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class TMVA::MethodLikelihood
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library: libTMVA
#include "MethodLikelihood.h"
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class TMVA::MethodLikelihood: public TMVA::MethodBase
/* Likelihood analysis ("non-parametric approach")

Also implemented is a "diagonalized likelihood approach", which improves over the uncorrelated likelihood ansatz by transforming linearly the input variables into a diagonal space, using the square-root of the covariance matrix

The method of maximum likelihood is the most straightforward, and certainly among the most elegant multivariate analyser approaches. We define the likelihood ratio, RL, for event i, by:

Here the signal and background likelihoods, LS, LB, are products of the corresponding probability densities, pS, pB, of the Nvar discriminating variables used in the MVA:
and accordingly for LB. In practise, TMVA uses polynomial splines to estimate the probability density functions (PDF) obtained from the distributions of the training variables.

Note that in TMVA the output of the likelihood ratio is transformed by

to avoid the occurrence of heavy peaks at RL=0,1. Decorrelated (or "diagonalized") Likelihood

The biggest drawback of the Likelihood approach is that it assumes that the discriminant variables are uncorrelated. If it were the case, it can be proven that the discrimination obtained by the above likelihood ratio is optimal, ie, no other method can beat it. However, in most practical applications of MVAs correlations are present.

Linear correlations, measured from the training sample, can be taken into account in a straightforward manner through the square-root of the covariance matrix. The square-root of a matrix C is the matrix C′ that multiplied with itself yields C: C=C′C′. We compute the square-root matrix (SQM) by means of diagonalising (D) the covariance matrix:

and the linear transformation of the linearly correlated into the uncorrelated variables space is then given by multiplying the measured variable tuple by the inverse of the SQM. Note that these transformations are performed for both signal and background separately, since the correlation pattern is not the same in the two samples.

The above diagonalisation is complete for linearly correlated, Gaussian distributed variables only. In real-world examples this is not often the case, so that only little additional information may be recovered by the diagonalisation procedure. In these cases, non-linear methods must be applied. */

Function Members (Methods)

