Implementação inicial do particionamento por entropia

common/FuzzySet.py

@@ -1,5 +1,6 @@
 import numpy as np
 from pyFTS import *
+from pyFTS.common import Membership
 
 
 class FuzzySet:
@@ -8,8 +9,12 @@ class FuzzySet:
         self.mf = mf
         self.parameters = parameters
         self.centroid = centroid
-        self.lower = min(parameters)
-        self.upper = max(parameters)
+        if self.mf == Membership.trimf:
+            self.lower = min(parameters)
+            self.upper = max(parameters)
+        elif self.mf == Membership.gaussmf:
+            self.lower = parameters[0] - parameters[1]*3
+            self.upper = parameters[0] + parameters[1]*3
 
     def membership(self, x):
         return self.mf(x, self.parameters)

common/Membership.py

@@ -29,7 +29,8 @@ def trapmf(x, parameters):
 
 
 def gaussmf(x, parameters):
-    return math.exp(-0.5 * ((x - parameters[0]) / parameters[1]) ** 2)
+    return math.exp((-(x - parameters[0])**2)/(2 * parameters[1]**2))
+    #return math.exp(-0.5 * ((x - parameters[0]) / parameters[1]) ** 2)
 
 
 def bellmf(x, parameters):

partitioners/Entropy.py

@@ -0,0 +1,76 @@
+import numpy as np
+import math
+import random as rnd
+import functools, operator
+from pyFTS.common import FuzzySet, Membership
+
+
+# C. H. Cheng, R. J. Chang, and C. A. Yeh, “Entropy-based and trapezoidal fuzzification-based fuzzy time series approach for forecasting IT project cost,”
+# Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524–542, Jun. 2006.
+
+def splitBelow(data,threshold):
+    return [k for k in data if k <= threshold]
+
+def splitAbove(data,threshold):
+    return [k for k in data if k > threshold]
+
+def PMF(data, threshold):
+    a = sum([1.0 for k in splitBelow(data,threshold)])
+    b = sum([1.0 for k in splitAbove(data, threshold)])
+    l = len(data)
+    return [a / l, b / l]
+
+
+def entropy(data, threshold):
+    pmf = PMF(data, threshold)
+    return - sum([pmf[0] * math.log(pmf[0]), pmf[1] * math.log(pmf[1])])
+
+
+def informationGain(data, thres1, thres2):
+    return entropy(data, thres1) - entropy(data, thres2)
+
+
+def bestSplit(data, npart):
+    if len(data) < 2:
+        return None
+    count = 2
+    ndata = list(set(data))
+    ndata.sort()
+    threshold = 0
+    while informationGain(data, ndata[count - 1], ndata[count]) <= 0:
+        threshold = ndata[count]
+        count += 1
+
+    rem = npart % 2
+
+    if (npart - rem)/2 > 1:
+        p1 = splitBelow(data,threshold)
+        p2 = splitAbove(data,threshold)
+
+        if len(p1) > len(p2):
+            np1 = (npart - rem)/2 + rem
+            np2 = (npart - rem)/2
+        else:
+            np1 = (npart - rem) / 2
+            np2 = (npart - rem) / 2 + rem
+
+        return [ threshold, bestSplit(p1, np1 ), bestSplit(p2, np2 )  ]
+
+    else:
+        return threshold
+
+def EntropyPartitionerTrimf(data, npart, prefix="A"):
+    dmax = max(data)
+    dmax += dmax * 0.10
+    dmin = min(data)
+    dmin -= dmin * 0.10
+
+    sets = [dmin, bestSplit(data, npart), dmax]
+
+    sets.sort()
+    for c in np.arange(1, len(sets) - 1):
+        sets.append(FuzzySet.FuzzySet(prefix + str(c), Membership.trimf,
+                                      [round(sets[c - 1], 3), round(sets[c], 3),
+                                       round(sets[c + 1], 3)],round(sets[c], 3)))
+
+    return sets

partitioners/Grid.py

@@ -23,3 +23,21 @@ def GridPartitionerTrimf(data, npart, names=None, prefix="A"):
         partition += partlen
 
     return sets
+
+
+def GridPartitionerGaussmf(data, npart, names=None, prefix="A"):
+    sets = []
+    dmax = max(data)
+    dmax += dmax * 0.10
+    dmin = min(data)
+    dmin -= dmin * 0.10
+    dlen = dmax - dmin
+    partlen = math.ceil(dlen / npart)
+    partition = math.ceil(dmin)
+    for c in range(npart):
+        sets.append(
+            FuzzySet.FuzzySet(prefix + str(c), Membership.gaussmf, [partition, partlen/3],
+                     partition))
+        partition += partlen
+
+    return sets

partitioners/Huarng.py

@@ -12,8 +12,6 @@ def GridPartitionerTrimf(data, prefix="A"):
     data2 = Transformations.differential(data)
     davg = np.abs( np.mean(data2) / 2 )
 
-    print(davg)
-
     if davg <= 1.0:
         base = 0.1
     elif 1 < davg <= 10: