/**
* It creates a new Population with only the sufficiently experienced classifiers.
*
* @param maxReward is the maximum reward of the environment.
* @return a Population with the experienced population
*/
public Population deleteNotExpClassifiers(double maxReward) {
// We create a Config.popSize population instead of a macroClSum classifier, because, if it's
// used in a training execution, this population can increase (new classifiers can be added).
Population pExp = new Population(Config.popSize);
for (int i = 0; i < macroClSum; i++) {
if (set[i].couldReduce(maxReward)) {
pExp.addClassifier(set[i]);
}
}
return pExp;
} // end deleteNotExpClassifiers
/**
* It creates the D population defined by Wilson 2002. It creates a population with the minimum
* number of classifiers that cover all the input examples.
*
* @param env Environment to be set in the new population.
* @return the D population created
*/
public Population createMCompPopulation(Environment env) {
int moreMatches = 0, maxMatched = 0;
// We create a Config.popSize population instead of a macroClSum classifier, because, if it's
// used in a training execution, this population can increase (new classifiers can be added).
Population Mcomp = new Population(Config.popSize);
while (env.getNumberOfExamples() > 0 && macroClSum > 0) {
moreMatches = 0;
maxMatched = set[0].getNumberMatches();
for (int i = 1; i < macroClSum; i++) {
if (set[i].getNumberMatches() > maxMatched) {
maxMatched = set[i].getNumberMatches();
moreMatches = i;
}
}
Mcomp.addClassifier(set[moreMatches]);
env.deleteMatchedExamples(set[moreMatches]);
deleteClassifier(moreMatches);
}
return Mcomp;
} // end createMcompPopulation
