class TMVA::MethodCuts: public TMVA::MethodBase, public TMVA::IFitterTarget

 standard constructor

 construction from weight file

 Cuts can only handle classification with 2 classes

 default initialisation called by all constructors

 destructor

 define the options (their key words) that can be set in the option string
 know options:
 Method             <string> Minimisation method
    available values are:        MC Monte Carlo <default>
                                 GA Genetic Algorithm
                                 SA Simulated annealing

 EffMethod          <string> Efficiency selection method
    available values are:        EffSel <default>
                                 EffPDF

 VarProp            <string> Property of variable 1 for the MC method (taking precedence over the
    globale setting. The same values as for the global option are available. Variables 1..10 can be
    set this way

 CutRangeMin/Max    <float>  user-defined ranges in which cuts are varied

 process user options
 sanity check, do not allow the input variables to be normalised, because this
 only creates problems when interpreting the cuts

 cut evaluation: returns 1.0 if event passed, 0.0 otherwise

 print cuts

 retrieve cut values for given signal efficiency
 assume vector of correct size !!

 retrieve cut values for given signal efficiency

 training method: here the cuts are optimised for the training sample

 nothing to test

 for full event scan

 returns estimator for "cut fitness" used by GA

 returns estimator for "cut fitness" used by GA
 there are two requirements:
 1) the signal efficiency must be equal to the required one in the
    efficiency scan
 2) the background efficiency must be as small as possible
 the requirement 1) has priority over 2)

 translates parameters into cuts

 translate the cuts into parameters (obsolete function)

 translates cuts into parameters

 compute signal and background efficiencies from PDFs
 for given cut sample

 compute signal and background efficiencies from event counting
 for given cut sample

 for PDF method: create efficiency reference histograms and PDFs

 read the cuts from stream

 create XML description for LD classification and regression
 (for arbitrary number of output classes/targets)

 read coefficients from xml weight file

 write histograms and PDFs to file for monitoring purposes

 - overloaded function to create background efficiency (rejection) versus
   signal efficiency plot (first call of this function)
 - the function returns the signal efficiency at background efficiency
   indicated in theString

 "theString" must have two entries:
 [0]: "Efficiency"
 [1]: the value of background efficiency at which the signal efficiency
      is to be returned

 - overloaded function to create background efficiency (rejection) versus
   signal efficiency plot (first call of this function)
 - the function returns the signal efficiency at background efficiency
   indicated in theString

 "theString" must have two entries:
 [0]: "Efficiency"
 [1]: the value of background efficiency at which the signal efficiency
      is to be returned

 write specific classifier response

 get help message text

 typical length of text line:
         "|--------------------------------------------------------------|"

 this is a workaround which is necessary since CINT is not capable of handling dynamic casts

 also overwrite --> not computed for cuts

 rarity distributions (signal or background (default) is uniform in [0,1])

 ranking of input variables (not available for cuts)

 no check of options at this place

 the definition of fit parameters can be different from the actual
 cut requirements; these functions provide the matching