Refatoração dos métodos de exibir e salvar gráfico

benchmarks/benchmarks.py

@@ -10,10 +10,8 @@ from mpl_toolkits.mplot3d import Axes3D
 #from sklearn.cross_validation import KFold
 from pyFTS.benchmarks import Measures
 from pyFTS.partitioners import Grid
-from pyFTS.common import Membership, FuzzySet, FLR, Transformations
+from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-import time
 
-current_milli_time = lambda: int(round(time.time() * 1000))
 
 def getIntervalStatistics(original, models):
     ret = "Model		& RMSE		& MAPE		& Sharpness		& Resolution		& Coverage	\\ \n"
@@ -37,15 +35,6 @@ def plotDistribution(dist):
                     vmin=0, vmax=1, edgecolors=None)
 
 
-def uniquefilename(name):
-    if '.' in name:
-        tmp = name.split('.')
-        return  tmp[0] + str(current_milli_time()) + '.' + tmp[1]
-    else:
-        return name + str(current_milli_time())
-
-
-
 def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5]):
     fig = plt.figure(figsize=tam)
     ax = fig.add_subplot(111)
@@ -89,10 +78,9 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
     ax.set_xlabel('T')
     ax.set_xlim([0, len(original)])
 
-    if save:
+    Util.showAndSaveImage(fig,file,save)
-        plt.show()
+
-        fig.savefig(uniquefilename(file))
+
-        plt.close(fig)
 
 
 def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to,
@@ -158,10 +146,7 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
     ax.set_xlabel('T')
     ax.set_xlim([0, len(original)])
 
-    if save:
+    Util.showAndSaveImage(fig, file, save)
-        plt.show()
-        fig.savefig(uniquefilename(file))
-        plt.close(fig)
 
 
 def plotCompared(original, forecasts, labels, title):

common/Transformations.py

@@ -17,3 +17,10 @@ def boxcox(original, plambda):
     else:
         modified = [math.log(original[t]) for t in np.arange(0, n)]
     return np.array(modified)
+
+
+def Z(original):
+    mu = np.mean(original)
+    sigma = np.std(original)
+    z = [(k - mu)/sigma for k in original]
+    return z

common/Util.py

@@ -0,0 +1,20 @@
+import time
+import matplotlib.pyplot as plt
+
+
+current_milli_time = lambda: int(round(time.time() * 1000))
+
+
+def uniquefilename(name):
+    if '.' in name:
+        tmp = name.split('.')
+        return  tmp[0] + str(current_milli_time()) + '.' + tmp[1]
+    else:
+        return name + str(current_milli_time())
+
+
+def showAndSaveImage(fig,file,flag):
+    if flag:
+        plt.show()
+        fig.savefig(uniquefilename(file))
+        plt.close(fig)

partitioners/Util.py

@@ -4,12 +4,12 @@ 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
+from pyFTS.common import Membership, Util
 
 
-def plotSets(data, sets, titles):
+def plotSets(data, sets, titles, tam=[12, 10], save=False, file=None):
     num = len(sets)
-    fig = plt.figure(figsize=[12, 10])
+    fig = plt.figure(figsize=tam)
     maxx = max(data)
     minx = min(data)
     h = 1/num
@@ -24,4 +24,6 @@ def plotSets(data, sets, titles):
             elif s.mf == Membership.gaussmf:
                 tmpx = [ kk for kk in np.arange(s.lower, s.upper)]
                 tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)]
-                ax0.plot(tmpx, tmpy)
+                ax0.plot(tmpx, tmpy)
+
+    Util.showAndSaveImage(fig, file, save)

pfts.py

@@ -204,7 +204,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
             # gerar o intervalo
             norm = sum(norms)
             if norm == 0:
-                ret.append([0, 0])
+                ret.append(0)
             else:
                 ret.append(sum(mp) / norm)