86 lines
1.8 KiB
Python
86 lines
1.8 KiB
Python
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import numpy as np
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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.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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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 __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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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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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 = HighOrderFLRG(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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return (flrgs)
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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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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 forecast(self,data):
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ret = []
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l = len(data)
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if l <= self.order:
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return data
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for k in np.arange(self.order, l):
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tmpdata = common.fuzzySeries(data[k-self.order : k],self.sets)
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tmpflrg = HighOrderFLRG(self.order)
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for s in tmpdata: tmpflrg.appendLHS(s)
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if tmpflrg.strLHS() not in self.flrgs:
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ret.append(tmpdata[-1].centroid)
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else:
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flrg = self.flrgs[tmpflrg.strLHS()]
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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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