public override TSAFuzzySystem Generate(TSAFuzzySystem Approximate, IGeneratorConf config)
{
type_alg = ((kMeanRulesGeneratorConfig)config).KMRGTypeAlg;
count_rules = ((kMeanRulesGeneratorConfig)config).KMRGCountRules;
type_func = ((kMeanRulesGeneratorConfig)config).KMRGTypeFunc;
nebulisation_factor = ((kMeanRulesGeneratorConfig)config).KMRGExponentialWeight;
Max_iteration = ((kMeanRulesGeneratorConfig)config).KMRGIteraton;
need_precision = ((kMeanRulesGeneratorConfig)config).KMRGAccuracy;
Approxk_mean_base K_Agl = null;
switch (type_alg)
{
case Type_k_mean_algorithm.GathGeva: K_Agl = new Approxk_mean_Gath_Geva(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.GustafsonKessel: K_Agl = new Approxk_mean_Gustafson_kessel(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.FCM: K_Agl = new Approxk_mean_base(Approximate.LearnSamplesSet, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
}
K_Agl.Calc();
KnowlegeBaseTSARules New_Rules = new KnowlegeBaseTSARules();
for (int i = 0; i < count_rules; i++)
{
int [] order_terms = new int [Approximate.LearnSamplesSet.CountVars];
List <Term> term_set = new List <Term>();
for (int j = 0; j < Approximate.LearnSamplesSet.CountVars; j++)
{
Term temp_term = Term.MakeTerm(K_Agl.Centroid_cordinate_S[i][j], Math.Sqrt(Calc_distance_for_member_ship_function_for_Clust(i, j, K_Agl)) * 3, type_func, j);
term_set.Add(temp_term);
}
New_Rules.ConstructNewRule(term_set, Approximate);
}
TSAFuzzySystem Result = Approximate;
if (Result.RulesDatabaseSet.Count > 0)
{
Result.RulesDatabaseSet[0] = New_Rules;
}
else
{
Result.RulesDatabaseSet.Add(New_Rules);
}
Result.UnlaidProtectionFix(Result.RulesDatabaseSet[0]);
GC.Collect();
Result.RulesDatabaseSet[0].TermsSet.Trim();
return(Result);
}
private double Calc_distance_for_member_ship_function_for_Clust(int number_cluster, int number_var, Approxk_mean_base Alg)
{
double nominator = 0;
double denominator = 0;
for (int e = 0; e < Alg.Learn_table.CountSamples; e++)
{
nominator += Math.Pow(Alg.U_matrix[number_cluster][e], 2) * Math.Pow(Alg.Centroid_cordinate_S[number_cluster][number_var] - Alg.Learn_table.DataRows[e].InputAttributeValue[number_var], 2);
denominator += Math.Pow(Alg.U_matrix[number_cluster][e], 2);
}
return(nominator / denominator);
}