Padronização dos nomes dos atributos das FLRGs

benchmarks.py

@@ -3,6 +3,7 @@ import pandas as pd
 import matplotlib as plt
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
+from sklearn.cross_validation import KFold
 
 from pyFTS import common
 
@@ -76,7 +77,7 @@ def SelecaoKFold_MenorRMSE(original,parameters,modelo):
 		errors_fold = []
 		pc = 0 #Parameter count
 		for p in parameters:
-			sets = GridPartitionerTrimf(train,p)
+			sets = common.GridPartitionerTrimf(train,p)
 			fts = modelo(str(p)+ " particoes")
 			fts.learn(train,sets)
 			predicted = [fts.predict(xx) for xx in test]
@@ -306,6 +307,8 @@ def compareModelsTable(original,models_fo,models_ho):
         
     return sup + header + body + "\\end{tabular}"
 
+from pyFTS import hwang
+
 def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
 	ret = []
 	errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
@@ -326,8 +329,8 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
 	for p in parameters:
 		oc = 0
 		for o in orders:
-			sets = GridPartitionerTrimf(original,p)
+			sets = common.GridPartitionerTrimf(original,p)
-			fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
+			fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
 			fts.learn(original,sets)
 			predicted = [fts.predict(original, xx) for xx in range(o,len(original))]
 			error = rmse(np.array(predicted),np.array(original[o:]))
@@ -373,7 +376,7 @@ def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
 		oc = 0
 		for o in orders:
 			sets = common.GridPartitionerTrimf(common.differential(original),p)
-			fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
+			fts = hwang.HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
 			fts.learn(original,sets)
 			predicted = [fts.predictDiff(original, xx) for xx in range(o,len(original))]
 			error = rmse(np.array(predicted),np.array(original[o:]))

chen.py

@@ -1,17 +1,17 @@
 from pyFTS import *
 
 class ConventionalFLRG:
-	def __init__(self,premiss):
+	def __init__(self,LHS):
-		self.premiss = premiss
+		self.LHS = LHS
-		self.consequent = set()
+		self.RHS = set()
 	
 	def append(self,c):
-		self.consequent.add(c)
+		self.RHS.add(c)
 
 	def __str__(self):
-		tmp = self.premiss + " -> "
+		tmp = self.LHS + " -> "
 		tmp2 = ""
-		for c in self.consequent:
+		for c in self.RHS:
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
 			tmp2 = tmp2 + c
@@ -34,7 +34,7 @@ class ConventionalFTS(fts.FTS):
 		count = 0.0
 		denom = 0.0
 
-		for s in flrg.consequent:
+		for s in flrg.RHS:
 			denom = denom + self.sets[s].centroid
 			count = count + 1.0
 

hwang.py

@@ -1,3 +1,4 @@
+import numpy as np
 from pyFTS import *
 
 class HighOrderFTS(fts.FTS):
@@ -33,4 +34,4 @@ class HighOrderFTS(fts.FTS):
 		return self.defuzzy(data,t)
 
 	def predictDiff(self,data,t):
-		return data[t] + self.defuzzy(diferencas(data),t)
+		return data[t] + self.defuzzy(common.differential(data),t)

ismailefendi.py

@@ -1,28 +1,29 @@
+import numpy as np
 from pyFTS import *
 
 class ImprovedWeightedFLRG:
-	def __init__(self,premiss):
+	def __init__(self,LHS):
-		self.premiss = premiss
+		self.LHS = LHS
-		self.consequent = {}
+		self.RHS = {}
 		self.count = 0.0
 
 	def append(self,c):
-		if c not in self.consequent:
+		if c not in self.RHS:
-			self.consequent[c] = 1.0
+			self.RHS[c] = 1.0
 		else:
-			self.consequent[c] = self.consequent[c] + 1.0
+			self.RHS[c] = self.RHS[c] + 1.0
 		self.count = self.count + 1.0
 
 	def weights(self):
-		return np.array([ self.consequent[c]/self.count for c in self.consequent.keys() ])
+		return np.array([ self.RHS[c]/self.count for c in self.RHS.keys() ])
         
 	def __str__(self):
-		tmp = self.premiss + " -> "
+		tmp = self.LHS + " -> "
 		tmp2 = ""
-		for c in self.consequent.keys():
+		for c in self.RHS.keys():
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
-			tmp2 = tmp2 + c + "(" + str(round(self.consequent[c]/self.count,3)) + ")"
+			tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.count,3)) + ")"
 		return tmp + tmp2
 		
