/**
* Update the mean fitness variable.
*
* @param population a set representing the population.
*/
private void updateMeanPopulationFitness(final ClassifierSet population) {
meanPopulationFitness = 0;
final int populationSize = population.getNumberOfMacroclassifiers();
for (int i = 0; i < populationSize; i++) {
final int clNumerosity = population.getClassifierNumerosity(i);
final Classifier cl = population.getClassifier(i);
final double fitness = ((UCSClassifierData) cl.getUpdateDataObject()).fitness;
meanPopulationFitness += fitness * clNumerosity;
}
meanPopulationFitness /= population.getTotalNumerosity();
}
/*
* (non-Javadoc)
*
* @see
* gr.auth.ee.lcs.data.UpdateAlgorithmFactoryAndStrategy#getComparisonValue
* (gr.auth.ee.lcs.classifiers.Classifier, int)
*/
@Override
public double getComparisonValue(final Classifier aClassifier, final int mode) {
final UCSClassifierData data = (UCSClassifierData) aClassifier.getUpdateDataObject();
switch (mode) {
case COMPARISON_MODE_EXPLORATION:
case COMPARISON_MODE_EXPLOITATION:
final double value =
data.fitness
* ((aClassifier.experience < deleteAge)
? ((double) aClassifier.experience) / ((double) deleteAge)
: 1);
return Double.isNaN(value) ? 0 : value;
case COMPARISON_MODE_DELETION:
if (aClassifier.experience < deleteAge) {
final double result = (data.cs * meanPopulationFitness) / data.fitness;
return Double.isNaN(result) ? 1 : result;
}
return data.cs;
/*
* final double acc = (((double) (data.tp)) / (double) (data.msa));
* final double exploitValue = acc (aClassifier.experience <
* deleteAge ? 0 : 1); return Double.isNaN(exploitValue) ? 0 :
* exploitValue;
*/
default:
return 0;
}
}
/**
* Perform an update to the set.
*
* @param matchSet the match set used for the update
* @param correctSet the correct set used for the update
*/
@Override
public void performUpdate(final ClassifierSet matchSet, final ClassifierSet correctSet) {
double strengthSum = 0;
final int matchSetMacroclassifiers = matchSet.getNumberOfMacroclassifiers();
final int correctSetSize = correctSet.getTotalNumerosity();
for (int i = 0; i < matchSetMacroclassifiers; i++) {
final Classifier cl = matchSet.getClassifier(i);
UCSClassifierData data = ((UCSClassifierData) cl.getUpdateDataObject());
cl.experience++;
data.msa += 1;
if (correctSet.getClassifierNumerosity(cl) > 0) {
data.cs = data.cs + (b * (correctSetSize - data.cs));
data.tp += 1;
final double accuracy = ((double) data.tp) / ((double) data.msa);
if (accuracy > accuracy0) {
data.fitness0 = 1;
// Check subsumption
if (cl.experience >= this.subsumptionExperienceThreshold) cl.setSubsumptionAbility(true);
} else {
data.fitness0 = a * Math.pow(accuracy / accuracy0, n);
cl.setSubsumptionAbility(false);
}
strengthSum += data.fitness0 * matchSet.getClassifierNumerosity(i);
} else {
data.fp += 1;
data.fitness0 = 0;
}
}
// Fix for avoiding problems...
if (strengthSum == 0) strengthSum = 1;
// double fitnessSum = 0;
final int msSize = matchSet.getNumberOfMacroclassifiers();
for (int i = 0; i < msSize; i++) {
Classifier cl = matchSet.getClassifier(i);
UCSClassifierData data = ((UCSClassifierData) cl.getUpdateDataObject());
data.fitness += b * ((data.fitness0 / strengthSum) - data.fitness); // TODO:
// Something
// else?
// fitnessSum += data.fitness * matchSet.getClassifierNumerosity(i);
}
}
@Override
public void setComparisonValue(
final Classifier aClassifier, final int mode, final double comparisonValue) {
final UCSClassifierData data = ((UCSClassifierData) aClassifier.getUpdateDataObject());
data.fitness = comparisonValue;
}
@Override
public String getData(final Classifier aClassifier) {
final UCSClassifierData data = ((UCSClassifierData) aClassifier.getUpdateDataObject());
return "tp:" + data.tp;
}