public:
virtual~MethodLikelihood()
voidTObject::AbstractMethod(const char* method) const
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::AppendPad(Option_t* option = "")
TDirectory*TMVA::MethodBase::BaseDir() const
virtual voidTObject::Browse(TBrowser* b)
virtual voidTObject::Browse(TBrowser* b)
voidTMVA::Configurable::CheckForUnusedOptions() const
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual Int_tTObject::Compare(const TObject* obj) const
TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")
virtual voidTObject::Copy(TObject& object) const
virtual voidTObject::Copy(TObject& object) const
voidTMVA::MethodBase::CreateMVAPdfs()
virtual const TMVA::Ranking*CreateRanking()
TMVA::DataSet&TMVA::MethodBase::Data() const
virtual voidTObject::Delete(Option_t* option = "")
virtual voidTObject::Delete(Option_t* option = "")
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() const
virtual voidTObject::DrawClass() const
virtual TObject*TObject::DrawClone(Option_t* option = "") const
virtual TObject*TObject::DrawClone(Option_t* option = "") const
virtual voidTObject::Dump() const
virtual voidTObject::Dump() const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual Option_t*TObject::GetDrawOption() const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
static Long_tTObject::GetDtorOnly()
Double_tTMVA::MethodBase::GetEffForRoot(Double_t)
virtual Double_tTMVA::MethodBase::GetEfficiency(TString, TTree*, Double_t& err)
TMVA::Event&TMVA::MethodBase::GetEvent() const
Double_tTMVA::MethodBase::GetEventVal(Int_t ivar) const
Double_tTMVA::MethodBase::GetEventWeight() const
virtual const char*TObject::GetIconName() const
virtual const char*TObject::GetIconName() const
const TString&TMVA::MethodBase::GetInputExp(int i) const
const TString&TMVA::MethodBase::GetInputVar(int i) const
const TString&TMVA::MethodBase::GetJobName() const
const TString&TMVA::MethodBase::GetMethodName() const
const TString&TMVA::MethodBase::GetMethodTitle() const
TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const
virtual Double_tTMVA::MethodBase::GetmuTransform(TTree*)
virtual Double_tGetMvaValue()
virtual const char*TMVA::MethodBase::GetName() const
Int_tTMVA::MethodBase::GetNvar() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
static Bool_tTObject::GetObjectStat()
virtual Double_tTMVA::MethodBase::GetOptimalSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
virtual Option_t*TObject::GetOption() const
virtual Option_t*TObject::GetOption() const
const TString&TMVA::Configurable::GetOptions() const
virtual Double_tTMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TStringTMVA::MethodBase::GetProbaName() const
virtual Double_tTMVA::MethodBase::GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
Double_tTMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_tTMVA::MethodBase::GetSeparation(TH1*, TH1*) const
virtual Double_tTMVA::MethodBase::GetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
Double_tTMVA::MethodBase::GetSignalReferenceCut() const
virtual Double_tTMVA::MethodBase::GetSignificance() const
TTree*TMVA::MethodBase::GetTestTree() const
const TString&TMVA::MethodBase::GetTestvarName() const
static TMVA::MethodBase*TMVA::MethodBase::GetThisBase()
virtual const char*TObject::GetTitle() const
virtual const char*TObject::GetTitle() const
virtual Double_tTMVA::MethodBase::GetTrainingEfficiency(TString)
TTree*TMVA::MethodBase::GetTrainingTree() const
virtual UInt_tTObject::GetUniqueID() const
virtual UInt_tTObject::GetUniqueID() const
TMVA::Types::EVariableTransformTMVA::MethodBase::GetVariableTransform() const
TMVA::VariableTransformBase&TMVA::MethodBase::GetVarTransform() const
TStringTMVA::MethodBase::GetWeightFileDir() const
TStringTMVA::MethodBase::GetWeightFileName() const
Double_tTMVA::MethodBase::GetXmax(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmax(const TString& var) const
Double_tTMVA::MethodBase::GetXmin(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmin(const TString& var) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual ULong_tTObject::Hash() const
Bool_tTMVA::MethodBase::HasTrainingTree() const
Bool_tTMVA::MethodBase::Help() const
static Double_tTMVA::MethodBase::IGetEffForRoot(Double_t)
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() const
virtual voidTObject::Inspect() const
voidTObject::InvertBit(UInt_t f)
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
virtual Bool_tTObject::IsFolder() const
Bool_tTMVA::MethodBase::IsMVAPdfs() const
Bool_tTMVA::MethodBase::IsNormalised() const
Bool_tTMVA::MethodBase::IsOK() const
Bool_tTObject::IsOnHeap() const
Bool_tTObject::IsOnHeap() const
Bool_tTMVA::MethodBase::IsSignalEvent() const
virtual Bool_tTMVA::MethodBase::IsSignalLike()
virtual Bool_tTObject::IsSortable() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTMVA::MethodBase::MakeClass(const TString& classFileName = "") const
voidTObject::MayNotUse(const char* method) const
voidTObject::MayNotUse(const char* method) const
TDirectory*TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodLikelihoodMethodLikelihood(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)
TMVA::MethodLikelihoodMethodLikelihood(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "", TDirectory* theTargetDir = 0)
