High Order Nostationary Fuzzy Time Series - HONSFTS
This commit is contained in:
Petrônio Cândido
parent
6f455f3215
commit
2ea599fdb8
@ -124,18 +124,18 @@ class FuzzySet(FS.FuzzySet):
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def get_midpoint(self, t):
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self.perturbate_parameters(t)
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param = self.perturbated_parameters[t]
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if self.mf == Membership.gaussmf:
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return self.perturbated_parameters[t][0]
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return param[0]
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elif self.mf == Membership.sigmf:
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return self.perturbated_parameters[t][1]
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return param[1]
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elif self.mf == Membership.trimf:
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return self.perturbated_parameters[t][1]
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return param[1]
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elif self.mf == Membership.trapmf:
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param = self.perturbated_parameters[t]
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return (param[2] - param[1]) / 2
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else:
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return self.perturbated_parameters[t]
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return param
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def get_lower(self, t):
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@ -13,7 +13,7 @@ class NonStationaryFLRG(flrg.FLRG):
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def get_membership(self, data, t, window_size=1):
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ret = 0.0
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if isinstance(self.LHS, (list, set)):
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assert len(self.LHS) == len(data)
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#assert len(self.LHS) == len(data)
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ret = min([self.LHS[ct].membership(dat, common.window_index(t - (self.order - ct), window_size))
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for ct, dat in enumerate(data)])
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@ -31,20 +31,20 @@ class NonStationaryFLRG(flrg.FLRG):
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else:
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return self.LHS[-1].get_midpoint(common.window_index(t, window_size))
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def get_lower(self, t, window_size=1):
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if self.lower is None:
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if len(self.RHS) > 0:
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self.lower = min([r.get_lower(common.window_index(t, window_size)) for r in self.RHS])
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if isinstance(self.RHS, (list, set)):
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return min([r.get_lower(common.window_index(t, window_size)) for r in self.RHS])
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elif isinstance(self.RHS, dict):
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return min([self.RHS[r].get_lower(common.window_index(t, window_size)) for r in self.RHS.keys()])
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else:
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self.lower = self.LHS[-1].get_lower(common.window_index(t, window_size))
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return self.lower
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return self.LHS[-1].get_lower(common.window_index(t, window_size))
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def get_upper(self, t, window_size=1):
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if self.upper is None:
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if len(self.RHS) > 0:
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self.upper = min([r.get_upper(common.window_index(t, window_size)) for r in self.RHS])
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if isinstance(self.RHS, (list, set)):
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return max([r.get_upper(common.window_index(t, window_size)) for r in self.RHS])
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elif isinstance(self.RHS, dict):
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return max([self.RHS[r].get_upper(common.window_index(t, window_size)) for r in self.RHS.keys()])
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else:
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self.upper = self.LHS[-1].get_upper(common.window_index(t, window_size))
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return self.upper
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return self.LHS[-1].get_upper(common.window_index(t, window_size))
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@ -91,6 +91,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
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for st in rhs:
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flrgs[flrg.strLHS()].appendRHS(st)
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# flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))
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return flrgs
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def train(self, data, sets=None, order=2, parameters=None):
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@ -108,31 +110,14 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
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window_size = parameters if parameters is not None else 1
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self.flrgs = self.generate_flrg(ndata, window_size=window_size)
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def forecast(self, data, **kwargs):
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time_displacement = kwargs.get("time_displacement",0)
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window_size = kwargs.get("window_size", 1)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(self.order, l+1):
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def _affected_flrgs(self, sample, k, time_displacement, window_size):
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# print("input: " + str(ndata[k]))
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disp = common.window_index(k + time_displacement, window_size)
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affected_flrgs = []
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affected_flrgs_memberships = []
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lags = {}
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sample = ndata[k - self.order: k]
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for ct, dat in enumerate(sample):
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tdisp = common.window_index((k + time_displacement) - (self.order - ct), window_size)
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sel = [ct for ct, set in enumerate(self.sets) if set.membership(dat, tdisp) > 0.0]
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@ -158,18 +143,72 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
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flrg.appendLHS(self.sets[kk])
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affected_flrgs.append(flrg)
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affected_flrgs_memberships.append(flrg.get_membership(ndata[k - self.order: k], disp))
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# affected_flrgs_memberships.append(flrg.get_membership(sample, disp))
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#print(affected_sets)
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# print(flrg.strLHS())
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# the FLRG is here because of the bounds verification
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mv = []
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for ct, dat in enumerate(sample):
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td = common.window_index((k + time_displacement) - (self.order - ct), window_size)
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tmp = flrg.LHS[ct].membership(dat, td)
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# print('td',td)
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# print('dat',dat)
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# print(flrg.LHS[ct].name, flrg.LHS[ct].perturbated_parameters[td])
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# print(tmp)
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if (tmp == 0.0 and flrg.LHS[ct].name == self.sets[0].name and dat < self.sets[0].get_lower(td)) \
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or (tmp == 0.0 and flrg.LHS[ct].name == self.sets[-1].name and dat > self.sets[-1].get_upper(
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td)):
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mv.append(1.0)
