111 lines
2.6 KiB
Python
111 lines
2.6 KiB
Python
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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self.flrgs = {}
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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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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 = min(np.array([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 getSequenceMembership(self, data, fuzzySets):
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mb = [ fuzzySets[k].membership( data[k] ) for k in np.arange(0,len(data)) ]
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return mb
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def buildTree(self,node, lags, level):
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if level >= self.order:
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return
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for s in lags[level]:
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node.appendChild(tree.FLRGTreeNode(s))
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for child in node.getChildren():
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self.buildTree(child,lags,level+1)
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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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flrs.append(flrg)
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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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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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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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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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