145 lines
3.8 KiB
Python
145 lines
3.8 KiB
Python
import numpy as np
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from pyFTS import *
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class ProbabilisticIntervalFLRG(hofts.HighOrderFLRG):
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def __init__(self,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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def appendRHS(self,c):
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self.frequencyCount = self.frequencyCount + 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.RHS[c.name] = 1
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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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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(ProbabilisticIntervalFTS, self).__init__("PIFTS")
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self.name = "Probabilistic Interval FTS"
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self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
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self.flrgs = {}
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self.globalFrequency = 0
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self.isInterval = True
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def generateFLRG(self, flrs):
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flrgs = {}
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l = len(flrs)
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for k in np.arange(self.order +1, l):
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flrg = ProbabilisticIntervalFLRG(self.order)
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for kk in np.arange(k - self.order, k):
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flrg.appendLHS( flrs[kk].LHS )
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if flrg.strLHS() in flrgs:
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flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
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else:
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flrgs[flrg.strLHS()] = flrg;
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flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
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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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l = len(ndata)
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ret = []
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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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up = []
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lo = []
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# Achar os conjuntos que tem pert > 0 para cada lag
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count = 0
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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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idx = np.ravel(tmp) #flat the array
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lags[count] = idx
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count = count + 1
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# Constrói uma árvore com todos os caminhos possíveis
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root = tree.FLRGTreeNode(None)
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self.buildTree(root,lags,0)
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# Traça os possíveis caminhos e costrói as HOFLRG's
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for p in root.paths():
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path = list(reversed(list(filter(None.__ne__, p))))
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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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##
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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) # 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( 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( 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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ret.append( [ sum(lo), sum(up) ] )
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return ret
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