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

 default constructur

 constructor used for Testing + Application of the MVA, only (no training),
 using given weight file
 default destructur

 declaration, processing and checking of configuration options

 ---------- main training and testing methods ------------------------------
 prepare tree branch with the method's discriminating variable

 performs classifier training
 calls methods Train() implemented by derived classes

 optimize tuning parameters

 store and retrieve time used for training

 store and retrieve time used for testing

 performs classifier testing

 performs multiclass classifier testing

 performs regression testing

 options treatment

 reset the Method --> As if it was not yet trained, just instantiated
      virtual void     Reset()          = 0;
for the moment, I provide a dummy (that would not work) default, just to make
 compilation/running w/o parameter optimisation still possible

 classifier response:
 some methods may return a per-event error estimate
 error calculation is skipped if err==0

 signal/background classification response

 helper function to set errors to -1

 signal/background classification response for all current set of data

 regression response

 multiclass classification response

 probability of classifier response (mvaval) to be signal (requires "CreateMvaPdf" option set)

 Rarity of classifier response (signal or background (default) is uniform in [0,1])

 create ranking

 make ROOT-independent C++ class

 print help message

 streamer methods for training information (creates "weight" files) --------

 the actual "weights"

 the variable information

 write evaluation histograms into target file

 write classifier-specific monitoring information to target file

 ---------- public evaluation methods --------------------------------------
 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"
 variables (and private menber functions) for the Evaluation:
 get the effiency. It fills a histogram for efficiency/vs/bkg
 and returns the one value fo the efficiency demanded for
 in the TString argument. (Watch the string format)

 ---------- public accessors -----------------------------------------------

 classifier naming (a lot of names ... aren't they ;-)

 build classifier name in Test tree
 MVA prefix (e.g., "TMVA_")

 number of input variable used by classifier

 internal names and expressions of input variables

 normalisation and limit accessors

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

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

 pointers to ROOT directories

Silent file

Model Persistence

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

 event reference and update
 NOTE: these Event accessors make sure that you get the events transformed according to the
        particular clasifiers transformation chosen

 ---------- public auxiliary methods ---------------------------------------
 this method is used to decide whether an event is signal- or background-like
 the reference cut "xC" is taken to be where
 Int_[-oo,xC] { PDF_S(x) dx } = Int_[xC,+oo] { PDF_B(x) dx }

 setter method for suppressing writing to XML and writing of standalone classes

 initializing IPythonInteractive class (for JsMVA only)

 get training errors (for JsMVA only)

 stop's the training process (for JsMVA only)

 check's if the training ended (for JsMVA only)

 get fIPyMaxIter

 get fIPyCurrentIter

 ---------- protected acccessors -------------------------------------------
TDirectory*  LocalTDir() const { return Data().LocalRootDir(); }
 weight file name and directory (given by global config variable)

 are input variables normalised ?

 set number of input variables (only used by MethodCuts, could perhaps be removed)
      void SetNvar( Int_t n ) { fNvar = n; }
 verbose and help flags

 ---------- protected event and tree accessors -----------------------------
 names of input variables (if the original names are expressions, they are
 transformed into regexps)

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

 header and auxiliary classes

 static pointer to this object - required for ROOT finder (to be solved differently)(solved by Omar)
static MethodBase* GetThisBase();
 some basic statistical analysis

 if TRUE, write weights only to text files
 access to event information that needs method-specific information

 ---------- private definitions --------------------------------------------
 Initialisation

 ---------- private acccessors ---------------------------------------------
 reset required for RootFinder

 ---------- private auxiliary methods --------------------------------------
 PDFs for classifier response (required to compute signal probability and Rarity)

 for root finder
virtual method to find ROOT

 used for file parsing

 fill test tree with classification or regression results

 ========== class members ==================================================
 direct accessors