54 :
FitterBase( target, name, ranges, theOption )
75 "Saves the best n results from each generation. They are included in the last cycle" );
77 "Saves the best n results from each cycle. They are included in the last cycle. The value should be set to at least 1.0" );
80 "Trim the population to PopSize after assessing the fitness of each individual" );
109 Log() <<
kHEADER <<
"<GeneticFitter> Optimisation, please be patient " 110 <<
"... (inaccurate progress timing for GA)" <<
Endl;
134 if ( pars.size() ==
fRanges.size() ){
137 if (cycle==fCycles-1) {
const std::vector< TMVA::Interval * > fRanges
MsgLogger & Endl(MsgLogger &ml)
void GiveHint(std::vector< Double_t > &hint, Double_t fitness=0)
add an individual (a set of variables) to the population if there is a set of variables which is know...
virtual Double_t CalculateFitness()
starts the evaluation of the fitness of all different individuals of the population.
GeneticPopulation & GetGeneticPopulation()
virtual Double_t SpreadControl(Int_t steps, Int_t ofSteps, Double_t factor)
this function provides the ability to change the stepSize of a mutation according to the success of t...
OptionBase * DeclareOptionRef(T &ref, const TString &name, const TString &desc="")
void DrawProgressBar(Int_t, const TString &comment="")
draws progress bar in color or B&W caution:
TString GetElapsedTime(Bool_t Scientific=kTRUE)
void AddPopulation(GeneticPopulation *strangers)
add another population (strangers) to the one of this GeneticPopulation
Double_t Run()
estimator function interface for fitting
virtual void ProgressNotifier(TString, TString)
const char * GetName() const
Returns name of object.
IFitterTarget & GetFitterTarget() const
void TrimPopulation()
trim the population to the predefined size
GeneticGenes * GetGenes(Int_t index)
gives back the "Genes" of the population with the given index.
void Sort()
sort the genepool according to the fitness of the individuals
Double_t GetFitness() const
virtual Bool_t HasConverged(Int_t steps=10, Double_t ratio=0.1)
gives back true if the last "steps" steps have lead to an improvement of the "fitness" of the "indivi...
std::vector< Double_t > & GetFactors()
void SetParameters(Int_t cycles, Int_t nsteps, Int_t popSize, Int_t SC_steps, Int_t SC_rate, Double_t SC_factor, Double_t convCrit)
set GA configuration parameters
void DeclareOptions()
declare GA options
Abstract ClassifierFactory template that handles arbitrary types.
void Init()
calls evolution, but if it is not the first time.
Int_t fSaveBestFromGeneration