当前位置: 首页>>代码示例>>C++>>正文

C++ Reader::FindCutsMVA方法代码示例

本文整理汇总了C++中tmva::Reader::FindCutsMVA方法的典型用法代码示例。如果您正苦于以下问题：C++ Reader::FindCutsMVA方法的具体用法？C++ Reader::FindCutsMVA怎么用？C++ Reader::FindCutsMVA使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tmva::Reader的用法示例。


在下文中一共展示了Reader::FindCutsMVA方法的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: TMVAClassificationApplication


//.........这里部分代码省略.........
      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 );         
         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( "TMVApp.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();
开发者ID:beknapp，项目名称:usercode，代码行数:67，代码来源:TMVAClassificationApplication_MC_ZH150.C

示例2: TMVAClassificationApplication_TX


//.........这里部分代码省略.........
      var8 = intVar8;
      var9 = intVar9;
      var10 = dVar10;
      var11 = dVar11;
      var12 = dVar12;
      var13 = dVar13;
      var14 = dVar14;
      var15 = dVar15;
      if(vecVar16->size()>0){
      	var16 = vecVar16->at(0);
      }
      else{
      	var16 = 0;
      }
      if(vecVar17->size()>0){
      	var17 = vecVar17->at(0);
      }
      else{
      	var18 = 0;
      }
      if(vecVar19->size()>0){
      	var19 = vecVar19->at(0);
      }
      else{
      	var19 = 0;
      }     
      if(vecVar20->size()>0){
      	var20 = vecVar20->at(0);
      }
      else{
      	var20 = 0;
      }
      if(vecVar16->size()>1){
	  	var21 = vecVar16->at(1);
	  }
	  else{
	  	var21 = 0;
	  }
	  var22 = dVar22;
	  var23 = dVar23;
	  var24 = dVar24;
	  var25 = dVar25;
	  var26 = dVar26;
	  var27 = dVar27;
	  var28 = dVar28;
	  var29 = dVar29;      // --- 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++;
      }
      BDT = reader->EvaluateMVA( "BDT method");
      histBdt->Fill(BDT);
      branchBDT->Fill();
   }

   // 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

   newTree->Write("",TObject::kOverwrite);
   target->Close();

   std::cout << "--- Created root file: \""<<oFileName<<"\" containing the MVA output histograms" << std::endl;
  
   delete reader;
    
   std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
开发者ID:cms-ljmet，项目名称:singletPrime，代码行数:101，代码来源:TMVAClassificationApplication_TX.C

示例3: Classify_HWW


//.........这里部分代码省略.........
      if (Use["Category"     ])   histCat    ->Fill( reader->EvaluateMVA( "Category method"      ) , weight);
      if (Use["Plugin"       ])   histPBdt   ->Fill( reader->EvaluateMVA( "P_BDT method"         ) , weight);

      // 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 , weight);
      }         

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

    std::cout << npass << " events passing selection, yield " << yield << std::endl;
 
    // 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
    cout << "dir " << dir << endl;
    char* mydir = outdir;
    TFile *target  = new TFile( Form("%s/%s.root",mydir,samples.at(i) ) ,"RECREATE" );
    cout << "Writing to file " << Form("%s/%s.root",mydir,samples.at(i) ) << endl;

    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();
开发者ID:cmstas，项目名称:SingleLepton2012，代码行数:67，代码来源:Classify.C

示例4: TMVAClassificationApplication_new


//.........这里部分代码省略.........
   Float_t BDT;
   TBranch *branchBDT = newTree->Branch("BDT_"+bkgSample,&BDT,"BDT/F");
   std::vector<Double_t> *vecVar1;
   std::vector<Double_t> *vecVar5;
   std::vector<Double_t> *vecVar7;
   theTree->SetBranchAddress( "svMass", &vecVar1);
   theTree->SetBranchAddress( "dRTauTau", &var3);
   theTree->SetBranchAddress( "dRJJ", &var4 );
//    theTree->SetBranchAddress( "svPt", &vecVar5 );
//    theTree->SetBranchAddress( "dRhh", &var6 );
   theTree->SetBranchAddress( "met", &vecVar7 );
   theTree->SetBranchAddress( "mJJ", &var8 );
//    theTree->SetBranchAddress( "metTau1DPhi", &var9 );
//    theTree->SetBranchAddress( "metTau2DPhi", &var10);
//    theTree->SetBranchAddress( "metJ1DPhi", &var11);
//    theTree->SetBranchAddress( "metJ2DPhi", &var12 );
//    theTree->SetBranchAddress( "metTauPairDPhi", &var13 );
//    theTree->SetBranchAddress( "metSvTauPairDPhi", &var14 );
//    theTree->SetBranchAddress( "metJetPairDPhi", &var15 );
//    theTree->SetBranchAddress( "CSVJ1", &var16 );
//    theTree->SetBranchAddress( "CSVJ2", &var17 );
   theTree->SetBranchAddress( "fMassKinFit", &var2);
   theTree->SetBranchAddress( "chi2KinFit2", &var18);

   //to get initial pre-processed events
   TH1F* cutFlow = (TH1F*)input->Get("preselection");

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

      if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
      theTree->GetEntry(ievt);
      var1 = vecVar1->at(0);
//       var5 = vecVar5->at(0);
      var7 = vecVar7->at(0);
      // --- 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++;
      }
      BDT = reader->EvaluateMVA( "BDT method");
      histBdt->Fill(BDT);
      branchBDT->Fill();
   }

