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0001 // @(#)root/tmva $Id$ 0002 // Author: Peter Speckmayer 0003 0004 /********************************************************************************** 0005 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * 0006 * Package: TMVA * 0007 * Class : GeneticFitter * 0008 * * 0009 * * 0010 * Description: * 0011 * Fitter using a Genetic Algorithm * 0012 * * 0013 * Authors (alphabetical): * 0014 * Peter Speckmayer <speckmay@mail.cern.ch> - CERN, Switzerland * 0015 * * 0016 * Copyright (c) 2005: * 0017 * CERN, Switzerland * 0018 * MPI-K Heidelberg, Germany * 0019 * * 0020 * Redistribution and use in source and binary forms, with or without * 0021 * modification, are permitted according to the terms listed in LICENSE * 0022 * (see tmva/doc/LICENSE) * 0023 **********************************************************************************/ 0024 0025 #ifndef ROOT_TMVA_GeneticFitter 0026 #define ROOT_TMVA_GeneticFitter 0027 0028 ////////////////////////////////////////////////////////////////////////// 0029 // // 0030 // GeneticFitter // 0031 // // 0032 // Fitter using a Genetic Algorithm // 0033 // // 0034 ////////////////////////////////////////////////////////////////////////// 0035 0036 #include "TMVA/FitterBase.h" 0037 #include <vector> 0038 0039 namespace TMVA { 0040 0041 class IFitterTarget; 0042 class Interval; 0043 0044 class GeneticFitter : public FitterBase { 0045 0046 public: 0047 0048 GeneticFitter( IFitterTarget& target, const TString& name, 0049 const std::vector<TMVA::Interval*>& ranges, const TString& theOption ); 0050 0051 virtual ~GeneticFitter() {} 0052 0053 void SetParameters( Int_t cycles, 0054 Int_t nsteps, 0055 Int_t popSize, 0056 Int_t SC_steps, 0057 Int_t SC_rate, 0058 Double_t SC_factor, 0059 Double_t convCrit ); 0060 0061 Double_t Run( std::vector<Double_t>& pars ); 0062 0063 Double_t NewFitness( Double_t oldF, Double_t newF ) { return oldF + newF; } 0064 0065 private: 0066 0067 void DeclareOptions(); 0068 0069 Int_t fCycles; ///< number of (nearly) independent calculation cycles 0070 Int_t fNsteps; ///< convergence criteria: if no improvements > fConvCrit was achieved within the last fNsteps: cycle has "converged" 0071 Int_t fPopSize; ///< number of individuals to start with 0072 Int_t fSC_steps; ///< regulates how strong the mutations for the coordinates are: if within fSC_steps there were more than... 0073 Int_t fSC_rate; ///< ... fSC_rate improvements, than multiply the sigma of the gaussian which defines how the random numbers are generated ... 0074 Double_t fSC_factor; ///< ... with fSC_factor; if there were less improvements: divide by that factor; if there were exactly fSC_rate improvements, dont change anything 0075 Double_t fConvCrit; ///< improvements bigger than fConvCrit are counted as "improvement" 0076 Int_t fSaveBestFromGeneration; ///< store the best individuals from one generation (these are included as "hints" in the last cycle of GA calculation) 0077 Int_t fSaveBestFromCycle; ///< store the best individuals from one cycle (these are included as "hints" in the last cycle of GA calculation) 0078 Bool_t fTrim; ///< take care, that the number of individuals is less fPopSize (trimming is done after the fitness of the individuals is assessed) 0079 UInt_t fSeed; ///< Seed for the random generator (0 takes random seeds) 0080 0081 ClassDef(GeneticFitter,0); // Fitter using a Genetic Algorithm 0082 }; 0083 0084 } // namespace TMVA 0085 0086 #endif 0087 0088
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