pyFTS/sadaei.py

91 lines
2.5 KiB
Python
Raw Normal View History

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts
class ExponentialyWeightedFLRG(object):
def __init__(self, LHS, c):
self.LHS = LHS
self.RHS = []
self.count = 0.0
self.c = c
def append(self, c):
self.RHS.append(c)
self.count = self.count + 1.0
def weights(self):
wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
tot = sum(wei)
return np.array([k / tot for k in wei])
def __str__(self):
tmp = self.LHS.name + " -> "
tmp2 = ""
cc = 0
wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
tot = sum(wei)
for c in sorted(self.RHS, key=lambda s: s.name):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c.name + "(" + str(wei[cc] / tot) + ")"
cc = cc + 1
return tmp + tmp2
def __len__(self):
return len(self.RHS)
class ExponentialyWeightedFTS(fts.FTS):
def __init__(self, name):
super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS")
self.name = "Exponentialy Weighted FTS"
self.detail = "Sadaei"
self.c = 1
def generateFLRG(self, flrs, c):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
def train(self, data, sets,order=1,parameters=2):
self.c = parameters
self.sets = sets
ndata = self.doTransformations(data)
tmpdata = FuzzySet.fuzzySeries(ndata, sets)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs, self.c)
def forecast(self, data):
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