import numpy as np from pyFTS import * class ConventionalFLRG: 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 class ConventionalFTS(fts.FTS): def __init__(self,name): super(ConventionalFTS, self).__init__(1,"CFTS") 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): self.sets = sets tmpdata = common.fuzzySeries(data,sets) flrs = common.generateNonRecurrentFLRs(tmpdata) self.flrgs = self.generateFLRG(flrs) def forecast(self,data): 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(sum(mp)/len(mp)) return ret