Double_tTMVA::MethodBase::Norm(Int_t ivar, Double_t x) const
Double_tTMVA::MethodBase::Norm(TString var, Double_t x) const
virtual Bool_tTObject::Notify()
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
void*TObject::operator new[](size_t sz, void* vp)
TMVA::IMethod&TMVA::IMethod::operator=(const TMVA::IMethod&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Paint(Option_t* option = "")
voidTMVA::Configurable::ParseOptions(Bool_t verbose = kTRUE)
virtual voidTObject::Pop()
virtual voidTObject::Pop()
virtual voidTMVA::MethodBase::PrepareEvaluationTree(TTree* theTestTree)
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTMVA::MethodBase::PrintHelpMessage() const
voidTMVA::Configurable::PrintOptions() const
virtual Int_tTObject::Read(const char* name)
virtual Int_tTObject::Read(const char* name)
virtual Bool_tTMVA::MethodBase::ReadEvent(TTree* tr, UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
voidTMVA::MethodBase::ReadStateFromFile()
voidTMVA::MethodBase::ReadStateFromStream(istream& tf)
voidTMVA::MethodBase::ReadStateFromStream(TFile& rf)
virtual Bool_tTMVA::MethodBase::ReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
virtual Bool_tTMVA::MethodBase::ReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
virtual voidReadWeightsFromStream(istream& istr)
virtual voidReadWeightsFromStream(TFile& istr)
virtual voidTObject::RecursiveRemove(TObject* obj)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTObject::SetBit(UInt_t f, Bool_t set)
virtual voidTObject::SetDrawOption(Option_t* option = "")
virtual voidTObject::SetDrawOption(Option_t* option = "")
static voidTObject::SetDtorOnly(void* obj)
static voidTObject::SetDtorOnly(void* obj)
voidTMVA::MethodBase::SetHelp(Bool_t h = kTRUE)
voidTMVA::MethodBase::SetJobName(TString jobName)
voidTMVA::MethodBase::SetMethodName(TString methodName)
voidTMVA::MethodBase::SetMethodTitle(TString methodTitle)
voidTMVA::MethodBase::SetMethodType(TMVA::Types::EMVA methodType)
voidTMVA::Configurable::SetName(const char* n)
voidTMVA::MethodBase::SetNormalised(Bool_t norm)
voidTMVA::MethodBase::SetNvar(Int_t n)
static voidTObject::SetObjectStat(Bool_t stat)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::Configurable::SetOptions(const TString& s)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidTMVA::MethodBase::SetVariableTransform(TMVA::Types::EVariableTransform m)
voidTMVA::MethodBase::SetVerbose(Bool_t v = kTRUE)
voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)
voidTMVA::MethodBase::SetWeightFileName(TString)
voidTMVA::MethodBase::SetXmax(Int_t ivar, Double_t x)
voidTMVA::MethodBase::SetXmax(const TString& var, Double_t x)
voidTMVA::MethodBase::SetXmin(Int_t ivar, Double_t x)
voidTMVA::MethodBase::SetXmin(const TString& var, Double_t x)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTMVA::MethodBase::Test(TTree* theTestTree = 0)
Bool_tTObject::TestBit(UInt_t f) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTMVA::MethodBase::TestInit(TTree* theTestTree = 0)
virtual voidTrain()
voidTMVA::MethodBase::TrainMethod()
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::UseCurrentStyle()
Bool_tTMVA::MethodBase::Verbose() const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
voidTMVA::MethodBase::WriteEvaluationHistosToFile(TDirectory* targetDir = 0)
virtual voidWriteMonitoringHistosToFile() const
voidTMVA::MethodBase::WriteStateToFile() const
voidTMVA::MethodBase::WriteStateToStream(TFile& rf) const
voidTMVA::MethodBase::WriteStateToStream(ostream& tf, Bool_t isClass = kFALSE) const
virtual voidWriteWeightsToStream(ostream& o) const
virtual voidWriteWeightsToStream(TFile& rf) const
protected:
Bool_tTMVA::MethodBase::CheckSanity(TTree* theTree = 0)
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::GetCutOrientation() const
virtual voidGetHelpMessage() const
const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
const TString&TMVA::MethodBase::GetTestvarPrefix() const
UInt_tTMVA::MethodBase::GetTrainingROOTVersionCode() const
TStringTMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_tTMVA::MethodBase::GetTrainingTMVAVersionCode() const
TStringTMVA::MethodBase::GetTrainingTMVAVersionString() const
TMVA::Types::ESBTypeTMVA::MethodBase::GetVariableTransformType() const
TDirectory*TMVA::MethodBase::LocalTDir() const
Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const
virtual voidMakeClassSpecific(ostream&, const TString&) const
virtual voidMakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTObject::MakeZombie()
voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)
voidTMVA::Configurable::ResetSetFlag()
voidTMVA::MethodBase::ResetThisBase()
voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
voidTMVA::MethodBase::SetTestvarName(const TString& v = "")
voidTMVA::MethodBase::SetTestvarPrefix(TString prefix)
voidTMVA::MethodBase::SetVariableTransformType(TMVA::Types::ESBType t)
voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Bool_t norm = kFALSE)
Bool_tTMVA::MethodBase::TxtWeightsOnly() const
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
private:
virtual voidDeclareOptions()
voidInitLik()
virtual voidProcessOptions()
Double_tTransformLikelihoodOutput(Double_t ps, Double_t pb) const

