pyFTS/hofts.py

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import numpy as np
from pyFTS import *
class HighOrderFLRG:
def __init__(self,order):
self.LHS = []
self.RHS = []
self.order = order
self.strlhs = ""
def appendRHS(self,c):
self.RHS.append(c)
def strLHS(self):
if len(self.strlhs) == 0:
for c in self.LHS:
if len(self.strlhs) > 0:
self.strlhs = self.strlhs + ","
self.strlhs = self.strlhs + c.name
return self.strlhs
def appendLHS(self,c):
self.LHS.append(c)
def __str__(self):
tmp = ""
for c in sorted(self.RHS, key=lambda s: s.name):
if len(tmp) > 0:
tmp = tmp + ","
tmp = tmp + c.name
return self.strLHS() + " -> " + tmp
class HighOrderFTS(fts.FTS):
def __init__(self,name):
super(HighOrderFTS, self).__init__(1,name)
self.order = 1
def generateFLRG(self, flrs):
flrgs = {}
l = len(flrs)
for k in np.arange(self.order +1, l):
flrg = HighOrderFLRG(self.order)
for kk in np.arange(k - self.order, k):
flrg.appendLHS( flrs[kk].LHS )
if flrg.strLHS() in flrgs:
flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
else:
flrgs[flrg.strLHS()] = flrg;
flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
return (flrgs)
def train(self, data, sets, order):
self.order = order
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self,data):
ret = []
l = len(data)
if l <= self.order:
return data
for k in np.arange(self.order, l):
tmpdata = common.fuzzySeries(data[k-self.order : k],self.sets)
tmpflrg = HighOrderFLRG(self.order)
for s in tmpdata: tmpflrg.appendLHS(s)
if tmpflrg.strLHS() not in self.flrgs:
ret.append(tmpdata[-1].centroid)
else:
flrg = self.flrgs[tmpflrg.strLHS()]
mp = self.getMidpoints(flrg)
ret.append(sum(mp)/len(mp))
return ret