Refatoração dos códigos para padronizar com a rfts
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@ -189,7 +189,10 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
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sets = partitioner.GridPartitionerTrimf(original,p)
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fts = modelo(str(p)+ " particoes")
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fts.train(original,sets)
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forecasted = [fts.forecast(xx) for xx in original]
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#print(original)
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forecasted = fts.forecast(original)
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forecasted.insert(0,original[0])
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#print(forecasted)
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ax0.plot(forecasted,label=fts.name)
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error = rmse(np.array(forecasted),np.array(original))
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print(p,error)
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@ -226,8 +229,8 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
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sets = partitioner.GridPartitionerTrimf(difffts,p)
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fts = modelo(str(p)+ " particoes")
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fts.train(difffts,sets)
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forecasted = [fts.forecast(xx) for xx in difffts]
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#forecasted.insert(0,difffts[0])
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forecasted = fts.forecast(difffts)
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forecasted.insert(0,difffts[0])
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ax2.plot(forecasted,label=fts.name)
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error = rmse(np.array(forecasted),np.array(difffts))
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print(p,error)
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48
chen.py
48
chen.py
@ -24,14 +24,14 @@ class ConventionalFTS(fts.FTS):
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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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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] = ConventionalFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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flrgs[flr.LHS.name] = ConventionalFLRG(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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@ -39,26 +39,32 @@ class ConventionalFTS(fts.FTS):
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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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mv = common.fuzzyInstance(data, self.sets)
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l = 1
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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ndata = np.array(data)
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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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mv = common.fuzzyInstance(ndata[k], self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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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.name]
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count = 0.0
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denom = 0.0
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for s in flrg.RHS:
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denom = denom + self.sets[s].centroid
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count = count + 1.0
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return denom/count
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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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else:
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flrg = self.flrgs[actual.name]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp)/len(mp))
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return ret
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5
fts.py
5
fts.py
@ -1,3 +1,4 @@
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import numpy as np
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from pyFTS import *
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class FTS:
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@ -23,6 +24,10 @@ class FTS:
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def train(self, data, sets):
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pass
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def getMidpoints(self,flrg):
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ret = np.array([s.centroid for s in flrg.RHS])
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return ret
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def __str__(self):
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tmp = self.name + ":\n"
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@ -48,14 +48,26 @@ class ImprovedWeightedFTS(fts.FTS):
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
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mv = common.fuzzyInstance(data, self.sets)
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l = 1
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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ndata = np.array(data)
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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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mv = common.fuzzyInstance(ndata[k], self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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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.name]
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mi = np.array([self.sets[s].centroid for s in flrg.RHS.keys()])
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return mi.dot( flrg.weights() )
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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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else:
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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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return ret
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31
sadaei.py
31
sadaei.py
@ -53,16 +53,27 @@ class ExponentialyWeightedFTS(fts.FTS):
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self.flrgs = self.generateFLRG(flrs,c)
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def forecast(self,data):
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mv = common.fuzzyInstance(data, self.sets)
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l = 1
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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ndata = np.array(data)
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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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mv = common.fuzzyInstance(ndata[k], self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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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.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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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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else:
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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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return ret
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41
yu.py
41
yu.py
@ -35,11 +35,11 @@ class WeightedFTS(fts.FTS):
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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] = WeightedFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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flrgs[flr.LHS.name] = WeightedFLRG(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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@ -49,17 +49,26 @@ class WeightedFTS(fts.FTS):
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
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mv = common.fuzzyInstance(data, self.sets)
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l = 1
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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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.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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ndata = np.array(data)
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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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mv = common.fuzzyInstance(ndata[k], self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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else:
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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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return ret
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