public KnowlegeBaseTSARules(KnowlegeBaseTSARules source, List <bool> used_rules = null)
{
for (int i = 0; i < source.TermsSet.Count; i++)
{
Term temp_term = new Term(source.TermsSet[i]);
TermsSet.Add(temp_term);
}
for (int j = 0; j < source.RulesDatabase.Count; j++)
{
if ((used_rules == null) || (used_rules[j]))
{
int[] order = new int[source.RulesDatabase[j].ListTermsInRule.Count];
for (int k = 0; k < source.RulesDatabase[j].ListTermsInRule.Count; k++)
{
Term temp_term = source.RulesDatabase[j].ListTermsInRule[k];
order[k] = source.TermsSet.FindIndex(x => x == temp_term);
}
double temp_DoubleOutputs = source.RulesDatabase[j].IndependentConstantConsequent;
double[] temp_approx_RegressionConstantConsequent = source.RulesDatabase[j].RegressionConstantConsequent.Clone() as double [];
TSARule temp_rule = new TSARule(TermsSet, order, temp_DoubleOutputs, temp_approx_RegressionConstantConsequent);
RulesDatabase.Add(temp_rule);
}
}
}
public void constuct__and_add_the_Rule(List <Term> terms, SAFuzzySystem FS)
{
SARule Result;
int[] order_of_terms = new int[terms.Count()];
for (int i = 0; i < terms.Count(); i++)
{
order_of_terms[i] = TermsSet.Count;
TermsSet.Add(terms[i]);
}
double kons_Value = KNNConsequent.NearestApprox(FS, TermsSet);
Result = new SARule(TermsSet, order_of_terms, kons_Value);
RulesDatabase.Add(Result);
}
public void ConstructNewRule(List <Term> terms, PCFuzzySystem FS)
{
PCRule Result;
int[] order_of_terms = new int[terms.Count()];
for (int i = 0; i < terms.Count(); i++)
{
order_of_terms[i] = TermsSet.Count;
TermsSet.Add(terms[i]);
}
string kons_Value = KNNClassName.NearestClass(FS, TermsSet);
Result = new PCRule(TermsSet, order_of_terms, kons_Value, 1.0);
RulesDatabase.Add(Result);
}
public void ConstructNewRule(List <Term> terms, TSAFuzzySystem FS)
{
TSARule Result;
int[] order_of_terms = new int[terms.Count()];
for (int i = 0; i < terms.Count(); i++)
{
order_of_terms[i] = TermsSet.Count;
TermsSet.Add(terms[i]);
}
double [] temp_regressionCoefficent;
double kons_Value = LSMWeghtReqursiveSimple.EvaluteConsiquent(FS, TermsSet, out temp_regressionCoefficent);
Result = new TSARule(TermsSet, order_of_terms, kons_Value, temp_regressionCoefficent);
RulesDatabase.Add(Result);
}
public override void TrimTerms()
{
{
bool need_to_cut_this_term;
for (int j = TermsSet.Count - 1; j >= 0; j--)
{
need_to_cut_this_term = true;
for (int i = 0; i < RulesDatabase.Count; i++)
{
if (RulesDatabase[i].ListTermsInRule.Contains(TermsSet[j]))
{
need_to_cut_this_term = false; break;
}
}
if (need_to_cut_this_term)
{
TermsSet.RemoveAt(j);
}
}
}
}
public KnowlegeBasePCRules(KnowlegeBasePCRules source, List <bool> used_rules = null)
{
for (int i = 0; i < source.TermsSet.Count; i++)
{
Term temp_term = new Term(source.TermsSet[i]);
TermsSet.Add(temp_term);
}
for (int j = 0; j < source.RulesDatabase.Count; j++)
{
if ((used_rules == null) || (used_rules[j]))
{
int [] order = new int[source.RulesDatabase[j].ListTermsInRule.Count];
for (int k = 0; k < source.RulesDatabase[j].ListTermsInRule.Count; k++)
{
Term temp_term = source.RulesDatabase[j].ListTermsInRule[k];
order[k] = source.TermsSet.FindIndex(x => x == temp_term);
}
string temp_class_label = source.RulesDatabase[j].LabelOfClass;
double temp_cf = source.RulesDatabase[j].CF;
PCRule temp_rule = new PCRule(TermsSet, order, temp_class_label, temp_cf);
RulesDatabase.Add(temp_rule);
}
}
}