   // 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

   histBdt->Write();
   cutFlow->Write();
   newTree->Write();
   target->Close();

   std::cout << "--- Created root file: \""<<oFileName<<"\" containing the MVA output histograms" << std::endl;
  
   delete reader;
    
   std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
开发者ID:zaixingmao，项目名称:nTupleProduction，代码行数:101，代码来源:TMVAClassificationApplication_new.C

示例5: DYPtZ_HF_BDTCut


//.........这里部分代码省略.........
		   hdelRjj_OpenSelection->Fill(delRjj, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hdelRemu_OpenSelection->Fill(delRemu, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hDphiZMET_OpenSelection->Fill(DphiZMET, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hDphiemu_OpenSelection->Fill(Dphiemu, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hDeltaPhijetMETmin_OpenSelection->Fill(DeltaPhijetMETmin, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hAngleHemu_OpenSelection->Fill(AngleHemu, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hProjVisT_OpenSelection->Fill(ProjVisT, DY_PtZ_weight*Trigweight*B2011PUweight );
		   htopMass_OpenSelection->Fill(topMass, DY_PtZ_weight*Trigweight*B2011PUweight );
		   htopPt_OpenSelection->Fill(topPt, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hVMt_OpenSelection->Fill(Mt, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hZmassSVD_OpenSelection->Fill(ZmassSVD, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hZmassSVDnegSol_OpenSelection->Fill(ZmassSVDnegSol, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hZmass_OpenSelection->Fill(Zmass, DY_PtZ_weight*Trigweight*B2011PUweight );
		   hZmassNegInclu_OpenSelection->Fill(ZmassNegInclu, DY_PtZ_weight*Trigweight*B2011PUweight );

	   }
	   treeWithBDT->Fill();
	   
   }//end event loop
   
   // Get elapsed time
   sw.Stop();
   std::cout << "--- End of event loop: "; sw.Print();
std::cout << "Number of Events: "<< Nevents << " Events passed BDT " << NpassBDT<< endl;
   // 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( "BDTCut_DYPtZ_HF.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();
开发者ID:rbartek，项目名称:usercode，代码行数:67，代码来源:DYPtZ_HF_BDTCut.C

示例6: TMVAClassificationApplication


//.........这里部分代码省略.........
      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( "TMVApp.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();
开发者ID:stopsnt，项目名称:SingleLepton2012，代码行数:67，代码来源:TMVA_apply.C

示例7: ZTMVAClassificationApplication


//.........这里部分代码省略.........
    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;
    }
  }

 newtree->Write();
  f_out->Close();  


  // --- Write histograms

  TFile *target  = new TFile( "TMVApp.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();
开发者ID:abmorris，项目名称:BsphiKK，代码行数:67，代码来源:ZTMVAClassificationApplication.C

示例8: TMVAClassificationApplicationLambda


//.........这里部分代码省略.........
      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



   

   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();
开发者ID:KongTu，项目名称:TMVA，代码行数:67，代码来源:TMVAClassificationApplicationLambda.C

示例9: rezamyTMVAClassificationApplication1systematic


//.........这里部分代码省略.........
hists[idx] ->Fill( reader->EvaluateMVA( "BDT method"           ),finalweight* scales[idx] );

//cout<<reader->EvaluateMVA( "BDT method")<<"                            "<<finalweight<<endl;   
//cout<<(*myweight)[0]<<endl;
      // 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" ) );
      }
//delete finalweight;
   }

   // 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;
      }
   }

delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
开发者ID:rgoldouz，项目名称:tqA，代码行数:67，代码来源:rezamyTMVAClassificationApplication1systematic.C

示例10: TMVAClassificationApplication


//.........这里部分代码省略.........
      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( "results_"+decay_mode+".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();
开发者ID:sigamani，项目名称:Stops-dilepton，代码行数:67，代码来源:TMVAClassificationApplication.C

示例11: TMVAClassificationApplication


//.........这里部分代码省略.........
      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["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 );         
         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" ) );
      }
*/
cout<<"End of processing"<<endl;

   }
   // 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( filename+"_out"+".root","RECREATE" );
   if (Use["Likelihood"   ])   {if (_Weight>0)histLk->Scale(_Weight);histLk     ->Write();}
   if (Use["LikelihoodD"  ])   {if (_Weight>0)histLkD->Scale(_Weight);histLkD     ->Write();}
   if (Use["LikelihoodPCA"])   {if (_Weight>0)histLkPCA->Scale(_Weight);histLkPCA     ->Write();}
   if (Use["LikelihoodKDE"])   {if (_Weight>0)histLkKDE->Scale(_Weight);histLkKDE     ->Write();}  
   if (Use["LikelihoodMIX"])   {if (_Weight>0)histLkMIX->Scale(_Weight);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["CFMlpANN"     ])   histNnC    ->Write();
   if (Use["TMlpANN"      ])   histNnT    ->Write();
   if (Use["BDT"          ])   histBdt    ->Write();
   if (Use["BDTD"         ])   histBdtD   ->Write();
   if (Use["BDTG"         ])   histBdtG   ->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["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.root\" containing the MVA output histograms" << std::endl;
  
   delete reader;
    
   std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
开发者ID:igormarfin，项目名称:TMA-Optimization，代码行数:101，代码来源:TMVAClassificationApplication_old.C


注：本文中的tmva::Reader::FindCutsMVA方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。