38#ifndef ROOT_TMVA_MethodLikelihood
39#define ROOT_TMVA_MethodLikelihood
79 void Train()
override;
120 void Init()
override;
float Float_t
Float 4 bytes (float)
#define ClassDefOverride(name, id)
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A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
1-D histogram with a double per channel (see TH1 documentation)
Class that contains all the data information.
Virtual base Class for all MVA method.
Likelihood analysis ("non-parametric approach")
Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets) override
FDA can handle classification with 2 classes.
Int_t fDropVariable
for ranking test
TString fKDEtypeString
Kernel type to use for KDE (string)
void ProcessOptions() override
process user options reference cut value to distinguish signal-like from background-like events
virtual void WriteOptionsToStream(std::ostream &o, const TString &prefix) const
write options to stream
Int_t * fNsmoothVarB
number of smooth passes
void MakeClassSpecific(std::ostream &, const TString &) const override
write specific classifier response
Int_t * fAverageEvtPerBinVarS
average events per bin; used to calculate fNbins
const Ranking * CreateRanking() override
computes ranking of input variables
void ReadWeightsFromStream(std::istream &istr) override
read weight info from file nothing to do for this method
std::vector< TH1 * > * fHistSig
signal PDFs (histograms)
void Train() override
create reference distributions (PDFs) from signal and background events: fill histograms and smooth t...
void GetHelpMessage() const override
get help message text
virtual ~MethodLikelihood()
destructor
void DeclareOptions() override
define the options (their key words) that can be set in the option string
void ReadWeightsFromXML(void *wghtnode) override
read weights from XML
std::vector< TH1 * > * fHistBgd
background PDFs (histograms)
std::vector< PDF * > * fPDFSig
list of PDFs (signal)
Double_t fEpsilon
minimum number of likelihood (to avoid zero)
TString * fInterpolateString
which interpolation method used for reference histograms (individual for each variable)
MethodLikelihood(const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
standard constructor
void WriteMonitoringHistosToFile() const override
write histograms and PDFs to file for monitoring purposes
void Init() override
default initialisation called by all constructors
void DeclareCompatibilityOptions() override
options that are used ONLY for the READER to ensure backward compatibility they are hence without any...
std::vector< TH1 * > * fHistSig_smooth
signal PDFs (smoothed histograms)
Bool_t fTransformLikelihoodOutput
likelihood output is sigmoid-transformed
void AddWeightsXMLTo(void *parent) const override
write weights to XML
TString fKDEiterString
Number of iterations (string)
Double_t TransformLikelihoodOutput(Double_t ps, Double_t pb) const
returns transformed or non-transformed output
std::vector< TH1 * > * fHistBgd_smooth
background PDFs (smoothed histograms)
TString fBorderMethodString
the method to take care about "border" effects (string)
Double_t GetMvaValue(Double_t *err=nullptr, Double_t *errUpper=nullptr) override
returns the likelihood estimator for signal fill a new Likelihood branch into the testTree
std::vector< PDF * > * fPDFBgd
list of PDFs (background)
void WriteWeightsToStream(TFile &rf) const
write reference PDFs to ROOT file
Int_t * fNsmoothVarS
number of smooth passes
Int_t fNsmooth
number of smooth passes
Float_t fKDEfineFactor
fine tuning factor for Adaptive KDE
Int_t * fAverageEvtPerBinVarB
average events per bin; used to calculate fNbins
Int_t fAverageEvtPerBin
average events per bin; used to calculate fNbins
void MakeClassSpecificHeader(std::ostream &, const TString &="") const override
write specific header of the classifier (mostly include files)
PDF * fDefaultPDFLik
pdf that contains default definitions
PDF wrapper for histograms; uses user-defined spline interpolation.
Ranking for variables in method (implementation)
create variable transformations