import numpy as np from pyFTS.common import FuzzySet,FLR from pyFTS import fts 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,order=1,parameters=2): self.c = parameters self.sets = sets tmpdata = FuzzySet.fuzzySeries(data, sets) flrs = FLR.generateRecurrentFLRs(tmpdata) self.flrgs = self.generateFLRG(flrs, self.c) def forecast(self, data): l = 1 ndata = np.array(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())) return ret