void BasicProjectMedeaAgentObserver::pickRandomGenome()
{
if(_wm->_genomesList.size() != 0)
{
int randomIndex = rand()%_wm->_genomesList.size();
std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin();
while (randomIndex !=0 )
{
it ++;
randomIndex --;
}
_wm->_currentGenome = (*it).second;
if ( BasicProjectMedeaSharedData::gDynamicSigma == true )
{
mutateWithBouncingBounds(_wm->_sigmaList[(*it).first]);
}
else
{
mutateWithBouncingBounds(-1.00);
}
_wm->setNewGenomeStatus(true);
gLogFile << "Info(" << gWorld->getIterations() << ") : robot nb." << _wm->_agentId << " take the genome from the robot nb." << (*it).first << std::endl;
if (_wm->_agentId == 1 && gVerbose) // debug
std::cout << " Sigma is " << _wm->_sigmaList[(*it).first] << "." << std::endl;
_wm->_genomesList.clear();
}
}
void MedeaSpAgentObserver::pickRandomGenome()
{
if(_wm->_genomesList.size() != 0)//optional, should always be true : pickRandomGenome should be called only when liste>0
{
int randomIndex = rand()%_wm->_genomesList.size();
std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin();
while (randomIndex !=0 )
{
it ++;
randomIndex --;
}
_wm->_currentGenome = (*it).second;
if ( MedeaSpSharedData::gDynamicSigma == true )
{
mutateWithBouncingBounds(_wm->_sigmaList[(*it).first]);
}
else
{
mutateWithBouncingBounds(-1.00);
}
_wm->setNewGenomeStatus(true);
_wm->setFatherId((*it).first);
//gLogFile << gWorld->getIterations() << " : " << _wm->_agentId + 1000 * _wm->getDateOfBirth() << " take " << _wm->getFatherId()<<std::endl;
if (_wm->_agentId == 1 && gVerbose) // debug
std::cout << " Sigma is " << _wm->_sigmaList[(*it).first] << "." << std::endl;
_wm->_genomesList.clear();
}
//never reached
}
void MedeaSpAgentObserver::elitSelection()
{
if(_wm->_genomesList.size() != 0)//optional, should always be true : pickRandomGenome should be called only when liste>0
{
std::map<int, std::vector<double> > tournamentListGenome;
std::map<int, double > tournamentListFitness;
std::cout<<"size???"<<(int)MedeaSpSharedData::gTournamentSize<<std::endl;
unsigned int aimedSize = std::min((int)MedeaSpSharedData::gTournamentSize,(int)_wm->_genomesList.size());
//unsigned int aimedSize = std::min(1,(int)_wm->_genomesList.size());
while ( tournamentListGenome.size() < aimedSize)
{
int randomIndex = rand()%_wm->_genomesList.size();
std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin();
while (randomIndex !=0 )
{
it ++;
randomIndex --;
}
tournamentListGenome[(*it).first] = (*it).second;
tournamentListFitness[(*it).first] = _wm->_fitnessList[(*it).first];
_wm->_genomesList.erase((*it).first);
}
double bestFitness = (*tournamentListFitness.begin()).second;
int idBest = (*tournamentListFitness.begin()).first;
for (std::map<int, double>::iterator it = tournamentListFitness.begin(); it != tournamentListFitness.end() ; it++)
{
if ( (*it).second > bestFitness )
{
bestFitness = (*it).second;
idBest = (*it).first;
}
}
_wm->_currentGenome = tournamentListGenome[idBest];
if ( MedeaSpSharedData::gDynamicSigma == true )
{
mutateWithBouncingBounds(_wm->_sigmaList[idBest]);
}
else
{
mutateWithBouncingBounds(-1.00);
}
_wm->setNewGenomeStatus(true);
_wm->setFatherId(idBest);
//gLogFile << gWorld->getIterations() << " : " << _wm->_agentId + 1000 * _wm->getDateOfBirth() << " take " << _wm->getFatherId()<<std::endl;
if (_wm->_agentId == 1 && gVerbose) // debug
std::cout << " Sigma is " << _wm->_sigmaList[idBest] << "." << std::endl;
_wm->_genomesList.clear();
_wm->_fitnessList.clear();
}
}
void StrongCausalityExperimentAgentObserver::step()
{
if ( _wm->_age == _lifetimeIterations )
{
// stats for current agent
_sumOfFitnesses = _sumOfFitnesses + _wm->_fitnessValue;
if ( initPos[_wm->_generation][0] == -1 ) // genome iteration ends? display stats and restart with new genome
{
// * Display stats.