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else:
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mv.append(tmp)
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# print(mv)
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affected_flrgs_memberships.append(np.prod(mv))
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return [affected_flrgs, affected_flrgs_memberships]
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def forecast(self, data, **kwargs):
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time_displacement = kwargs.get("time_displacement",0)
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window_size = kwargs.get("window_size", 1)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(self.order, l+1):
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sample = ndata[k - self.order: k]
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affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
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time_displacement, window_size)
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#print([str(k) for k in affected_flrgs])
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#print(affected_flrgs_memberships)
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tmp = []
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tdisp = common.window_index(k + time_displacement, window_size)
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if len(affected_flrgs) == 0:
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tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
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elif len(affected_flrgs) == 1:
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if affected_flrgs[0].strLHS() in self.flrgs:
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flrg = affected_flrgs[0]
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tmp.append(self.flrgs[flrg.strLHS()].get_midpoint(tdisp))
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else:
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tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
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else:
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for ct, aset in enumerate(affected_flrgs):
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if aset.strLHS() in self.flrgs:
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tmp.append(self.flrgs[aset.strLHS()].get_midpoint(tdisp) *
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affected_flrgs_memberships[ct])
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else:
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tmp.append(aset.LHS[-1].get_midpoint(tdisp))
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tmp.append(aset.LHS[-1].get_midpoint(tdisp)*
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affected_flrgs_memberships[ct])
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pto = sum(tmp)
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#print(pto)
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@ -184,27 +223,56 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
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time_displacement = kwargs.get("time_displacement", 0)
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window_size = kwargs.get("window_size", 1)
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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for k in np.arange(0, l):
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for k in np.arange(self.order, l + 1):
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tdisp = k + time_displacement
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sample = ndata[k - self.order: k]
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affected_sets = [ [set.name, set.membership(ndata[k], tdisp)]
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for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
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affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
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time_displacement, window_size)
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# print([str(k) for k in affected_flrgs])
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# print(affected_flrgs_memberships)
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upper = []
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lower = []
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for aset in affected_sets:
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lower.append(self.flrgs[aset[0]].get_lower(tdisp) * aset[1])
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upper.append(self.flrgs[aset[0]].get_upper(tdisp) * aset[1])
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tdisp = common.window_index(k + time_displacement, window_size)
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if len(affected_flrgs) == 0:
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aset = common.check_bounds(sample[-1], self.sets, tdisp)
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lower.append(aset.get_lower(tdisp))
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upper.append(aset.get_upper(tdisp))
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elif len(affected_flrgs) == 1:
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if affected_flrgs[0].strLHS() in self.flrgs:
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flrg = affected_flrgs[0]
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lower.append(self.flrgs[flrg.strLHS()].get_lower(tdisp))
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upper.append(self.flrgs[flrg.strLHS()].get_upper(tdisp))
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else:
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lower.append(flrg.LHS[-1].get_lower(tdisp))
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upper.append(flrg.LHS[-1].get_upper(tdisp))
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else:
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for ct, aset in enumerate(affected_flrgs):
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if aset.strLHS() in self.flrgs:
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lower.append(self.flrgs[aset.strLHS()].get_lower(tdisp) *
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affected_flrgs_memberships[ct])
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upper.append(self.flrgs[aset.strLHS()].get_upper(tdisp) *
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affected_flrgs_memberships[ct])
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else:
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lower.append(aset.LHS[-1].get_lower(tdisp) *
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affected_flrgs_memberships[ct])
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upper.append(aset.LHS[-1].get_upper(tdisp) *
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affected_flrgs_memberships[ct])
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ret.append([sum(lower), sum(upper)])
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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@ -96,12 +96,18 @@ class NonStationaryFTS(fts.FTS):
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tmp = []
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if len(affected_sets) == 1 and self.method == 'fuzzy':
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tmp.append(affected_sets[0][0].get_midpoint(tdisp))
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aset = affected_sets[0][0]
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if aset.name in self.flrgs:
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tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
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else:
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tmp.append(aset.get_midpoint(tdisp))
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else:
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for aset in affected_sets:
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if self.method == 'fuzzy':
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if aset[0].name in self.flrgs:
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tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1])
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else:
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tmp.append(aset[0].get_midpoint(tdisp) * aset[1])
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elif self.method == 'maximum':
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if aset.name in self.flrgs:
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tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
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@ -132,16 +138,46 @@ class NonStationaryFTS(fts.FTS):
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for k in np.arange(0, l):
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# print("input: " + str(ndata[k]))
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tdisp = common.window_index(k + time_displacement, window_size)
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affected_sets = [ [set.name, set.membership(ndata[k], tdisp)]
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if self.method == 'fuzzy':
