29 #ifndef ROOT_TMVA_DataSet 30 #define ROOT_TMVA_DataSet 152 std::vector< std::map< TString, Results* > >
fResults;
Random number generator class based on M.
const Event * GetTestEvent(Long64_t ievt) const
void SetCurrentEvent(Long64_t ievt) const
Long64_t fTrainingBlockSize
std::vector< std::vector< std::pair< Float_t, Long64_t > > > fSamplingSelected
void AddEvent(Event *, Types::ETreeType)
add event to event list after which the event is owned by the dataset
std::vector< std::vector< std::pair< Float_t, Long64_t > > > fSamplingEventList
std::vector< std::vector< Event * > > fEventCollection
void CreateSampling() const
create an event sampling (random or importance sampling)
TRandom3 * fSamplingRandom
UInt_t GetNVariables() const
access the number of variables through the datasetinfo
std::vector< Char_t > fBlockBelongToTraining
void ClearNClassEvents(Int_t type)
const std::vector< Event * > & GetEventCollection(Types::ETreeType type=Types::kMaxTreeType) const
Long64_t GetNEvtBkgdTrain()
return number of background training events in dataset
UInt_t TreeIndex(Types::ETreeType type) const
#define ClassDef(name, id)
UInt_t GetNSpectators() const
access the number of targets through the datasetinfo
The TNamed class is the base class for all named ROOT classes.
virtual ~DataSet()
destructor
TTree * GetTree(Types::ETreeType type)
create the test/trainings tree with all the variables, the weights, the classes, the targets...
Types::ETreeType GetCurrentType() const
const Event * GetEvent(Long64_t ievt, Types::ETreeType type) const
Class that contains all the data information.
Long64_t GetNTrainingEvents() const
Bool_t fHasNegativeEventWeights
void MoveTrainingBlock(Int_t blockInd, Types::ETreeType dest, Bool_t applyChanges=kTRUE)
move training block
const Event * GetTrainingEvent(Long64_t ievt) const
Class that contains all the data information.
void ApplyTrainingSetDivision()
apply division of data set
Bool_t HasNegativeEventWeights() const
Results * GetResults(const TString &, Types::ETreeType type, Types::EAnalysisType analysistype)
std::vector< std::vector< Long64_t > > fClassEvents
Long64_t GetNEvtSigTest()
return number of signal test events in dataset
void DeleteResults(const TString &, Types::ETreeType type, Types::EAnalysisType analysistype)
delete the results stored for this particular Method instance.
void DivideTrainingSet(UInt_t blockNum)
divide training set
void DestroyCollection(Types::ETreeType type, Bool_t deleteEvents)
destroys the event collection (events + vector)
const Event * GetEvent(Long64_t ievt) const
Long64_t GetNEvtBkgdTest()
return number of background test events in dataset
Long64_t GetNTestEvents() const
void IncrementNClassEvents(Int_t type, UInt_t classNumber)
std::vector< Char_t > fSampling
void EventResult(Bool_t successful, Long64_t evtNumber=-1)
increase the importance sampling weight of the event when not successful and decrease it when success...
std::vector< Float_t > fSamplingWeight
Long64_t fCurrentEventIdx
UInt_t fCurrentTreeIdx
[train/test/...][method-identifier]
Long64_t GetNEvtSigTrain()
return number of signal training events in dataset
MsgLogger & Log() const
message logger
void SetCurrentType(Types::ETreeType type) const
void SetEventCollection(std::vector< Event *> *, Types::ETreeType, Bool_t deleteEvents=true)
Sets the event collection (by DataSetFactory)
void ApplyTrainingBlockDivision()
std::vector< Int_t > fSamplingNEvents
Long64_t GetNClassEvents(Int_t type, UInt_t classNumber)
ostringstream derivative to redirect and format output
Abstract ClassifierFactory template that handles arbitrary types.
#define dest(otri, vertexptr)
Class that is the base-class for a vector of result.
Long64_t GetNEvents(Types::ETreeType type=Types::kMaxTreeType) const
A TTree object has a header with a name and a title.
const TTree * GetEventCollectionAsTree()
UInt_t GetNTargets() const
access the number of targets through the datasetinfo
void InitSampling(Float_t fraction, Float_t weight, UInt_t seed=0)
initialize random or importance sampling
const Event * GetEvent() const
std::vector< std::map< TString, Results *> > fResults