本文整理汇总了Python中Classifier.Classifier.action方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.action方法的具体用法？Python Classifier.action怎么用？Python Classifier.action使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Classifier.Classifier的用法示例。
在下文中一共展示了Classifier.action方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: gen_match_set
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import action [as 别名]
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示例2: cover_triple
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import action [as 别名]
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示例3: apply_mutation
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import action [as 别名]
def apply_mutation(cl: Classifier, perception: list):
"""
:type cl: Classifier
:param cl:
:type perception: list
:param perception:
:return:
"""
for i in range(len(cl.condition)):
if rd.random() < cons.nu:
if cl.condition[i] == cons.dontCare:
cl.condition[i] = perception[i]
else:
cl.condition[i] = cons.dontCare
if rd.random() < cons.nu:
c = rd.choice([i for i in range(0, (cons.nbAction - 1))])
cl.action = c示例4: generate_covering_classifier
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import action [as 别名]
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