Refatoração dos códigos para padronizar com a rfts - Co Yu
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32
chen.py
32
chen.py
@ -23,6 +23,22 @@ class ConventionalFTS(fts.FTS):
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def __init__(self,name):
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super(ConventionalFTS, self).__init__(1,name)
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self.flrgs = {}
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if flr.LHS in flrgs:
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flrgs[flr.LHS].append(flr.RHS)
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else:
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flrgs[flr.LHS] = ConventionalFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets):
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self.sets = sets
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tmpdata = common.fuzzySeries(data,sets)
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flrs = common.generateNonRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
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@ -44,19 +60,5 @@ class ConventionalFTS(fts.FTS):
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return denom/count
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if flr.LHS in flrgs:
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flrgs[flr.LHS].append(flr.RHS)
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else:
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flrgs[flr.LHS] = ConventionalFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets):
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self.sets = sets
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tmpdata = common.fuzzySeries(data,sets)
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flrs = common.generateNonRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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@ -87,6 +87,6 @@ def generateNonRecurrentFLRs(fuzzyData):
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def generateRecurrentFLRs(fuzzyData):
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flrs = []
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for i in range(2,len(fuzzyData)):
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flrs[i-1] = FLR(fuzzyData[i-1],fuzzyData[i])
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for i in np.arange(1,len(fuzzyData)):
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flrs.append(FLR(fuzzyData[i-1],fuzzyData[i]))
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return flrs
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45
yu.py
45
yu.py
@ -31,36 +31,35 @@ class WeightedFLRG(fts.FTS):
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class WeightedFTS(fts.FTS):
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def __init__(self,name):
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super(WeightedFTS, self).__init__(1,name)
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if flr.LHS in flrgs:
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flrgs[flr.LHS].append(flr.RHS)
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else:
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flrgs[flr.LHS] = WeightedFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets):
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self.sets = sets
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tmpdata = common.fuzzySeries(data,sets)
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flrs = common.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
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actual = self.fuzzy(data)
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mv = common.fuzzyInstance(data, self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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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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if actual.name not in self.flrgs:
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return actual.centroid
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flrg = self.flrgs[actual["fuzzyset"]]
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flrg = self.flrgs[actual.name]
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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"]] = WeightedFLRG(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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