69 lines
1.4 KiB
Python
69 lines
1.4 KiB
Python
import numpy as np
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from pyFTS import *
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class SeasonalFLRG(fts.FTS):
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def __init__(self,seasonality):
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self.LHS = seasonality
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self.RHS = []
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def append(self,c):
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self.RHS.append(c)
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def __str__(self):
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tmp = str(self.LHS) + " -> "
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tmp2 = ""
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for c in sorted(self.RHS, key=lambda s: s.name):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c.name
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return tmp + tmp2
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class SeasonalFTS(fts.FTS):
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def __init__(self,name):
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super(SeasonalFTS, self).__init__(1,"SFTS")
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self.name = "Seasonal FTS"
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self.detail = "Chen"
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self.seasonality = 1
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self.isSeasonal = True
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def generateFLRG(self, flrs):
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flrgs = []
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season = 1
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for flr in flrs:
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if len(flrgs) < self.seasonality:
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flrgs.append(SeasonalFLRG(season))
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flrgs[season].append(flr.RHS)
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season = (season + 1) % (self.seasonality + 1)
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if season == 0: season = 1
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return (flrgs)
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def train(self, data, sets, seasonality):
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self.sets = sets
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self.seasonality = seasonality
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tmpdata = common.fuzzySeries(data,sets)
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flrs = common.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
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ndata = np.array(data)
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l = len(ndata)
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ret = []
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for k in np.arange(1,l):
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flrg = self.flrgs[ data[k] ]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp)/len(mp))
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return ret
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