109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
"""
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First Order Weighted Fuzzy Time Series by Yu(2005)
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H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,”
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Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR, fts, flrg
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from pyFTS.models import chen
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class WeightedFLRG(flrg.FLRG):
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"""First Order Weighted Fuzzy Logical Relationship Group"""
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def __init__(self, LHS, **kwargs):
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super(WeightedFLRG, self).__init__(1, **kwargs)
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self.LHS = LHS
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self.RHS = []
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self.count = 1.0
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self.w = None
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def append_rhs(self, c, **kwargs):
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count = kwargs.get('count', 1.0)
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self.RHS.append(c)
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self.count += count
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def weights(self, sets):
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if self.w is None:
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tot = sum(np.arange(1.0, self.count, 1.0))
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self.w = np.array([k / tot for k in np.arange(1.0, self.count, 1.0)])
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return self.w
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def __str__(self):
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tmp = self.LHS + " -> "
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tmp2 = ""
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cc = 1.0
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tot = sum(np.arange(1.0, self.count, 1.0))
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for c in sorted(self.RHS):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c + "(" + str(round(cc / tot, 3)) + ")"
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cc = cc + 1.0
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return tmp + tmp2
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class WeightedFTS(fts.FTS):
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"""First Order Weighted Fuzzy Time Series"""
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def __init__(self, **kwargs):
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super(WeightedFTS, self).__init__(order=1, name="WFTS", **kwargs)
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self.name = "Weighted FTS"
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self.detail = "Yu"
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def generate_FLRG(self, flrs):
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for flr in flrs:
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if flr.LHS in self.flrgs:
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self.flrgs[flr.LHS].append_rhs(flr.RHS)
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else:
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self.flrgs[flr.LHS] = WeightedFLRG(flr.LHS);
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self.flrgs[flr.LHS].append_rhs(flr.RHS)
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def train(self, ndata, **kwargs):
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tmpdata = FuzzySet.fuzzyfy(ndata, partitioner=self.partitioner, method='maximum', mode='sets')
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flrs = FLR.generate_recurrent_flrs(tmpdata)
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self.generate_FLRG(flrs)
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def forecast(self, ndata, **kwargs):
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explain = kwargs.get('explain', False)
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if self.partitioner is not None:
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ordered_sets = self.partitioner.ordered_sets
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else:
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ordered_sets = FuzzySet.set_ordered(self.sets)
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ndata = np.array(ndata)
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l = len(ndata) if not explain else 1
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ret = []
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for k in np.arange(0, l):
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actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets)
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if explain:
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print("Fuzzyfication:\n\n {} -> {} \n\n".format(ndata[k], actual.name))
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if actual.name not in self.flrgs:
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ret.append(actual.centroid)
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if explain:
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print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid))
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else:
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flrg = self.flrgs[actual.name]
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mp = flrg.get_midpoints(self.sets)
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final = mp.dot(flrg.weights(self.sets))
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ret.append(final)
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if explain:
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print("Rules:\n\n {} \n\n ".format(str(flrg)))
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print("Midpoints: \n\n {}\n\n".format(mp))
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print("Deffuzyfied value: {} \n".format(final))
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return ret
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