def generate_negative_rules(self, train, confident_value_pass):
confident_value = 0
class_value_arr = self.get_class_value_array(train)
for i in range(0, len(self.ruleBase)):
rule_negative = Rule()
rule_negative.antecedent = self.ruleBase[i].antecedent
positive_rule_class_value = self.ruleBase[i].get_class()
print("the positive rule class value is " +
str(positive_rule_class_value) + " ,the i is :" + str(i))
rule_negative.setClass(positive_rule_class_value)
for j in range(0, len(class_value_arr)):
class_type = int(class_value_arr[j])
if positive_rule_class_value != class_type: # need to get another class value for negative rule
rule_negative.setClass(
class_type) # change the class type in the rule
confident_value = rule_negative.calculate_confident(
self.data_row_array)
print("The calculation confident value is :" +
str(confident_value))
if confident_value >= confident_value_pass:
rule_negative.weight = confident_value
if not (self.duplicated_negative_rule(rule_negative)):
for k in range(0, len(rule_negative.antecedent)):
print("antecedent L_ " +
str(rule_negative.antecedent[j].label))
print("class value " +
str(rule_negative.get_class()))
print(" weight " + str(rule_negative.weight))
print("positive_rule_class_value" +
str(positive_rule_class_value))
print("class_type" + str(class_type))
self.negative_rule_base_array.append(rule_negative)
