C++ Reader::GetMVAError方法代码示例

//.........这里部分代码省略.........
      if (Use["LikelihoodD"  ])   histLkD    ->Fill( reader->EvaluateMVA( "LikelihoodD method"   ) );
      if (Use["LikelihoodPCA"])   histLkPCA  ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
      if (Use["LikelihoodKDE"])   histLkKDE  ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
      if (Use["LikelihoodMIX"])   histLkMIX  ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) );
      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) );
      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) );
      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) );
      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) );
      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) );
      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) );
      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) );
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) );
      if (Use["MLPBFGS"          ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"           ) );
      if (Use["MLPBNN"          ])   histNnbnn ->Fill( reader->EvaluateMVA( "MLPBNN method"           ) );
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
      if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ) );
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
      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++) {

//.........这里部分代码省略.........
      if (Use["LikelihoodD"  ])   histLkD    ->Fill( reader->EvaluateMVA( "LikelihoodD method"   ) );
      if (Use["LikelihoodPCA"])   histLkPCA  ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
      if (Use["LikelihoodKDE"])   histLkKDE  ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
      if (Use["LikelihoodMIX"])   histLkMIX  ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) );
      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) );
      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) );
      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) );
      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) );
      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) );
      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) );
      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) );
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) );
      if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) );
      if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) );
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
      if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ) );
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
      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 );         
         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;

//.........这里部分代码省略.........
      if (Use["LikelihoodD"  ])   histLkD    ->Fill( reader->EvaluateMVA( "LikelihoodD method"   ) , weight);
      if (Use["LikelihoodPCA"])   histLkPCA  ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) , weight);
      if (Use["LikelihoodKDE"])   histLkKDE  ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) , weight);
      if (Use["LikelihoodMIX"])   histLkMIX  ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) , weight);
      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) , weight);
      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) , weight);
      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) , weight);
      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) , weight);
      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) , weight);
      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) , weight);
      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) , weight);
      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) , weight);
      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) , weight);
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) , weight);
      if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) , weight);
      if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) , weight);
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) , weight);
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) , weight);
      if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ) , weight);
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) , weight);
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) , weight);
      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) , weight);
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) , weight);
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) , weight);
      if (Use["SVM_Lin"      ])   histSVML   ->Fill( reader->EvaluateMVA( "SVM_Lin method"       ) , weight);
      if (Use["FDA_MT"       ])   histFDAMT  ->Fill( reader->EvaluateMVA( "FDA_MT method"        ) , weight);
      if (Use["FDA_GA"       ])   histFDAGA  ->Fill( reader->EvaluateMVA( "FDA_GA method"        ) , weight);
      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 );

//.........这里部分代码省略.........
      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) );
      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) );
      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) );
      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) );
      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) );
      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) );
      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) );
      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) );
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) );
      if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) );
      if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) );
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
	   if (Use["BDT"          ]) {
	   BDTvalue = reader->EvaluateMVA( "BDT method"           );
		   histMattBdt    ->Fill( BDTvalue,DY_PtZ_weight*Trigweight*B2011PUweight );
		   histTMVABdt    ->Fill( BDTvalue,DY_PtZ_weight*Trigweight*B2011PUweight );
	   }
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
      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 );         
         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" ) );
      }
   
