pyFTS/yu.py

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import numpy as np
from pyFTS import *
class WeightedFLRG(fts.FTS):
def __init__(self,LHS):
self.LHS = LHS
self.RHS = []
self.count = 1.0
def append(self,c):
self.RHS.append(c)
self.count = self.count + 1.0
def weights(self):
tot = sum( np.arange(1.0,self.count,1.0) )
return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
def __str__(self):
tmp = self.LHS.name + " -> "
tmp2 = ""
cc = 1.0
tot = sum( np.arange(1.0,self.count,1.0) )
for c in sorted(self.RHS, key=lambda s: s.name):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c.name + "(" + str(round(cc/tot,3)) + ")"
cc = cc + 1.0
return tmp + tmp2
class WeightedFTS(fts.FTS):
def __init__(self,name):
super(WeightedFTS, self).__init__(1,name)
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
def train(self, data, sets):
self.sets = sets
tmpdata = common.fuzzySeries(data,sets)
flrs = common.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
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def forecast(self,data):
l = 1
ndata = np.array(data)
l = len(ndata)
ret = []
for k in np.arange(1,l):
mv = common.fuzzyInstance(ndata[k], self.sets)
actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
flrg = self.flrgs[actual.name]
mp = self.getMidpoints(flrg)
ret.append( mp.dot( flrg.weights() ))
return ret