public override a_Fuzzy_System Generate(a_Fuzzy_System Approximate, Abstract_generator_conf config)
{
type_alg = ((k_mean_rules_generator_conf)config).Алгоритм;
count_rules = ((k_mean_rules_generator_conf)config).Количество_правил;
type_func = ((k_mean_rules_generator_conf)config).Функция_принадлежности;
nebulisation_factor = ((k_mean_rules_generator_conf)config).Экспоненциальный_вес_алгоритма;
Max_iteration = ((k_mean_rules_generator_conf)config).Итераций;
need_precision = ((k_mean_rules_generator_conf)config).Точность;
k_mean_base K_Agl = null;
switch (type_alg)
{
case Type_k_mean_algorithm.Gath_geva: K_Agl = new k_mean_Gath_Geva(Approximate.Learn_Samples_set, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.Gustafson_Kessel: K_Agl = new k_mean_Gustafson_kessel(Approximate.Learn_Samples_set, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
case Type_k_mean_algorithm.FCM: K_Agl = new k_mean_base(Approximate.Learn_Samples_set, Max_iteration, need_precision, count_rules, nebulisation_factor); break;
}
K_Agl.Calc();
Knowlege_base_ARules New_Rules = new Knowlege_base_ARules();
for (int i = 0; i < count_rules; i++)
{
int [] order_terms = new int [Approximate.Learn_Samples_set.Count_Vars];
List <Term> term_set = new List <Term>();
for (int j = 0; j < Approximate.Learn_Samples_set.Count_Vars; j++)
{
Term temp_term = Term.Make_Term(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.constuct__and_add_the_Rule(term_set, Approximate);
}
a_Fuzzy_System Result = Approximate;
if (Result.Rulles_Database_Set.Count > 0)
{
Result.Rulles_Database_Set[0] = New_Rules;
}
else
{
Result.Rulles_Database_Set.Add(New_Rules);
}
Result.unlaid_protection_fix();
GC.Collect();
return(Result);
}
private double Calc_distance_for_member_ship_function_for_Clust(int number_cluster, int number_var, k_mean_base Alg)
{
double nominator = 0;
double denominator = 0;
for (int e = 0; e < Alg.Learn_table.Count_Samples; e++)
{
nominator += Math.Pow(Alg.U_matrix[number_cluster][e], 2) * Math.Pow(Alg.Centroid_cordinate_S[number_cluster][number_var] - Alg.Learn_table.Data_Rows[e].Input_Attribute_Value[number_var], 2);
denominator += Math.Pow(Alg.U_matrix[number_cluster][e], 2);
}
return(nominator / denominator);
}
