58 lines
1.2 KiB
Python
58 lines
1.2 KiB
Python
import numpy as np
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from pyFTS import *
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class SeasonalFLRG(fts.FTS):
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def __init__(self,seasonality):
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self.LHS = seasonality
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self.RHS = []
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def append(self,c):
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self.RHS.append(c)
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp2 = ""
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for c in self.RHS:
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c
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return tmp + tmp2
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class SeasonalFTS(fts.FTS):
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def __init__(self,name):
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super(SeasonalFTS, self).__init__(1,name)
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def forecast(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.RHS])
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return mi.dot( flrg.weights() )
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def train(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"]] = SeasonalFLRG(last["fuzzyset"])
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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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