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affected_sets = [[set, set.membership(ndata[k], tdisp)]
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for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
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elif self.method == 'maximum':
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mv = [set.membership(ndata[k], tdisp) for set in self.sets]
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ix = np.ravel(np.argwhere(mv == max(mv)))
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affected_sets = [self.sets[x] for x in ix]
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if len(affected_sets) == 0:
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if self.method == 'fuzzy':
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affected_sets.append([common.check_bounds(ndata[k], self.sets, tdisp), 1.0])
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else:
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affected_sets.append(common.check_bounds(ndata[k], self.sets, tdisp))
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upper = []
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lower = []
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if len(affected_sets) == 1:
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#print(2)
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aset = affected_sets[0][0]
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if aset.name in self.flrgs:
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lower.append(self.flrgs[aset.name].get_lower(tdisp))
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upper.append(self.flrgs[aset.name].get_upper(tdisp))
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else:
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lower.append(aset.get_lower(tdisp))
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upper.append(aset.get_upper(tdisp))
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else:
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for aset in affected_sets:
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lower.append(self.flrgs[aset[0]].get_lower(tdisp) * aset[1])
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upper.append(self.flrgs[aset[0]].get_upper(tdisp) * aset[1])
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#print(aset)
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if aset[0].name in self.flrgs:
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lower.append(self.flrgs[aset[0].name].get_lower(tdisp) * aset[1])
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upper.append(self.flrgs[aset[0].name].get_upper(tdisp) * aset[1])
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else:
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lower.append(aset[0].get_lower(tdisp) * aset[1])
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upper.append(aset[0].get_upper(tdisp) * aset[1])
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ret.append([sum(lower), sum(upper)])
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@ -60,7 +60,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
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for count, set in enumerate(self.LHS):
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mv.append(set.membership(x[count]))
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min_mv = np.prod(mv)
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min_mv = np.min(mv)
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return min_mv
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def partition_function(self, uod, nbins=100):
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@ -73,6 +73,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
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return self.Z
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def get_midpoint(self):
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'''Return the expectation of the PWFLRG, the weighted sum'''
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return sum(np.array([self.get_RHSprobability(s) * self.RHS[s].centroid
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for s in self.RHS.keys()]))
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@ -495,6 +496,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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def forecastDistribution(self, data, **kwargs):
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if not isinstance(data, (list, set, np.ndarray)):
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data = [data]
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smooth = kwargs.get("smooth", "none")
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nbins = kwargs.get("num_bins", 100)
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@ -56,24 +56,28 @@ ws=12
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trainp = passengers[:ts]
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testp = passengers[ts:]
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tmp_fsp = Grid.GridPartitioner(trainp[:ws], 15)
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tmp_fsp = Grid.GridPartitioner(trainp[:50], 10)
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fsp = common.PolynomialNonStationaryPartitioner(trainp, tmp_fsp, window_size=ws, degree=1)
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#nsftsp = honsfts.HighOrderNonStationaryFTS("", partitioner=fsp)
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nsftsp = nsfts.NonStationaryFTS("", partitioner=fsp, method='fuzzy')
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nsftsp = honsfts.HighOrderNonStationaryFTS("", partitioner=fsp)
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#nsftsp = nsfts.NonStationaryFTS("", partitioner=fsp, method='fuzzy')
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#nsftsp.train(trainp, order=1, parameters=ws)
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nsftsp.train(trainp, order=2, parameters=ws)
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print(fsp)
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#print(fsp)
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#print(nsftsp)
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#tmpp = nsftsp.forecast(passengers[55:65], time_displacement=55, window_size=ws)
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tmpp = nsftsp.forecast(passengers[101:104], time_displacement=101, window_size=ws)
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tmpi = nsftsp.forecastInterval(passengers[101:104], time_displacement=101, window_size=ws)
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#print(passengers[100:120])
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#print(tmpp)
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#print(passengers[101:104])
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print([k[0] for k in tmpi])
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print(tmpp)
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print([k[1] for k in tmpi])
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#util.plot_sets(fsp.sets,tam=[10, 5], start=0, end=100, step=2, data=passengers[:100],
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# window_size=ws, only_lines=False)
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@ -54,20 +54,28 @@ pfts1.shortname = "1st Order"
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#print(pfts1_enrollments)
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tmp = pfts1.forecast(data[3000:3020])
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#tmp = pfts1.forecast(data[3000:3020])
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tmp = pfts1.forecastInterval(data[3000:3020])
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#tmp = pfts1.forecastInterval(data[3000:3020])
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tmp = pfts1.forecastAheadInterval(data[3000:3020],20)
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tmp = pfts1.forecastDistribution(data[3500])
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tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
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print(tmp[0])
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p = 0
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for b in tmp[0].bins:
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p += tmp[0].density(b)
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print(p)
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#tmp = pfts1.forecastAheadInterval(data[3000:3020],20)
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#tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
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#print(tmp[0])
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#print(tmp[0].quantile([0.05, 0.95]))
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#pfts1_enrollments.AprioriPDF
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||||
#norm = pfts1_enrollments.global_frequency_count
|
||||
#uod = pfts1_enrollments.get_UoD()
|
||||
#uod = pfts1.get_UoD()
|
||||
|
||||
#for k in sorted(pfts1_enrollments.flrgs.keys())
|
||||
# flrg = pfts1_enrollments.flrgs[k]
|
||||
|
||||
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Reference in New Issue
Block a user