Refatoração dos códigos para padronizar com a rfts e HOFTS
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Petrônio Cândido de Lima e Silva
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30ccb6a88f
commit
a04eabf9ed
6
chen.py
6
chen.py
@ -10,12 +10,12 @@ class ConventionalFLRG:
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self.RHS.add(c)
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp = self.LHS.name + " -> "
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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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2
fts.py
2
fts.py
@ -31,6 +31,6 @@ class FTS:
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def __str__(self):
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tmp = self.name + ":\n"
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for r in self.flrgs.keys():
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for r in sorted(self.flrgs):
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tmp = tmp + str(self.flrgs[r]) + "\n"
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return tmp
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@ -8,19 +8,19 @@ class ImprovedWeightedFLRG:
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self.count = 0.0
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def append(self,c):
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if c not in self.RHS:
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self.RHS[c] = 1.0
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if c.name not in self.RHS:
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self.RHS[c.name] = 1.0
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else:
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self.RHS[c] = self.RHS[c] + 1.0
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self.RHS[c.name] = self.RHS[c.name] + 1.0
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self.count = self.count + 1.0
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def weights(self):
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return np.array([ self.RHS[c]/self.count for c in self.RHS.keys() ])
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp = self.LHS.name + " -> "
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tmp2 = ""
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for c in self.RHS.keys():
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for c in sorted(self.RHS):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.count,3)) + ")"
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@ -30,23 +30,31 @@ class ImprovedWeightedFLRG:
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class ImprovedWeightedFTS(fts.FTS):
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def __init__(self,name):
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super(ImprovedWeightedFTS, self).__init__(1,name)
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self.setsDict = {}
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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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if flr.LHS.name in flrgs:
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flrgs[flr.LHS.name].append(flr.RHS)
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else:
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flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
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flrgs[flr.LHS.name].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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for s in self.sets: self.setsDict[s.name] = s
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tmpdata = common.fuzzySeries(data,self.sets)
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flrs = common.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def getMidpoints(self,flrg):
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ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
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return ret
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def forecast(self,data):
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l = 1
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@ -68,6 +76,6 @@ class ImprovedWeightedFTS(fts.FTS):
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flrg = self.flrgs[actual.name]
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mp = self.getMidpoints(flrg)
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ret.append( mi.dot( flrg.weights() ))
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ret.append( mp.dot( flrg.weights() ))
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return ret
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20
sadaei.py
20
sadaei.py
@ -18,35 +18,35 @@ class ExponentialyWeightedFLRG:
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return np.array([ k/tot for k in wei ])
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp = self.LHS.name + " -> "
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tmp2 = ""
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cc = 0
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wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
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tot = sum( wei )
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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 + "(" + str(wei[cc]/tot) + ")"
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tmp2 = tmp2 + c.name + "(" + str(wei[cc]/tot) + ")"
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cc = cc + 1
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return tmp + tmp2
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class ExponentialyWeightedFTS(fts.FTS):
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def __init__(self,name):
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super(ExponentialyWeightedFTS, self).__init__(1,name)
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this.c = 1
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self.c = 1
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def generateFLRG(self, flrs, c):
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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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if flr.LHS.name in flrgs:
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flrgs[flr.LHS.name].append(flr.RHS)
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else:
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flrgs[flr.LHS] = ExponentialyWeightedFLRG(flr.LHS, c);
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flrgs[flr.LHS].append(flr.RHS)
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flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c);
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flrgs[flr.LHS.name].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets, c):
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this.c = c
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self.c = c
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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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@ -73,7 +73,7 @@ class ExponentialyWeightedFTS(fts.FTS):
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flrg = self.flrgs[actual.name]
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mp = self.getMidpoints(flrg)
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ret.append( mi.dot( flrg.weights() ))
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ret.append( mp.dot( flrg.weights() ))
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return ret
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8
yu.py
8
yu.py
@ -16,14 +16,14 @@ class WeightedFLRG(fts.FTS):
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return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp = self.LHS.name + " -> "
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tmp2 = ""
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cc = 1.0
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tot = sum( np.arange(1.0,self.count,1.0) )
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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 + "(" + str(round(cc/tot,3)) + ")"
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tmp2 = tmp2 + c.name + "(" + str(round(cc/tot,3)) + ")"
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cc = cc + 1.0
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return tmp + tmp2
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@ -69,6 +69,6 @@ class WeightedFTS(fts.FTS):
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flrg = self.flrgs[actual.name]
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mp = self.getMidpoints(flrg)
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ret.append( mi.dot( flrg.weights() ))
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ret.append( mp.dot( flrg.weights() ))
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return ret
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