Split ifts.IntervalFTS(HighOrderFTS) from WeightedIntervalFTS(WeightedHighOrderFTS)
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@ -13,7 +13,7 @@ from pyFTS.common import FuzzySet, FLR, fts, tree
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from pyFTS.models import hofts
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class IntervalFTS(hofts.WeightedHighOrderFTS):
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class IntervalFTS(hofts.HighOrderFTS):
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"""
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High Order Interval Fuzzy Time Series
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"""
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@ -87,3 +87,79 @@ class IntervalFTS(hofts.WeightedHighOrderFTS):
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ret.append([lo_, up_])
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return ret
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class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
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"""
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Weighted High Order Interval Fuzzy Time Series
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"""
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def __init__(self, **kwargs):
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super(IntervalFTS, self).__init__(**kwargs)
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self.shortname = "IFTS"
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self.name = "Interval FTS"
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self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)"
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self.flrgs = {}
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self.has_point_forecasting = False
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self.has_interval_forecasting = True
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self.is_high_order = True
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self.min_order = 1
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def get_upper(self, flrg):
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ret = np.nan
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if len(flrg.LHS) > 0:
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if flrg.get_key() in self.flrgs:
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tmp = self.flrgs[flrg.get_key()]
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ret = tmp.get_upper(self.sets)
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else:
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ret = self.sets[flrg.LHS[-1]].upper
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return ret
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def get_lower(self, flrg):
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ret = np.nan
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if len(flrg.LHS) > 0:
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if flrg.get_key() in self.flrgs:
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tmp = self.flrgs[flrg.get_key()]
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ret = tmp.get_lower(self.partitioner.sets)
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else:
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ret = self.partitioner.sets[flrg.LHS[-1]].lower
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return ret
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def get_sequence_membership(self, data, fuzzySets):
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mb = [fuzzySets[k].membership(data[k]) for k in np.arange(0, len(data))]
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return mb
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def forecast_interval(self, ndata, **kwargs):
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ret = []
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l = len(ndata)
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if l <= self.order:
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return ndata
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for k in np.arange(self.max_lag, l+1):
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sample = ndata[k - self.max_lag: k]
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flrgs = self.generate_lhs_flrg(sample)
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up = []
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lo = []
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affected_flrgs_memberships = []
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for flrg in flrgs:
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if len(flrg.LHS) > 0:
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mv = flrg.get_membership(sample, self.sets)
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up.append(mv * self.get_upper(flrg))
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lo.append(mv * self.get_lower(flrg))
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affected_flrgs_memberships.append(mv)
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# gerar o intervalo
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norm = sum(affected_flrgs_memberships)
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lo_ = sum(lo) / norm
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up_ = sum(up) / norm
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ret.append([lo_, up_])
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return ret
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