def searchForBestAntecedent(self, example, clas):
ruleInstance = Rule()
ruleInstance.setTwoParameters(self.n_variables, self.compatibilityType)
# print("In searchForBestAntecedent ,self.n_variables is :" + str(self.n_variables))
ruleInstance.setClass(clas)
# print("In searchForBestAntecedent ,self.n_labels is :" + str(self.n_labels))
example_feature_array = []
for f_variable in range(0, self.n_variables):
# print("The f_variable is :"+str(f_variable))
# print("The example is :" + str(example))
example_feature_array.append(example[f_variable])
label_array = []
for i in range(0, self.n_variables):
max_value = 0.0
etq = -1
per = None
for j in range(0, self.n_labels):
# print("Inside the second loop of searchForBestAntecedent......")
per = self.dataBase.membershipFunction(i, j, example[i])
if per > max_value:
max_value = per
etq = j
if max_value == 0.0:
# print("There was an Error while searching for the antecedent of the rule")
# print("Example: ")
for j in range(0, self.n_variables):
print(example[j] + "\t")
print("Variable " + str(i))
exit(1)
# print(" The max_value is : " + str(max_value))
# print(" ,the j value is : " + str(j))
ruleInstance.antecedent[i] = self.dataBase.clone(
i, etq) # self.dataBase[i][j]
label_array.append(etq)
data_row_temp = data_row()
data_row_temp.set_three_parameters(clas, example_feature_array,
label_array)
ruleInstance.data_row_here = data_row_temp
return ruleInstance
