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kukartse |
1.1 |
/**********************************************************************************
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* Project : TMVA - a Root-integrated toolkit for multivariate data analysis *
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* Package : TMVA *
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* Exectuable: TMVApplication *
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* *
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* This macro provides a simple example on how to use the trained classifiers *
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* within an analysis module *
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* *
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* ------------------------------------------------------------------------------ *
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* see also the alternative (slightly faster) way to retrieve the MVA values in *
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* examples/TMVApplicationAlternative.cxx *
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* ------------------------------------------------------------------------------ *
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**********************************************************************************/
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kukartse |
1.2 |
#include <iostream>
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#include "TCut.h"
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#include "TFile.h"
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#include "TSystem.h"
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#include "TTree.h"
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#include "TStopwatch.h"
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// requires links
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#include "TMVA/Factory.h"
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#include "TMVA/Tools.h"
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#include "TMVA/Config.h"
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#include "TMVA/Reader.h"
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kukartse |
1.1 |
// ---------------------------------------------------------------
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// choose MVA methods to be applied
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Bool_t Use_Cuts = 0;
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Bool_t Use_CutsD = 0;
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Bool_t Use_CutsGA = 1;
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Bool_t Use_Likelihood = 1;
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Bool_t Use_LikelihoodD = 0; // the "D" extension indicates decorrelated input variables (see option strings)
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Bool_t Use_LikelihoodPCA = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
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Bool_t Use_PDERS = 1;
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Bool_t Use_PDERSD = 0;
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Bool_t Use_PDERSPCA = 0;
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Bool_t Use_KNN = 1;
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Bool_t Use_HMatrix = 1;
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Bool_t Use_Fisher = 1;
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Bool_t Use_FDA_GA = 0;
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Bool_t Use_FDA_MT = 1;
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Bool_t Use_MLP = 1; // this is the recommended ANN
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Bool_t Use_CFMlpANN = 0;
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Bool_t Use_TMlpANN = 0;
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Bool_t Use_SVM_Gauss = 1;
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Bool_t Use_SVM_Poly = 0;
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Bool_t Use_SVM_Lin = 0;
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Bool_t Use_BDT = 1;
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Bool_t Use_BDTD = 0;
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Bool_t Use_RuleFit = 1;
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// ---------------------------------------------------------------
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void TMVApplication( TString myMethodList = "" )
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{
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cout << endl;
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cout << "==> start TMVApplication" << endl;
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if (myMethodList != "") {
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Use_CutsGA = Use_CutsD = Use_Cuts
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= Use_LikelihoodPCA = Use_LikelihoodD = Use_Likelihood
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= Use_PDERSPCA = Use_PDERSD = Use_PDERS
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= Use_KNN
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= Use_MLP = Use_CFMlpANN = Use_TMlpANN
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= Use_HMatrix = Use_Fisher = Use_BDTD = Use_BDT = Use_RuleFit
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= Use_SVM_Gauss = Use_SVM_Poly = Use_SVM_Lin
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= Use_FDA_GA = Use_FDA_MT
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= 0;
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TList* mlist = TMVA::Tools::ParseFormatLine( myMethodList, " :," );
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if (mlist->FindObject( "Cuts" ) != 0) Use_Cuts = 1;
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if (mlist->FindObject( "CutsD" ) != 0) Use_CutsD = 1;
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if (mlist->FindObject( "CutsGA" ) != 0) Use_CutsGA = 1;
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if (mlist->FindObject( "Likelihood" ) != 0) Use_Likelihood = 1;
