import numpy as np from pyFTS import * class ImprovedWeightedFLRG: def __init__(self,LHS): self.LHS = LHS self.RHS = {} self.count = 0.0 def append(self,c): if c.name not in self.RHS: self.RHS[c.name] = 1.0 else: self.RHS[c.name] = self.RHS[c.name] + 1.0 self.count = self.count + 1.0 def weights(self): return np.array([ self.RHS[c]/self.count for c in self.RHS.keys() ]) def __str__(self): tmp = self.LHS.name + " -> " tmp2 = "" for c in sorted(self.RHS): if len(tmp2) > 0: tmp2 = tmp2 + "," tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.count,3)) + ")" return tmp + tmp2 class ImprovedWeightedFTS(fts.FTS): def __init__(self,name): super(ImprovedWeightedFTS, self).__init__(1,name) self.setsDict = {} 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] = ImprovedWeightedFLRG(flr.LHS); flrgs[flr.LHS.name].append(flr.RHS) return (flrgs) def train(self, data, sets): self.sets = sets for s in self.sets: self.setsDict[s.name] = s tmpdata = common.fuzzySeries(data,self.sets) flrs = common.generateRecurrentFLRs(tmpdata) self.flrgs = self.generateFLRG(flrs) def getMidpoints(self,flrg): ret = np.array([self.setsDict[s].centroid for s in flrg.RHS]) return ret 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