class TMVA::MethodBase: public TMVA::IMethod, public TMVA::Configurable

 default destructur

 default initialization called by all constructors

 define the options (their key words) that can be set in the option string
 here the options valid for ALL MVA methods are declared.
 know options: VariableTransform=None,Decorrelated,PCA  to use transformed variables
                                                        instead of the original ones
               VariableTransformType=Signal,Background  which decorrelation matrix to use
                                                        in the method. Only the Likelihood
                                                        Method can make proper use of independent
                                                        transformations of signal and background
               fNbinsMVAPdf   = 50 Number of bins used to create a PDF of MVA
               fNsmoothMVAPdf =  2 Number of times a histogram is smoothed before creating the PDF
               fIsMVAPdfs          create PDFs for the MVA outputs
               V                   for Verbose output (!V) for non verbos
               H                   for Help message

 the option string is decoded, for availabel options see "DeclareOptions"

 prints out classifier-specific help method

 train the classifier method

 compute likelihood ratio

 compute rarity:
 R(x) = Integrate_[-oo..x] { PDF(x') dx' }
 where PDF(x) is the PDF of the classifier's signal or background distribution

 Create PDFs of the MVA output variables

 general method used in writing the header of the weight files where
 the used variables, variable transformation type etc. is specified

 write reference MVA distributions (and other information)
 to a ROOT type weight file

 write reference MVA distributions (and other information)
 to a ROOT type weight file

 write options and weights to file
 note that each one text file for the main configuration information
 and one ROOT file for ROOT objects are created

 Function to write options and weights to file

 reads one line from the input stream
 checks for certain keywords and interprets
 the line if keywords are found

 read the header from the weight files of the different MVA methods

 returns the ROOT directory where info/histograms etc of the
 corresponding MVA method instance are stored

 returns the ROOT directory where all instances of the
 corresponding MVA method are stored

 set the weight file name (depreciated)

 retrieve weight file name

 tree sanity checks

 set directory of weight file

 renormalises variable with respect to its min and max

 renormalises variable with respect to its min and max

 initialization of MVA testing

 prepare tree branch with the method's discriminating variable

 test the method - not much is done here... mainly furthor initialization

 fill background efficiency (resp. rejection) versus signal efficiency plots
 returns signal efficiency at background efficiency indicated in theString

 fill background efficiency (resp. rejection) versus signal efficiency plots
 returns signal efficiency at background efficiency indicated in theString

 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 }

 compute "separation" defined as
 <s2> = (1/2) Int_-oo..+oo { (S(x)2 - B(x)2)/(S(x) + B(x)) dx }

 plot significance, S/Sqrt(S^2 + B^2), curve for given number
 of signal and background events; returns cut for optimal significance
 also returned via reference is the optimal significance

 computes Mu-transform

 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'

 calculates rms,mean, xmin, xmax of the event variable
 this can be either done for the variables as they are or for
 normalised variables (in the range of 0-1) if "norm" is set to kTRUE

 writes all MVA evaluation histograms to file

 create reader class for classifier

 interface for RootFinder

 returns efficiency as function of cut

 write special monitoring histograms to file - not implemented for this method

 calculates the TMVA version string from the training version code on the fly

 calculates the ROOT version string from the training version code on the fly

 individual initialistion for testing of each method
 overload this one for individual initialisation of the testing,
 it is then called automatically within the global "TestInit"
 the new way to get the MVA value

 accessors

 normalisation accessors

 are variables normalised ?

 member functions for the "evaluation"
 accessors

 TestVar (the variable name used for the MVA)

 retrieve variable transformer

 sets the minimum requirement on the MVA output to declare an event signal-like

 make ROOT-independent C++ class for classifier response (classifier-specific implementation)

 header and auxiliary classes

 static pointer to this object

 reset required for RootFinder

 sets the minimum requirement on the MVA output to declare an event signal-like

 the versions can be checked using
 if(GetTrainingTMVAVersionCode()>TMVA_VERSION(3,7,2)) {...}
 or
 if(GetTrainingROOTVersionCode()>ROOT_VERSION(5,15,5)) {...}

 protected accessors for derived classes
      TDirectory*      BaseDir() const;

 TestVar (the variable name used for the MVA)

 MVA prefix (e.g., "TMVA_")

 if TRUE, write weights only to text files

 normalises input variables