BoxCox transformation; Particionamento de Huarng, implementação inicial

benchmarks/benchmarks.py

@@ -9,12 +9,6 @@ import Measures
 from pyFTS.partitioners import Grid
 from pyFTS.common import Membership,FuzzySet,FLR,Transformations
 
-def Teste(par):
-	x = np.arange(1,par)
-	y = [ yy**yy for yyy in x]
-	plt.plot(x,y)
-
-
 def getIntervalStatistics(original,models):
 	ret = "Model		& RMSE		& MAPE		& Sharpness		& Resolution		& Coverage	\\ \n"
 	for fts in models:
@@ -126,8 +120,8 @@ def plotCompared(original,forecasts,labels,title):
 	fig = plt.figure(figsize=[13,6])
 	ax = fig.add_subplot(111)
 	ax.plot(original,color='k',label="Original")
-	for c in range(0,len(forecasted)):
+	for c in range(0,len(forecasts)):
-		ax.plot(forecasted[c],label=labels[c])
+		ax.plot(forecasts[c],label=labels[c])
 	handles0, labels0 = ax.get_legend_handles_labels()
 	ax.legend(handles0,labels0)
 	ax.set_title(title)

common/Transformations.py

@@ -1,8 +1,19 @@
 import numpy as np
+import math
 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 boxcox(original, plambda):
+    n = len(original)
+    if plambda != 0:
+        modified = [(original[t] ** plambda - 1) / plambda for t in np.arange(0, n)]
+    else:
+        modified = [math.log(original[t]) for t in np.arange(0, n)]
+    return np.array(modified)

partitioners/Huarng.py

@@ -0,0 +1,34 @@
+import numpy as np
+import math
+import random as rnd
+import functools,operator
+from pyFTS.common import FuzzySet,Membership,Transformations
+
+#print(common.__dict__)
+
+def GridPartitionerTrimf(data,npart,names = None,prefix = "A"):
+    data2 = Transformations.differential(data)
+    davg = np.mean(data2)/2
+    if davg <= 1.0:
+        base = 0.1
+    elif davg > 1 and davg <= 10:
+        base = 1.0
+    elif davg > 10 and davg <= 100:
+        base = 10
+    else:
+        base = 100
+
+    sets = []
+    dmax = max(data)
+    dmax = dmax + dmax*0.10
+    dmin = min(data)
+    dmin = dmin - dmin*0.10
+    dlen = dmax - dmin
+    partlen = math.ceil(dlen / npart)
+    partition = math.ceil(dmin)
+    for c in range(npart):
+        sets.append(FuzzySet(prefix+str(c),Membership.trimf,[round(partition-partlen,3), partition, partition+partlen], partition ) )
+        partition = partition + partlen
+
+    return sets
+