/// \file
/// \ingroup tutorial_tmva
/// \notebook -nodraw
/// This macro provides a simple example on how to use the trained classifiers
/// within an analysis module
/// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
/// - Package   : TMVA
/// - Exectuable: TMVAClassificationApplication
///
/// \macro_output
/// \macro_code
/// \author Andreas Hoecker

#include <cstdlib>
#include <vector>
#include <iostream>
#include <map>
#include <string>

#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"

#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
#include "TMVA/MethodCuts.h"

#include "TXMLEngine.h"

using namespace TMVA;

void TMVAClassificationApplication_socialfinal( TString myMethodList = "" )
{

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

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

   // 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
   Use["DNN_CPU"] = 0;         // CUDA-accelerated DNN training.
   Use["DNN_GPU"] = 0;         // Multi-core accelerated DNN.
   
   // Boosted Decision Trees
   Use["BDT"]             = 1; // uses Adaptive Boost
   Use["BDTG"]            = 0; // uses Gradient Boost
   Use["BDTB"]            = 0; // uses Bagging
   Use["BDTD"]            = 0; // decorrelation + Adaptive Boost
   Use["BDTF"]            = 0; // allow usage of fisher discriminant for node splitting
   //
//-------------------------------------------------------------------------------------------------------------------------
   std::cout << std::endl;
   std::cout << "==> Start TMVAClassificationApplication_socialfinal" << std::endl;

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

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

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

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

   // Create the Reader object

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

   // Create a set of variables and declare them to the reader
   // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
//   Float_t var1, var2;
//   Float_t var3, var4;
//   reader->AddVariable( "myvar1 := var1+var2", &var1 );
//   reader->AddVariable( "myvar2 := var1-var2", &var2 );
//   reader->AddVariable( "var3",                &var3 );
//   reader->AddVariable( "var4",                &var4 );

   Float_t Age, Workc, Educ, Marit, Occup, Rel, Race, Sex, Hrs, Pais;

//variables utilizadas en el entrenamiento

   reader->AddVariable( "Age",                &Age );
   reader->AddVariable( "Workc",            &Workc );
   reader->AddVariable( "Educ",              &Educ );
   reader->AddVariable( "Marit",            &Marit );
   reader->AddVariable( "Occup",            &Occup );
   reader->AddVariable( "Rel",                &Rel );
   reader->AddVariable( "Race",              &Race );
   reader->AddVariable( "Sex",                &Sex );
//   reader->AddVariable( "Gain",            &Gain );
//   reader->AddVariable( "Loss",            &Loss );
   reader->AddVariable( "Hrs",                &Hrs );
   reader->AddVariable( "Pais",              &Pais );


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



   // Book the MVA methods

   TString dir    = "/home/karen/Desktop/Prueba_clasificacion_social_02082018/Data_socialfinal/weights/";
   TString prefix = "TMVAClassification_socialfinal";

   // Book method(s)
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      if (it->second) {
         TString methodName = TString(it->first) + TString(" method");
         TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
         reader->BookMVA( methodName, weightfile );
      }
   }

   // Book output histograms
   UInt_t nbin = 100;
//   TH1F *histLk(0);
//   TH1F *histLkD(0);
//   TH1F *histLkPCA(0);
//   TH1F *histLkKDE(0);
//   TH1F *histLkMIX(0);
//   TH1F *histPD(0);
//   TH1F *histPDD(0);
//   TH1F *histPDPCA(0);
//   TH1F *histPDEFoam(0);
//   TH1F *histPDEFoamErr(0);
//   TH1F *histPDEFoamSig(0);
//   TH1F *histKNN(0);
//   TH1F *histHm(0);
//   TH1F *histFi(0);
//   TH1F *histFiG(0);
//   TH1F *histFiB(0);
//   TH1F *histLD(0);
   TH1F *histNn(0);
   TH1F *histNnbfgs(0);
   TH1F *histNnbnn(0);
   TH1F *histNnC(0);
   TH1F *histNnT(0);
   TH1F *histBdt(0);
   TH1F *histBdtG(0);
   TH1F *histBdtB(0);
   TH1F *histBdtD(0);
   TH1F *histBdtF(0);
//   TH1F *histRf(0);
//   TH1F *histSVMG(0);
//   TH1F *histSVMP(0);
//   TH1F *histSVML(0);
//   TH1F *histFDAMT(0);
//   TH1F *histFDAGA(0);
//   TH1F *histCat(0);
//   TH1F *histPBdt(0);
   TH1F *histDnnGpu(0);
   TH1F *histDnnCpu(0);

