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factors={}
attributes = ()
def __init__(self,attr):
self.attributes = attr
for i in attr:
self.factors[i]=[]
def add_values(self,factor,values):
self.factors[factor]=values
class CandidateElimination:
Positive={}
Negative={}
def __init__(self,data,fact):
self.num_factors = len(data[0][0])
self.factors = fact.factors
self.attr = fact.attributes
self.dataset = data
def run_algorithm(self):
G = self.initializeG()
S = self.initializeS()
count=0
for trial_set in self.dataset:
if self.is_positive(trial_set):
G = self.remove_inconsistent_G(G,trial_set[0])
S_new = S[:]
print (S_new)
for s in S:
if not self.consistent(s,trial_set[0]):
S_new.remove(s)
generalization = self.generalize_inconsistent_S(s,trial_set[0])
generalization = self.get_general(generalization,G)
if generalization:
S_new.append(generalization)
S = S_new[:]
S = self.remove_more_general(S)
print(S)
else:
S = self.remove_inconsistent_S(S,trial_set[0])
G_new = G[:]
print (G_new)
for g in G:
if self.consistent(g,trial_set[0]):
G_new.remove(g)
specializations = self.specialize_inconsistent_G(g,trial_set[0])
specializationss = self.get_specific(specializations,S)
if specializations != []:
G_new += specializationss
G = G_new[:]
G = self.remove_more_specific(G)
print(G)
print(S)
print(G)
def initializeS(self):
S = tuple(['-' for factor in range(self.num_factors)])
return [S]
def initializeG(self):
G = tuple(['?' for factor in range(self.num_factors)])
return [G]
def is_positive(self,trial_set):
if trial_set[1] == 'Y':
return True
elif trial_set[1] == 'N':
return False
else:
raise TypeError("invalid target value")
def match_factor(self,value1,value2):
if value1 == '?' or value2 == '?':
return True
elif value1 == value2 :
return True
return False
def consistent(self,hypothesis,instance):
for i,factor in enumerate(hypothesis):
if not self.match_factor(factor,instance[i]):
return False
return True
def remove_inconsistent_G(self,hypotheses,instance):
G_new = hypotheses[:]
for g in hypotheses:
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