGui( outfileName );

   return 0;
}

int main( int argc, char** argv )
{
   // Select methods (don't look at this code - not of interest)
   TString methodList;
   for (int i=1; i<argc; i++) {
      TString regMethod(argv[i]);
      if(regMethod=="-b" || regMethod=="--batch") continue;
      if (!methodList.IsNull()) methodList += TString(",");
      methodList += regMethod;
   }
   return TMVAClassification(methodList);
}