	  // std::cout << "Ht is "<< Ht << endl;
	   if(BDTvalue>-1.50){
NpassBDT++;
hMjj_OpenSelection->Fill(Hmass,DY_PtZ_weight*Trigweight*B2011PUweight );
hMmumu_OpenSelection->Fill(Emumass,DY_PtZ_weight*Trigweight*B2011PUweight );
hPtjj_OpenSelection->Fill(Hpt,DY_PtZ_weight*Trigweight*B2011PUweight );
hPtmumu_OpenSelection->Fill(Zpt,DY_PtZ_weight*Trigweight*B2011PUweight );
hCSV0_OpenSelection->Fill(CSV0,DY_PtZ_weight*Trigweight*B2011PUweight );
hCSV1_OpenSelection->Fill(CSV1,DY_PtZ_weight*Trigweight*B2011PUweight );
hdphiVH_OpenSelection->Fill(DeltaPhiHV,DY_PtZ_weight*Trigweight*B2011PUweight );
hdetaJJ_OpenSelection->Fill(DetaJJ,DY_PtZ_weight*Trigweight*B2011PUweight );
hUnweightedEta_OpenSelection->Fill(UnweightedEta,DY_PtZ_weight*Trigweight*B2011PUweight );
hPtmu0_OpenSelection->Fill(lep0pt,DY_PtZ_weight*Trigweight*B2011PUweight );
	   hHt_OpenSelection->Fill(Ht,DY_PtZ_weight*Trigweight*B2011PUweight );
	   hCircularity_OpenSelection->Fill(EvntShpCircularity,DY_PtZ_weight*Trigweight*B2011PUweight );
	   hCHFb0_OpenSelection->Fill(jetCHF0, DY_PtZ_weight*Trigweight*B2011PUweight );
	   hCHFb1_OpenSelection->Fill(jetCHF1, DY_PtZ_weight*Trigweight*B2011PUweight );
	   hPtbalZH_OpenSelection->Fill(PtbalZH, DY_PtZ_weight*Trigweight*B2011PUweight );
	   hPtmu1_OpenSelection->Fill(lep1pt, DY_PtZ_weight*Trigweight*B2011PUweight );
	   hPFRelIsomu0_OpenSelection->Fill(lep_pfCombRelIso0, DY_PtZ_weight*Trigweight*B2011PUweight );
	   hPFRelIsomu1_OpenSelection->Fill(lep_pfCombRelIso1, DY_PtZ_weight*Trigweight*B2011PUweight );

//.........这里部分代码省略.........
    }
    if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) );
    if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) );
    if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
    if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
    if (Use["BDT"          ])  {
       histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ) );
       bdt = reader->EvaluateMVA( "BDT method"           ) ;
 	b_bdt->Fill();
    }
    if (Use["BDTD"         ])  {
      histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      bdtd =  reader->EvaluateMVA( "BDTD method"          );
	b_bdtd->Fill();
    }
    if (Use["BDTG"         ])  {
      histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      bdtg =  reader->EvaluateMVA( "BDTG method"        );
	b_bdtg->Fill();
	//cout <<  reader->EvaluateMVA( "BDTG method" )  <<endl;
    }
    if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
    if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
    if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
    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 );         
       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;

//.........这里部分代码省略.........
          n1->Fill(Lam_mass,MVA);

      }

      if (Use["BDTG"         ]) {

          double Lam_mass = la_mass;
          
          histBdtG    ->Fill( reader->EvaluateMVA( "BDTG method"           ) );
          

          double MVA = 0.0;
          MVA = reader->EvaluateMVA("BDTG method");


          n1->Fill(Lam_mass,MVA);



      }

      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
      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 );         
         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;

//.........这里部分代码省略.........
//cout<<(*myweight)[0]<<endl;
eventwight["__PU__plus"]=(*myweight)[0]*(*mypileupSFup)[0]/(*mypileupSF)[0];
eventwight["__PU__minus"]=(*myweight)[0]*(*mypileupSFdown)[0]/(*mypileupSF)[0];
eventwight["__TRIG__plus"]=(*myweight)[0]*(*mytriggerSFup)[0]/(*mytriggerSF)[0];
eventwight["__TRIG__minus"]=(*myweight)[0]*(*mytriggerSFdown)[0]/(*mytriggerSF)[0];
eventwight["__BTAG__plus"]=(*myweight)[0]*(*mybtagSFup)[0]/(*mybtagSF)[0];
eventwight["__BTAG__minus"]=(*myweight)[0]*(*mybtagSFdown)[0]/(*mybtagSF)[0];
eventwight["__MISSTAG__plus"]=(*myweight)[0]*(*mymistagSFup)[0]/(*mybtagSF)[0];
eventwight["__MISSTAG__minus"]=(*myweight)[0]*(*mymistagSFdown)[0]/(*mybtagSF)[0];
eventwight["__MUON__plus"]=(*myweight)[0]*(*mymuonSFup)[0]/(*mymuonSF)[0];
eventwight["__MUON__minus"]=(*myweight)[0]*(*mymuonSFdown)[0]/(*mymuonSF)[0];
eventwight["__PHOTON__plus"]=(*myweight)[0]*(*myphotonSFup)[0]/(*myphotonSF)[0];
eventwight["__PHOTON__minus"]=(*myweight)[0]*(*myphotonSFdown)[0]/(*myphotonSF)[0];
eventwight[""]=(*myweight)[0];