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if (mlist->FindObject( "LikelihoodD" ) != 0) Use_LikelihoodD = 1;
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if (mlist->FindObject( "LikelihoodPCA" ) != 0) Use_LikelihoodPCA = 1;
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if (mlist->FindObject( "PDERS" ) != 0) Use_PDERS = 1;
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if (mlist->FindObject( "PDERSD" ) != 0) Use_PDERSD = 1;
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if (mlist->FindObject( "PDERSPCA" ) != 0) Use_PDERSPCA = 1;
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if (mlist->FindObject( "KNN" ) != 0) Use_KNN = 1;
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if (mlist->FindObject( "HMatrix" ) != 0) Use_HMatrix = 1;
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if (mlist->FindObject( "Fisher" ) != 0) Use_Fisher = 1;
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if (mlist->FindObject( "MLP" ) != 0) Use_MLP = 1;
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if (mlist->FindObject( "CFMlpANN" ) != 0) Use_CFMlpANN = 1;
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if (mlist->FindObject( "TMlpANN" ) != 0) Use_TMlpANN = 1;
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if (mlist->FindObject( "BDTD" ) != 0) Use_BDTD = 1;
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if (mlist->FindObject( "BDT" ) != 0) Use_BDT = 1;
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if (mlist->FindObject( "RuleFit" ) != 0) Use_RuleFit = 1;
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if (mlist->FindObject( "SVM_Gauss" ) != 0) Use_SVM_Gauss = 1;
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if (mlist->FindObject( "SVM_Poly" ) != 0) Use_SVM_Poly = 1;
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if (mlist->FindObject( "SVM_Lin" ) != 0) Use_SVM_Lin = 1;
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if (mlist->FindObject( "FDA_MT" ) != 0) Use_FDA_MT = 1;
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if (mlist->FindObject( "FDA_GA" ) != 0) Use_FDA_GA = 1;
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delete mlist;
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}
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//
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// create the Reader object
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//
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TMVA::Reader *reader = new TMVA::Reader("!Color");
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// create a set of variables and declare them to the reader
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// - the variable names must corresponds in name and type to
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// those given in the weight file(s) that you use
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kukartse |
1.2 |
Float_t _aplanarity;
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Float_t _getHt3;
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Float_t _ktMinPrime;
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Float_t _DphiJMET;
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Float_t _dPhiLMet;
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Float_t _getMt;
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reader->AddVariable("aplanarity", &_aplanarity);
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reader->AddVariable("getHt3", &_getHt3);
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reader->AddVariable("ktMinPrime", &_ktMinPrime);
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reader->AddVariable("DphiJMET", &_DphiJMET);
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//reader->AddVariable("dPhiLMet", &_dPhiLMet);
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reader->AddVariable("getMt", &_getMt);
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kukartse |
1.1 |
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//
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// book the MVA methods
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//
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string dir = "weights/";
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string prefix = "TMVAnalysis";
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if (Use_Cuts) reader->BookMVA( "Cuts method", dir + prefix + "_Cuts.weights.txt" );
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if (Use_CutsD) reader->BookMVA( "CutsD method", dir + prefix + "_CutsD.weights.txt" );
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if (Use_CutsGA) reader->BookMVA( "CutsGA method", dir + prefix + "_CutsGA.weights.txt" );
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if (Use_Likelihood) reader->BookMVA( "Likelihood method", dir + prefix + "_Likelihood.weights.txt" );
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if (Use_LikelihoodD) reader->BookMVA( "LikelihoodD method", dir + prefix + "_LikelihoodD.weights.txt" );
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if (Use_LikelihoodPCA) reader->BookMVA( "LikelihoodPCA method", dir + prefix + "_LikelihoodPCA.weights.txt" );
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if (Use_PDERS) reader->BookMVA( "PDERS method", dir + prefix + "_PDERS.weights.txt" );
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if (Use_PDERSD) reader->BookMVA( "PDERSD method", dir + prefix + "_PDERSD.weights.txt" );
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if (Use_PDERSPCA) reader->BookMVA( "PDERSPCA method", dir + prefix + "_PDERSPCA.weights.txt" );
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if (Use_KNN) reader->BookMVA( "KNN method", dir + prefix + "_KNN.weights.txt" );
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if (Use_HMatrix) reader->BookMVA( "HMatrix method", dir + prefix + "_HMatrix.weights.txt" );
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if (Use_Fisher) reader->BookMVA( "Fisher method", dir + prefix + "_Fisher.weights.txt" );
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if (Use_MLP) reader->BookMVA( "MLP method", dir + prefix + "_MLP.weights.txt" );
