class TMVA::GeneticPopulation

library: libTMVA
#include "TMVA/GeneticPopulation.h"

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class TMVA::GeneticPopulation

 Population definition for genetic algorithm

Function Members (Methods)

public:

virtual	~GeneticPopulation()
void	AddPopulation(TMVA::GeneticPopulation* strangers)
void	AddPopulation(TMVA::GeneticPopulation& strangers)
static TClass*	Class()
TMVA::GeneticPopulation	GeneticPopulation(const TMVA::GeneticPopulation&)
TMVA::GeneticPopulation	GeneticPopulation(const vector<TMVA::Interval*>& ranges, Int_t size, UInt_t seed = 0)
Double_t	GetFitness() const
const vector<TMVA::GeneticGenes>&	GetGenePool() const
vector<TMVA::GeneticGenes>&	GetGenePool()
TMVA::GeneticGenes*	GetGenes(Int_t index)
Int_t	GetPopulationSize() const
const vector<TMVA::GeneticRange*>&	GetRanges() const
vector<TMVA::GeneticRange*>&	GetRanges()
void	GiveHint(vector<Double_t>& hint, Double_t fitness = 0)
virtual TClass*	IsA() const
void	MakeChildren()
void	MakeCopies(int number)
void	Mutate(Double_t probability = 20, Int_t startIndex = 0, Bool_t near = kFALSE, Double_t spread = 0.10000000000000001, Bool_t mirror = kFALSE)
void	NextGeneration()
TMVA::GeneticPopulation&	operator=(const TMVA::GeneticPopulation&)
void	Print(Int_t untilIndex = -1)
void	Print(ostream& out, Int_t utilIndex = -1)
void	SetRandomSeed(UInt_t seed = 0)
virtual void	ShowMembers(TMemberInspector& insp) const
void	Sort()
virtual void	Streamer(TBuffer&)
void	StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
void	TrimPopulation()
vector<Double_t>	VariableDistribution(Int_t varNumber)
TH1F*	VariableDistribution(Int_t varNumber, Int_t bins, Int_t min, Int_t max)

private:

TMVA::MsgLogger&	Log() const
TMVA::GeneticGenes	MakeSex(TMVA::GeneticGenes male, TMVA::GeneticGenes female)

Data Members

private:

vector<TMVA::GeneticGenes>	fGenePool	the "genePool" where the individuals of the current generation are stored
TMVA::MsgLogger*	fLogger	message logger
Int_t	fPopulationSizeLimit
TRandom3*	fRandomGenerator	random Generator for this population
vector<TMVA::GeneticRange*>	fRanges	contains the ranges inbetween the values of the coefficients have to be

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

GeneticPopulation(const vector<TMVA::Interval*>& ranges, Int_t size, UInt_t seed = 0)

 Constructor

~GeneticPopulation()

 destructor

void SetRandomSeed(UInt_t seed = 0)

 the random seed of the random generator

void MakeCopies(int number)

 produces offspring which is are copies of their parents
 Parameters:
         int number : the number of the last individual to be copied

void MakeChildren()

 does what the name says,... it creates children out of members of the
 current generation
 children have a combination of the coefficients of their parents

TMVA::GeneticGenes MakeSex(TMVA::GeneticGenes male, TMVA::GeneticGenes female)

 this function takes two individuals and produces offspring by mixing (recombining) their
 coefficients

void Mutate(Double_t probability = 20, Int_t startIndex = 0, Bool_t near = kFALSE, Double_t spread = 0.10000000000000001, Bool_t mirror = kFALSE)

 mutates the individuals in the genePool
 Parameters:
         double probability : gives the probability (in percent) of a mutation of a coefficient
         int startIndex : leaves unchanged (without mutation) the individuals which are better ranked
                     than indicated by "startIndex". This means: if "startIndex==3", the first (and best)
                     three individuals are not mutaded. This allows to preserve the best result of the
                     current Generation for the next generation.
         Bool_t near : if true, the mutation will produce a new coefficient which is "near" the old one
                     (gaussian around the current value)
         double spread : if near==true, spread gives the sigma of the gaussian
         Bool_t mirror : if the new value obtained would be outside of the given constraints
                    the value is mapped between the constraints again. This can be done either
                    by a kind of periodic boundary conditions or mirrored at the boundary.
                    (mirror = true seems more "natural")

TMVA::GeneticGenes* GetGenes(Int_t index)

 gives back the "Genes" of the population with the given index.

void Print(Int_t untilIndex = -1)

 make a little printout of the individuals up to index "untilIndex"
 this means, .. write out the best "untilIndex" individuals.

void Print(ostream& out, Int_t utilIndex = -1)

 make a little printout to the stream "out" of the individuals up to index "untilIndex"
 this means, .. write out the best "untilIndex" individuals.

TH1F* VariableDistribution(Int_t varNumber, Int_t bins, Int_t min, Int_t max)

 give back a histogram with the distribution of the coefficients
 parameters:
          int bins : number of bins of the histogram
          int min : histogram minimum
          int max : maximum value of the histogram

vector<Double_t> VariableDistribution(Int_t varNumber)

 gives back all the values of coefficient "varNumber" of the current generation

void AddPopulation( GeneticPopulation *strangers )

 add another population (strangers) to the one of this GeneticPopulation

void AddPopulation( GeneticPopulation &strangers )

 add another population (strangers) to the one of this GeneticPopulation

void TrimPopulation()

 trim the population to the predefined size

void GiveHint(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 known to perform good, they can be given as a hint to the population

void Sort()

 sort the genepool according to the fitness of the individuals

GeneticPopulation(const vector<TMVA::Interval*>& ranges, Int_t size, UInt_t seed = 0)

Int_t GetPopulationSize() const

{ return fGenePool.size(); }

Double_t GetFitness() const

{ return fGenePool.size()>0? fGenePool[0].GetFitness() : 0; }

const std::vector<TMVA::GeneticGenes>& GetGenePool() const

{ return fGenePool; }

const std::vector<TMVA::GeneticRange*>& GetRanges() const

{ return fRanges; }

std::vector<TMVA::GeneticGenes>& GetGenePool()

{ return fGenePool; }

std::vector<TMVA::GeneticRange*>& GetRanges()

{ return fRanges; }

void NextGeneration()

{}