pyFTS/sadaei.py
Petrônio Cândido de Lima e Silva b613c6db8a Acréscimo de informações aos modelos
2016-10-25 16:21:32 -02:00

82 lines
1.8 KiB
Python

import numpy as np
from pyFTS import *
class ExponentialyWeightedFLRG:
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
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, c):
self.c = c
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs,c)
def forecast(self,data):
l = 1
ndata = np.array(data)
l = len(ndata)
ret = []
for k in np.arange(0,l):
mv = common.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() ))
return ret