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if (Use_CFMlpANN) reader->BookMVA( "CFMlpANN method", dir + prefix + "_CFMlpANN.weights.txt" );
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if (Use_TMlpANN) reader->BookMVA( "TMlpANN method", dir + prefix + "_TMlpANN.weights.txt" );
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if (Use_BDT) reader->BookMVA( "BDT method", dir + prefix + "_BDT.weights.txt" );
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if (Use_BDTD) reader->BookMVA( "BDTD method", dir + prefix + "_BDTD.weights.txt" );
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if (Use_RuleFit) reader->BookMVA( "RuleFit method", dir + prefix + "_RuleFitTMVA.weights.txt" );
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if (Use_SVM_Gauss) reader->BookMVA( "SVM_Gauss method", dir + prefix + "_SVM_Gauss.weights.txt" );
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if (Use_SVM_Poly) reader->BookMVA( "SVM_Poly method", dir + prefix + "_SVM_Poly.weights.txt" );
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if (Use_SVM_Lin) reader->BookMVA( "SVM_Lin method", dir + prefix + "_SVM_Lin.weights.txt" );
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if (Use_FDA_MT) reader->BookMVA( "FDA_MT method", dir + prefix + "_FDA_MT.weights.txt" );
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if (Use_FDA_GA) reader->BookMVA( "FDA_GA method", dir + prefix + "_FDA_GA.weights.txt" );
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// book output histograms
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UInt_t nbin = 100;
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kukartse |
1.2 |
TFile *target = new TFile( "TMVApp.root","RECREATE" );
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kukartse |
1.1 |
TH1F *histLk, *histLkD, *histLkPCA, *histPD, *histPDD, *histPDPCA, *histKNN, *histHm, *histFi;
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TH1F *histNn, *histNnC, *histNnT, *histBdt, *histBdtD, *histRf;
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TH1F *histSVMG, *histSVMP, *histSVML;
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TH1F *histFDAMT, *histFDAGA;
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if (Use_Likelihood) histLk = new TH1F( "MVA_Likelihood", "MVA_Likelihood", nbin, 0, 1 );
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kukartse |
1.2 |
TTree * _tree = new TTree("classifier","classifier");
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Double_t _ll = -1.0;
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TBranch * b_ll = _tree->Branch("MVA_Likelihood", &_ll, "classifier/D");
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kukartse |
1.1 |
if (Use_LikelihoodD) histLkD = new TH1F( "MVA_LikelihoodD", "MVA_LikelihoodD", nbin, 0.000001, 0.9999 );
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if (Use_LikelihoodPCA) histLkPCA = new TH1F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin, 0, 1 );
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if (Use_PDERS) histPD = new TH1F( "MVA_PDERS", "MVA_PDERS", nbin, 0, 1 );
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if (Use_PDERSD) histPDD = new TH1F( "MVA_PDERSD", "MVA_PDERSD", nbin, 0, 1 );
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if (Use_PDERSPCA) histPDPCA = new TH1F( "MVA_PDERSPCA", "MVA_PDERSPCA", nbin, 0, 1 );
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if (Use_KNN) histKNN = new TH1F( "MVA_KNN", "MVA_KNN", nbin, 0, 1 );
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if (Use_HMatrix) histHm = new TH1F( "MVA_HMatrix", "MVA_HMatrix", nbin, -0.95, 1.55 );
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if (Use_Fisher) histFi = new TH1F( "MVA_Fisher", "MVA_Fisher", nbin, -4, 4 );
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if (Use_MLP) histNn = new TH1F( "MVA_MLP", "MVA_MLP", nbin, -0.25, 1.5 );
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if (Use_CFMlpANN) histNnC = new TH1F( "MVA_CFMlpANN", "MVA_CFMlpANN", nbin, 0, 1 );
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if (Use_TMlpANN) histNnT = new TH1F( "MVA_TMlpANN", "MVA_TMlpANN", nbin, -1.3, 1.3 );
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if (Use_BDT) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
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if (Use_BDTD) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.4, 0.6 );
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if (Use_RuleFit) histRf = new TH1F( "MVA_RuleFitTMVA", "MVA_RuleFitTMVA", nbin, -2.0, 2.0 );
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if (Use_SVM_Gauss) histSVMG = new TH1F( "MVA_SVM_Gauss", "MVA_SVM_Gauss", nbin, 0.0, 1.0 );
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if (Use_SVM_Poly) histSVMP = new TH1F( "MVA_SVM_Poly", "MVA_SVM_Poly", nbin, 0.0, 1.0 );
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if (Use_SVM_Lin) histSVML = new TH1F( "MVA_SVM_Lin", "MVA_SVM_Lin", nbin, 0.0, 1.0 );
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if (Use_FDA_MT) histFDAMT = new TH1F( "MVA_FDA_MT", "MVA_FDA_MT", nbin, -2.0, 3.0 );
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if (Use_FDA_GA) histFDAGA = new TH1F( "MVA_FDA_GA", "MVA_FDA_GA", nbin, -2.0, 3.0 );
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// book examsple histogram for probability (the other methods are done similarly)
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TH1F *probHistFi, *rarityHistFi;
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if (Use_Fisher) {
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probHistFi = new TH1F( "PROBA_MVA_Fisher", "PROBA_MVA_Fisher", nbin, 0, 1 );
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rarityHistFi = new TH1F( "RARITY_MVA_Fisher", "RARITY_MVA_Fisher", nbin, 0, 1 );
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}
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// Prepare input tree (this must be replaced by your data source)
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// in this example, there is a toy tree with signal and one with background events
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// we'll later on use only the "signal" events for the test in this example.
