tmva » test

PlotDecisionBoundary.C

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00001 /**********************************************************************************
00002  * Project   : TMVA - a Root-integrated toolkit for multivariate data analysis    *
00003  * Package   : TMVA                                                               *
00004  * Exectuable: PlotDecisionBoundary                                               *
00005  *                                                                                *
00006  * Plots decision boundary for simple cases in 2 (3?) dimensions                  *
00007  **********************************************************************************/
00008 
00009 #include <cstdlib>
00010 #include <vector>
00011 #include <iostream>
00012 #include <map>
00013 #include <string>
00014 
00015 #include "TFile.h"
00016 #include "TTree.h"
00017 #include "TString.h"
00018 #include "TSystem.h"
00019 #include "TROOT.h"
00020 #include "TStopwatch.h"
00021 #include "TGraph.h"
00022 #include "TH2F.h"
00023 #include "TH3F.h"
00024 #include "TMVAGui.C"
00025 
00026 #if not defined(__CINT__) || defined(__MAKECINT__)
00027 #include "TMVA/Tools.h"
00028 #include "TMVA/Reader.h"
00029 #include "TMVA/MethodCuts.h"
00030 #endif
00031 
00032 using namespace TMVA;
00033 
00034 
00035 void plot(TH2D *sig, TH2D *bkg, TH2F *MVA, TString v0="var0", TString v1="var1"){
00036 
00037    TCanvas *c = new TCanvas(Form("DecisionBoundary%s",MVA->GetTitle()),MVA->GetTitle(),800,800);
00038 
00039    gStyle->SetPalette(1);
00040    MVA->SetXTitle(v0);
00041    MVA->SetYTitle(v1);
00042    MVA->SetStats(0);
00043    MVA->Draw("cont1");
00044    sig->SetMarkerColor(2);
00045    bkg->SetMarkerColor(4);
00046    sig->SetMarkerStyle(20);
00047    bkg->SetMarkerStyle(20);
00048    sig->SetMarkerSize(.5);
00049    bkg->SetMarkerSize(.5);
00050    sig->Draw("same");
00051    bkg->Draw("same");
00052 }
00053 
00054 
00055 void PlotDecisionBoundary( TString myMethodList = "",TString v0="var0", TString v1="var1", TString dataFileName = "/home/hvoss/TMVA/TMVA_data/data/data_3Bumps.root", TString weightFilePrefix="TMVA") 
00056 {   
00057    //---------------------------------------------------------------
00058    // default MVA methods to be trained + tested
00059 
00060    // this loads the library
00061    TMVA::Tools::Instance();
00062 
00063    std::map<std::string,int> Use;
00064 
00065    Use["CutsGA"]          = 0; // other "Cuts" methods work identically
00066    // ---
00067    Use["Likelihood"]      = 0;
00068    Use["LikelihoodD"]     = 0; // the "D" extension indicates decorrelated input variables (see option strings)
00069    Use["LikelihoodPCA"]   = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
00070    Use["LikelihoodKDE"]   = 0;
00071    Use["LikelihoodMIX"]   = 0;
00072    // ---
00073    Use["PDERS"]           = 0;
00074    Use["PDERSD"]          = 0;
00075    Use["PDERSPCA"]        = 0;
00076    Use["PDERSkNN"]        = 0; // depreciated until further notice
00077    Use["PDEFoam"]         = 0;
00078    // --
00079    Use["KNN"]             = 0;
00080    // ---
00081    Use["HMatrix"]         = 0;
00082    Use["Fisher"]          = 0;
00083    Use["FisherG"]         = 0;
00084    Use["BoostedFisher"]   = 0;
00085    Use["LD"]              = 0;
00086    // ---
00087    Use["FDA_GA"]          = 0;
00088    Use["FDA_SA"]          = 0;
00089    Use["FDA_MC"]          = 0;
00090    Use["FDA_MT"]          = 0;
00091    Use["FDA_GAMT"]        = 0;
00092    Use["FDA_MCMT"]        = 0;
00093    // ---
00094    Use["MLP"]             = 0; // this is the recommended ANN
00095    Use["MLPBFGS"]         = 0; // recommended ANN with optional training method
00096    Use["CFMlpANN"]        = 0; // *** missing
00097    Use["TMlpANN"]         = 0; 
00098    // ---
00099    Use["SVM"]             = 0;
00100    // ---
00101    Use["BDT"]             = 0;
00102    Use["BDTD"]            = 0;
