TMVA::MethodCuts

 standard constructor
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 format of option string: "OptMethod:EffMethod:Option_var1:...:Option_varn:Decorr"
 "OptMethod" can be:
     - "GA"    : Genetic Algorithm (recommended)
     - "SA"    : Simulated Annealing
     - "MC"    : Monte-Carlo optimization 
 "EffMethod" can be:
     - "EffSel": compute efficiency by event counting
     - "EffPDF": compute efficiency from PDFs
 === For "GA" method ======
 "Option_var1++" are (see GA for explanation of parameters):
     - fGA_nsteps        
     - fGA_cycles        
     - fGA_popSize
     - fGA_SC_steps        
     - fGA_SC_offsteps 
     - fGA_SC_factor   
 === For "SA" method ======
 "Option_var1++" are (see SA for explanation of parameters):
     - fSA_MaxCalls                
     - fSA_TemperatureGradient      
     - fSA_UseAdaptiveTemperature    
     - fSA_InitialTemperature        
     - fSA_MinTemperature        
     - fSA_Eps                       
     - fSA_NFunLoops                 
     - fSA_NEps                      
 === For "MC" method ======
 "Option_var1" is number of random samples
 "Option_var2++" can be 
     - "FMax"  : ForceMax   (the max cut is fixed to maximum of variable i)
     - "FMin"  : ForceMin   (the min cut is fixed to minimum of variable i)
     - "FSmart": ForceSmart (the min or max cut is fixed to min/max, based on mean value)
     - Adding "All" to "option_vari", eg, "AllFSmart" will use this option for all variables
     - if "option_vari" is empty (== ""), no assumptions on cut min/max are made
 "Decorr" can be:
     - omitted : Decorrelation not used
     - "D"     : Decorrelates variables, evaluation events decorrelated with signal decorrelation matrix
     - "DS"    : Decorrelates variables, evaluation events decorrelated with signal decorrelation matrix
     - "DB"    : Decorrelates variables, evaluation events decorrelated with background decorrelation matrix
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 construction from weight file

 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> Minimization 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

 MC_NRandCuts       <int>    Number of random cuts to estimate the efficiency for the MC method
 MC_AllVarProp      <string> Property of all variables for the MC method
    available values are:        AllNotEnforced <default>
                                 AllFMax
                                 AllFMin
                                 AllFSmart
                                 AllFVerySmart
 MC_Var1Prop        <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


 GA_nsteps           <int>   Number of steps for the genetic algorithm
 GA_cycles           <int>   Number of generations for the genetic algorithm
 GA_popSize          <int>   Size of the population for the genetic algorithm
 GA_SC_steps         <int>   Number of steps for the genetic algorithm
 GA_SC_offsteps      <int>    for the genetic algorithm
 GA_SC_factor        <float>  for the genetic algorithm


 SA_MaxCalls                 <int>      maximum number of calls for simulated annealing
 SA_TemperatureGradient      <float>    temperature gradient for simulated annealing
 SA_UseAdaptiveTemperature   <bool>     use of adaptive temperature for simulated annealing
 SA_InitialTemperature       <float>    initial temperature for simulated annealing
 SA_MinTemperature           <float>    minimum temperature for simulated annealing 
 SA_Eps                      <int>      number of epochs for simulated annealing
 SA_NFunLoops                <int>      number of loops for simulated annealing      
 SA_NEps                     <int>      number of epochs for simulated annealing

 process user options

 cut evaluation: returns 1.0 if event passed, 0.0 otherwise

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

 not used 

 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

 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

 basic checks to ensure that assumptions on variable order are satisfied

 first the dimensions

 read the cuts from stream

 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

 also overwrite:

 static pointer to this object

 ranking of input variables

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