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//
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TFile *input(0);
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kukartse |
1.2 |
//TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/fake_data/tmva_fake_data_100pb-summer08-09jan2009.root";
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TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/fake_data/tmva_fake_data_100pb-summer08-09jan2009.root";
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kukartse |
1.1 |
if (!gSystem->AccessPathName( fname )) {
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// first we try to find tmva_example.root in the local directory
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cout << "--- accessing data file: " << fname << endl;
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input = TFile::Open( fname );
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}
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else {
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// second we try accessing the file via the web from
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// http://root.cern.ch/files/tmva_example.root
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cout << "--- accessing tmva_example.root file from http://root.cern.ch/files" << endl;
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cout << "--- for faster startup you may consider downloading it into you local directory" << endl;
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input = TFile::Open("http://root.cern.ch/files/tmva_example.root");
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}
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if (!input) {
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std::cout << "ERROR: could not open data file: " << fname << std::endl;
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exit(1);
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}
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//
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// prepare the tree
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// - here the variable names have to corresponds to your tree
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// - you can use the same variables as above which is slightly faster,
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// but of course you can use different ones and copy the values inside the event loop
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//
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kukartse |
1.2 |
TTree* theTree = (TTree*)input->Get("data");
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kukartse |
1.1 |
cout << "--- select signal sample" << endl;
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kukartse |
1.2 |
//Float_t userVar1, userVar2;
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Double_t aplanarity;
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Double_t getHt3;
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Double_t ktMinPrime;
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Double_t DphiJMET;
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Double_t dPhiLMet;
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Double_t getMt;
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theTree->SetBranchAddress( "aplanarity", &aplanarity );
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theTree->SetBranchAddress( "getHt3", &getHt3 );
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theTree->SetBranchAddress( "ktMinPrime", &ktMinPrime );