00103    Use["BDTG"]            = 0;
00104    Use["BDTB"]            = 0;
00105    // ---
00106    Use["RuleFit"]         = 0;
00107    // ---
00108    Use["Category"]        = 0;
00109    // ---
00110    Use["Plugin"]          = 0;
00111    // ---------------------------------------------------------------
00112 
00113    std::cout << std::endl;
00114    std::cout << "==> Start TMVAClassificationApplication" << std::endl;
00115 
00116    if (myMethodList != "") {
00117       for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
00118 
00119       std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
00120       for (UInt_t i=0; i<mlist.size(); i++) {
00121          std::string regMethod(mlist[i]);
00122 
00123          if (Use.find(regMethod) == Use.end()) {
00124             std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
00125             for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
00126             std::cout << std::endl;
00127             return;
00128          }
00129          Use[regMethod] = 1;
00130       }
00131    }
00132 
00133    //
00134    // create the Reader object
00135    //
00136    TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );    
00137 
00138    // create a set of variables and declare them to the reader
00139    // - the variable names must corresponds in name and type to 
00140    // those given in the weight file(s) that you use
00141    Float_t var0, var1;
00142    reader->AddVariable( v0,                &var0 );
00143    reader->AddVariable( v1,                &var1 );
00144 
00145    //
00146    // book the MVA methods
00147    //
00148    TString dir    = "weights/";
00149    TString prefix = weightFilePrefix;
00150 
00151    // book method(s)
00152    for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
00153       if (it->second) {
00154          TString methodName = it->first + " method";
00155          TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
00156          reader->BookMVA( methodName, weightfile ); 
00157       }
00158    }
00159    
00160    TFile *f = new TFile(dataFileName);
00161    TTree *signal     = (TTree*)f->Get("TreeS");
00162    TTree *background = (TTree*)f->Get("TreeB");
00163 
00164 
00165    
00166 
00167 //Declaration of leaves types
00168    Float_t         svar0;
00169    Float_t         svar1;
00170    Float_t         bvar0;
00171    Float_t         bvar1;
00172 
00173    // Set branch addresses.
00174    signal->SetBranchAddress(v0,&svar0);
00175    signal->SetBranchAddress(v1,&svar1);
00176    background->SetBranchAddress(v0,&bvar0);
00177    background->SetBranchAddress(v1,&bvar1);
00178 
00179 
00180    UInt_t nbin = 50;
00181    Float_t xmax = signal->GetMaximum(v0.Data());
00182    Float_t xmin = signal->GetMinimum(v0.Data());
00183    Float_t ymax = signal->GetMaximum(v1.Data());
00184    Float_t ymin = signal->GetMinimum(v1.Data());
00185 
00186    TH2D *hs=new TH2D("hs","",nbin,xmin,xmax,nbin,ymin,ymax);   
00187    TH2D *hb=new TH2D("hb","",nbin,xmin,xmax,nbin,ymin,ymax);   
00188 
00189 
00190    Long64_t nentries;
00191    nentries = TreeS->GetEntries();
00192    for (Long64_t is=0; is<nentries;is++) {
00193       signal->GetEntry(is);
00194       hs->Fill(svar0,svar1);
00195    }
00196    nentries = TreeB->GetEntries();
00197    for (Long64_t ib=0; ib<nentries;ib++) {
00198       background->GetEntry(ib);
00199       hb->Fill(bvar0,bvar1);
00200    }
00201 
00202 
00203    hb->SetMarkerColor(4);
00204    hs->SetMarkerColor(2);
00205 
00206 
00207    // book output histograms
00208    TH2F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
00209    TH2F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
00210    TH2F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0), *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0);
00211    TH2F *histRf(0), *histSVMG(0), *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
00212 
