%%cpp -d

#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)) );
       }
};
%%cpp -d


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();
       //    }
      // }
};

std::cout << "Start Test TMVAGAexample" << std::endl
          << "========================" << std::endl
          << "\nEXAMPLE"              << std::endl;

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();

MyGA2nd mg( *myFitness, 100, ranges );

#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++;
}