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PIFTS 100% funcional

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Petrônio Cândido de Lima e Silva 2016-10-26 13:01:30 -02:00
parent c1c8f90fc9
commit 37ce2a3931
3 changed files with 28 additions and 20 deletions
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2
hofts.py
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@ -16,7 +16,7 @@ class HighOrderFLRG:
if len(self.strlhs) == 0: if len(self.strlhs) == 0:
for c in self.LHS: for c in self.LHS:
if len(self.strlhs) > 0: if len(self.strlhs) > 0:
self.strlhs = self.strlhs + "," self.strlhs = self.strlhs + ", "
self.strlhs = self.strlhs + c.name self.strlhs = self.strlhs + c.name
return self.strlhs return self.strlhs

6
ifts.py
View File

@ -68,16 +68,12 @@ class IntervalFTS(hofts.HighOrderFTS):
lags[count] = idx lags[count] = idx
count = count + 1 count = count + 1
#print(lags)
# Constrói uma árvore com todos os caminhos possíveis # Constrói uma árvore com todos os caminhos possíveis
root = tree.FLRGTreeNode(None) root = tree.FLRGTreeNode(None)
self.buildTree(root,lags,0) self.buildTree(root,lags,0)
#print(root)
# Traça os possíveis caminhos e costrói as HOFLRG's # Traça os possíveis caminhos e costrói as HOFLRG's
for p in root.paths(): for p in root.paths():

40
pifts.py
View File

@ -1,9 +1,9 @@
import numpy as np import numpy as np
from pyFTS import * from pyFTS import *
class ProbabilisticIntervalFLRG(hofts.HighOrderFLRG): class ProbabilisticFLRG(hofts.HighOrderFLRG):
def __init__(self,order): def __init__(self,order):
super(ProbabilisticIntervalFLRG, self).__init__(order) super(ProbabilisticFLRG, self).__init__(order)
self.RHS = {} self.RHS = {}
self.frequencyCount = 0 self.frequencyCount = 0
@ -21,7 +21,7 @@ class ProbabilisticIntervalFLRG(hofts.HighOrderFLRG):
tmp2 = "" tmp2 = ""
for c in sorted(self.RHS): for c in sorted(self.RHS):
if len(tmp2) > 0: if len(tmp2) > 0:
tmp2 = tmp2 + "," tmp2 = tmp2 + ", "
tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.frequencyCount,3)) + ")" tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.frequencyCount,3)) + ")"
return self.strLHS() + " -> " + tmp2 return self.strLHS() + " -> " + tmp2
@ -38,7 +38,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
flrgs = {} flrgs = {}
l = len(flrs) l = len(flrs)
for k in np.arange(self.order +1, l): for k in np.arange(self.order +1, l):
flrg = ProbabilisticIntervalFLRG(self.order) flrg = ProbabilisticFLRG(self.order)
for kk in np.arange(k - self.order, k): for kk in np.arange(k - self.order, k):
flrg.appendLHS( flrs[kk].LHS ) flrg.appendLHS( flrs[kk].LHS )
@ -53,7 +53,10 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
return (flrgs) return (flrgs)
def getProbability(self, flrg): def getProbability(self, flrg):
return flrg.frequencyCount / self.globalFrequency if flrg.strLHS() in self.flrgs:
return self.flrgs[ flrg.strLHS() ].frequencyCount / self.globalFrequency
else:
return 0
def getUpper(self,flrg): def getUpper(self,flrg):
if flrg.strLHS() in self.flrgs: if flrg.strLHS() in self.flrgs:
@ -81,10 +84,9 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
for k in np.arange(self.order,l): for k in np.arange(self.order,l):
print(k)
flrs = [] flrs = []
mvs = [] mvs = []
norms = []
up = [] up = []
lo = [] lo = []
@ -94,7 +96,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
lags = {} lags = {}
if self.order > 1: if self.order > 1:
subset = ndata[k-self.order : k ] subset = ndata[k-self.order : k ]
print(subset)
for instance in subset: for instance in subset:
mb = common.fuzzyInstance(instance, self.sets) mb = common.fuzzyInstance(instance, self.sets)
tmp = np.argwhere( mb ) tmp = np.argwhere( mb )
@ -108,7 +110,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
self.buildTree(root,lags,0) self.buildTree(root,lags,0)
# Traça os possíveis caminhos e costrói as HOFLRG's # Traça os possíveis caminhos e costrói as PFLRG's
for p in root.paths(): for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p)))) path = list(reversed(list(filter(None.__ne__, p))))
@ -116,7 +118,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
for kk in path: flrg.appendLHS(self.sets[ kk ]) for kk in path: flrg.appendLHS(self.sets[ kk ])
## ##
flrs.append( self.flrgs[ flrg.strLHS() ] ) flrs.append( flrg )
# Acha a pertinência geral de cada FLRG # Acha a pertinência geral de cada FLRG
mvs.append(min(self.getSequenceMembership(subset, flrg.LHS))) mvs.append(min(self.getSequenceMembership(subset, flrg.LHS)))
@ -128,17 +130,27 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
for kk in idx: for kk in idx:
flrg = hofts.HighOrderFLRG(self.order) flrg = hofts.HighOrderFLRG(self.order)
flrg.appendLHS(self.sets[ kk ]) flrg.appendLHS(self.sets[ kk ])
flrs.append( self.flrgs[ flrg.strLHS() ] ) flrs.append( flrg )
mvs.append(mv[kk]) mvs.append(mv[kk])
count = 0 count = 0
for flrg in flrs: for flrg in flrs:
# achar o os bounds de cada FLRG, ponderados pela pertinência # achar o os bounds de cada FLRG, ponderados pela pertinência
up.append( self.getProbability(flrg) * mvs[count] * self.getUpper(flrg) ) norm = self.getProbability(flrg) * mvs[count]
lo.append( self.getProbability(flrg) * mvs[count] * self.getLower(flrg) ) up.append( norm * self.getUpper(flrg) )
lo.append( norm * self.getLower(flrg) )
norms.append(norm)
count = count + 1 count = count + 1
# gerar o intervalo # gerar o intervalo
ret.append( [ sum(lo), sum(up) ] ) norm = sum(norms)
ret.append( [ sum(lo)/norm, sum(up)/norm ] )
return ret return ret
def __str__(self):
tmp = self.name + ":\n"
for r in sorted(self.flrgs):
p = round(self.flrgs[r].frequencyCount / self.globalFrequency,3)
tmp = tmp + "(" + str(p) + ") " + str(self.flrgs[r]) + "\n"
return tmp