00213    if (Use["Likelihood"])    histLk      = new TH2F( "MVA_Likelihood",    "MVA_Likelihood",    nbin,xmin,xmax,nbin,ymin,ymax);
00214    if (Use["LikelihoodD"])   histLkD     = new TH2F( "MVA_LikelihoodD",   "MVA_LikelihoodD",   nbin,xmin,xmax,nbin,ymin,ymax);
00215    if (Use["LikelihoodPCA"]) histLkPCA   = new TH2F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin,xmin,xmax,nbin,ymin,ymax);
00216    if (Use["LikelihoodKDE"]) histLkKDE   = new TH2F( "MVA_LikelihoodKDE", "MVA_LikelihoodKDE", nbin,xmin,xmax,nbin,ymin,ymax);
00217    if (Use["LikelihoodMIX"]) histLkMIX   = new TH2F( "MVA_LikelihoodMIX", "MVA_LikelihoodMIX", nbin,xmin,xmax,nbin,ymin,ymax);
00218    if (Use["PDERS"])         histPD      = new TH2F( "MVA_PDERS",         "MVA_PDERS",         nbin,xmin,xmax,nbin,ymin,ymax);
00219    if (Use["PDERSD"])        histPDD     = new TH2F( "MVA_PDERSD",        "MVA_PDERSD",        nbin,xmin,xmax,nbin,ymin,ymax);
00220    if (Use["PDERSPCA"])      histPDPCA   = new TH2F( "MVA_PDERSPCA",      "MVA_PDERSPCA",      nbin,xmin,xmax,nbin,ymin,ymax);
00221    if (Use["KNN"])           histKNN     = new TH2F( "MVA_KNN",           "MVA_KNN",           nbin,xmin,xmax,nbin,ymin,ymax);
00222    if (Use["HMatrix"])       histHm      = new TH2F( "MVA_HMatrix",       "MVA_HMatrix",       nbin,xmin,xmax,nbin,ymin,ymax);
00223    if (Use["Fisher"])        histFi      = new TH2F( "MVA_Fisher",        "MVA_Fisher",        nbin,xmin,xmax,nbin,ymin,ymax);
00224    if (Use["FisherG"])        histFiG    = new TH2F( "MVA_FisherG",       "MVA_FisherG",       nbin,xmin,xmax,nbin,ymin,ymax);
00225    if (Use["BoostedFisher"])  histFiB    = new TH2F( "MVA_BoostedFisher", "MVA_BoostedFisher", nbin,xmin,xmax,nbin,ymin,ymax);
00226    if (Use["LD"])            histLD      = new TH2F( "MVA_LD",            "MVA_LD",            nbin,xmin,xmax,nbin,ymin,ymax);
00227    if (Use["MLP"])           histNn      = new TH2F( "MVA_MLP",           "MVA_MLP",           nbin,xmin,xmax,nbin,ymin,ymax);
00228    if (Use["CFMlpANN"])      histNnC     = new TH2F( "MVA_CFMlpANN",      "MVA_CFMlpANN",      nbin,xmin,xmax,nbin,ymin,ymax);
00229    if (Use["TMlpANN"])       histNnT     = new TH2F( "MVA_TMlpANN",       "MVA_TMlpANN",       nbin,xmin,xmax,nbin,ymin,ymax);
00230    if (Use["BDT"])           histBdt     = new TH2F( "MVA_BDT",           "MVA_BDT",           nbin,xmin,xmax,nbin,ymin,ymax);
00231    if (Use["BDTD"])          histBdtD    = new TH2F( "MVA_BDTD",          "MVA_BDTD",          nbin,xmin,xmax,nbin,ymin,ymax);
00232    if (Use["BDTG"])          histBdtG    = new TH2F( "MVA_BDTG",          "MVA_BDTG",          nbin,xmin,xmax,nbin,ymin,ymax);
00233    if (Use["RuleFit"])       histRf      = new TH2F( "MVA_RuleFit",       "MVA_RuleFit",       nbin,xmin,xmax,nbin,ymin,ymax);
00234    if (Use["SVM_Gauss"])     histSVMG    = new TH2F( "MVA_SVM_Gauss",     "MVA_SVM_Gauss",     nbin,xmin,xmax,nbin,ymin,ymax);
00235    if (Use["SVM_Poly"])      histSVMP    = new TH2F( "MVA_SVM_Poly",      "MVA_SVM_Poly",      nbin,xmin,xmax,nbin,ymin,ymax);
00236    if (Use["SVM_Lin"])       histSVML    = new TH2F( "MVA_SVM_Lin",       "MVA_SVM_Lin",       nbin,xmin,xmax,nbin,ymin,ymax);
00237    if (Use["FDA_MT"])        histFDAMT   = new TH2F( "MVA_FDA_MT",        "MVA_FDA_MT",        nbin,xmin,xmax,nbin,ymin,ymax);
00238    if (Use["FDA_GA"])        histFDAGA   = new TH2F( "MVA_FDA_GA",        "MVA_FDA_GA",        nbin,xmin,xmax,nbin,ymin,ymax);
00239    if (Use["Category"])      histCat     = new TH2F( "MVA_Category",      "MVA_Category",      nbin,xmin,xmax,nbin,ymin,ymax);
00240    if (Use["Plugin"])        histPBdt    = new TH2F( "MVA_PBDT",          "MVA_BDT",           nbin,xmin,xmax,nbin,ymin,ymax); 
00241 
00242 
00243 
00244    // Prepare input tree (this must be replaced by your data source)
00245    // in this example, there is a toy tree with signal and one with background events
00246    // we'll later on use only the "signal" events for the test in this example.
