class TMVA::QuickMVAProbEstimator

Function Members (Methods)

public:

virtual	~QuickMVAProbEstimator()
void	AddEvent(Double_t val, Double_t weight, Int_t type)
static TClass*	Class()
static bool	compare(TMVA::QuickMVAProbEstimator::EventInfo e1, TMVA::QuickMVAProbEstimator::EventInfo e2)
Double_t	GetMVAProbAt(Double_t value)
virtual TClass*	IsA() const
TMVA::QuickMVAProbEstimator&	operator=(const TMVA::QuickMVAProbEstimator&)
TMVA::QuickMVAProbEstimator	QuickMVAProbEstimator(const TMVA::QuickMVAProbEstimator&)
TMVA::QuickMVAProbEstimator	QuickMVAProbEstimator(Int_t nMin = 40, Int_t nMax = 5000)
virtual void	ShowMembers(TMemberInspector&)
virtual void	Streamer(TBuffer&)
void	StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)

private:

TMVA::MsgLogger&

Log() const

Data Members

private:

vector<EventInfo>	fEvtVector
Bool_t	fIsSorted
TMVA::MsgLogger*	fLogger
UInt_t	fNMax
UInt_t	fNMin

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

void AddEvent(Double_t val, Double_t weight, Int_t type)

Double_t GetMVAProbAt(Double_t value)

 Well.. if it's fast is actually another question all together, merely
 it's a quick and dirty simple kNN approach to the 1-Dim signal/backgr. MVA
 distributions.

bool compare(TMVA::QuickMVAProbEstimator::EventInfo e1, TMVA::QuickMVAProbEstimator::EventInfo e2)

{return e1.eventValue < e2.eventValue;}

QuickMVAProbEstimator(Int_t nMin = 40, Int_t nMax = 5000)

{ fLogger = new MsgLogger("QuickMVAProbEstimator");}

virtual ~QuickMVAProbEstimator()

{delete fLogger;}