Data Members

public:
enum TMVA::MethodBase::EWeightFileType { kROOT
kTEXT
};
enum TMVA::MethodBase::ECutOrientation { kNegative
kPositive
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
protected:
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::fCutOrientation+1 if Sig>Bkg, -1 otherwise
TH1*TMVA::MethodBase::fEffBefficiency plot (background)
TH1*TMVA::MethodBase::fEffBvsSbackground efficiency versus signal efficiency
TH1*TMVA::MethodBase::fEffSefficiency plot (signal)
TGraph*TMVA::MethodBase::fGraphBgraphs used for splines for efficiency (background)
TGraph*TMVA::MethodBase::fGraphSgraphs used for splines for efficiency (signal)
TGraph*TMVA::MethodBase::fGraphTrainBgraphs used for splines for training efficiency (background)
TGraph*TMVA::MethodBase::fGraphTrainEffBvsSgraphs used for splines for training signal eff. versus background eff.
TGraph*TMVA::MethodBase::fGraphTrainSgraphs used for splines for training efficiency (signal)
TGraph*TMVA::MethodBase::fGrapheffBvsSgraphs used for splines for signal eff. versus background eff.
TH1*TMVA::MethodBase::fHistB_highbinMVA plots used for efficiency calculations (background)
TH1*TMVA::MethodBase::fHistB_plotbinMVA plots used for graphics representation (background)
TH1*TMVA::MethodBase::fHistBhatBworking histograms needed for mu-transform (background)
TH1*TMVA::MethodBase::fHistBhatSworking histograms needed for mu-transform (signal)
TH1*TMVA::MethodBase::fHistMuBmu-transform (background)
TH1*TMVA::MethodBase::fHistMuSmu-transform (signal)
TH1*TMVA::MethodBase::fHistS_highbinMVA plots used for efficiency calculations (signal)
TH1*TMVA::MethodBase::fHistS_plotbinMVA plots used for graphics representation (signal)
vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA
Bool_tTMVA::MethodBase::fIsOKstatus of sanity checks
TMVA::MsgLoggerTMVA::MethodBase::fLoggermessage logger
TMVA::PDF*TMVA::MethodBase::fMVAPdfBbackground MVA PDF
TMVA::PDF*TMVA::MethodBase::fMVAPdfSsignal MVA PDF
Double_tTMVA::MethodBase::fMode
Int_tTMVA::MethodBase::fNbinsnumber of bins in representative histograms
Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms
Int_tTMVA::MethodBase::fNbinsMVAPdfnumber of bins used in histogram that creates PDF
Int_tTMVA::MethodBase::fNsmoothMVAPdfnumber of times a histogram is smoothed before creating the PDF
TH1*TMVA::MethodBase::fProbaB_plotbinP(MVA) plots used for graphics representation (background)
TH1*TMVA::MethodBase::fProbaS_plotbinP(MVA) plots used for graphics representation (signal)
TMVA::Ranking*TMVA::MethodBase::fRankingranking
TH1*TMVA::MethodBase::fRarityB_plotbinR(MVA) plots used for graphics representation (background)
TH1*TMVA::MethodBase::fRarityS_plotbinR(MVA) plots used for graphics representation (signal)
TH1*TMVA::MethodBase::fRejBvsSbackground rejection (=1-eff.) versus signal efficiency
TMVA::PDF*TMVA::MethodBase::fSplBPDFs of MVA distribution (background)
TMVA::TSpline1*TMVA::MethodBase::fSplRefBhelper splines for RootFinder (background)
TMVA::TSpline1*TMVA::MethodBase::fSplRefShelper splines for RootFinder (signal)
TMVA::PDF*TMVA::MethodBase::fSplSPDFs of MVA distribution (signal)
TMVA::PDF*TMVA::MethodBase::fSplTrainBPDFs of training MVA distribution (background)
TSpline*TMVA::MethodBase::fSplTrainEffBvsSsplines for training signal eff. versus background eff.
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefBhelper splines for RootFinder (background)
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefShelper splines for RootFinder (signal)
TMVA::PDF*TMVA::MethodBase::fSplTrainSPDFs of training MVA distribution (signal)
TSpline*TMVA::MethodBase::fSpleffBvsSsplines for signal eff. versus background eff.
TH1*TMVA::MethodBase::fTrainEffBTraining efficiency plot (background)
TH1*TMVA::MethodBase::fTrainEffBvsSTraining background efficiency versus signal efficiency
TH1*TMVA::MethodBase::fTrainEffSTraining efficiency plot (signal)
TH1*TMVA::MethodBase::fTrainRejBvsSTraining background rejection (=1-eff.) versus signal efficiency
Double_tTMVA::MethodBase::fX
TH1*TMVA::MethodBase::finvBeffvsSeffinverse background eff (1/eff.) versus signal efficiency
private:
Int_tfAverageEvtPerBinaverage events per bin; used to calculate fNbins
Int_t*fAverageEvtPerBinVarBaverage events per bin; used to calculate fNbins
Int_t*fAverageEvtPerBinVarSaverage events per bin; used to calculate fNbins
TMVA::KDEKernel::EKernelBorderfBorderMethodthe method to take care about "border" effects
TStringfBorderMethodStringthe method to take care about "border" effects (string)
Int_tfDropVariablefor ranking test
Double_tfEpsilonminimum number of likelihood (to avoid zero)
vector<TH1*>*fHistBgdbackground PDFs (histograms)
vector<TH1*>*fHistBgd_smoothbackground PDFs (smoothed histograms)
vector<TH1*>*fHistSigsignal PDFs (histograms)
vector<TH1*>*fHistSig_smoothsignal PDFs (smoothed histograms)
TMVA::PDF::EInterpolateMethod*fInterpolateMethodenumerators encoding the interpolation method
TString*fInterpolateStringwhich interpolation method used for reference histograms (individual for each variable)
Float_tfKDEfineFactorfine tuning factor for Adaptive KDE: factor to multiply the "width" of the Kernel function
TMVA::KDEKernel::EKernelIterfKDEiterNumber of iterations
TStringfKDEiterStringNumber of iterations (string)
TMVA::KDEKernel::EKernelTypefKDEtypeKernel type to use for KDE
TStringfKDEtypeStringKernel type to use for KDE (string) (if KDE is selected for interpolation)
Int_tfNsmoothnumber of smooth passes
Int_t*fNsmoothVarBnumber of smooth passes
Int_t*fNsmoothVarSnumber of smooth passes
vector<PDF*>*fPDFBgdlist of PDFs (background)
vector<PDF*>*fPDFSiglist of PDFs (signal)
Int_tfSplineSpline order to smooth histograms (if spline is selected for interpolation)
Bool_tfTransformLikelihoodOutputlikelihood output is sigmoid-transformed