00247 
00248 
00249 
00250    for (Int_t ibin=1; ibin<nbin+1; ibin++){
00251       for (Int_t jbin=1; jbin<nbin+1; jbin++){
00252          var0 = hs->GetXaxis()->GetBinCenter(ibin);
00253          var1 = hs->GetYaxis()->GetBinCenter(jbin);
00254          
00255          
00256          if (Use["Likelihood"   ])   histLk     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "Likelihood method"    ) );
00257          if (Use["LikelihoodD"  ])   histLkD    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "LikelihoodD method"   ) );
00258          if (Use["LikelihoodPCA"])   histLkPCA  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "LikelihoodPCA method" ) );
00259          if (Use["LikelihoodKDE"])   histLkKDE  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "LikelihoodKDE method" ) );
00260          if (Use["LikelihoodMIX"])   histLkMIX  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "LikelihoodMIX method" ) );
00261          if (Use["PDERS"        ])   histPD     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "PDERS method"         ) );
00262          if (Use["PDERSD"       ])   histPDD    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "PDERSD method"        ) );
00263          if (Use["PDERSPCA"     ])   histPDPCA  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "PDERSPCA method"      ) );
00264          if (Use["KNN"          ])   histKNN    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "KNN method"           ) );
00265          if (Use["HMatrix"      ])   histHm     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "HMatrix method"       ) );
00266          if (Use["Fisher"       ])   histFi     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "Fisher method"        ) );
00267          if (Use["FisherG"      ])   histFiG    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "FisherG method"        ) );
00268          if (Use["BoostedFisher"])   histFiB    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "BoostedFisher method"        ) );
00269          if (Use["LD"           ])   histLD     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "LD method"            ) );
00270          if (Use["MLP"          ])   histNn     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "MLP method"           ) );
00271          if (Use["CFMlpANN"     ])   histNnC    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "CFMlpANN method"      ) );
00272          if (Use["TMlpANN"      ])   histNnT    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "TMlpANN method"       ) );
00273          if (Use["BDT"          ])   histBdt    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "BDT method"           ) );
00274          if (Use["BDTD"         ])   histBdtD   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "BDTD method"          ) );
00275          if (Use["BDTG"         ])   histBdtG   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "BDTG method"          ) );
00276          if (Use["RuleFit"      ])   histRf     ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "RuleFit method"       ) );
00277          if (Use["SVM_Gauss"    ])   histSVMG   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "SVM_Gauss method"     ) );
00278          if (Use["SVM_Poly"     ])   histSVMP   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "SVM_Poly method"      ) );
00279          if (Use["SVM_Lin"      ])   histSVML   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "SVM_Lin method"       ) );
00280          if (Use["FDA_MT"       ])   histFDAMT  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "FDA_MT method"        ) );
00281          if (Use["FDA_GA"       ])   histFDAGA  ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "FDA_GA method"        ) );
00282          if (Use["Category"     ])   histCat    ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "Category method"         ) );
00283          if (Use["Plugin"       ])   histPBdt   ->SetBinContent(ibin,jbin, reader->EvaluateMVA( "P_BDT method"         ) );
00284       }
00285    }
