Padronização dos nomes dos atributos das FLRGs
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Petrônio Cândido de Lima e Silva
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7200053651
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82c2e56f4d
@ -3,6 +3,7 @@ import pandas as pd
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import matplotlib as plt
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn.cross_validation import KFold
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from pyFTS import common
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@ -76,7 +77,7 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
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errors_fold = []
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pc = 0 #Parameter count
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for p in parameters:
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sets = GridPartitionerTrimf(train,p)
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sets = common.GridPartitionerTrimf(train,p)
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fts = modelo(str(p)+ " particoes")
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fts.learn(train,sets)
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predicted = [fts.predict(xx) for xx in test]
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@ -306,6 +307,8 @@ def compareModelsTable(original,models_fo,models_ho):
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return sup + header + body + "\\end{tabular}"
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from pyFTS import hwang
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def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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ret = []
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errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
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@ -326,8 +329,8 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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for p in parameters:
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oc = 0
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for o in orders:
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sets = GridPartitionerTrimf(original,p)
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fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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sets = common.GridPartitionerTrimf(original,p)
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fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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fts.learn(original,sets)
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predicted = [fts.predict(original, xx) for xx in range(o,len(original))]
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error = rmse(np.array(predicted),np.array(original[o:]))
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@ -373,7 +376,7 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
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oc = 0
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for o in orders:
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sets = common.GridPartitionerTrimf(common.differential(original),p)
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fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
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fts.learn(original,sets)
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predicted = [fts.predictDiff(original, xx) for xx in range(o,len(original))]
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error = rmse(np.array(predicted),np.array(original[o:]))
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14
chen.py
14
chen.py
@ -1,17 +1,17 @@
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from pyFTS import *
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class ConventionalFLRG:
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def __init__(self,premiss):
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self.premiss = premiss
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self.consequent = set()
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def __init__(self,LHS):
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self.LHS = LHS
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self.RHS = set()
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def append(self,c):
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self.consequent.add(c)
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self.RHS.add(c)
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def __str__(self):
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tmp = self.premiss + " -> "
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tmp = self.LHS + " -> "
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tmp2 = ""
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for c in self.consequent:
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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
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@ -34,7 +34,7 @@ class ConventionalFTS(fts.FTS):
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count = 0.0
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denom = 0.0
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for s in flrg.consequent:
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for s in flrg.RHS:
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denom = denom + self.sets[s].centroid
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count = count + 1.0
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3
hwang.py
3
hwang.py
@ -1,3 +1,4 @@
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import numpy as np
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from pyFTS import *
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class HighOrderFTS(fts.FTS):
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@ -33,4 +34,4 @@ class HighOrderFTS(fts.FTS):
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return self.defuzzy(data,t)
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def predictDiff(self,data,t):
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return data[t] + self.defuzzy(diferencas(data),t)
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return data[t] + self.defuzzy(common.differential(data),t)
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@ -1,28 +1,29 @@
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import numpy as np
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from pyFTS import *
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class ImprovedWeightedFLRG:
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def __init__(self,premiss):
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self.premiss = premiss
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self.consequent = {}
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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 = 0.0
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def append(self,c):
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if c not in self.consequent:
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self.consequent[c] = 1.0
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if c not in self.RHS:
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self.RHS[c] = 1.0
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else:
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self.consequent[c] = self.consequent[c] + 1.0
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self.RHS[c] = self.RHS[c] + 1.0
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self.count = self.count + 1.0
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def weights(self):
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return np.array([ self.consequent[c]/self.count for c in self.consequent.keys() ])
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return np.array([ self.RHS[c]/self.count for c in self.RHS.keys() ])
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def __str__(self):
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tmp = self.premiss + " -> "
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tmp = self.LHS + " -> "
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tmp2 = ""
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for c in self.consequent.keys():
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for c in self.RHS.keys():
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if len(tmp2) > 0:
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tmp2 = tmp2 + ","
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tmp2 = tmp2 + c + "(" + str(round(self.consequent[c]/self.count,3)) + ")"
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tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.count,3)) + ")"
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return tmp + tmp2
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@ -35,7 +36,7 @@ class ImprovedWeightedFTS(fts.FTS):
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if actual["fuzzyset"] not in self.flrgs:
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return self.sets[actual["fuzzyset"]].centroid
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flrg = self.flrgs[actual["fuzzyset"]]
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mi = np.array([self.sets[s].centroid for s in flrg.consequent.keys()])
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mi = np.array([self.sets[s].centroid for s in flrg.RHS.keys()])
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return mi.dot( flrg.weights() )
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def learn(self, data, sets):
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15
sadaei.py
15
sadaei.py
@ -1,14 +1,15 @@
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import numpy as np
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from pyFTS import *
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class ExponentialyWeightedFLRG:
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def __init__(self,premiss,c):
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self.premiss = premiss
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self.consequent = []
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def __init__(self,LHS,c):
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self.LHS = LHS
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self.RHS = []
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self.count = 0.0
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self.c = c
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def append(self,c):
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self.consequent.append(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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@ -17,12 +18,12 @@ class ExponentialyWeightedFLRG:
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return np.array([ k/tot for k in wei ])
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def __str__(self):
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tmp = self.premiss + " -> "
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tmp = self.LHS + " -> "
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tmp2 = ""
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cc = 0
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wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
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tot = sum( wei )
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for c in self.consequent:
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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(wei[cc]/tot) + ")"
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@ -42,7 +43,7 @@ class ExponentialyWeightedFTS(fts.FTS):
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flrg = self.flrgs[actual["fuzzyset"]]
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mi = np.array([self.sets[s].centroid for s in flrg.consequent])
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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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15
yu.py
15
yu.py
@ -1,13 +1,14 @@
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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,premiss):
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self.premiss = premiss
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self.consequent = []
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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.consequent.append(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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@ -15,11 +16,11 @@ class WeightedFLRG(fts.FTS):
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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.premiss + " -> "
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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.consequent:
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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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@ -40,7 +41,7 @@ class WeightedFTS(fts.FTS):
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flrg = self.flrgs[actual["fuzzyset"]]
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mi = np.array([self.sets[s].centroid for s in flrg.consequent])
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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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