//   if (Use["Likelihood"])    histLk      = new TH1F( "MVA_Likelihood",    "MVA_Likelihood",    nbin, -1, 1 );
//   if (Use["LikelihoodD"])   histLkD     = new TH1F( "MVA_LikelihoodD",   "MVA_LikelihoodD",   nbin, -1, 0.9999 );
//   if (Use["LikelihoodPCA"]) histLkPCA   = new TH1F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin, -1, 1 );
//   if (Use["LikelihoodKDE"]) histLkKDE   = new TH1F( "MVA_LikelihoodKDE", "MVA_LikelihoodKDE", nbin,  -0.00001, 0.99999 );
//   if (Use["LikelihoodMIX"]) histLkMIX   = new TH1F( "MVA_LikelihoodMIX", "MVA_LikelihoodMIX", nbin,  0, 1 );
//   if (Use["PDERS"])         histPD      = new TH1F( "MVA_PDERS",         "MVA_PDERS",         nbin,  0, 1 );
//   if (Use["PDERSD"])        histPDD     = new TH1F( "MVA_PDERSD",        "MVA_PDERSD",        nbin,  0, 1 );
//   if (Use["PDERSPCA"])      histPDPCA   = new TH1F( "MVA_PDERSPCA",      "MVA_PDERSPCA",      nbin,  0, 1 );
//   if (Use["KNN"])           histKNN     = new TH1F( "MVA_KNN",           "MVA_KNN",           nbin,  0, 1 );
//   if (Use["HMatrix"])       histHm      = new TH1F( "MVA_HMatrix",       "MVA_HMatrix",       nbin, -0.95, 1.55 );
//   if (Use["Fisher"])        histFi      = new TH1F( "MVA_Fisher",        "MVA_Fisher",        nbin, -4, 4 );
//   if (Use["FisherG"])       histFiG     = new TH1F( "MVA_FisherG",       "MVA_FisherG",       nbin, -1, 1 );
//   if (Use["BoostedFisher"]) histFiB     = new TH1F( "MVA_BoostedFisher", "MVA_BoostedFisher", nbin, -2, 2 );
//   if (Use["LD"])            histLD      = new TH1F( "MVA_LD",            "MVA_LD",            nbin, -2, 2 );
   if (Use["MLP"])           histNn      = new TH1F( "MVA_MLP",           "MVA_MLP",           nbin, -1.25, 1.5 );
   if (Use["MLPBFGS"])       histNnbfgs  = new TH1F( "MVA_MLPBFGS",       "MVA_MLPBFGS",       nbin, -1.25, 1.5 );
   if (Use["MLPBNN"])        histNnbnn   = new TH1F( "MVA_MLPBNN",        "MVA_MLPBNN",        nbin, -1.25, 1.5 );
   if (Use["CFMlpANN"])      histNnC     = new TH1F( "MVA_CFMlpANN",      "MVA_CFMlpANN",      nbin,  0, 1 );
   if (Use["TMlpANN"])       histNnT     = new TH1F( "MVA_TMlpANN",       "MVA_TMlpANN",       nbin, -1.3, 1.3 );
   if (Use["DNN_GPU"]) histDnnGpu = new TH1F("MVA_DNN_GPU", "MVA_DNN_GPU", nbin, -0.1, 1.1);
   if (Use["DNN_CPU"]) histDnnCpu = new TH1F("MVA_DNN_CPU", "MVA_DNN_CPU", nbin, -0.1, 1.1);
   if (Use["BDT"])           histBdt     = new TH1F( "MVA_BDT",           "MVA_BDT",           nbin, -0.8, 0.8 );
   if (Use["BDTG"])          histBdtG    = new TH1F( "MVA_BDTG",          "MVA_BDTG",          nbin, -1.0, 1.0 );
   if (Use["BDTB"])          histBdtB    = new TH1F( "MVA_BDTB",          "MVA_BDTB",          nbin, -1.0, 1.0 );
   if (Use["BDTD"])          histBdtD    = new TH1F( "MVA_BDTD",          "MVA_BDTD",          nbin, -0.8, 0.8 );
   if (Use["BDTF"])          histBdtF    = new TH1F( "MVA_BDTF",          "MVA_BDTF",          nbin, -1.0, 1.0 );
//   if (Use["RuleFit"])       histRf      = new TH1F( "MVA_RuleFit",       "MVA_RuleFit",       nbin, -2.0, 2.0 );
//   if (Use["SVM_Gauss"])     histSVMG    = new TH1F( "MVA_SVM_Gauss",     "MVA_SVM_Gauss",     nbin,  0.0, 1.0 );
//   if (Use["SVM_Poly"])      histSVMP    = new TH1F( "MVA_SVM_Poly",      "MVA_SVM_Poly",      nbin,  0.0, 1.0 );
//   if (Use["SVM_Lin"])       histSVML    = new TH1F( "MVA_SVM_Lin",       "MVA_SVM_Lin",       nbin,  0.0, 1.0 );
//   if (Use["FDA_MT"])        histFDAMT   = new TH1F( "MVA_FDA_MT",        "MVA_FDA_MT",        nbin, -2.0, 3.0 );
//   if (Use["FDA_GA"])        histFDAGA   = new TH1F( "MVA_FDA_GA",        "MVA_FDA_GA",        nbin, -2.0, 3.0 );
//   if (Use["Category"])      histCat     = new TH1F( "MVA_Category",      "MVA_Category",      nbin, -2., 2. );
//   if (Use["Plugin"])        histPBdt    = new TH1F( "MVA_PBDT",          "MVA_BDT",           nbin, -0.8, 0.8 );

