66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
class ExponentialyWeightedFLRG:
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def __init__(self,premiss,c):
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self.premiss = premiss
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self.consequent = []
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self.count = 0.0
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self.c = c
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def append(self,c):
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self.consequent.append(c)
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self.count = self.count + 1.0
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def weights(self):
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wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
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tot = sum( wei )
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return np.array([ k/tot for k in wei ])
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def __str__(self):
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tmp = self.premiss + " -> "
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tmp2 = ""
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cc = 0
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wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
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tot = sum( wei )
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for c in self.consequent:
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c + "(" + str(wei[cc]/tot) + ")"
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cc = cc + 1
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return tmp + tmp2
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class ExponentialyWeightedFTS(FTS):
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def __init__(self,name):
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super(ExponentialyWeightedFTS, self).__init__(1,name)
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def defuzzy(self,data):
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actual = self.fuzzy(data)
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if actual["fuzzyset"] not in self.flrgs:
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return self.sets[actual["fuzzyset"]].centroid
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flrg = self.flrgs[actual["fuzzyset"]]
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mi = np.array([self.sets[s].centroid for s in flrg.consequent])
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return mi.dot( flrg.weights() )
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def learn(self, data, sets):
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last = {"fuzzyset":"", "membership":0.0}
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actual = {"fuzzyset":"", "membership":0.0}
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for s in sets:
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self.sets[s.name] = s
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self.flrgs = {}
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count = 1
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for inst in data:
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actual = self.fuzzy(inst)
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if count > self.order:
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if last["fuzzyset"] not in self.flrgs:
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self.flrgs[last["fuzzyset"]] = ExponentialyWeightedFLRG(last["fuzzyset"],2)
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self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
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count = count + 1
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last = actual
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