Refatoração e criação da package common

common.py

@@ -1,97 +0,0 @@
-import numpy as np
-from pyFTS import *
-
-def differential(original):
-    n = len(original)
-    diff = [ original[t-1]-original[t] for t in np.arange(1,n) ]
-    diff.insert(0,0)
-    return np.array(diff)
-
-def trimf(x,parameters):
-	xx = round(x,3)
-	if(xx < parameters[0]):
-		return 0
-	elif(xx >= parameters[0] and xx < parameters[1]):
-		return (x-parameters[0])/(parameters[1]-parameters[0])
-	elif(xx >= parameters[1] and xx <= parameters[2]):
-		return (parameters[2]-xx)/(parameters[2]-parameters[1])
-	else: 
-		return 0
-
-def trapmf(x, parameters):
-		if(x < parameters[0]):
-			return 0
-		elif(x >= parameters[0] and x < parameters[1]):
-			return (x-parameters[0])/(parameters[1]-parameters[0])
-		elif(x >= parameters[1] and x <= parameters[2]):
-			return 1
-		elif(x >= parameters[2] and x <= parameters[3]):
-			return (parameters[3]-x)/(parameters[3]-parameters[2])
-		else: 
-			return 0
-
-def gaussmf(x,parameters):
-		return math.exp(-0.5*((x-parameters[0]) / parameters[1] )**2)
-
-
-def bellmf(x,parameters):
-		return 1 / (1 + abs((xx - parameters[2])/parameters[0])**(2*parameters[1]))
-
-
-def sigmf(x,parameters):
-		return 1 / (1 + math.exp(-parameters[0] * (x - parameters[1])))
-
-
-class FuzzySet:
-	def __init__(self,name,mf,parameters,centroid):
-		self.name = name
-		self.mf = mf
-		self.parameters = parameters
-		self.centroid = centroid
-		self.lower = min(parameters)
-		self.upper = max(parameters)
-        
-	def membership(self,x):
-		return self.mf(x,self.parameters)
-    
-	def __str__(self):
-		return self.name + ": " + str(self.mf) + "(" + str(self.parameters) + ")"
-    
-class FLR:
-	def __init__(self,LHS,RHS):
-		self.LHS = LHS
-		self.RHS = RHS
-	
-	def __str__(self):
-		return str(self.LHS) + " -> " + str(self.RHS)
-    
-def fuzzyInstance(inst, fuzzySets):
-	mv = np.array([ fs.membership(inst) for fs in fuzzySets])
-	return mv
-	
-	
-def getMaxMembershipFuzzySet(inst, fuzzySets):
-	mv = fuzzyInstance(inst,fuzzySets)
-	return fuzzySets[  np.argwhere(mv == max(mv) )[0,0] ]
-
-
-def fuzzySeries(data,fuzzySets):
-	fts = []
-	for item in data:
-		fts.append(getMaxMembershipFuzzySet(item,fuzzySets))
-	return fts
-
-
-def generateNonRecurrentFLRs(fuzzyData):
-	flrs = {}
-	for i in range(2,len(fuzzyData)):
-		tmp = FLR(fuzzyData[i-1],fuzzyData[i])
-		flrs[str(tmp)] = tmp
-	ret = [value for key, value in flrs.items()]
-	return ret
-
-def generateRecurrentFLRs(fuzzyData):
-	flrs = []
-	for i in np.arange(1,len(fuzzyData)):
-		flrs.append(FLR(fuzzyData[i-1],fuzzyData[i]))
-	return flrs

common/FLR.py

@@ -0,0 +1,21 @@
+class FLR:
+    def __init__(self, LHS, RHS):
+        self.LHS = LHS
+        self.RHS = RHS
+
+    def __str__(self):
+        return str(self.LHS) + " -> " + str(self.RHS)
+
+def generateNonRecurrentFLRs(fuzzyData):
+    flrs = {}
+    for i in range(2,len(fuzzyData)):
+        tmp = FLR(fuzzyData[i-1],fuzzyData[i])
+        flrs[str(tmp)] = tmp
+    ret = [value for key, value in flrs.items()]
+    return ret
+
+def generateRecurrentFLRs(fuzzyData):
+    flrs = []
+    for i in np.arange(1,len(fuzzyData)):
+        flrs.append(FLR(fuzzyData[i-1],fuzzyData[i]))
+    return flrs

common/FuzzySet.py

@@ -0,0 +1,35 @@
+import numpy as np
+from pyFTS import *
+
+
+class FuzzySet:
+    def __init__(self, name, mf, parameters, centroid):
+        self.name = name
+        self.mf = mf
+        self.parameters = parameters
+        self.centroid = centroid
+        self.lower = min(parameters)
+        self.upper = max(parameters)
+
+    def membership(self, x):
+        return self.mf(x, self.parameters)
+
+    def __str__(self):
+        return self.name + ": " + str(self.mf) + "(" + str(self.parameters) + ")"
+
+
+def fuzzyInstance(inst, fuzzySets):
+    mv = np.array([fs.membership(inst) for fs in fuzzySets])
+    return mv
+
+
+def getMaxMembershipFuzzySet(inst, fuzzySets):
+    mv = fuzzyInstance(inst, fuzzySets)
+    return fuzzySets[np.argwhere(mv == max(mv))[0, 0]]
+
+
+def fuzzySeries(data, fuzzySets):
+    fts = []
+    for item in data:
+        fts.append(getMaxMembershipFuzzySet(item, fuzzySets))
+    return fts

common/Membership.py

@@ -0,0 +1,40 @@
+import numpy as np
+import math
+from pyFTS import *
+
+
+def trimf(x, parameters):
+    xx = round(x, 3)
+    if (xx < parameters[0]):
+        return 0
+    elif (xx >= parameters[0] and xx < parameters[1]):
+        return (x - parameters[0]) / (parameters[1] - parameters[0])
+    elif (xx >= parameters[1] and xx <= parameters[2]):
+        return (parameters[2] - xx) / (parameters[2] - parameters[1])
+    else:
+        return 0
+
+
+def trapmf(x, parameters):
+    if (x < parameters[0]):
+        return 0
+    elif (x >= parameters[0] and x < parameters[1]):
+        return (x - parameters[0]) / (parameters[1] - parameters[0])
+    elif (x >= parameters[1] and x <= parameters[2]):
+        return 1
+    elif (x >= parameters[2] and x <= parameters[3]):
+        return (parameters[3] - x) / (parameters[3] - parameters[2])
+    else:
+        return 0
+
+
+def gaussmf(x, parameters):
+    return math.exp(-0.5 * ((x - parameters[0]) / parameters[1]) ** 2)
+
+
+def bellmf(x, parameters):
+    return 1 / (1 + abs((x - parameters[2]) / parameters[0]) ** (2 * parameters[1]))
+
+
+def sigmf(x, parameters):
+    return 1 / (1 + math.exp(-parameters[0] * (x - parameters[1])))

common/Transformations.py

@@ -0,0 +1,8 @@
+import numpy as np
+from pyFTS import *
+
+def differential(original):
+    n = len(original)
+    diff = [ original[t-1]-original[t] for t in np.arange(1,n) ]
+    diff.insert(0,0)
+    return np.array(diff)