   // PDEFoam also returns per-event error, fill in histogram, and also fill significance
//   if (Use["PDEFoam"]) {
//      histPDEFoam    = new TH1F( "MVA_PDEFoam",       "MVA_PDEFoam",              nbin,  0, 1 );
//      histPDEFoamErr = new TH1F( "MVA_PDEFoamErr",    "MVA_PDEFoam error",        nbin,  0, 1 );
//      histPDEFoamSig = new TH1F( "MVA_PDEFoamSig",    "MVA_PDEFoam significance", nbin,  0, 10 );
//   }

   // Book example histogram for probability (the other methods are done similarly)
//   TH1F *probHistFi(0), *rarityHistFi(0);
//   if (Use["Fisher"]) {
//      probHistFi   = new TH1F( "MVA_Fisher_Proba",  "MVA_Fisher_Proba",  nbin, 0, 1 );
//      rarityHistFi = new TH1F( "MVA_Fisher_Rarity", "MVA_Fisher_Rarity", nbin, 0, 1 );
//   }

   // Prepare input tree (this must be replaced by your data source)
   // in this example, there is a toy tree with signal and one with background events
   // we'll later on use only the "signal" events for the test in this example.
   //
   TFile *input(0);
   TString fname = "./home/karen/Desktop/Prueba_clasificacion_social_02082018/Data_social_training.root";
//   if (!gSystem->AccessPathName( fname )) {
//      input = TFile::Open( fname ); // check if file in local directory exists
//   }
//   else {
//      TFile::SetCacheFileDir(".");
//      input = TFile::Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD"); // if not: download from ROOT server
//   }
//   if (!input) {
//      std::cout << "ERROR: could not open data file" << std::endl;
//      exit(1);
//   }

   //Real data input
//   TFile* datafile = new TFile("/home/karen/Desktop/Prueba_clasificacion_social_02082018/Data_social_real.root");  //open the file
//   TTree* datatree = (TTree*)datafile->Get("TreeD"); 

   std::cout << "--- TMVAClassificationApp    : Using input file: " << input->GetName() << std::endl;
//   std::cout << "--- TMVAClassificationApp    : Using real data file "<< std::endl;

   // Event loop

   // Prepare the event tree
   // - Here the variable names have to corresponds to your tree
   // - You can use the same variables as above which is slightly faster,
   //   but of course you can use different ones and copy the values inside the event loop
   //
   std::cout << "--- Select signal sample" << std::endl;
//   TTree* theTree = (TTree*)input->Get("TreeS");
//   Float_t userVar1, userVar2;
//   theTree->SetBranchAddress( "var1", &userVar1 );
//   theTree->SetBranchAddress( "var2", &userVar2 );
//   theTree->SetBranchAddress( "var3", &var3 );
//   theTree->SetBranchAddress( "var4", &var4 );

