doc/master/TMVAGAexample_8C.html

#include <iostream> // Stream declarations

#include <vector>


#include "TMVA/GeneticAlgorithm.h"

#include "TMVA/GeneticFitter.h"

#include "TMVA/IFitterTarget.h"


using std::vector;


using namespace TMVA;


class MyFitness : public IFitterTarget {

    public:

       MyFitness() : IFitterTarget() {

       }


       // the fitness-function goes here

       // the factors are optimized such that the return-value of this function is minimized

       // take care!! the fitness-function must never fail, .. means: you have to prevent

       // the function from reaching undefined values (such as x=0 for 1/x or so)

       //

       // HINT: to use INTEGER variables, it is sufficient to cast the "factor" in the fitness-function

       // to (int). In this case the variable-range has to be chosen +1 ( to get 0..5, take Interval(0,6) )

       // since the introduction of "Interval" ranges can be defined with a third parameter

       // which gives the number of bins within the interval. With that technique discrete values

       // can be achieved easier. The random selection out of this discrete numbers is completely uniform.

       //

       Double_t EstimatorFunction( std::vector<Double_t> & factors ){

           //return (10.- (int)factors.at(0) *factors.at(1) + (int)factors.at(2));

           return (10.- factors.at(0) *factors.at(1) + factors.at(2));


           //return 100.- (10 + factors.at(1)) *factors.at(2)* TMath::Abs( TMath::Sin(factors.at(0)) );

       }

};


class MyGA2nd : public GeneticAlgorithm {

    public:

       MyGA2nd( IFitterTarget& target, Int_t size, vector<Interval*>& ranges ) : GeneticAlgorithm(target,

       size, ranges ){

       }


       // this method has to be activated if one wants to change the behaviour of the evolution

       // works only with the head version

       //void Evolution(){

       //    fSexual = true;

       //    if (fSexual) {

       //       fPopulation.MakeCopies( 5 );

       //       fPopulation.MakeChildren();

       //       fPopulation.NextGeneration();


       //       fPopulation.Mutate( 10, 3, kTRUE, fSpread, fMirror );

       //       fPopulation.Mutate( 40, fPopulation.GetPopulationSize()*3/4 );

       //    } else {

       //       fPopulation.MakeCopies( 3 );

       //       fPopulation.MakeMutants(100,true, 0.1, true);

       //       fPopulation.NextGeneration();

       //    }

      // }

};


void TMVAGAexample() {


   std::cout << "Start Test TMVAGAexample" << std::endl

             << "========================" << std::endl

             << "\nEXAMPLE"              << std::endl;

   // define all the parameters by their minimum and maximum value

   // in this example 3 parameters are defined.

   vector<Interval*> ranges;

   ranges.push_back( new Interval(0,15,30) );

   ranges.push_back( new Interval(0,13) );

   ranges.push_back( new Interval(0,5,3) );


   for( std::vector<Interval*>::iterator it = ranges.begin(); it != ranges.end(); it++ ){

      std::cout << " range: " << (*it)->GetMin() << "   " << (*it)->GetMax() << std::endl;

   }


   IFitterTarget* myFitness = new MyFitness();


   // prepare the genetic algorithm with an initial population size of 20

   // mind: big population sizes will help in searching the domain space of the solution

   // but you have to weight this out to the number of generations

   // the extreme case of 1 generation and populationsize n is equal to

   // a Monte Carlo calculation with n tries


   MyGA2nd mg( *myFitness, 100, ranges );

   // mg.SetParameters( 4, 30, 200, 10,5, 0.95, 0.001 );


   #define CONVSTEPS 20

   #define CONVCRIT 0.0001

   #define SCSTEPS 10

   #define SCRATE 5

   #define SCFACTOR 0.95


   do {

      // prepares the new generation and does evolution

      mg.Init();


      // assess the quality of the individuals

      mg.CalculateFitness();


      mg.GetGeneticPopulation().Print(0);

      std::cout << "---" << std::endl;


      // reduce the population size to the initially defined one

      mg.GetGeneticPopulation().TrimPopulation();


      // tricky thing: control the speed of how fast the "solution space" is searched through

      // this function basically influences the sigma of a gaussian around the actual value

      // of the parameter where the new value will be randomly thrown.

      // when the number of improvements within the last SCSTEPS

      // A) smaller than SCRATE: divide the preset sigma by SCFACTOR

      // B) equal to SCRATE: do nothing

      // C) greater than SCRATE: multiply the preset sigma by SCFACTOR

      // if you don't know what to do, leave it unchanged or even delete this function call

      mg.SpreadControl( SCSTEPS, SCRATE, SCFACTOR );


   } while (!mg.HasConverged( CONVSTEPS, CONVCRIT ));  // converged if: fitness-improvement < CONVCRIT within the last CONVSTEPS loops


   GeneticGenes* genes = mg.GetGeneticPopulation().GetGenes( 0 );

   std::vector<Double_t> gvec;

   gvec = genes->GetFactors();

   int n = 0;

   for( std::vector<Double_t>::iterator it = gvec.begin(); it<gvec.end(); it++ ){

      std::cout << "FACTOR " << n << " : " << (*it) << std::endl;

      n++;

   }

}


int main( int argc, char** argv )

{

   TMVAGAexample();

}

GeneticAlgorithm.h

GeneticFitter.h

IFitterTarget.h

main
int main()
Definition Prototype.cxx:12

size
size_t size(const MatrixT &matrix)
retrieve the size of a square matrix

Int_t
int Int_t
Signed integer 4 bytes (int)
Definition RtypesCore.h:59

Double_t
double Double_t
Double 8 bytes.
Definition RtypesCore.h:73

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:360

target
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t target
Definition TGWin32VirtualXProxy.cxx:247

ROOT::Detail::TRangeCast
Definition TCollection.h:313

ROOT::RRangeCast::begin
const_iterator begin() const
Definition RRangeCast.hxx:104

ROOT::RRangeCast::end
const_iterator end() const
Definition RRangeCast.hxx:105

TMVA::GeneticAlgorithm
Base definition for genetic algorithm.
Definition GeneticAlgorithm.h:50

TMVA::GeneticGenes
Cut optimisation interface class for genetic algorithm.
Definition GeneticGenes.h:41

TMVA::IFitterTarget
Interface for a fitter 'target'.
Definition IFitterTarget.h:44

TMVA::Interval
The TMVA::Interval Class.
Definition Interval.h:61

n
const Int_t n
Definition legend1.C:16

TMVA
create variable transformations
Definition GeneticMinimizer.h:22