void makeKons(FS CurrentFS, List <Term> forClass)
{
#if PClass
string Kons = FuzzySystem.PittsburghClassifier.KNNClassName.NearestClass(CurrentFS, forClass);
theRule = new Rule(thePositionOfBee.TermsSet, numOfTerms, Kons);
#elif SApprox
double Kons = FuzzySystem.SingletoneApproximate.KNNConsequent.NearestApprox(CurrentFS, forClass);
theRule = new Rule(thePositionOfBee.TermsSet, numOfTerms, Kons);
#elif TSApprox
double[] koefs;
double val = FuzzySystem.TakagiSugenoApproximate.LSMWeghtReqursiveSimple.EvaluteConsiquent(CurrentFS, forClass, out koefs);
theRule = new Rule(thePositionOfBee.TermsSet, numOfTerms, val, koefs);
#else
double Kons = FuzzySystem.SingletoneApproximate.KNNConsequent.NearestApprox(CurrentFS, forClass);
theRule = new Rule(thePositionOfBee.TermsSet, numOfTerms, Kons);
#endif
}
public void WorkerFly(int numOfRule, Random rand)
{
Rule optimised = thePositionOfBee.RulesDatabase[numOfRule];
double coust = 0;
for (int i = 0; i < optimised.ListTermsInRule.Count; i++)
{
int numOfFreature = optimised.ListTermsInRule[i].NumVar;
for (int j = 0; j < optimised.ListTermsInRule[i].Parametrs.Count(); j++)
{
coust = rand.Next(100) / 500.0;
if (rand.Next(2) > 0)
{
optimised.ListTermsInRule[i].Parametrs[j] *= (1 - coust);
}
else
{
optimised.ListTermsInRule[i].Parametrs[j] *= (1 + coust);
}
}
}
}