import numpy as np from pyFTS.common import FuzzySet,FLR from pyFTS import fts class ImprovedWeightedFLRG(object): 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 def __len__(self): return len(self.RHS) class ImprovedWeightedFTS(fts.FTS): def __init__(self, order, **kwargs): super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name) self.name = "Improved Weighted FTS" self.detail = "Ismail & Efendi" 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,order=1,parameters=None): self.sets = sets for s in self.sets: self.setsDict[s.name] = s ndata = self.doTransformations(data) tmpdata = FuzzySet.fuzzySeries(ndata, self.sets) flrs = FLR.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, **kwargs): l = 1 data = np.array(data) ndata = 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(mp.dot(flrg.weights())) ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]]) return ret