66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
import numpy as np
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from pyFTS import *
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class WeightedFLRG(fts.FTS):
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def __init__(self,LHS):
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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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def append(self,c):
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self.RHS.append(c)
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self.count = self.count + 1.0
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def weights(self):
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tot = sum( np.arange(1.0,self.count,1.0) )
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return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
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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 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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def __init__(self,name):
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super(WeightedFTS, self).__init__(1,name)
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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 in flrgs:
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flrgs[flr.LHS].append(flr.RHS)
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else:
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flrgs[flr.LHS] = WeightedFLRG(flr.LHS);
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flrgs[flr.LHS].append(flr.RHS)
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return (flrgs)
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def train(self, data, sets):
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self.sets = sets
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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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mv = common.fuzzyInstance(data, self.sets)
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actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
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if actual.name not in self.flrgs:
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return actual.centroid
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flrg = self.flrgs[actual.name]
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mi = np.array([self.sets[s].centroid for s in flrg.RHS])
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return mi.dot( flrg.weights() )
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