def apply_ga(aset: list, t: int, pop: list):
sumNum = 0
sumTgaN = 0
for CL in aset:
sumTgaN += CL.tga * CL.num
sumNum += CL.num
if (t - sumTgaN) / sumNum > cons.thetaGA:
for CL in aset:
CL.tga = t
parent1 = select_offspring(aset)
parent2 = select_offspring(aset)
child1 = Classifier(parent1)
child2 = Classifier(parent2)
child1.num += 1
child2.num += 1
child1.exp += 1
child2.exp += 1
apply_ga_mutation(child1)
apply_ga_mutation(child2)
if random() < cons.x:
apply_crossover(child1, child2)
child1.r = (parent1.r + parent2.r) / 2
child2.r = (parent1.r + parent2.r) / 2
child1.q = (parent1.q + parent2.q) / 2
child2.q = (parent1.q + parent2.q) / 2
child1.q /= 2
child2.q /= 2
delete_classifier(aset, pop)
if child1.condition != [cons.symbol] * cons.lenCondition:
add_ga_classifier(aset, pop, child1)
if child2.condition != [cons.symbol] * cons.lenCondition:
add_ga_classifier(aset, pop, child2)
def cover_triple(percept_: list, action: int, percept: list, t: int) -> Classifier:
child = Classifier()
for i in range(len(percept)):
if percept_[i] != percept[i]:
child.condition[i] = percept_[i]
child.effect[i] = percept[i]
child.action = action
child.exp = 0
child.r = 0
child.aav = 0
child.alp = t
child.tga = t
child.t = t
child.q = 0.5
child.num = 1
return child