finalweight=eventwight[systematics[phi].c_str()];
if (samples_[idx]=="SIGNAL.root") finalweight=(*myweight)[0];
//if (samples_[idx]=="WPHJET")  finalweight=(*mypileupSF)[0]*(*mytriggerSF)[0]*(*mybtagSF)[0]*(*mymuonSF)[0]*(*myphotonSF)[0];
//if (finalweight<0) finalweight=30;
//cout<<"negative event weight"<<finalweight<<"            "<<ptphoton<<endl;
      if (Use["CutsGA"]) {
         // Cuts is a special case: give the desired signal efficienciy
         Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
         if (passed) nSelCutsGA++;
      }

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;

//.........这里部分代码省略.........
      if (Use["LikelihoodPCA"])   histLkPCA  ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
      if (Use["LikelihoodKDE"])   histLkKDE  ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
      if (Use["LikelihoodMIX"])   histLkMIX  ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
      if (Use["PDERS"        ])   histPD     ->Fill( reader->EvaluateMVA( "PDERS method"         ) );
      if (Use["PDERSD"       ])   histPDD    ->Fill( reader->EvaluateMVA( "PDERSD method"        ) );
      if (Use["PDERSPCA"     ])   histPDPCA  ->Fill( reader->EvaluateMVA( "PDERSPCA method"      ) );
      if (Use["KNN"          ])   histKNN    ->Fill( reader->EvaluateMVA( "KNN method"           ) );
      if (Use["HMatrix"      ])   histHm     ->Fill( reader->EvaluateMVA( "HMatrix method"       ) );
      if (Use["Fisher"       ])   histFi     ->Fill( reader->EvaluateMVA( "Fisher method"        ) );
      if (Use["FisherG"      ])   histFiG    ->Fill( reader->EvaluateMVA( "FisherG method"       ) );
      if (Use["BoostedFisher"])   histFiB    ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
      if (Use["LD"           ])   histLD     ->Fill( reader->EvaluateMVA( "LD method"            ) );
      if (Use["MLP"          ])   histNn     ->Fill( reader->EvaluateMVA( "MLP method"           ) );
      if (Use["MLPBFGS"      ])   histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method"       ) );
      if (Use["MLPBNN"       ])   histNnbnn  ->Fill( reader->EvaluateMVA( "MLPBNN method"        ) );
      if (Use["CFMlpANN"     ])   histNnC    ->Fill( reader->EvaluateMVA( "CFMlpANN method"      ) );
      if (Use["TMlpANN"      ])   histNnT    ->Fill( reader->EvaluateMVA( "TMlpANN method"       ) );
      if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           ), event_weight ); 
      //if (Use["BDT"          ])   histBdt    ->Fill( reader->EvaluateMVA( "BDT method"           )); 
      if (Use["BDTD"         ])   histBdtD   ->Fill( reader->EvaluateMVA( "BDTD method"          ) );
      if (Use["BDTG"         ])   histBdtG   ->Fill( reader->EvaluateMVA( "BDTG method"          ) );
      if (Use["RuleFit"      ])   histRf     ->Fill( reader->EvaluateMVA( "RuleFit method"       ) );
      if (Use["SVM_Gauss"    ])   histSVMG   ->Fill( reader->EvaluateMVA( "SVM_Gauss method"     ) );
      if (Use["SVM_Poly"     ])   histSVMP   ->Fill( reader->EvaluateMVA( "SVM_Poly method"      ) );
      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 );         
         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;