pyFTS/ismailefendi.py

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import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class ImprovedWeightedFLRG(object):
"""First Order Improved Weighted Fuzzy Logical Relationship Group"""
def __init__(self, LHS):
self.LHS = LHS
self.RHS = {}
self.count = 0.0
def append(self, c):
if c.name not in self.RHS:
self.RHS[c.name] = 1.0
else:
self.RHS[c.name] = self.RHS[c.name] + 1.0
self.count = self.count + 1.0
def weights(self):
return np.array([self.RHS[c] / self.count for c in self.RHS.keys()])
def __str__(self):
tmp = self.LHS.name + " -> "
tmp2 = ""
for c in sorted(self.RHS):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c + "(" + str(round(self.RHS[c] / self.count, 3)) + ")"
return tmp + tmp2
def __len__(self):
return len(self.RHS)
class ImprovedWeightedFTS(fts.FTS):
"""First Order Improved Weighted Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name)
self.name = "Improved Weighted FTS"
self.detail = "Ismail & Efendi"
self.setsDict = {}
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
for s in self.sets: self.setsDict[s.name] = s
ndata = self.doTransformations(data)
tmpdata = FuzzySet.fuzzySeries(ndata, self.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):
l = 1
data = np.array(data)
ndata = self.doTransformations(data)
l = len(ndata)
ret = []
for k in np.arange(0, l):
mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)
actual = self.sets[np.argwhere(mv == max(mv))[0, 0]]
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
flrg = self.flrgs[actual.name]
mp = self.getMidpoints(flrg)
ret.append(mp.dot(flrg.weights()))
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
return ret