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Reference Guide
TMVAGAexample2.C File Reference

Detailed Description

View in nbviewer Open in SWAN 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
Start Test TMVAGAexample
========================
EXAMPLE
range: 0 15
range: 0 13
range: 0 5
FitterBase : <GeneticFitter> Optimisation, please be patient ... (inaccurate progress timing for GA)
: Elapsed time: 0.0118 sec
FACTOR 0 : 15
FACTOR 1 : 13
FACTOR 2 : 0
#include <iostream> // Stream declarations
#include <vector>
using namespace std;
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)) );
}
};
void exampleGA(){
std::cout << "\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
const TString name( "exampleGA" );
const TString opts( "PopSize=100:Steps=30" );
GeneticFitter mg( *myFitness, name, ranges, opts);
// mg.SetParameters( 4, 30, 200, 10,5, 0.95, 0.001 );
std::vector<Double_t> result;
Double_t estimator = mg.Run(result);
int n = 0;
for( std::vector<Double_t>::iterator it = result.begin(); it<result.end(); it++ ){
std::cout << "FACTOR " << n << " : " << (*it) << std::endl;
n++;
}
}
} // namespace TMVA
void TMVAGAexample2() {
cout << "Start Test TMVAGAexample" << endl
<< "========================" << endl
<< endl;
TMVA::exampleGA();
}
int main( int argc, char** argv )
{
TMVAGAexample2();
return 0;
}
Author
Andreas Hoecker

Definition in file TMVAGAexample2.C.

TGeant4Unit::mg
static constexpr double mg
Definition: TGeant4SystemOfUnits.h:210
n
const Int_t n
Definition: legend1.C:16
TString
Definition: TString.h:136
main
int main(int argc, char **argv)
Definition: histspeedtest.cxx:751
GeneticFitter.h
Double_t
double Double_t
Definition: RtypesCore.h:59
IFitterTarget.h
name
char name[80]
Definition: TGX11.cxx:110
GeneticAlgorithm.h
TMVA::IFitterTarget
Definition: IFitterTarget.h:64
TMVA
create variable transformations
Definition: GeneticMinimizer.h:22
TMVA::Interval
Definition: Interval.h:61