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Refatoração dos códigos para padronizar com a rfts - Co Yu

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Petrônio Cândido de Lima e Silva 2016-10-18 17:44:03 -02:00
parent 9ad28b07f2
commit aa7389e228
3 changed files with 41 additions and 40 deletions
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32
chen.py
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@ -23,6 +23,22 @@ class ConventionalFTS(fts.FTS):
def __init__(self,name): def __init__(self,name):
super(ConventionalFTS, self).__init__(1,name) super(ConventionalFTS, self).__init__(1,name)
self.flrgs = {} self.flrgs = {}
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS in flrgs:
flrgs[flr.LHS].append(flr.RHS)
else:
flrgs[flr.LHS] = ConventionalFLRG(flr.LHS);
flrgs[flr.LHS].append(flr.RHS)
return (flrgs)
def train(self, data, sets):
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateNonRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self,data): def forecast(self,data):
@ -44,19 +60,5 @@ class ConventionalFTS(fts.FTS):
return denom/count return denom/count
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS in flrgs:
flrgs[flr.LHS].append(flr.RHS)
else:
flrgs[flr.LHS] = ConventionalFLRG(flr.LHS);
flrgs[flr.LHS].append(flr.RHS)
return (flrgs)
def train(self, data, sets):
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateNonRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)

4
common.py
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@ -87,6 +87,6 @@ def generateNonRecurrentFLRs(fuzzyData):
def generateRecurrentFLRs(fuzzyData): def generateRecurrentFLRs(fuzzyData):
flrs = [] flrs = []
for i in range(2,len(fuzzyData)): for i in np.arange(1,len(fuzzyData)):
flrs[i-1] = FLR(fuzzyData[i-1],fuzzyData[i]) flrs.append(FLR(fuzzyData[i-1],fuzzyData[i]))
return flrs return flrs

45
yu.py
View File

@ -31,36 +31,35 @@ class WeightedFLRG(fts.FTS):
class WeightedFTS(fts.FTS): class WeightedFTS(fts.FTS):
def __init__(self,name): def __init__(self,name):
super(WeightedFTS, self).__init__(1,name) super(WeightedFTS, self).__init__(1,name)
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS in flrgs:
flrgs[flr.LHS].append(flr.RHS)
else:
flrgs[flr.LHS] = WeightedFLRG(flr.LHS);
flrgs[flr.LHS].append(flr.RHS)
return (flrgs)
def train(self, data, sets):
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self,data): def forecast(self,data):
actual = self.fuzzy(data) mv = common.fuzzyInstance(data, self.sets)
actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
if actual["fuzzyset"] not in self.flrgs: if actual.name not in self.flrgs:
return self.sets[actual["fuzzyset"]].centroid return actual.centroid
flrg = self.flrgs[actual["fuzzyset"]] flrg = self.flrgs[actual.name]
mi = np.array([self.sets[s].centroid for s in flrg.RHS]) mi = np.array([self.sets[s].centroid for s in flrg.RHS])
return mi.dot( flrg.weights() ) return mi.dot( flrg.weights() )
def train(self, data, sets):
last = {"fuzzyset":"", "membership":0.0}
actual = {"fuzzyset":"", "membership":0.0}
for s in sets:
self.sets[s.name] = s
self.flrgs = {}
count = 1
for inst in data:
actual = self.fuzzy(inst)
if count > self.order:
if last["fuzzyset"] not in self.flrgs:
self.flrgs[last["fuzzyset"]] = WeightedFLRG(last["fuzzyset"])
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
count = count + 1
last = actual