//   TFile* signalfile = new TFile("/home/karen/Desktop/Prueba_clasificacion_social_02082018/Data_social_training.root");  //open the file
   TTree* signaltree = (TTree*)input->Get("TreeS");

   signaltree->SetBranchAddress( "Age",                &Age );
   signaltree->SetBranchAddress( "Workc",            &Workc );
   signaltree->SetBranchAddress( "Educ",              &Educ );
   signaltree->SetBranchAddress( "Marit",            &Marit );
   signaltree->SetBranchAddress( "Occup",            &Occup );
   signaltree->SetBranchAddress( "Rel",                &Rel );
   signaltree->SetBranchAddress( "Race",              &Race );
   signaltree->SetBranchAddress( "Sex",                &Sex );
//   signaltree->SetBranchAddress( "Gain",            &Gain );
//   signaltree->SetBranchAddress( "Loss",            &Loss );
   signaltree->SetBranchAddress( "Hrs",                &Hrs );
   signaltree->SetBranchAddress( "Pais",              &Pais );


   // Efficiency calculator for cut method
//   Int_t    nSelCutsGA = 0;
//   Double_t effS       = 0.7;

   std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests

   std::cout << "--- Processing: " << signaltree->GetEntries() << " events" << std::endl;
   TStopwatch sw;
   sw.Start();
   for (Long64_t ievt=0; ievt<signaltree->GetEntries();ievt++) {

      if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;

      signaltree->GetEntry(ievt);

//      var1 = userVar1 + userVar2;
//      var2 = userVar1 - userVar2;

      // Return the MVA outputs and fill into histograms

//      if (Use["CutsGA"]) {
//         // Cuts is a special case: give the desired signal efficienciy
//         Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
//         if (passed) nSelCutsGA++;
//      }

//      if (Use["Likelihood"   ])   histLk     ->Fill( reader->EvaluateMVA( "Likelihood method"    ) );
//      if (Use["LikelihoodD"  ])   histLkD    ->Fill( reader->EvaluateMVA( "LikelihoodD method"   ) );
//      if (Use["LikelihoodPCA"])   histLkPCA  ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
//      if (Use["LikelihoodKDE"])   histLkKDE  ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
//      if (Use["LikelihoodMIX"])   histLkMIX  ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
//      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) );
//      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) );
//      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) );
//      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) );
//      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) );
//      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) );
//      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) );
//      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
//      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) );
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) );
      if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) );
      if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) );
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
      if (Use["DNN_GPU"]) histDnnGpu->Fill(reader->EvaluateMVA("DNN_GPU method"));
      if (Use["DNN_CPU"]) histDnnCpu->Fill(reader->EvaluateMVA("DNN_CPU method"));
      if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ) );
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      if (Use["BDTB"         ])   histBdtB   ->Fill( reader->EvaluateMVA( "BDTB method"          ) );
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      if (Use["BDTF"         ])   histBdtF   ->Fill( reader->EvaluateMVA( "BDTF method"          ) );
//      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
//      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
//      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
//      if (Use["SVM_Lin"      ])   histSVML   ->Fill( reader->EvaluateMVA( "SVM_Lin method"       ) );
//      if (Use["FDA_MT"       ])   histFDAMT  ->Fill( reader->EvaluateMVA( "FDA_MT method"        ) );
//      if (Use["FDA_GA"       ])   histFDAGA  ->Fill( reader->EvaluateMVA( "FDA_GA method"        ) );
//      if (Use["Category"     ])   histCat    ->Fill( reader->EvaluateMVA( "Category method"      ) );
//      if (Use["Plugin"       ])   histPBdt   ->Fill( reader->EvaluateMVA( "P_BDT method"         ) );

      // Retrieve also per-event error
//      if (Use["PDEFoam"]) {
//         Double_t val = reader->EvaluateMVA( "PDEFoam method" );
//         Double_t err = reader->GetMVAError();
//         histPDEFoam   ->Fill( val );
//         histPDEFoamErr->Fill( err );
//         if (err>1.e-50) histPDEFoamSig->Fill( val/err );
//      }

