35 lines
1021 B
Python
35 lines
1021 B
Python
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class HighOrderFTS(FTS):
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def __init__(self,order,name):
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super(HighOrderFTS, self).__init__(order,name)
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def defuzzy(self,data,t):
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cn = np.array([0.0 for k in range(len(self.sets))])
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ow = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
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rn = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
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ft = np.array([0.0 for k in range(len(self.sets))])
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for s in range(len(self.sets)):
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cn[s] = self.sets[s].membership(data[t])
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for w in range(self.order-1):
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ow[w,s] = self.sets[s].membership(data[t-w])
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rn[w,s] = ow[w,s] * cn[s]
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ft[s] = max(ft[s],rn[w,s])
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mft = max(ft)
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out = 0.0
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count = 0.0
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for s in range(len(self.sets)):
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if ft[s] == mft:
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out = out + self.sets[s].centroid
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count = count + 1.0
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return out / count
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def learn(self, data, sets):
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self.sets = sets
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def predict(self,data,t):
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return self.defuzzy(data,t)
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def predictDiff(self,data,t):
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return data[t] + self.defuzzy(diferencas(data),t)
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