void FireflyOptimizator::init() {
generationRanked = false;
optmized=false;
groundTruth.getRegions();
normalizeDis = sqrt( groundTruth.getMask().rows*groundTruth.getMask().rows + groundTruth.getMask().cols*groundTruth.getMask().cols);
createPopulation();
}
void ExperimentRun::setupExperimentInProgressUseDat(string _datFile, string populationFileName,string _outputFileName)
{
outputFileName = _outputFileName;
//datFile = _datFile;
PRINT(populationFileName);
{
TiXmlDocument doc(populationFileName);
bool loadStatus;
if (iends_with(populationFileName,".gz"))
{
loadStatus = doc.LoadFileGZ();
}
else
{
loadStatus = doc.LoadFile();
}
if (!loadStatus)
{
cerr << "Error trying to load the XML file!" << endl;
throw CREATE_LOCATEDEXCEPTION_INFO("Error trying to load the XML file!");
}
TiXmlElement *element = doc.FirstChildElement();
NEAT::Globals* globals = NEAT::Globals::init(element);
(void) globals; //to get rid of unused variable warning
NEAT::Globals::getSingleton()->overrideParametersFromFile(_datFile);
NEAT::Globals::getSingleton()->setOutputFilePrefix(outputFileName); //set the name of the outputPrefixFile
//Destroy the document
}
int experimentType = int(NEAT::Globals::getSingleton()->getParameterValue("ExperimentType")+0.001);
cout << "Loading Experiment: " << experimentType << endl;
setupExperiment(experimentType,_outputFileName);
cout << "Experiment set up. Creating population...\n";
createPopulation(populationFileName);
cout << "Population Created\n";
}
int main() {
srand(time(NULL));
cfg_t cfg = {0};
configureGA(&cfg);
printf("Initial population: %d\nUniform rate: %f\nMutation rate: %f\nTarget genome: %s\n"
, cfg.initialPopulation
, cfg.uniformRate
, cfg.mutationRate
, cfg.targetGenome);
population p = createPopulation(cfg.initialPopulation, true);
for (uint8_t i = 0; i < cfg.initialPopulation; i++) {
calcFitness(p[i], cfg.targetGenome);
printf("%s (fit: %d)\n", p[i]->genes, p[i]->fitnessValue);
}
individual * ind = createIndividual(false);
uint16_t counter = 0;
evolve(p, &cfg, ind, counter);
return 0;
}
void ExperimentRun::setupExperimentInProgress(
string populationFileName,
string _outputFileName
)
{
outputFileName = _outputFileName;
{
TiXmlDocument *doc = new TiXmlDocument(populationFileName);
cout << "Loading population\n";
bool loadStatus;
if (iends_with(populationFileName,".gz"))
{
loadStatus = doc->LoadFileGZ();
}
else
{
loadStatus = doc->LoadFile();
}
if (!loadStatus)
{
throw CREATE_LOCATEDEXCEPTION_INFO("Error trying to load the XML file!");
}
TiXmlElement *element = doc->FirstChildElement();
NEAT::Globals* globals = NEAT::Globals::init(element);
//Destroy the document
delete doc;
cout << "Population loaded\n";
}
int experimentType = int(NEAT::Globals::getSingleton()->getParameterValue("ExperimentType")+0.001);
NEAT::Globals::getSingleton()->seedRandom((unsigned int)(NEAT::Globals::getSingleton()->getParameterValue("ActualRandomSeed")+0.001));
cout << "Loading Experiment: " << experimentType << endl;
setupExperiment(experimentType,_outputFileName);
cout << "Experiment set up. Creating population...\n";
createPopulation(populationFileName);
cout << "Population Created\n";
/*cout << "Cleaning up old generations\n";
population->cleanupOld(population->getGenerationCount()-1);
cout << "Done cleaning\n";*/
}
SEXP evolutionRun(SEXP numberOfRuns_ext, SEXP popSize_ext, SEXP sampleSize_ext, SEXP actualParameters, SEXP targetValues, SEXP funcSet, SEXP inSet, SEXP maxDepth_ext, SEXP maxLeafs_ext, SEXP maxNodes_ext, SEXP constProb_ext, SEXP constScaling_ext, SEXP subtreeProb_ext, SEXP RMSElimit_ext, SEXP returnRMSE_ext, SEXP silent_ext, SEXP timeLimit_ext) {
SEXP population;
PROTECT(population= createPopulation(popSize_ext, funcSet, inSet, maxDepth_ext, constProb_ext, subtreeProb_ext, constScaling_ext));
PROTECT(numberOfRuns_ext= coerceVector(numberOfRuns_ext, INTSXP));
int numberOfRuns= INTEGER(numberOfRuns_ext)[0];
PROTECT(RMSElimit_ext= coerceVector(RMSElimit_ext, REALSXP));
double RMSElimit= REAL(RMSElimit_ext)[0];
PROTECT(silent_ext= coerceVector(silent_ext, INTSXP));
int silent= INTEGER(silent_ext)[0];
PROTECT(timeLimit_ext= coerceVector(timeLimit_ext, INTSXP));
int timeLimit= INTEGER(timeLimit_ext)[0];
PROTECT(returnRMSE_ext= coerceVector(returnRMSE_ext, INTSXP));
int rRMSE= INTEGER(returnRMSE_ext)[0];
clock_t prgstart, prgende;
prgstart=clock();
double bestRMSE= 1000000;
for(int i=0; i < numberOfRuns; i++) {
PROTECT(population= selection(population, sampleSize_ext, actualParameters, targetValues, funcSet, inSet, maxDepth_ext, constProb_ext, subtreeProb_ext, maxLeafs_ext, maxNodes_ext, constScaling_ext, &bestRMSE));
if(silent == 0) {
Rprintf(" \n StepNumber: %d", i+1);
Rprintf(" best RMSE: %f", bestRMSE);
}
if(RMSElimit > 0) {
if (RMSElimit >= bestRMSE) {
UNPROTECT(7);
if(rRMSE == 0) {
if(silent != 1) {
summary(population, actualParameters, targetValues, &bestRMSE);
}
return population;
} else {
SEXP returnRMSE;
PROTECT(returnRMSE = allocVector(REALSXP, 1));
REAL(returnRMSE)[0] = bestRMSE;
UNPROTECT(1);
return returnRMSE; } }
}
prgende=clock();
float runtime= (float)(prgende-prgstart) / CLOCKS_PER_SEC;
if(silent != 1) {
Rprintf(" runtime: %.2f sec", runtime);
}
if(timeLimit > 0) {
if ((int)runtime >= timeLimit) {
UNPROTECT(7);
if(rRMSE == 0) {
if(silent != 1) {
summary(population, actualParameters, targetValues, &bestRMSE);
}
return population;
} else {
SEXP returnRMSE;
PROTECT(returnRMSE = allocVector(REALSXP, 1));
REAL(returnRMSE)[0] = bestRMSE;
UNPROTECT(1);
return returnRMSE; } }
}
UNPROTECT(1);
}
if(silent != 1) {
summary(population, actualParameters, targetValues, &bestRMSE);
}
UNPROTECT(6);
if(rRMSE == 0) {
return population;
} else {
SEXP returnRMSE;
PROTECT(returnRMSE = allocVector(REALSXP, 1));
REAL(returnRMSE)[0] = bestRMSE;
UNPROTECT(1);
return returnRMSE; }
}
