#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"

#if not defined(__CINT__) || defined(__MAKECINT__)

// needs to be included when makecint runs (ACLIC)

#include "TMVA/Factory.h"

#include "TMVA/Tools.h"

#endif

//require mumucl>0.6

//opening angle >10

//coplanarity >90

//pang<90

void TMVAClassification_cc1pcoh_bdt_ver6noveract( TString myMethodList = "" )

{

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

// This loads the library

TMVA::Tools::Instance();

// to get access to the GUI and all tmva macros

TString thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName());

gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath());

gROOT->ProcessLine(".L TMVAGui.C");

// Default MVA methods to be trained + tested

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

// --- Cut optimisation

Use["Cuts"] = 1;

Use["CutsD"] = 1;

Use["CutsPCA"] = 0;

Use["CutsGA"] = 0;

Use["CutsSA"] = 0;

//

// --- 1-dimensional likelihood ("naive Bayes estimator")

Use["Likelihood"] = 1;

Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)

Use["LikelihoodPCA"] = 1; // 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"] = 1;

Use["PDERSD"] = 0;

Use["PDERSPCA"] = 0;

Use["PDEFoam"] = 1;

Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting

Use["KNN"] = 1; // k-nearest neighbour method

//

// --- Linear Discriminant Analysis

Use["LD"] = 1; // 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"] = 1; // 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"] = 1; // 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"] = 1;

//

// --- 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

//

// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")

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";

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

// --- Add sample

TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot.root";

TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe.root";

TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot.root";

TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot.root";

/*TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pm_merged_ccqe_tot.root";

TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pmbar_merged_ccqe.root";

TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/wall_merged_ccqe_tot.root";

TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/ingrid_merged_nd3_ccqe_tot.root";*/

TFile *pfileSignal = new TFile(fsignalStr);

TFile *pfileBar = new TFile(fbarStr);

TFile *pfileBkg = new TFile(fbkgStr);

TFile *pfileBkg2 = new TFile(fbkg2Str);

TFile *pfileRatio = new TFile(fratioStr);

TTree *ptree_sig = (TTree*)pfileSignal->Get("tree");

TTree *ptree_bar = (TTree*)pfileBar->Get("tree");

TTree *ptree_bkg = (TTree*)pfileBkg->Get("tree");

TTree *ptree_bkg2 = (TTree*)pfileBkg2->Get("tree");

// POT normalization

const int nmcFile = 3950;

const int nbarFile = 986;

const int nbkgFile = 55546;//(31085+24461);

const int nbkg2File = 7882;//(3941+3941);

// global event weights per tree (see below for setting event-wise weights)

// adding for signal sample

// using this as standard and add other later

Double_t signalWeight_sig = 1.0;

Double_t backgroundWeight_sig = 1.0;

factory->AddSignalTree ( ptree_sig, signalWeight_sig );

factory->AddBackgroundTree( ptree_sig, backgroundWeight_sig );

// Add Numubar sample

//Double_t signalWeight_bar = nmcFile/float(nbarFile);

Double_t backgroundWeight_bar = nmcFile/float(nbarFile);

//factory->AddSignalTree ( ptree_bar, signalWeight_bar );

factory->AddBackgroundTree( ptree_bar, backgroundWeight_bar );

// Add wall background

//Double_t signalWeight_bkg = nmcFile/float(nbkgFile);

Double_t backgroundWeight_bkg = nmcFile/float(nbkgFile);

//factory->AddSignalTree ( ptree_bkg, signalWeight_bkg );

factory->AddBackgroundTree( ptree_bkg, backgroundWeight_bkg );

// Add INGRID background

//Double_t signalWeight_bkg2 = nmcFile/float(nbkg2File);

Double_t backgroundWeight_bkg2 = nmcFile/float(nbkg2File);

//factory->AddSignalTree ( ptree_bkg2, signalWeight_bkg2 );

factory->AddBackgroundTree( ptree_bkg2, backgroundWeight_bkg2 );

//factory->SetSignalWeightExpression ("norm*totcrsne*2.8647e-13");

//factory->SetBackgroundWeightExpression( "norm*totcrsne*2.8647e-13" );

// Apply additional cuts on the signal and background samples (can be different)

TCut mycuts = "Ntrack==2 && abs(inttype)==16 && fileIndex==1 && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";

TCut mycutb = "Ntrack==2 && (abs(inttype)!=16 || fileIndex>1) && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // 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" );

// 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=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=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:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );*/

if (Use["BDT"]) // Adaptive Boost

factory->BookMethod( TMVA::Types::kBDT, "BDT",

"!H:!V:NTrees=850:MaxDepth=3:BoostType=AdaBoost: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: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

// ---- STILL EXPERIMENTAL and only implemented for BDT's !

// 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 );

}