def expected_case(cli: Classifier, percept: list) -> Classifier:
"""
:rtype: Classifier
"""
diff = get_differences(cli.mark, percept)
if diff == [cons.symbol] * cons.lenCondition:
cli.q += cons.beta * (1 - cli.q)
return None
else:
spec = number_of_spec(cli.condition)
spec_new = number_of_spec(diff)
child = Classifier(cli)
if spec == cons.uMax:
remove_random_spec_att(child.condition)
spec -= 1
while spec + spec_new > cons.beta:
if spec > 0 and random() < 0.5:
remove_random_spec_att(child.condition)
spec -= 1
else:
remove_random_spec_att(diff)
spec_new -= 1
else:
while spec + spec_new > cons.beta:
remove_random_spec_att(diff)
spec_new -= 1
child.condition = diff
if child.q < 0.5:
child.q = 0.5
child.exp = 1
assert isinstance(child, Classifier), 'Should be a Classifier'
return child
def gen_match_set(pop: list, percept: list):
"""
Generate a list of Classifier thats match current perception
:param pop:
:type pop: list
:param percept:
:type percept: list
:return:
:rtype: list
"""
ma = []
if time == 0 or len(pop) == 0:
for i in range(cons.nbAction):
newcl = Classifier()
newcl.condition = [cons.symbol] * cons.lenCondition
newcl.action = i
newcl.effect = [cons.symbol] * cons.lenCondition
newcl.exp = 0
newcl.t = time
newcl.q = 0.5
pop.append(newcl)
for c in pop:
if does_match(c, percept):
ma.append(c)
return ma
def generate_covering_classifier(env, action):
newcl = Classifier()
newcl.condition = ['0'] * len(env)
for i in range(len(env)):
if rd.random() < cons.P_dontcare:
newcl.condition[i] = cons.dontCare
else:
newcl.condition[i] = env[i]
for i in range(len(action)):
if action[i] is False:
newcl.action = i
newcl.prediction = cons.predictionIni
newcl.predictionError = cons.predictionErrorIni
newcl.fitness = cons.fitnessIni
newcl.experience = 0
newcl.timeStamp = time
newcl.actionSetSize = 1
newcl.numerosity = 1
return newcl
