/**
* Constructor with parameters. It creates a specific rule from a given sample
*
* @param database Data Base associated to the general rule base that includes this rule
* @param n_classes Number of classes in the training set
* @param tnorm T-norm used to compute the compatibility degree
* @param tconorm T-conorm used to compute the compatibility degree
* @param rule_weight Way of computing the rule weight
* @param sample Data sample used to generate this rule
* @param sample_class Output class associated to the data sample used to generate this rule
*/
public Rule(
DataBase database,
int n_classes,
int tnorm,
int tconorm,
int rule_weight,
double[] sample,
int sample_class) {
antecedent = new ArrayList<FuzzyAntecedent>(database.numVariables());
clas = sample_class;
t_norm = tnorm;
t_conorm = tconorm;
ruleWeight = rule_weight;
level = 2;
n_e = true;
for (int i = 0; i < database.numVariables(); i++) {
double max = 0.0;
int etq = -1;
double per;
for (int j = 0; j < database.numLabels(); j++) {
per = database.membershipFunction(i, j, sample[i]);
if (per > max) {
max = per;
etq = j;
}
}
if (max == 0.0) {
System.err.println("There was an error while searching for the antecedent of the rule");
System.err.println("Example: ");
for (int j = 0; j < database.numVariables(); j++) {
System.err.print(sample[j] + "\t");
}
System.err.println("Variable " + i);
System.exit(1);
}
FuzzyAntecedent new_antecedent =
new FuzzyAntecedent(database.clone(i, etq), i, database.numLabels());
antecedent.add((FuzzyAntecedent) new_antecedent);
}
}