 
@@ -35,7 +36,7 @@ class ImprovedWeightedFTS(fts.FTS):
 		if actual["fuzzyset"] not in self.flrgs:
 			return self.sets[actual["fuzzyset"]].centroid
 		flrg = self.flrgs[actual["fuzzyset"]]
-		mi = np.array([self.sets[s].centroid for s in flrg.consequent.keys()])
+		mi = np.array([self.sets[s].centroid for s in flrg.RHS.keys()])
 		return mi.dot( flrg.weights() )
         
 	def learn(self, data, sets):

sadaei.py

@@ -1,14 +1,15 @@
+import numpy as np
 from pyFTS import *
 
 class ExponentialyWeightedFLRG:
-	def __init__(self,premiss,c):
+	def __init__(self,LHS,c):
-		self.premiss = premiss
+		self.LHS = LHS
-		self.consequent = []
+		self.RHS = []
 		self.count = 0.0
 		self.c = c
 
 	def append(self,c):
-		self.consequent.append(c)
+		self.RHS.append(c)
 		self.count = self.count + 1.0
 
 	def weights(self):
@@ -17,12 +18,12 @@ class ExponentialyWeightedFLRG:
 		return np.array([ k/tot for k in wei ])
         
 	def __str__(self):
-		tmp = self.premiss + " -> "
+		tmp = self.LHS + " -> "
 		tmp2 = ""
 		cc = 0
 		wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
 		tot = sum( wei )
-		for c in self.consequent:
+		for c in self.RHS:
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
 			tmp2 = tmp2 + c + "(" + str(wei[cc]/tot) + ")"
@@ -42,7 +43,7 @@ class ExponentialyWeightedFTS(fts.FTS):
 
 		flrg = self.flrgs[actual["fuzzyset"]]
 
-		mi = np.array([self.sets[s].centroid for s in flrg.consequent])
+		mi = np.array([self.sets[s].centroid for s in flrg.RHS])
         
 		return mi.dot( flrg.weights() )
         

yu.py

@@ -1,13 +1,14 @@
+import numpy as np
 from pyFTS import *
 
 class WeightedFLRG(fts.FTS):
-	def __init__(self,premiss):
+	def __init__(self,LHS):
-		self.premiss = premiss
+		self.LHS = LHS
-		self.consequent = []
+		self.RHS = []
 		self.count = 1.0
 
 	def append(self,c):
-		self.consequent.append(c)
+		self.RHS.append(c)
 		self.count = self.count + 1.0
 
 	def weights(self):
@@ -15,11 +16,11 @@ class WeightedFLRG(fts.FTS):
 		return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
         
 	def __str__(self):
-		tmp = self.premiss + " -> "
+		tmp = self.LHS + " -> "
 		tmp2 = ""
 		cc = 1.0
 		tot = sum( np.arange(1.0,self.count,1.0) )
-		for c in self.consequent:
+		for c in self.RHS:
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
 			tmp2 = tmp2 + c + "(" + str(round(cc/tot,3)) + ")"
@@ -40,7 +41,7 @@ class WeightedFTS(fts.FTS):
 
 		flrg = self.flrgs[actual["fuzzyset"]]
 
-		mi = np.array([self.sets[s].centroid for s in flrg.consequent])
+		mi = np.array([self.sets[s].centroid for s in flrg.RHS])
         
 		return mi.dot( flrg.weights() )