29#ifndef ROOT_TMVA_DataSetFactory
30#define ROOT_TMVA_DataSetFactory
59 class DataInputHandler;
84 template<
typename T >
109 template <
typename F>
#define ClassDef(name, id)
winID h TVirtualViewer3D TVirtualGLPainter p
Float_t cutScaling() const
Int_t nTrainingEventsRequested
Float_t TrainTestSplitRequested
Int_t nTestingEventsRequested
Class that contains all the data information.
~DataSetFactory()
destructor
std::vector< TTreeFormula * > fSpectatorFormulas
spectators
std::vector< TTreeFormula * > fWeightFormula
weights
TTree * fCurrentTree
the tree, events are currently read from
DataSet * BuildInitialDataSet(DataSetInfo &, TMVA::DataInputHandler &)
if no entries, than create a DataSet with one Event which uses dynamic variables (pointers to variabl...
DataSetFactory()
constructor
std::map< Types::ETreeType, EventVectorOfClasses > EventVectorOfClassesOfTreeType
UInt_t fCurrentEvtIdx
the current event (to avoid reading of the same event)
void ChangeToNewTree(TreeInfo &, const DataSetInfo &)
While the data gets copied into the local training and testing trees, the input tree can change (for ...
std::map< Types::ETreeType, EventVector > EventVectorOfTreeType
Bool_t fScaleWithPreselEff
how to deal with requested #events in connection with preselection cuts
std::vector< std::pair< TTreeFormula *, Int_t > > fInputTableFormulas
! input variables expression for arrays
void BuildEventVector(DataSetInfo &dsi, DataInputHandler &dataInput, EventVectorOfClassesOfTreeType &eventsmap, EvtStatsPerClass &eventCounts)
build empty event vectors distributes events between kTraining/kTesting/kMaxTreeType
DataSet * CreateDataSet(DataSetInfo &, DataInputHandler &)
steering the creation of a new dataset
DataSet * MixEvents(DataSetInfo &dsi, EventVectorOfClassesOfTreeType &eventsmap, EvtStatsPerClass &eventCounts, const TString &splitMode, const TString &mixMode, const TString &normMode, UInt_t splitSeed)
Select and distribute unassigned events to kTraining and kTesting.
std::vector< int > NumberPerClass
std::vector< TTreeFormula * > fInputFormulas
input variables
std::vector< EventVector > EventVectorOfClasses
void InitOptions(DataSetInfo &dsi, EvtStatsPerClass &eventsmap, TString &normMode, UInt_t &splitSeed, TString &splitMode, TString &mixMode)
the dataset splitting
Bool_t fVerbose
Verbosity.
void CalcMinMax(DataSet *, DataSetInfo &dsi)
compute covariance matrix
std::vector< Double_t > ValuePerClass
DataSet * BuildDynamicDataSet(DataSetInfo &)
std::vector< EventStats > EvtStatsPerClass
TString fVerboseLevel
VerboseLevel.
std::vector< TTreeFormula * > fTargetFormulas
targets
Bool_t CheckTTreeFormula(TTreeFormula *ttf, const TString &expression, Bool_t &hasDollar)
checks a TTreeFormula for problems
std::vector< TTreeFormula * > fCutFormulas
cuts
void ResetBranchAndEventAddresses(TTree *)
Bool_t fCorrelations
Whether to print correlations or not.
MsgLogger * fLogger
! message logger
std::map< Types::ETreeType, ValuePerClass > ValuePerClassOfTreeType
void RenormEvents(DataSetInfo &dsi, EventVectorOfClassesOfTreeType &eventsmap, const EvtStatsPerClass &eventCounts, const TString &normMode)
renormalisation of the TRAINING event weights
Bool_t fComputeCorrelations
Whether to force computation of correlations or not.
TMatrixD * CalcCorrelationMatrix(DataSet *, const UInt_t classNumber)
computes correlation matrix for variables "theVars" in tree; "theType" defines the required event "ty...
TMatrixD * CalcCovarianceMatrix(DataSet *, const UInt_t classNumber)
compute covariance matrix
std::vector< Event * > EventVector
Class that contains all the data information.
Class that contains all the data information.
ostringstream derivative to redirect and format output
F operator()(const F &argF) const
Mother of all ROOT objects.
A TTree represents a columnar dataset.
create variable transformations
DeleteFunctor_t< const T > DeleteFunctor()
DeleteFunctor_t & operator()(const T *p)