Refatoração dos códigos para padronizar com a rfts

benchmarks.py

@@ -189,7 +189,10 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
 		sets = partitioner.GridPartitionerTrimf(original,p)
 		fts = modelo(str(p)+ " particoes")
 		fts.train(original,sets)
-		forecasted = [fts.forecast(xx) for xx in original]
+		#print(original)
+		forecasted = fts.forecast(original)
+		forecasted.insert(0,original[0])
+		#print(forecasted)
 		ax0.plot(forecasted,label=fts.name)
 		error = rmse(np.array(forecasted),np.array(original))
 		print(p,error)
@@ -226,8 +229,8 @@ def SelecaoSimples_MenorRMSE(original,parameters,modelo):
 		sets = partitioner.GridPartitionerTrimf(difffts,p)
 		fts = modelo(str(p)+ " particoes")
 		fts.train(difffts,sets)
-		forecasted = [fts.forecast(xx) for xx in difffts]
+		forecasted = fts.forecast(difffts)
-		#forecasted.insert(0,difffts[0])
+		forecasted.insert(0,difffts[0])
 		ax2.plot(forecasted,label=fts.name)
 		error = rmse(np.array(forecasted),np.array(difffts))
 		print(p,error)

chen.py

@@ -24,14 +24,14 @@ class ConventionalFTS(fts.FTS):
 		super(ConventionalFTS, self).__init__(1,name)
 		self.flrgs = {}
     
-    def generateFLRG(self, flrs):
+	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] = ConventionalFLRG(flr.LHS);
+				flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS);
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 		return (flrgs)
 
 	def train(self, data, sets):
@@ -42,23 +42,29 @@ class ConventionalFTS(fts.FTS):
 		
 	def forecast(self,data):
 		
-		mv = common.fuzzyInstance(data, self.sets)
+		l = 1
 		
-		actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
+		ndata = np.array(data)
 		
-		if actual.name not in self.flrgs:
+		l = len(ndata)
-			return actual.centroid
 		
-		flrg = self.flrgs[actual.name]
+		ret = []
 		
-		count = 0.0
+		for k in np.arange(1,l):
-		denom = 0.0
 			
-		for s in flrg.RHS:
+			mv = common.fuzzyInstance(ndata[k], self.sets)
-			denom = denom + self.sets[s].centroid
-			count = count + 1.0
 		
-		return denom/count
+			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(sum(mp)/len(mp))
+			
+		return ret
 		
 	
 		 

fts.py

@@ -1,3 +1,4 @@
+import numpy as np
 from pyFTS import *
 
 class FTS:
@@ -24,6 +25,10 @@ class FTS:
 	def train(self, data, sets):
 		pass  
 		
+	def getMidpoints(self,flrg):
+		ret = np.array([s.centroid for s in flrg.RHS])
+		return ret
+
 	def __str__(self):
 		tmp = self.name + ":\n"
 		for r in self.flrgs.keys():

ismailefendi.py

@@ -48,14 +48,26 @@ class ImprovedWeightedFTS(fts.FTS):
 		self.flrgs = self.generateFLRG(flrs)
         
 	def forecast(self,data):
-		mv = common.fuzzyInstance(data, self.sets)
+		l = 1
 		
-		actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
+		ndata = np.array(data)
 		
-		if actual.name not in self.flrgs:
+		l = len(ndata)
-			return actual.centroid
 		
-		flrg = self.flrgs[actual.name]
+		ret = []
 		
-		mi = np.array([self.sets[s].centroid for s in flrg.RHS.keys()])
+		for k in np.arange(1,l):
-		return mi.dot( flrg.weights() )
+			
+			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( mi.dot( flrg.weights() ))
+			
+		return ret

sadaei.py

@@ -53,16 +53,27 @@ class ExponentialyWeightedFTS(fts.FTS):
 		self.flrgs = self.generateFLRG(flrs,c)
         
 	def forecast(self,data):
-        mv = common.fuzzyInstance(data, self.sets)
+       l = 1
 		
-		actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
+		ndata = np.array(data)
 		
-		if actual.name not in self.flrgs:
+		l = len(ndata)
-			return actual.centroid
 		
-		flrg = self.flrgs[actual.name]
+		ret = []
 		
-		mi = np.array([self.sets[s].centroid for s in flrg.RHS])
+		for k in np.arange(1,l):
 			
-		return mi.dot( flrg.weights() )
+			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( mi.dot( flrg.weights() ))
+			
+		return ret
         

yu.py

@@ -35,11 +35,11 @@ class WeightedFTS(fts.FTS):
 	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] = WeightedFLRG(flr.LHS);
+				flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
-				flrgs[flr.LHS].append(flr.RHS)
+				flrgs[flr.LHS.name].append(flr.RHS)
 		return (flrgs)
 
 	def train(self, data, sets):
@@ -49,17 +49,26 @@ class WeightedFTS(fts.FTS):
 		self.flrgs = self.generateFLRG(flrs)
         
 	def forecast(self,data):
+        l = 1
 		
-		mv = common.fuzzyInstance(data, self.sets)
+		ndata = np.array(data)
 		
-		actual = self.sets[ np.argwhere( mv == max(mv) )[0,0] ]
+		l = len(ndata)
 		
-		if actual.name not in self.flrgs:
+		ret = []
-			return actual.centroid
 		
-		flrg = self.flrgs[actual.name]
+		for k in np.arange(1,l):
 			
-		mi = np.array([self.sets[s].centroid for s in flrg.RHS])
+			mv = common.fuzzyInstance(ndata[k], self.sets)
 		
-		return mi.dot( flrg.weights() )
+			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( mi.dot( flrg.weights() ))
+			
+		return ret