def test_consistency(version_space,labels,features):
print(cl.count_of_T(h_1))
for boundaries in version_space:
for h in boundaries:
# print(h)
for idx,label in enumerate(labels):
cnstncy = cl.consistency(h,cl.get_data(features,idx))
# print(label)
print(cnstncy==label)
# print(cnstncy)
# print(label)
print(h)
print(cl.get_data(features,idx))
def test_consistency(version_space, labels, features):
print(cl.count_of_T(h_1))
for boundaries in version_space:
for h in boundaries:
# print(h)
for idx, label in enumerate(labels):
cnstncy = cl.consistency(h, cl.get_data(features, idx))
# print(label)
print(cnstncy == label)
# print(cnstncy)
# print(label)
print(h)
print(cl.get_data(features, idx))
print(h)
print(cl.get_data(features,idx))
sky = ("sunny","sunny","rainy","sunny")
temp = ("warm","warm","cold","warm")
humid = ("normal","high","high","high")
wind = ("strong","strong","strong","strong")
forecast = ("same","same","change","change")
enjoysport = (True,True,False,False)
examples = [sky,temp,humid,wind,forecast,enjoysport]
features = examples[:-1]
labels = examples[-1]
domains = cl.get_domains(features)
data = cl.get_data(features,3)
#print("min_specializations")
#print("domains: ",domains)
#print("data: ",data)
#cl.min_specializations()
#cl.candidate_elimination(examples)
#Implement a function min_specializations(h, domains, x) for a hypothesis h and an
#example x. The argument domains is a list of lists, in which the i-th sub-list contains the possible
#values of feature i. The function should return all minimal specializations of h with respect to
#domains which are not fulfilled by x. Example output:
#cl.min_specializations()
dummy_h = ("?","warm","strong","change")
specializations = cl.min_specializations(dummy_h,domains,data)
#print("hypthesis: ", dummy_h)
#print("specializations: ",specializations)
print(cl.get_data(features, idx))
sky = ("sunny", "sunny", "rainy", "sunny")
temp = ("warm", "warm", "cold", "warm")
humid = ("normal", "high", "high", "high")
wind = ("strong", "strong", "strong", "strong")
forecast = ("same", "same", "change", "change")
enjoysport = (True, True, False, False)
examples = [sky, temp, humid, wind, forecast, enjoysport]
features = examples[:-1]
labels = examples[-1]
domains = cl.get_domains(features)
data = cl.get_data(features, 3)
#print("min_specializations")
#print("domains: ",domains)
#print("data: ",data)
#cl.min_specializations()
#cl.candidate_elimination(examples)
#Implement a function min_specializations(h, domains, x) for a hypothesis h and an
#example x. The argument domains is a list of lists, in which the i-th sub-list contains the possible
#values of feature i. The function should return all minimal specializations of h with respect to
#domains which are not fulfilled by x. Example output:
#cl.min_specializations()
dummy_h = ("?", "warm", "strong", "change")
specializations = cl.min_specializations(dummy_h, domains, data)
#print("hypthesis: ", dummy_h)
#print("specializations: ",specializations)