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theTree->SetBranchAddress( "DphiJMET", &DphiJMET );
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//theTree->SetBranchAddress( "dPhiLMet", &dPhiLMet );
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theTree->SetBranchAddress( "getMt", &getMt );
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| 236 |
kukartse |
1.1 |
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// efficiency calculator for cut method
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Int_t nSelCuts = 0, nSelCutsD = 0, nSelCutsGA = 0;
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Double_t effS = 0.7;
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cout << "--- processing: " << theTree->GetEntries() << " events" << endl;
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| 242 |
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TStopwatch sw;
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| 243 |
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sw.Start();
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for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
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if (ievt%1000 == 0)
|
| 247 |
|
|
cout << "--- ... processing event: " << ievt << endl;
|
| 248 |
|
|
|
| 249 |
|
|
theTree->GetEntry(ievt);
|
| 250 |
|
|
|
| 251 |
kukartse |
1.2 |
//var1 = userVar1 + userVar2;
|
| 252 |
|
|
//var2 = userVar1 - userVar2;
|
| 253 |
|
|
_aplanarity = (Float_t)aplanarity;
|
| 254 |
|
|
_getHt3 = (Float_t)getHt3;
|
| 255 |
|
|
_ktMinPrime = (Float_t)ktMinPrime;
|
| 256 |
|
|
_DphiJMET = (Float_t)DphiJMET;
|
| 257 |
|
|
//_dPhiLMet = (Float_t)dPhiLMet;
|
| 258 |
|
|
_getMt = (Float_t)getMt;
|
| 259 |
kukartse |
1.1 |
|
| 260 |
|
|
//
|
| 261 |
|
|
// return the MVAs and fill to histograms
|
| 262 |
|
|
//
|
| 263 |
|
|
if (Use_Cuts) {
|
| 264 |
|
|
// Cuts is a special case: give the desired signal efficienciy
|
| 265 |
|
|
Bool_t passed = reader->EvaluateMVA( "Cuts method", effS );
|
| 266 |
|
|
if (passed) nSelCuts++;
|
| 267 |
|
|
}
|
| 268 |
|
|
if (Use_CutsD) {
|
| 269 |
|
|
// Cuts is a special case: give the desired signal efficienciy
|
| 270 |
|
|
Bool_t passed = reader->EvaluateMVA( "CutsD method", effS );
|
| 271 |
|
|
if (passed) nSelCutsD++;
|
| 272 |
|
|
}
|
| 273 |
|
|
if (Use_CutsGA) {
|
| 274 |
|
|
// Cuts is a special case: give the desired signal efficienciy
|
| 275 |
|
|
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
|
| 276 |
|
|
if (passed) nSelCutsGA++;
|
| 277 |
|
|
}
|
| 278 |
|
|
|
| 279 |
kukartse |
1.2 |
if (Use_Likelihood ){
|
| 280 |
|
|
_ll = (Double_t)(reader->EvaluateMVA("Likelihood method") );
|
| 281 |
|
|
histLk ->Fill( reader->EvaluateMVA( "Likelihood method" ) );
|
| 282 |
|
|
_tree ->Fill();
|
| 283 |
|
|
}
|
| 284 |
kukartse |
1.1 |
if (Use_LikelihoodD ) histLkD ->Fill( reader->EvaluateMVA( "LikelihoodD method" ) );
|
| 285 |
|
|
if (Use_LikelihoodPCA) histLkPCA ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
|
| 286 |
|
|
if (Use_PDERS ) histPD ->Fill( reader->EvaluateMVA( "PDERS method" ) );
|
| 287 |
|
|
if (Use_PDERSD ) histPDD ->Fill( reader->EvaluateMVA( "PDERSD method" ) );
|
| 288 |
|
|
if (Use_PDERSPCA ) histPDPCA ->Fill( reader->EvaluateMVA( "PDERSPCA method" ) );
|
| 289 |
|
|
if (Use_KNN ) histKNN ->Fill( reader->EvaluateMVA( "KNN method" ) );
|
| 290 |
|
|
if (Use_HMatrix ) histHm ->Fill( reader->EvaluateMVA( "HMatrix method" ) );
|
| 291 |
|
|
if (Use_Fisher ) histFi ->Fill( reader->EvaluateMVA( "Fisher method" ) );
|
| 292 |
|
|
if (Use_MLP ) histNn ->Fill( reader->EvaluateMVA( "MLP method" ) );
|
| 293 |
|
|
if (Use_CFMlpANN ) histNnC ->Fill( reader->EvaluateMVA( "CFMlpANN method" ) );
