Refatoração dos códigos para padronizar com a rfts e HOFTS

chen.py

@@ -10,12 +10,12 @@ class ConventionalFLRG:
 		self.RHS.add(c)
 
 	def __str__(self):
-		tmp = self.LHS + " -> "
+		tmp = self.LHS.name + " -> "
 		tmp2 = ""
-		for c in self.RHS:
+		for c in sorted(self.RHS, key=lambda s: s.name):
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
-			tmp2 = tmp2 + c
+			tmp2 = tmp2 + c.name
 		return tmp + tmp2
 
 

common.py

@@ -65,8 +65,8 @@ class FLR:
 		return str(self.LHS) + " -> " + str(self.RHS)
     
 def fuzzyInstance(inst, fuzzySets):
-    mv = np.array([ fs.membership(inst) for fs in fuzzySets])
+	mv = np.array([ fs.membership(inst) for fs in fuzzySets])
-    return mv
+	return mv
 
 
 def fuzzySeries(data,fuzzySets):

fts.py

@@ -31,6 +31,6 @@ class FTS:
 
 	def __str__(self):
 		tmp = self.name + ":\n"
-		for r in self.flrgs.keys():
+		for r in sorted(self.flrgs):
 			tmp = tmp + str(self.flrgs[r]) + "\n"
 		return tmp

ismailefendi.py

@@ -8,19 +8,19 @@ class ImprovedWeightedFLRG:
 		self.count = 0.0
 
 	def append(self,c):
-		if c not in self.RHS:
+		if c.name not in self.RHS:
-			self.RHS[c] = 1.0
+			self.RHS[c.name] = 1.0
 		else:
-			self.RHS[c] = self.RHS[c] + 1.0
+			self.RHS[c.name] = self.RHS[c.name] + 1.0
 		self.count = self.count + 1.0
 
 	def weights(self):
 		return np.array([ self.RHS[c]/self.count for c in self.RHS.keys() ])
         
 	def __str__(self):
-		tmp = self.LHS + " -> "
+		tmp = self.LHS.name + " -> "
 		tmp2 = ""
-		for c in self.RHS.keys():
+		for c in sorted(self.RHS):
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
 			tmp2 = tmp2 + c + "(" + str(round(self.RHS[c]/self.count,3)) + ")"
@@ -30,23 +30,31 @@ class ImprovedWeightedFLRG:
 class ImprovedWeightedFTS(fts.FTS):
 	def __init__(self,name):
 		super(ImprovedWeightedFTS, self).__init__(1,name)
+		self.setsDict = {}
 		
 	def generateFLRG(self, flrs):
 		flrgs = {}
 		for flr in flrs:
-			if flr.LHS in flrgs:
+			if flr.LHS.name in flrgs:
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 			else:
-				flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS);
+				flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 		return (flrgs)
 
 	def train(self, data, sets):
 		self.sets = sets
-		tmpdata = common.fuzzySeries(data,sets)
+		
+		for s in self.sets:	self.setsDict[s.name] = s
+		
+		tmpdata = common.fuzzySeries(data,self.sets)
 		flrs = common.generateRecurrentFLRs(tmpdata)
 		self.flrgs = self.generateFLRG(flrs)
 		
+	def getMidpoints(self,flrg):
+		ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
+		return ret
+        
 	def forecast(self,data):
 		l = 1
 		
@@ -68,6 +76,6 @@ class ImprovedWeightedFTS(fts.FTS):
 				flrg = self.flrgs[actual.name]
 				mp = self.getMidpoints(flrg)
 				
-				ret.append( mi.dot( flrg.weights() ))
+				ret.append( mp.dot( flrg.weights() ))
 			
 		return ret

sadaei.py

@@ -18,42 +18,42 @@ class ExponentialyWeightedFLRG:
 		return np.array([ k/tot for k in wei ])
         
 	def __str__(self):
-		tmp = self.LHS + " -> "
+		tmp = self.LHS.name + " -> "
 		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.RHS:
+		for c in sorted(self.RHS, key=lambda s: s.name):
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
-			tmp2 = tmp2 + c + "(" + str(wei[cc]/tot) + ")"
+			tmp2 = tmp2 + c.name + "(" + str(wei[cc]/tot) + ")"
 			cc = cc + 1
 		return tmp + tmp2
 		
 class ExponentialyWeightedFTS(fts.FTS):
 	def __init__(self,name):
 		super(ExponentialyWeightedFTS, self).__init__(1,name)
-		this.c = 1
+		self.c = 1
 		
 	def generateFLRG(self, flrs, c):
 		flrgs = {}
 		for flr in flrs:
-			if flr.LHS in flrgs:
+			if flr.LHS.name in flrgs:
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 			else:
-				flrgs[flr.LHS] = ExponentialyWeightedFLRG(flr.LHS, c);
+				flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c);
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 		return (flrgs)
 
 	def train(self, data, sets, c):
-		this.c = c
+		self.c = c
 		self.sets = sets
 		tmpdata = common.fuzzySeries(data,sets)
 		flrs = common.generateRecurrentFLRs(tmpdata)
 		self.flrgs = self.generateFLRG(flrs,c)
         
 	def forecast(self,data):
-       l = 1
+		l = 1
 		
 		ndata = np.array(data)
 		
@@ -73,7 +73,7 @@ class ExponentialyWeightedFTS(fts.FTS):
 				flrg = self.flrgs[actual.name]
 				mp = self.getMidpoints(flrg)
 				
-				ret.append( mi.dot( flrg.weights() ))
+				ret.append( mp.dot( flrg.weights() ))
 			
 		return ret
         

yu.py

@@ -16,14 +16,14 @@ 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.LHS + " -> "
+		tmp = self.LHS.name + " -> "
 		tmp2 = ""
 		cc = 1.0
 		tot = sum( np.arange(1.0,self.count,1.0) )
-		for c in self.RHS:
+		for c in sorted(self.RHS, key=lambda s: s.name):
 			if len(tmp2) > 0:
 				tmp2 = tmp2 + ","
-			tmp2 = tmp2 + c + "(" + str(round(cc/tot,3)) + ")"
+			tmp2 = tmp2 + c.name + "(" + str(round(cc/tot,3)) + ")"
 			cc = cc + 1.0
 		return tmp + tmp2
 		
@@ -49,7 +49,7 @@ class WeightedFTS(fts.FTS):
 		self.flrgs = self.generateFLRG(flrs)
         
 	def forecast(self,data):
-        l = 1
+		l = 1
 		
 		ndata = np.array(data)
 		
@@ -69,6 +69,6 @@ class WeightedFTS(fts.FTS):
 				flrg = self.flrgs[actual.name]
 				mp = self.getMidpoints(flrg)
 				
-				ret.append( mi.dot( flrg.weights() ))
+				ret.append( mp.dot( flrg.weights() ))
 			
 		return ret