当前位置: 首页>>代码示例>>C++>>正文


C++ TChain::SetCacheLearnEntries方法代码示例

本文整理汇总了C++中TChain::SetCacheLearnEntries方法的典型用法代码示例。如果您正苦于以下问题:C++ TChain::SetCacheLearnEntries方法的具体用法?C++ TChain::SetCacheLearnEntries怎么用?C++ TChain::SetCacheLearnEntries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TChain的用法示例。


在下文中一共展示了TChain::SetCacheLearnEntries方法的3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: makesimpleplot

void makesimpleplot(void)
{
  //  set_plot_style();
  TChain *chain = new TChain("OSTwoLepAna/summaryTree");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_1.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_10.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_11.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_12.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_13.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_14.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_15.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_16.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_17.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_18.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_19.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_2.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_20.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_3.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_4.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_5.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_6.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_7.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_8.root");
  chain->Add("root://eoscms.cern.ch//eos/cms/store/user/muell149/ttH-leptons_Skims/acceptance_study_v5/ttHJetToNonbb_M125_13TeV_amcatnloFXFX_madspin_pythia8/crab_ttH125/150916_225227/0000/multilep_michaeltest_deleteme_9.root");

  int chainentries = chain->GetEntries();   
  cout << "tree entries: " << chainentries << endl;  
  
  Int_t cachesize = 100000000;   //100 MBytes
  chain->SetCacheSize(cachesize);   //<<<
  chain->SetCacheLearnEntries(20); 
  
  double mcwgt_intree = -999.;
  double wgt_intree = -999.;
  int hDecay_intree = -999;
  int eventNum_intree = -999;

  vector<ttH::GenParticle> *pruned_genParticles_intree = 0; 
  vector<ttH::Electron> *raw_electrons_intree = 0; 
  vector<ttH::Electron> *preselected_electrons_intree = 0; 
  vector<ttH::Electron> *tight_electrons_intree = 0; 
  vector<ttH::Muon> *raw_muons_intree = 0; 
  vector<ttH::Muon> *preselected_muons_intree = 0; 
  vector<ttH::Muon> *tight_muons_intree = 0; 
  vector<ttH::Lepton> *tight_leptons_intree = 0; 
  vector<ttH::Lepton> *preselected_leptons_intree = 0; 
  vector<ttH::Lepton> raw_leptons;
  
  chain->SetBranchAddress("mcwgt", &mcwgt_intree);
  chain->SetBranchAddress("wgt", &wgt_intree);
  chain->SetBranchAddress("eventnum", &eventNum_intree);
  chain->SetBranchAddress("higgs_decay", &hDecay_intree);
  chain->SetBranchAddress("pruned_genParticles", &pruned_genParticles_intree);
  chain->SetBranchAddress("raw_electrons", &raw_electrons_intree);
  chain->SetBranchAddress("preselected_electrons", &preselected_electrons_intree);
  chain->SetBranchAddress("tightMvaBased_electrons", &tight_electrons_intree);
  chain->SetBranchAddress("raw_muons", &raw_muons_intree);
  chain->SetBranchAddress("preselected_muons", &preselected_muons_intree);
  chain->SetBranchAddress("tightMvaBased_muons", &tight_muons_intree);
  chain->SetBranchAddress("tightMvaBased_leptons", &tight_leptons_intree);
  chain->SetBranchAddress("preselected_leptons", &preselected_leptons_intree);

  int positiveCharge;
  int negativeCharge;
  double leadPt;
  double trailPt;

  int duplicate = 0;
  int total_count = 0;
  int ss2l_reco_count =0;
  int ss2l_reco_agree_count =0;
  int ss2l_gen_count =0;
  int ss2l_ee_gen_count =0;
  int ss2l_mm_gen_count =0;
  int ss2l_em_gen_count =0;
  int ss2l_me_gen_count =0;
  int l3_reco_count =0;
  int l3_reco_agree_count =0;
  int l3_gen_count =0;
  int l4_reco_count =0;
  int l4_reco_agree_count =0;
  int l4_gen_count =0;

  int ss2l_PS_count = 0;
  int ss2l_raw_count = 0;
  int l3_PS_count = 0;
  int l3_raw_count = 0;
  int l4_PS_count = 0;
  int l4_raw_count = 0;
  
  int wgt;

