import numpy as np from pyFTS.common import FuzzySet, FLR from pyFTS import fts class ConventionalFLRG(object): def __init__(self, LHS): self.LHS = LHS self.RHS = set() def append(self, c): self.RHS.add(c) def __str__(self): tmp = self.LHS.name + " -> " tmp2 = "" for c in sorted(self.RHS, key=lambda s: s.name): if len(tmp2) > 0: tmp2 = tmp2 + "," tmp2 = tmp2 + c.name return tmp + tmp2 def __len__(self): return len(self.RHS) class ConventionalFTS(fts.FTS): def __init__(self, name): super(ConventionalFTS, self).__init__(1, "CFTS " + name) self.name = "Conventional FTS" self.detail = "Chen" self.flrgs = {} 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] = ConventionalFLRG(flr.LHS) flrgs[flr.LHS.name].append(flr.RHS) return (flrgs) def train(self, data, sets,order=1,parameters=None): self.sets = sets ndata = self.doTransformations(data) tmpdata = FuzzySet.fuzzySeries(ndata, sets) flrs = FLR.generateNonRecurrentFLRs(tmpdata) self.flrgs = self.generateFLRG(flrs) def forecast(self, data): ndata = np.array(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(sum(mp) / len(mp)) ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]]) return ret