Particionamento por entropia

partitioners/Entropy.py

@@ -8,12 +8,15 @@ 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)])
@@ -23,6 +26,9 @@ def PMF(data, threshold):
 
 def entropy(data, threshold):
     pmf = PMF(data, threshold)
+    if pmf[0] == 0 or pmf[1] == 0:
+        return 1
+    else:
         return - sum([pmf[0] * math.log(pmf[0]), pmf[1] * math.log(pmf[1])])
 
 
@@ -33,13 +39,19 @@ def informationGain(data, thres1, thres2):
 def bestSplit(data, npart):
     if len(data) < 2:
         return None
-    count = 2
+    count = 1
     ndata = list(set(data))
     ndata.sort()
+    l = len(ndata)
     threshold = 0
-    while informationGain(data, ndata[count - 1], ndata[count]) <= 0:
+    try:
+        while count < l and informationGain(data, ndata[count - 1], ndata[count]) <= 0:
             threshold = ndata[count]
             count += 1
+    except IndexError:
+        print(threshold)
+        print (ndata)
+        print (count)
 
     rem = npart % 2
 
@@ -54,23 +66,31 @@ def bestSplit(data, npart):
             np1 = (npart - rem) / 2
             np2 = (npart - rem) / 2 + rem
 
-        return [ threshold, bestSplit(p1, np1 ), bestSplit(p2, np2 )  ]
+        tmp = [threshold]
+
+        for k in bestSplit(p1, np1 ): tmp.append(k)
+        for k in bestSplit(p2, np2 ): tmp.append(k)
+
+        return tmp
 
     else:
-        return threshold
+        return [threshold]
+
 
 def EntropyPartitionerTrimf(data, npart, prefix="A"):
+    sets = []
     dmax = max(data)
     dmax += dmax * 0.10
     dmin = min(data)
     dmin -= dmin * 0.10
 
-    sets = [dmin, bestSplit(data, npart), dmax]
+    partitions = bestSplit(data, npart)
-
+    partitions.append(dmin)
-    sets.sort()
+    partitions.append(dmax)
-    for c in np.arange(1, len(sets) - 1):
+    partitions = list(set(partitions))
+    partitions.sort()
+    for c in np.arange(1, len(partitions) - 1):
         sets.append(FuzzySet.FuzzySet(prefix + str(c), Membership.trimf,
-                                      [round(sets[c - 1], 3), round(sets[c], 3),
+                                      [partitions[c - 1], partitions[c], partitions[c + 1]],partitions[c]))
-                                       round(sets[c + 1], 3)],round(sets[c], 3)))
 
     return sets

partitioners/Util.py

@@ -4,17 +4,18 @@ import matplotlib as plt
 import matplotlib.colors as pltcolors
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
-
 from pyFTS.common import Membership
 
-def plotSets(data, sets):
+
+def plotSets(data, sets, titles):
     num = len(sets)
-    fig = plt.figure(figsize=[20, 10])
+    fig = plt.figure(figsize=[12, 10])
     maxx = max(data)
     minx = min(data)
     h = 1/num
     for k in range(num):
-        ax0 = fig.add_axes([0, (k+1)*h, 0.65, h])  # left, bottom, width, height
+        ax0 = fig.add_axes([0, (k+1)*h, 0.65, h*0.7])  # left, bottom, width, height
+        ax0.set_title(titles[k])
         ax0.set_ylim([0, 1])
         ax0.set_xlim([minx, maxx])
         for s in sets[k]: