31#ifndef ROOT_TMVA_MethodCategory
32#define ROOT_TMVA_MethodCategory
55 namespace Experimental {
67 const TString& theOption =
"" );
#define ClassDef(name, id)
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
A specialized string object used for TTree selections.
Class to perform two class classification.
Class that contains all the data information.
Class that contains all the data information.
This is the main MVA steering class.
Interface for all concrete MVA method implementations.
Class for boosting a TMVA method.
Class for categorizing the phase space.
void InitCircularTree(const DataSetInfo &dsi)
initialize the circular tree
void GetHelpMessage() const
Get help message text.
void Init()
initialize the method
Bool_t PassesCut(const Event *ev, UInt_t methodIdx)
virtual Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
check whether method category has analysis type the method type has to be the same for all sub-method...
void ProcessOptions()
process user options
virtual void MakeClass(const TString &=TString("")) const
create reader class for method (classification only at present)
virtual const std::vector< Float_t > & GetMulticlassValues()
returns the mva values of the multi-class right sub-classifier
std::vector< std::vector< UInt_t > > fVarMaps
Double_t GetMvaValue(Double_t *err=nullptr, Double_t *errUpper=nullptr)
returns the mva value of the right sub-classifier
TMVA::DataSetInfo & CreateCategoryDSI(const TCut &, const TString &, const TString &)
create a DataSetInfo object for a sub-classifier
std::vector< IMethod * > fMethods
void DeclareOptions()
options for this method
DataSetManager * fDataSetManager
void AddWeightsXMLTo(void *parent) const
create XML description of Category classifier
const Ranking * CreateRanking()
no ranking
std::vector< UInt_t > fCategorySpecIdx
std::vector< TString > fVars
virtual ~MethodCategory(void)
destructor
virtual const std::vector< Float_t > & GetRegressionValues()
returns the mva value of the right sub-classifier
std::vector< TTreeFormula * > fCatFormulas
needed in conjunction with TTreeFormulas for evaluation category expressions
std::vector< TCut > fCategoryCuts
TMVA::IMethod * AddMethod(const TCut &, const TString &theVariables, Types::EMVA theMethod, const TString &theTitle, const TString &theOptions)
adds sub-classifier for a category
virtual std::vector< Double_t > GetMvaValues(Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
returns the mva values of the right sub-classifier
void ReadWeightsFromXML(void *wghtnode)
read weights of sub-classifiers of MethodCategory from xml weight file
void Train(void)
train all sub-classifiers
Virtual base class for combining several TMVA method.
Ranking for variables in method (implementation)
The Reader class serves to use the MVAs in a specific analysis context.
A TTree represents a columnar dataset.
create variable transformations