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

MethodLikelihood(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "", TDirectory* theTargetDir = 0)
 standard constructor

 MethodLikelihood options:
 format and syntax of option string: "Spline2:0:25:D"

 where:
  SplineI [I=0,12,3,5] - which spline is used for smoothing the pdfs
                    0  - how often the input histos are smoothed
                    25 - average num of events per PDF bin
                    D  - use square-root-matrix to decorrelate variable space
MethodLikelihood(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)
 construct likelihood references from file
~MethodLikelihood( void )
 destructor
void InitLik( void )
 default initialisation called by all constructors
void DeclareOptions()
 define the options (their key words) that can be set in the option string
 know options:
 PDFInterpol[ivar] <string>   Spline0, Spline1, Spline2 <default>, Spline3, Spline5, KDE  used to interpolate reference histograms
             if no variable index is given, it is valid for ALL the variables

 NSmooth           <int>    how often the input histos are smoothed
 NAvEvtPerBin      <int>    minimum average number of events per PDF bin
 TransformOutput   <bool>   transform (often strongly peaked) likelihood output through sigmoid inversion
 fKDEtype          <KernelType>   type of the Kernel to use (1 is Gaussian)
 fKDEiter          <KerneIter>    number of iterations (1 --> "static KDE", 2 --> "adaptive KDE")
 fBorderMethod     <KernelBorder> the method to take care about "border" effects (1=no treatment , 2=kernel renormalization, 3=sample mirroring)
void ProcessOptions()
 process user options
void Train( void )
 create reference distributions (PDFs) from signal and background events:
 fill histograms and smooth them; if decorrelation is required, compute
 corresponding square-root matrices
Double_t GetMvaValue()
 returns the likelihood estimator for signal
Double_t TransformLikelihoodOutput(Double_t ps, Double_t pb) const
 returns transformed or non-transformed output
const TMVA::Ranking* CreateRanking()
 computes ranking of input variables
void WriteWeightsToStream( ostream& o )
 write weights to stream
void WriteWeightsToStream( TFile& )
 write reference PDFs to ROOT file
void ReadWeightsFromStream( istream & istr )
 read weight info from file
 nothing to do for this method
void ReadWeightsFromStream( TFile& rf )
 read reference PDF from ROOT file
void WriteMonitoringHistosToFile( void )
 write histograms and PDFs to file for monitoring purposes
void MakeClassSpecificHeader(ostream& , const TString& = "") const
 write specific header of the classifier (mostly include files)
void MakeClassSpecific(ostream& , const TString& ) const
 write specific classifier response
void GetHelpMessage()
 get help message text

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Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
Last update: root/tmva $Id: MethodLikelihood.cxx,v 1.21 2007/06/21 10:28:08 brun Exp $
Copyright (c) 2005: *
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