      // Retrieve probability instead of MVA output
//      if (Use["Fisher"])   {
//         probHistFi  ->Fill( reader->GetProba ( "Fisher method" ) );
//         rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
//      }
//   }

   // Get elapsed time
   sw.Stop();
   std::cout << "--- End of event loop: "; sw.Print();

   // Get efficiency for cuts classifier
//   if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
//                                << " (for a required signal efficiency of " << effS << ")" << std::endl;

//   if (Use["CutsGA"]) {

//      // test: retrieve cuts for particular signal efficiency
//      // CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
//      TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;

//      if (mcuts) {
//         std::vector<Double_t> cutsMin;
//         std::vector<Double_t> cutsMax;
//         mcuts->GetCuts( 0.7, cutsMin, cutsMax );
//         std::cout << "--- -------------------------------------------------------------" << std::endl;
//         std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
//         for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
//            std::cout << "... Cut: "
//                      << cutsMin[ivar]
//                      << " < \""
//                      << mcuts->GetInputVar(ivar)
//                      << "\" <= "
//                      << cutsMax[ivar] << std::endl;
//         }
//         std::cout << "--- -------------------------------------------------------------" << std::endl;
//      }
//   }

   // Write histograms

   TFile *target  = new TFile( "TMVAClassificationApp_socialfinal.root","RECREATE" );
//   if (Use["Likelihood"   ])   histLk     ->Write();
//   if (Use["LikelihoodD"  ])   histLkD    ->Write();
//   if (Use["LikelihoodPCA"])   histLkPCA  ->Write();
//   if (Use["LikelihoodKDE"])   histLkKDE  ->Write();
//   if (Use["LikelihoodMIX"])   histLkMIX  ->Write();
//   if (Use["PDERS"        ])   histPD     ->Write();
//   if (Use["PDERSD"       ])   histPDD    ->Write();
//   if (Use["PDERSPCA"     ])   histPDPCA  ->Write();
//   if (Use["KNN"          ])   histKNN    ->Write();
//   if (Use["HMatrix"      ])   histHm     ->Write();
//   if (Use["Fisher"       ])   histFi     ->Write();
//   if (Use["FisherG"      ])   histFiG    ->Write();
//   if (Use["BoostedFisher"])   histFiB    ->Write();
//   if (Use["LD"           ])   histLD     ->Write();
   if (Use["MLP"          ])   histNn     ->Write();
   if (Use["MLPBFGS"      ])   histNnbfgs ->Write();
   if (Use["MLPBNN"       ])   histNnbnn  ->Write();
   if (Use["CFMlpANN"     ])   histNnC    ->Write();
   if (Use["TMlpANN"      ])   histNnT    ->Write();
   if (Use["DNN_GPU"]) histDnnGpu->Write();
   if (Use["DNN_CPU"]) histDnnCpu->Write();
   if (Use["BDT"          ])   histBdt    ->Write();
   if (Use["BDTG"         ])   histBdtG   ->Write();
   if (Use["BDTB"         ])   histBdtB   ->Write();
   if (Use["BDTD"         ])   histBdtD   ->Write();
   if (Use["BDTF"         ])   histBdtF   ->Write();
//   if (Use["RuleFit"      ])   histRf     ->Write();
//   if (Use["SVM_Gauss"    ])   histSVMG   ->Write();
//   if (Use["SVM_Poly"     ])   histSVMP   ->Write();
//   if (Use["SVM_Lin"      ])   histSVML   ->Write();
//   if (Use["FDA_MT"       ])   histFDAMT  ->Write();
//   if (Use["FDA_GA"       ])   histFDAGA  ->Write();
//   if (Use["Category"     ])   histCat    ->Write();
//   if (Use["Plugin"       ])   histPBdt   ->Write();

   // Write also error and significance histos
//   if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }

   // Write also probability hists
//   if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
//   target->Close();

   std::cout << "--- Created root file: \"TMVApp_Classification_socialfinal.root\" containing the MVA output histograms" << std::endl;

   delete reader;

   std::cout << "==> TMVAClassificationApplication_socialfinal is done!" << std::endl;
}
}
int main( int argc, char** argv )
{
   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;
   }
   TMVAClassificationApplication_socialfinal(methodList);
   return 0;
}