Correções em HOFTS, IFTS e PIFTS
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Petrônio Cândido de Lima e Silva
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15
hofts.py
15
hofts.py
@ -4,12 +4,13 @@ from pyFTS import *
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class HighOrderFLRG:
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def __init__(self,order):
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self.LHS = []
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self.RHS = []
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self.RHS = {}
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self.order = order
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self.strlhs = ""
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def appendRHS(self,c):
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self.RHS.append(c)
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if c.name not in self.RHS:
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self.RHS[c.name] = c
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def strLHS(self):
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if len(self.strlhs) == 0:
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@ -24,16 +25,17 @@ class HighOrderFLRG:
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def __str__(self):
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tmp = ""
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for c in sorted(self.RHS, key=lambda s: s.name):
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for c in sorted(self.RHS):
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if len(tmp) > 0:
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tmp = tmp + ","
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tmp = tmp + c.name
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tmp = tmp + c
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return self.strLHS() + " -> " + tmp
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class HighOrderFTS(fts.FTS):
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def __init__(self,name):
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super(HighOrderFTS, self).__init__(1,name)
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self.order = 1
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self.setsDict = {}
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def generateFLRG(self, flrs):
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flrgs = {}
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@ -54,10 +56,15 @@ class HighOrderFTS(fts.FTS):
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def train(self, data, sets, order):
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self.order = order
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self.sets = sets
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for s in self.sets: self.setsDict[s.name] = s
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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 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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ret = []
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10
ifts.py
10
ifts.py
@ -1,8 +1,6 @@
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import numpy as np
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from pyFTS import *
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class IntervalFTS(hofts.HighOrderFTS):
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def __init__(self,name):
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super(IntervalFTS, self).__init__(name)
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@ -11,7 +9,7 @@ class IntervalFTS(hofts.HighOrderFTS):
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def getUpper(self,flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[ flrg.strLHS() ]
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ret = max(np.array([s.upper for s in tmp.RHS]))
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ret = max(np.array([self.setsDict[s].upper for s in tmp.RHS]))
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else:
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ret = flrg.LHS[-1].upper
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return ret
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@ -19,7 +17,7 @@ class IntervalFTS(hofts.HighOrderFTS):
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def getLower(self,flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[ flrg.strLHS() ]
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ret = min(np.array([s.lower for s in tmp.RHS]))
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ret = min(np.array([self.setsDict[s].lower for s in tmp.RHS]))
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else:
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ret = flrg.LHS[-1].lower
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return ret
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@ -48,8 +46,6 @@ class IntervalFTS(hofts.HighOrderFTS):
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for k in np.arange(self.order,l):
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print(k)
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flrs = []
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mvs = []
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@ -61,7 +57,7 @@ class IntervalFTS(hofts.HighOrderFTS):
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lags = {}
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if self.order > 1:
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subset = ndata[k-self.order : k ]
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print(subset)
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for instance in subset:
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mb = common.fuzzyInstance(instance, self.sets)
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tmp = np.argwhere( mb )
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76
pifts.py
76
pifts.py
@ -1,45 +1,33 @@
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import numpy as np
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from pyFTS import *
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class ProbabilisticIntervalFLRG:
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class ProbabilisticIntervalFLRG(hofts.HighOrderFLRG):
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def __init__(self,order):
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self.LHS = []
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self.RHS = []
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self.RHSfreqs = {}
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self.order = order
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super(ProbabilisticIntervalFLRG, self).__init__(order)
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self.RHS = {}
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self.frequencyCount = 0
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self.strlhs = ""
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def appendRHS(self,c):
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self.RHS.append(c)
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self.frequencyCount = self.frequencyCount + 1
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if c.name in self.RHSfreqs:
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self.RHSfreqs[c.name] = self.RHSfreqs[c.name] + 1
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if c.name in self.RHS:
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self.RHS[c.name] = self.RHS[c.name] + 1
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else:
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self.RHSfreqs[c.name] = 1
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self.RHS[c.name] = 1
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def strLHS(self):
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if len(self.strlhs) == 0:
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for c in self.LHS:
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if len(self.strlhs) > 0:
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self.strlhs = self.strlhs + ","
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self.strlhs = self.strlhs + c.name
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return self.strlhs
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def appendLHS(self,c):
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self.LHS.append(c)
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def getProbability(self,c):
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return self.RHS[c] / self.frequencyCount
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def __str__(self):
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tmp = ""
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for c in sorted(self.RHS, key=lambda s: s.name):
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if len(tmp) > 0:
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tmp = tmp + ","
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tmp = tmp + c.name
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return self.strLHS() + " -> " + tmp
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tmp2 = ""
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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.frequencyCount,3)) + ")"
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return self.strLHS() + " -> " + tmp2
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class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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def __init__(self,name):
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super(IntervalFTS, self).__init__(1,name)
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super(ProbabilisticIntervalFTS, self).__init__(name)
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self.flrgs = {}
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self.globalFrequency = 0
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@ -61,6 +49,25 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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self.globalFrequency = self.globalFrequency + 1
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return (flrgs)
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def getProbability(self, flrg):
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return flrg.frequencyCount / self.globalFrequency
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def getUpper(self,flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[ flrg.strLHS() ]
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ret = sum(np.array([ tmp.getProbability(s) * self.setsDict[s].upper for s in tmp.RHS]))
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else:
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ret = flrg.LHS[-1].upper
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return ret
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def getLower(self,flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[ flrg.strLHS() ]
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ret = sum(np.array([ tmp.getProbability(s) * self.setsDict[s].lower for s in tmp.RHS]))
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else:
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ret = flrg.LHS[-1].lower
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return ret
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def forecast(self,data):
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ndata = np.array(data)
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@ -105,26 +112,27 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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flrg = hofts.HighOrderFLRG(self.order)
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for kk in path: flrg.appendLHS(self.sets[ kk ])
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flrs.append(flrg)
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##
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flrs.append( self.flrgs[ flrg.strLHS() ] )
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# Acha a pertinência geral de cada FLRG
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mvs.append(min(self.getSequenceMembership(subset, flrg.LHS)))
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else:
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mv = common.fuzzyInstance(ndata[k],self.sets)
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tmp = np.argwhere( mv )
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idx = np.ravel(tmp)
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mv = common.fuzzyInstance(ndata[k],self.sets) # get all membership values
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tmp = np.argwhere( mv ) # get the indices of values > 0
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idx = np.ravel(tmp) # flatten the array
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for kk in idx:
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flrg = hofts.HighOrderFLRG(self.order)
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flrg.appendLHS(self.sets[ kk ])
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flrs.append(flrg)
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flrs.append( self.flrgs[ flrg.strLHS() ] )
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mvs.append(mv[kk])
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count = 0
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for flrg in flrs:
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# achar o os bounds de cada FLRG, ponderados pela pertinência
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up.append( mvs[count] * self.getUpper(flrg) )
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lo.append( mvs[count] * self.getLower(flrg) )
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up.append( self.getProbability(flrg) * mvs[count] * self.getUpper(flrg) )
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lo.append( self.getProbability(flrg) * mvs[count] * self.getLower(flrg) )
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count = count + 1
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# gerar o intervalo
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