| library: libTMVA #include "TMVA_MethodBase.h" |
private:
TH1* BookNormTH1(TString, Int_t, Double_t, Double_t, TString)
void Init()
protected:
Bool_t CheckSanity(TTree* theTree = 0)
void MuTransform()
void ResetThisBase()
public:
virtual ~TMVA_MethodBase()
void AppendToMethodName(TString methodNameSuffix)
static TClass* Class()
TMVA_MethodBase::CutOrientation GetCutOrientation()
Double_t GetEffForRoot(Double_t)
virtual Double_t GetEfficiency(TString, TTree*)
vector<TString>* GetInputVars() const
TString GetJobName() const
TMVA_Types::MVA GetMethod() const
TString GetMethodName() const
virtual Double_t GetmuTransform(TTree*)
virtual Double_t GetMvaValue(TMVA_Event* e)
Int_t GetNvar() const
TString GetOptions() const
virtual Double_t GetSeparation()
virtual Double_t GetSignificance()
static TMVA_MethodBase* GetThisBase()
TTree* GetTrainingTree() const
TString GetWeightFileDir() const
TString GetWeightFileExtension() const
TString GetWeightFileName()
Double_t GetXmaxNorm(Int_t ivar) const
Double_t GetXmaxNorm(TString var) const
Double_t GetXminNorm(Int_t ivar) const
Double_t GetXminNorm(TString var) const
static Double_t IGetEffForRoot(Double_t)
virtual void InitNorm(TTree* theTree)
virtual TClass* IsA() const
Bool_t IsOK() const
Double_t Norm(Int_t ivar, Double_t x) const
Double_t Norm(TString var, Double_t x) const
TMVA_MethodBase& operator=(const TMVA_MethodBase&)
virtual void PrepareEvaluationTree(TTree* theTestTree)
virtual void ReadWeightsFromFile()
void SetInputVars(vector<TString>* theInputVars)
void SetJobName(TString jobName)
void SetMethodName(TString methodName)
void SetWeightFileDir(TString fileDir)
void SetWeightFileExtension(TString fileExtension)
void SetWeightFileName()
void SetWeightFileName(TString)
void SetXmaxNorm(Int_t ivar, Double_t x)
void SetXmaxNorm(TString var, Double_t x)
void SetXminNorm(Int_t ivar, Double_t x)
void SetXminNorm(TString var, Double_t x)
virtual void ShowMembers(TMemberInspector& insp, char* parent)
virtual void Streamer(TBuffer& b)
void StreamerNVirtual(TBuffer& b)
virtual void Test(TTree* theTestTree)
virtual void TestInit(TTree* theTestTree)
virtual void TestInitLocal(TTree*)
virtual void Train()
void UpdateNorm(Int_t ivar, Double_t x)
Bool_t Verbose()
virtual void WriteHistosToFile()
void WriteHistosToFile(TDirectory* targetDir)
virtual void WriteWeightsToFile()
private:
TString fFileExtension
TString fFileDir
TString fWeightFile
Double_t fMeanS
Double_t fMeanB
Double_t fRmsS
Double_t fRmsB
Double_t fXmin
Double_t fXmax
Bool_t fVerbose
Double_t fSeparation
Double_t fSignificance
vector<Double_t>* fXminNorm
vector<Double_t>* fXmaxNorm
static TMVA_MethodBase* fgThisBase
protected:
TString fJobName
TString fMethodName
TMVA_Types::MVA fMethod
TTree* fTrainingTree
TString fTestvar
TString fTestvarPrefix
vector<TString>* fInputVars
TString fOptions
TDirectory* fBaseDir
TDirectory* fLocalTDir
Int_t fNvar
Bool_t fIsOK
TH1* fHistS_plotbin
TH1* fHistB_plotbin
TH1* fHistS_highbin
TH1* fHistB_highbin
TH1* fEffS
TH1* fEffB
TH1* fEffBvsS
TH1* fRejBvsS
TH1* fHistBhatS
TH1* fHistBhatB
TH1* fHistMuS
TH1* fHistMuB
Double_t fX
Double_t fMode
TGraph* fGraphS
TGraph* fGraphB
TGraph* fGrapheffBvsS
TMVA_PDF* fSplS
TMVA_PDF* fSplB
TSpline* fSpleffBvsS
Double_t fEffSatB
Int_t fNbins
Int_t fNbinsH
TMVA_MethodBase::CutOrientation fCutOrientation +1 if Sig>Bkg, -1 otherwise
TMVA_TSpline1* fSplRefS
TMVA_TSpline1* fSplRefB
public:
static const TMVA_MethodBase::CutOrientation kNegative
static const TMVA_MethodBase::CutOrientation kPositive
static const TMVA_MethodBase::Type kSignal
static const TMVA_MethodBase::Type kBackground
Virtual base class for all MVA method _______________________________________________________________________
/ default destructur
if trainingsTree exists, fill min/max vector
if no tree is given, use the trainingTree
sanity checks
basic statistics operations are made in base class note: cannot directly modify private class members
parse input string for required background efficiency
compute significance of mean difference significance = |<S> - <B>|/Sqrt(RMS_S2 + RMS_B2)
compute "separation" defined as
<s2> = (1/2) Int_-oo..+oo { (S(x)2 - B(x)2)/(S(x) + B(x)) dx }
---------------------------------------------------------------------------------------
Authors : Francois Le Diberder and Muriel Pivk
Reference : Muriel Pivk,
"Etude de la violation de CP dans la désintégration
B0 -> h+ h- (h = pi, K) auprès du détecteur BaBar à SLAC",
PhD thesis at Universite de Paris VI-VII, LPNHE (IN2P3/CNRS), Paris, 2003
http://tel.ccsd.cnrs.fr/documents/archives0/00/00/29/91/index_fr.html
Definitions : Bhat = PDFbackground(x)/(PDFbackground(x) + PDFsignal(x))
mu = mu(b) = Int_0B Bhat[b'] db'
---------------------------------------------------------------------------------------
void Train()
void WriteWeightsToFile()
void ReadWeightsFromFile()
Double_t GetMvaValue(TMVA_Event* e)
void TestInitLocal(TTree*)
TString GetMethodName() const
TMVA_Types::MVA GetMethod() const
TString GetOptions() const
void SetMethodName(TString methodName)
TString GetJobName() const
void SetJobName(TString jobName)
TString GetWeightFileExtension() const
void SetWeightFileExtension(TString fileExtension)
TString GetWeightFileDir() const
vector<TString>* GetInputVars() const
void SetInputVars(vector<TString>* theInputVars)
TTree* GetTrainingTree() const
Int_t GetNvar() const
Double_t GetXminNorm(TString var) const
Double_t GetXmaxNorm(TString var) const
void SetXminNorm(TString var, Double_t x)
void SetXmaxNorm(TString var, Double_t x)
Bool_t IsOK() const
void WriteHistosToFile(TDirectory* targetDir)
TMVA_MethodBase::CutOrientation GetCutOrientation()
Bool_t Verbose()
TMVA_MethodBase* GetThisBase()
void ResetThisBase()
TH1* BookNormTH1(TString, Int_t, Double_t, Double_t, TString)
void MuTransform()
TClass* Class()
TClass* IsA() const
void ShowMembers(TMemberInspector& insp, char* parent)
void Streamer(TBuffer& b)
void StreamerNVirtual(TBuffer& b)
TMVA_MethodBase& operator=(const TMVA_MethodBase&)