import numpy as np from pyFTS import * class WeightedFLRG(fts.FTS): def __init__(self,LHS): self.LHS = LHS self.RHS = [] self.count = 1.0 def append(self,c): self.RHS.append(c) self.count = self.count + 1.0 def weights(self): tot = sum( np.arange(1.0,self.count,1.0) ) return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ]) def __str__(self): tmp = self.LHS + " -> " tmp2 = "" cc = 1.0 tot = sum( np.arange(1.0,self.count,1.0) ) for c in self.RHS: if len(tmp2) > 0: tmp2 = tmp2 + "," tmp2 = tmp2 + c + "(" + str(round(cc/tot,3)) + ")" cc = cc + 1.0 return tmp + tmp2 class WeightedFTS(fts.FTS): def __init__(self,name): super(WeightedFTS, self).__init__(1,name) 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] = WeightedFLRG(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.generateRecurrentFLRs(tmpdata) self.flrgs = self.generateFLRG(flrs) def forecast(self,data): l = 1 ndata = np.array(data) l = len(ndata) ret = [] for k in np.arange(1,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( mi.dot( flrg.weights() )) return ret