SFTS
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2
chen.py
2
chen.py
@ -42,8 +42,6 @@ class ConventionalFTS(fts.FTS):
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def forecast(self,data):
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l = 1
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ndata = np.array(data)
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l = len(ndata)
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68
sfts.py
68
sfts.py
@ -10,48 +10,56 @@ class SeasonalFLRG(fts.FTS):
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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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tmp = str(self.LHS) + " -> "
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tmp2 = ""
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for c in self.RHS:
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for c in sorted(self.RHS, key=lambda s: s.name):
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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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tmp2 = tmp2 + c.name
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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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self.seasonality = 1
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def generateFLRG(self, flrs):
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flrgs = []
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season = 1
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for flr in flrs:
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if len(flrgs) < self.seasonality:
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flrgs.append(SeasonalFLRG(season))
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flrgs[season].append(flr.RHS)
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season = (season + 1) % (self.seasonality + 1)
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if season == 0: season = 1
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return (flrgs)
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def train(self, data, sets, seasonality):
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self.sets = sets
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self.seasonality = seasonality
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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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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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ndata = np.array(data)
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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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l = len(ndata)
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ret = []
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for k in np.arange(1,l):
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flrg = self.flrgs[ data[k] ]
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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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mp = self.getMidpoints(flrg)
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ret.append(sum(mp)/len(mp))
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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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return ret
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