//std::cout << _wm->_generation << "," << _wm->_fitnessValue;
if ( _testIt == 0 ) // reference genome
std::cout << "# ";
std::cout << _testIt << "," << _sumOfFitnesses;
if ( _testIt == 0 ) // reference genome
std::cout << ",0";
else
std::cout << "," << _sigma;
for ( int i = 0 ; i != 18 ; i++ ) // genome
{
std::cout << ",";
std::cout << _wm->_genome[i];
}
std::cout << std::endl;
reset();
if ( _triesPerSigma == _nbOfTriesPerSigmaValue )
{
if ( _sigma < _sigmaMax )
{
_sigma = _sigma * 2;
}
else
{
std::cout << "end." << std::endl;
exit(0);
}
_triesPerSigma = 0;
}
else
_triesPerSigma++;
mutateWithBouncingBounds();
_wm->_generation = 0;
}
// prepare for new evaluation
_wm->_generation++;
_wm->_xReal = initPos[_wm->_generation][0];
_wm->_yReal = initPos[_wm->_generation][1];
_wm->_agentAbsoluteOrientation = initPos[_wm->_generation][2];
_wm->_agentAbsoluteLinearSpeed = 0;
_wm->_fitnessValue = 0;
_wm->_age = 0; // age<0 means random mouvement until age=0D
}
else
{
// udpate fitness
updateFitness();
_wm->_age++;
}
}
void MedeaSpAgentObserver::selectRankProp()
{
if(_wm->_genomesList.size() != 0)
{
int idBest = 0;
if(_wm->_genomesList.size() == 1)
{
idBest = (*_wm->_genomesList.begin()).first;
}
else
{
double minimumExpected = MedeaSpSharedData::gMinimumExpectedValue;
double maximumExpected = MedeaSpSharedData::gMaximumExpectedValue;
//reverse the map to sort by fitness. Will put lowest first
std::multimap<double,int> sortedFitness = flip_map(_wm->_fitnessList);
std::map<int, double> f;
int rank = 0;
for (std::multimap<double, int >::iterator it = sortedFitness.begin(); it != sortedFitness.end() ; it++)
{
f[(*it).second] = ( maximumExpected - (maximumExpected - minimumExpected) * (rank / ( (double)_wm->_fitnessList.size() - 1.0) )) / (double)_wm->_fitnessList.size() ;
rank ++;
}
double sum1 = 0.0;
for( std::map<int, double>::iterator it = f.begin() ; it != f.end() ; it ++)
{
sum1 += f[(*it).first];
}
double sum2 = 0.0;
std::map<int, double> p;
for( std::map<int, double>::iterator it = f.begin() ; it != f.end() ; it ++)
{
if (f[(*it).first] > 0.0)
{
sum2 += f[(*it).first];
p[(*it).first] = sum2/sum1;
}
}
double r = ranf();
//Proportional selection
std::map<int, double >::iterator it = p.begin() ;
while( (r > p[(*it).first]) && (it != p.end()))
{
it ++;
}
idBest = (*it).first ;
_wm->_currentGenome =_wm->_genomesList[idBest];
if ( MedeaSpSharedData::gDynamicSigma == true )
{
mutateWithBouncingBounds(_wm->_sigmaList[idBest]);
}
else
{
mutateWithBouncingBounds(-1.00);
}
_wm->setNewGenomeStatus(true);
_wm->setFatherId(idBest);
//gLogFile << gWorld->getIterations() << " : " << _wm->_agentId + 1000 * _wm->getDateOfBirth() << " take " << _wm->getFatherId()<<std::endl;
if (_wm->_agentId == 1 && gVerbose) // debug
std::cout << " Sigma is " << _wm->_sigmaList[idBest] << "." << std::endl;
_wm->_genomesList.clear();
_wm->_fitnessList.clear();
}
}
}
void MedeaSpAgentObserver::selectFitnessProp()
{
if(_wm->_genomesList.size() != 0)
{
double sum1 = 0.0;
for( std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin() ; it != _wm->_genomesList.end() ; it ++)
{
sum1 += _wm->_fitnessList[(*it).first];
}
int selected = 0;
//if one has a fitness higher than 0 do the computations for proportional selection
if (sum1 > 0)
{
double sum2 = 0.0;
std::map<int, double> p;
for( std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin() ; it != _wm->_genomesList.end() ; it ++)
{
if (_wm->_fitnessList[(*it).first] > 0.0)
{
sum2 += _wm->_fitnessList[(*it).first];
p[(*it).first] = sum2/sum1;
}
}
double r = ranf();
//Proportional selection
std::map<int, double >::iterator it = p.begin() ;
while( (r > p[(*it).first]) && (it != p.end()))
{
it ++;
}
selected = (*it).first ;
std::cerr<<"okok"<<std::endl;
}
else
{
int randomIndex = rand()%_wm->_genomesList.size();
std::map<int, std::vector<double> >::iterator it = _wm->_genomesList.begin();
while (randomIndex != 0 )
{
it ++;
randomIndex --;
}
selected = (*it).first;
}
//_currentGenome = _wm->_genomesList[selected];
int idBest = selected;
_wm->_currentGenome =_wm->_genomesList[selected];
if ( MedeaSpSharedData::gDynamicSigma == true )
{
mutateWithBouncingBounds(_wm->_sigmaList[idBest]);
}
else
{
mutateWithBouncingBounds(-1.00);
}
_wm->setNewGenomeStatus(true);
_wm->setFatherId(idBest);
//gLogFile << gWorld->getIterations() << " : " << _wm->_agentId + 1000 * _wm->getDateOfBirth() << " take " << _wm->getFatherId()<<std::endl;
if (_wm->_agentId == 1 && gVerbose) // debug
std::cout << " Sigma is " << _wm->_sigmaList[idBest] << "." << std::endl;
_wm->_genomesList.clear();
_wm->_fitnessList.clear();
}
}