  //pure rate study
  vector<int> cut_vec_ele_int (7,0);
  vector<int> cut_vec_mu_int (7,0);
  int raw_ele_size = 0;
  int raw_mu_size = 0;
    
  //2D plot vars
  vector<ttH::GenParticle> genMuons;
//.........这里部分代码省略.........
开发者ID:RemKamal,项目名称:ttH-13TeVMultiLeptons,代码行数:101,代码来源:makesimpleplot.C

示例2: MuMcPrVKFV2012


//.........这里部分代码省略.........

         if      (p <  2)  hists[h][p] = new TH2D(Name, Title, nMcRecMult, xMult.GetArray(), nMcRecMult, xMult.GetArray());
         else if (p == 2)  hists[h][p] = new TH1D(Name, Title, nMcRecMult, xMult.GetArray());
      }
   }

   TNtuple *VertexG = new TNtuple("VertexG", "good vertex & global params info", vnames);
   TNtuple *VertexB = new TNtuple("VertexB", "bad  vertex & global params info", vnames);
   // ----------------------------------------------
   StMuDstMaker *maker = new StMuDstMaker(0, 0, "", file, "st:MuDst.root", 1e9); // set up maker in read mode
   //                       0,0                        this mean read mode
   //                           dir                    read all files in this directory
   //                               file               bla.lis real all file in this list, if (file!="") dir is ignored
   //                                    filter        apply filter to filenames, multiple filters are separated by ':'
   //                                          10      maximum number of file to read
   maker->SetStatus("*", 0);

   std::vector<std::string> activeBranchNames = {
      "MuEvent",
      "PrimaryVertices",
      "StStMuMcVertex",
      "StStMuMcTrack"
   };

   // Set Active braches
   for (const auto& branchName : activeBranchNames)
      maker->SetStatus(branchName.c_str(), 1);

   TChain *tree = maker->chain();
   Long64_t nentries = tree->GetEntries();
   nevent = TMath::Min(nevent, nentries);
   std::cout << nentries << " events in chain " << nevent << " will be read." << std::endl;
   tree->SetCacheSize(-1);        //by setting the read cache to -1 we set it to the AutoFlush value when writing
   tree->SetCacheLearnEntries(1); //one entry is sufficient to learn
   tree->SetCacheEntryRange(0, nevent);

   for (Long64_t ev = 0; ev < nevent; ev++) {
      if (maker->Make()) break;

      StMuDst *muDst = maker->muDst();   // get a pointer to the StMuDst class, the class that points to all the data
      StMuEvent *muEvent = muDst->event(); // get a pointer to the class holding event-wise information
      int referenceMultiplicity = muEvent->refMult(); // get the reference multiplicity

      TClonesArray *PrimaryVertices   = muDst->primaryVertices();
      int nPrimaryVertices = PrimaryVertices->GetEntriesFast();

      TClonesArray *MuMcVertices   = muDst->mcArray(0);
      int nMuMcVertices = MuMcVertices->GetEntriesFast();

      TClonesArray *MuMcTracks     = muDst->mcArray(1);
      int nMuMcTracks = MuMcTracks->GetEntriesFast();

      if ( nevent >= 10 && ev % int(nevent*0.1) == 0 )
      {
         std::cout << "Event #" << ev << "\tRun\t" << muEvent->runId()
                   << "\tId: " << muEvent->eventId()
                   << " refMult= " << referenceMultiplicity
                   << "\tPrimaryVertices " << nPrimaryVertices
                   << "\t" << " " << nMuMcVertices
                   << "\t" << " " << nMuMcTracks
                   << std::endl;
      }

      //    const Double_t field = muEvent->magneticField()*kilogauss;
      if (! nMuMcVertices || ! nMuMcTracks || nPrimaryVertices <= 0) {
         std::cout << "Ev. " << ev << " has no MC information ==> skip it" << std::endl;
开发者ID:star-bnl,项目名称:star-travex,代码行数:67,代码来源:MuMcPrVKFV2012.C

示例3: trainElectronEnergyRegression_ECAL

void trainElectronEnergyRegression_ECAL(char* trainingFile, char* outWeightFile, char* optionChar, int nTrees) {


  
  // Setting up training option
  std::string optionStr(optionChar);

