60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
"""
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Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)
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C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,”
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Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, yu
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class TrendWeightedFLRG(yu.WeightedFLRG):
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"""
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First Order Trend Weighted Fuzzy Logical Relationship Group
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"""
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def __init__(self, LHS, **kwargs):
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super(TrendWeightedFLRG, self).__init__(LHS)
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def weights(self):
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count_nochange = 0.0
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count_up = 0.0
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count_down = 0.0
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weights = []
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for c in self.RHS:
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tmp = 0
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if self.LHS.centroid == c.centroid:
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count_nochange += 1.0
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tmp = count_nochange
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elif self.LHS.centroid > c.centroid:
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count_down += 1.0
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tmp = count_down
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else:
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count_up += 1.0
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tmp = count_up
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weights.append(tmp)
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tot = sum(weights)
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return np.array([k / tot for k in weights])
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class TrendWeightedFTS(yu.WeightedFTS):
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"""First Order Trend Weighted Fuzzy Time Series"""
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def __init__(self, name, **kwargs):
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super(TrendWeightedFTS, self).__init__("")
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self.shortname = "TWFTS " + name
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self.name = "Trend Weighted FTS"
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self.detail = "Cheng"
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self.is_high_order = False
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if flr.LHS.name in flrgs:
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flrgs[flr.LHS.name].append(flr.RHS)
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else:
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flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
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flrgs[flr.LHS.name].append(flr.RHS)
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return (flrgs) |