|
| 294 |
|
|
if (Use_TMlpANN ) histNnT ->Fill( reader->EvaluateMVA( "TMlpANN method" ) );
|
| 295 |
|
|
if (Use_BDT ) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
|
| 296 |
|
|
if (Use_BDTD ) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
|
| 297 |
|
|
if (Use_RuleFit ) histRf ->Fill( reader->EvaluateMVA( "RuleFit method" ) );
|
| 298 |
|
|
if (Use_SVM_Gauss ) histSVMG ->Fill( reader->EvaluateMVA( "SVM_Gauss method" ) );
|
| 299 |
|
|
if (Use_SVM_Poly ) histSVMP ->Fill( reader->EvaluateMVA( "SVM_Poly method" ) );
|
| 300 |
|
|
if (Use_SVM_Lin ) histSVML ->Fill( reader->EvaluateMVA( "SVM_Lin method" ) );
|
| 301 |
|
|
if (Use_FDA_MT ) histFDAMT ->Fill( reader->EvaluateMVA( "FDA_MT method" ) );
|
| 302 |
|
|
if (Use_FDA_GA ) histFDAGA ->Fill( reader->EvaluateMVA( "FDA_GA method" ) );
|
| 303 |
|
|
|
| 304 |
|
|
// retrieve probability instead of MVA output
|
| 305 |
|
|
if (Use_Fisher ) {
|
| 306 |
|
|
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
|
| 307 |
|
|
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
|
| 308 |
|
|
}
|
| 309 |
|
|
}
|
| 310 |
|
|
sw.Stop();
|
| 311 |
|
|
cout << "--- end of event loop: "; sw.Print();
|
| 312 |
|
|
// get elapsed time
|
| 313 |
|
|
if (Use_Cuts) cout << "--- efficiency for Cuts method : " << double(nSelCuts)/theTree->GetEntries()
|
| 314 |
|
|
<< " (for a required signal efficiency of " << effS << ")" << endl;
|
| 315 |
|
|
if (Use_CutsD) cout << "--- efficiency for CutsD method : " << double(nSelCutsD)/theTree->GetEntries()
|
| 316 |
|
|
<< " (for a required signal efficiency of " << effS << ")" << endl;
|
| 317 |
|
|
if (Use_CutsGA) cout << "--- efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
|
| 318 |
|
|
<< " (for a required signal efficiency of " << effS << ")" << endl;
|
| 319 |
|
|
|
| 320 |
kukartse |
1.2 |
/*************************
|
| 321 |
kukartse |
1.1 |
//
|
| 322 |
|
|
// write histograms
|
| 323 |
|
|
//
|
| 324 |
|
|
TFile *target = new TFile( "TMVApp.root","RECREATE" );
|
| 325 |
|
|
if (Use_Likelihood ) histLk ->Write();
|
| 326 |
|
|
if (Use_LikelihoodD ) histLkD ->Write();
|
| 327 |
|
|
if (Use_LikelihoodPCA) histLkPCA ->Write();
|
| 328 |
|
|
if (Use_PDERS ) histPD ->Write();
|
| 329 |
|
|
if (Use_PDERSD ) histPDD ->Write();
|
| 330 |
|
|
if (Use_PDERSPCA ) histPDPCA ->Write();
|
| 331 |
|
|
if (Use_KNN ) histKNN ->Write();
|
| 332 |
|
|
if (Use_HMatrix ) histHm ->Write();
|
| 333 |
|
|
if (Use_Fisher ) histFi ->Write();
|
| 334 |
|
|
if (Use_MLP ) histNn ->Write();
|
| 335 |
|
|
if (Use_CFMlpANN ) histNnC ->Write();
|
| 336 |
|
|
if (Use_TMlpANN ) histNnT ->Write();
|
| 337 |
|
|
if (Use_BDT ) histBdt ->Write();
|
| 338 |
|
|
if (Use_BDTD ) histBdtD ->Write();
|
| 339 |
|
|
if (Use_RuleFit ) histRf ->Write();
|
| 340 |
|
|
if (Use_SVM_Gauss ) histSVMG ->Write();
|
| 341 |
|
|
if (Use_SVM_Poly ) histSVMP ->Write();
|
| 342 |
|
|
if (Use_SVM_Lin ) histSVML ->Write();
|
| 343 |
|
|
if (Use_FDA_MT ) histFDAMT ->Write();
|
| 344 |
|
|
if (Use_FDA_GA ) histFDAGA ->Write();
|
| 345 |
|
|
|
| 346 |
|
|
if (Use_Fisher ) { probHistFi->Write(); rarityHistFi->Write(); }
|
| 347 |
kukartse |
1.2 |
|
| 348 |
kukartse |
1.1 |
target->Close();
|
| 349 |
kukartse |
1.2 |
*******************************/
|
| 350 |
|
|
|
| 351 |
|
|
target->cd();
|
| 352 |
|
|
target->Write();
|
| 353 |
|
|
delete target;
|
| 354 |
kukartse |
1.1 |
|
| 355 |
|
|
cout << "--- created root file: \"TMVApp.root\" containing the MVA output histograms" << endl;
|
| 356 |
|
|
|
| 357 |
|
|
delete reader;
|
| 358 |
|
|
|
| 359 |
|
|
cout << "==> TMVApplication is done!" << endl << endl;
|
| 360 |
|
|
}
|