  // ******** If option is V00, V01, V02, etc. ********* //
  if (optionStr == "V00" || optionStr == "V01") {

    GBRTrainer *traineb = new GBRTrainer;
    GBRTrainer *trainebvar = new GBRTrainer;
    GBRTrainer *trainee = new GBRTrainer;
    GBRTrainer *traineevar = new GBRTrainer;

    TTree *intree = 0;

    cout << "Training on file " << trainingFile << " with version " << optionChar << endl;
    TChain *chainele = new TChain("eleIDdir/T1");
    chainele->Add(trainingFile);
    chainele->LoadTree(0);    
    chainele->SetCacheSize(64*1024*1024);
    chainele->SetCacheLearnEntries();
    intree = chainele;

    traineb->AddTree(chainele);
    trainebvar->AddTree(chainele);
    trainee->AddTree(chainele);
    traineevar->AddTree(chainele);

    TCut traincut = "pt>0";////////////////////////////////

    TCut evtcut;
    TCut evtcutvar;
    TCut statusenergycut;

    //if you want to train also energy variance
    evtcut = "event%2==0 ";
    evtcutvar = "event%2==1 ";


    statusenergycut="(GeneratedEnergyStatus3-GeneratedEnergyStatus1)/GeneratedEnergyStatus3<0.01 && GeneratedEnergyStatus3>=GeneratedEnergyStatus1";

    traineb->SetTrainingCut(std::string(traincut && evtcut && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
    trainee->SetTrainingCut(std::string(traincut && evtcut && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
    //turn this off for now

    trainebvar->SetTrainingCut(std::string(traincut && evtcutvar && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
    traineevar->SetTrainingCut(std::string(traincut && evtcutvar && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
   

    const double maxsig = 3.0;
    const double shrinkage = 0.1;

    traineb->SetMinEvents(200);
    traineb->SetShrinkage(shrinkage);  
    traineb->SetMinCutSignificance(maxsig);

    trainebvar->SetMinEvents(200);
    trainebvar->SetShrinkage(shrinkage);  
    trainebvar->SetMinCutSignificance(maxsig);  

    trainee->SetMinEvents(200);
    trainee->SetShrinkage(shrinkage);  
    trainee->SetMinCutSignificance(maxsig);  

    traineevar->SetMinEvents(200);
    traineevar->SetShrinkage(shrinkage);  
    traineevar->SetMinCutSignificance(maxsig);    

    traineb->SetTargetVar("GeneratedEnergyStatus3/SCRawEnergy");
    trainebvar->SetTargetVar("abs( targeteb - GeneratedEnergyStatus3/SCRawEnergy) ");
    trainee->SetTargetVar("GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))");
    traineevar->SetTargetVar("abs( targetee - GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))) ");

    std::vector<std::string> *varsf = new std::vector<std::string>;
    varsf->push_back("SCRawEnergy");
    varsf->push_back("scEta");
    varsf->push_back("scPhi");
    varsf->push_back("R9");  
    varsf->push_back("E5x5Seed/SCRawEnergy");  
    varsf->push_back("etawidth");
    varsf->push_back("phiwidth");  
    varsf->push_back("NClusters");
    varsf->push_back("HoE");
    varsf->push_back("rho");
    varsf->push_back("vertices");  
    varsf->push_back("EtaSeed-scEta");
    varsf->push_back("atan2(sin(PhiSeed-scPhi),cos(PhiSeed-scPhi))");
    varsf->push_back("ESeed/SCRawEnergy");
    varsf->push_back("E3x3Seed/ESeed");
    varsf->push_back("E5x5Seed/ESeed");
    varsf->push_back("see");   
    varsf->push_back("spp");   
    //    varsf->push_back("sep");
    varsf->push_back("EMaxSeed/ESeed");
    varsf->push_back("E2ndSeed/ESeed");
    varsf->push_back("ETopSeed/ESeed");
    varsf->push_back("EBottomSeed/ESeed");
    varsf->push_back("ELeftSeed/ESeed");
//.........这里部分代码省略.........
开发者ID:michelif,项目名称:usercode,代码行数:101,代码来源:trainElectronEnergyRegression_ECAL.C


注:本文中的TChain::SetCacheLearnEntries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。