TMVAGAexample.C File Reference

Detailed Description

This exectutable gives an example of a very simple use of the genetic algorithm of TMVA

• Project : TMVA - a Root-integrated toolkit for multivariate data analysis
• Package : TMVA
• Exectuable: TMVAGAexample

Processing /mnt/build/workspace/root-makedoc-v610/rootspi/rdoc/src/v6-10-00-patches/tutorials/tmva/TMVAGAexample.C...
Start Test TMVAGAexample
========================

EXAMPLE
 range: 0   15
 range: 0   13
 range: 0   5
                         : fitness: -167.597    f_0: 15     f_1: 12.0065     f_2: 2.5     
---
                         : fitness: -175.952    f_0: 15     f_1: 12.5635     f_2: 2.5     
---
                         : fitness: -175.952    f_0: 15     f_1: 12.5635     f_2: 2.5     
---
                         : fitness: -175.952    f_0: 15     f_1: 12.5635     f_2: 2.5     
---
                         : fitness: -175.952    f_0: 15     f_1: 12.5635     f_2: 2.5     
---
                         : fitness: -179.83    f_0: 15     f_1: 12.6553     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.3    f_0: 15     f_1: 12.9533     f_2: 0     
---
                         : fitness: -184.799    f_0: 15     f_1: 12.9866     f_2: 0     
---
                         : fitness: -184.799    f_0: 15     f_1: 12.9866     f_2: 0     
---
                         : fitness: -184.799    f_0: 15     f_1: 12.9866     f_2: 0     
---
                         : fitness: -184.799    f_0: 15     f_1: 12.9866     f_2: 0     
---
                         : fitness: -184.799    f_0: 15     f_1: 12.9866     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.808    f_0: 15     f_1: 12.9872     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.866    f_0: 15     f_1: 12.9911     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
                         : fitness: -184.958    f_0: 15     f_1: 12.9972     f_2: 0     
---
FACTOR 0 : 15
FACTOR 1 : 12.9972
FACTOR 2 : 0

#include <iostream> // Stream declarations
#include <vector>

#include "TMVA/GeneticAlgorithm.h"
#include "TMVA/GeneticFitter.h"
#include "TMVA/IFitterTarget.h"

using namespace std;

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 completly 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();
}

Author

Andreas Hoecker

Definition in file TMVAGAexample.C.