pyFTS/hofts.py
2017-05-07 11:41:31 -03:00

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"""
Simple High Order extension of Conventional FTS by Chen (1996)
[1] S.-M. Chen, “Forecasting enrollments based on fuzzy time series,”
Fuzzy Sets Syst., vol. 81, no. 3, pp. 311319, 1996.
"""
import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class HighOrderFLRG(object):
"""Conventional High Order Fuzzy Logical Relationship Group"""
def __init__(self, order):
self.LHS = []
self.RHS = {}
self.order = order
self.strlhs = ""
def appendRHS(self, c):
if c.name not in self.RHS:
self.RHS[c.name] = 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 + c.name
return self.strlhs
def appendLHS(self, c):
self.LHS.append(c)
def __str__(self):
tmp = ""
for c in sorted(self.RHS):
if len(tmp) > 0:
tmp = tmp + ","
tmp = tmp + c
return self.strLHS() + " -> " + tmp
def __len__(self):
return len(self.RHS)
class HighOrderFTS(fts.FTS):
"""Conventional High Order Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
self.name = "High Order FTS"
self.shortname = "HOFTS" + name
self.detail = "Chen"
self.order = 1
self.setsDict = {}
self.is_high_order = True
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=1,parameters=None):
data = self.doTransformations(data, updateUoD=True)
self.order = order
self.sets = sets
for s in self.sets: self.setsDict[s.name] = s
tmpdata = FuzzySet.fuzzySeries(data, sets)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def getMidpoints(self, flrg):
ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
return ret
def forecast(self, data, **kwargs):
ret = []
l = len(data)
if l <= self.order:
return data
ndata = self.doTransformations(data)
for k in np.arange(self.order, l+1):
tmpdata = FuzzySet.fuzzySeries(ndata[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))
ret = self.doInverseTransformations(ret, params=[data[self.order-1:]])
return ret