00286 
00287 
00288    std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
00289   
00290    delete reader;
00291 
00292 
00293 
00294     
00295    std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
00296 
00297 
00298 
00299    gStyle->SetPalette(1);
00300 
00301    if (Use["Likelihood"   ]) plot(hs,hb,histLk     ,v0,v1);
00302    if (Use["LikelihoodD"  ]) plot(hs,hb,histLkD    ,v0,v1);
00303    if (Use["LikelihoodPCA"]) plot(hs,hb,histLkPCA  ,v0,v1);
00304    if (Use["LikelihoodKDE"]) plot(hs,hb,histLkKDE  ,v0,v1);
00305    if (Use["LikelihoodMIX"]) plot(hs,hb,histLkMIX  ,v0,v1);
00306    if (Use["PDERS"        ]) plot(hs,hb,histPD     ,v0,v1);
00307    if (Use["PDERSD"       ]) plot(hs,hb,histPDD    ,v0,v1);
00308    if (Use["PDERSPCA"     ]) plot(hs,hb,histPDPCA  ,v0,v1);
00309    if (Use["KNN"          ]) plot(hs,hb,histKNN    ,v0,v1);
00310    if (Use["HMatrix"      ]) plot(hs,hb,histHm     ,v0,v1);
00311    if (Use["Fisher"       ]) plot(hs,hb,histFi     ,v0,v1);
00312    if (Use["FisherG"      ]) plot(hs,hb,histFiG    ,v0,v1);
00313    if (Use["BoostedFisher"]) plot(hs,hb,histFiB    ,v0,v1);
00314    if (Use["LD"           ]) plot(hs,hb,histLD     ,v0,v1);
00315    if (Use["MLP"          ]) plot(hs,hb,histNn     ,v0,v1);
00316    if (Use["CFMlpANN"     ]) plot(hs,hb,histNnC    ,v0,v1);
00317    if (Use["TMlpANN"      ]) plot(hs,hb,histNnT    ,v0,v1);
00318    if (Use["BDT"          ]) plot(hs,hb,histBdt    ,v0,v1);
00319    if (Use["BDTD"         ]) plot(hs,hb,histBdtD   ,v0,v1);
00320    if (Use["BDTG"         ]) plot(hs,hb,histBdtG   ,v0,v1); 
00321    if (Use["RuleFit"      ]) plot(hs,hb,histRf     ,v0,v1);
00322    if (Use["SVM_Gauss"    ]) plot(hs,hb,histSVMG   ,v0,v1);
00323    if (Use["SVM_Poly"     ]) plot(hs,hb,histSVMP   ,v0,v1);
00324    if (Use["SVM_Lin"      ]) plot(hs,hb,histSVML   ,v0,v1);
00325    if (Use["FDA_MT"       ]) plot(hs,hb,histFDAMT  ,v0,v1);
00326    if (Use["FDA_GA"       ]) plot(hs,hb,histFDAGA  ,v0,v1);
00327    if (Use["Category"     ]) plot(hs,hb,histCat    ,v0,v1);
00328    if (Use["Plugin"       ]) plot(hs,hb,histPBdt   ,v0,v1);
00329 
00330    
00331    //
00332    // write histograms
00333    //
00334    TFile *target  = new TFile( "TMVApp.root","RECREATE" );
00335 
00336    hs->Write();
00337    hb->Write();
00338 
00339    if (Use["Likelihood"   ])   histLk     ->Write();
00340    if (Use["LikelihoodD"  ])   histLkD    ->Write();
00341    if (Use["LikelihoodPCA"])   histLkPCA  ->Write();
00342    if (Use["LikelihoodKDE"])   histLkKDE  ->Write();
00343    if (Use["LikelihoodMIX"])   histLkMIX  ->Write();
00344    if (Use["PDERS"        ])   histPD     ->Write();
00345    if (Use["PDERSD"       ])   histPDD    ->Write();
00346    if (Use["PDERSPCA"     ])   histPDPCA  ->Write();
00347    if (Use["KNN"          ])   histKNN    ->Write();
00348    if (Use["HMatrix"      ])   histHm     ->Write();
00349    if (Use["Fisher"       ])   histFi     ->Write();
00350    if (Use["FisherG"      ])   histFiG    ->Write();
00351    if (Use["BoostedFisher"])   histFiB    ->Write();
00352    if (Use["LD"           ])   histLD     ->Write();
00353    if (Use["MLP"          ])   histNn     ->Write();
00354    if (Use["CFMlpANN"     ])   histNnC    ->Write();
00355    if (Use["TMlpANN"      ])   histNnT    ->Write();
00356    if (Use["BDT"          ])   histBdt    ->Write();
00357    if (Use["BDTD"         ])   histBdtD   ->Write();
00358    if (Use["BDTG"         ])   histBdtG   ->Write(); 
00359    if (Use["RuleFit"      ])   histRf     ->Write();
00360    if (Use["SVM_Gauss"    ])   histSVMG   ->Write();
00361    if (Use["SVM_Poly"     ])   histSVMP   ->Write();
00362    if (Use["SVM_Lin"      ])   histSVML   ->Write();
00363    if (Use["FDA_MT"       ])   histFDAMT  ->Write();
00364    if (Use["FDA_GA"       ])   histFDAGA  ->Write();
00365    if (Use["Category"     ])   histCat    ->Write();
00366    if (Use["Plugin"       ])   histPBdt   ->Write();
00367 
00368    target->Close();
00369 
00370 } 
00371
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