{

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

TChain signalTree ("shrinked") ;

signalTree.Add ("data/H160_VBF.root") ;

std::cout << "READ " << signalTree.GetEntries () << " signal events\n" ;

TMVAtest->AddSignalTree (&signalTree, 1) ;

//PG get the bkg and deliver it to the TMVA factory

TChain bkgTree ("shrinked") ;

bkgTree.Add ("data/H160_NOVBF.root") ;

std::cout << "READ " << bkgTree.GetEntries () << " bkg events\n" ;

TMVAtest->AddBackgroundTree (&bkgTree, 1) ;

//PG get the training and test samples and deliver them to the TMVA factory

TChain signalTrainTree ("shrinked") ;

signalTrainTree.Add ("data/H150_VBF.root") ;

std::cout << "READ " << signalTrainTree.GetEntries () << " signal train events\n" ;

TChain bkgTrainTree ("shrinked") ;

bkgTrainTree.Add ("data/H150_NOVBF.root") ;

std::cout << "READ " << bkgTrainTree.GetEntries () << " bkg train events\n" ;

// PG this is useless!!

TMVAtest->SetInputTrees (signalTrainTree.GetTree (), bkgTrainTree.GetTree (), 1., 1.) ;

//PG variables to be used for the selection

//PG must be defined in the TTrees

TMVAtest->AddVariable ("vars.Deta", 'F') ;

TMVAtest->AddVariable ("vars.Dphi" , 'F') ;

TMVAtest->AddVariable ("vars.Minv" , 'F') ;

int signalNumTrain = signalTrainTree.GetEntries () * 4 / 5 ;

int bkgNumTrain = bkgTrainTree.GetEntries () * 4 / 5 ;

int signalNumTest = signalTrainTree.GetEntries () - signalNumTrain ;

int bkgNumTest = bkgTrainTree.GetEntries () - bkgNumTrain ;

char trainOptions[120] ;

sprintf (trainOptions,"NSigTrain=%d:NBkgTrain=%d:NSigTest=%d:NBkgTest=%d",

signalNumTrain, bkgNumTrain,

signalNumTest, bkgNumTest) ;

sprintf (trainOptions,"NSigTrain=%d:NBkgTrain=%d:NSigTest=%d:NBkgTest=%d",

0,0,0,0) ;

std::cout << "TRAINING CONFIGURATION : " << trainOptions << "\n" ;

TMVAtest->PrepareTrainingAndTestTree ("",trainOptions) ;

//PG prepare the classifiers

//PG ------------------------

//PG cut-based, default params

TMVAtest->BookMethod (TMVA::Types::kCuts, "Cuts") ;

//PG multi layer perceptron

TMVAtest->BookMethod (TMVA::Types::kMLP, "MLP",

"H:!V:!Normalise:NeuronType=tanh:NCycles=200:HiddenLayers=N+1,N:TestRate=5");

TMVAtest->TrainAllMethods () ;

TMVAtest->TestAllMethods () ;

TMVAtest->EvaluateAllMethods () ;

delete TMVAtest ;

delete outputfile ;