- fixing exponential moving average

- adding MaxPooling and AveragePooling transformations

pyFTS/common/transformations/smoothing.py

@@ -1,10 +1,11 @@
 from pyFTS.common.transformations.transformation import Transformation 
 import numpy as np
+from math import ceil
 
 
 class MovingAverage(Transformation):
     def __init__(self, **kwargs):
-        super(MovingAverage, self).__init__()
+        super(MovingAverage, self).__init__(**kwargs)
         self.name = 'Moving Average Smoothing'
         self.steps = kwargs.get('steps',2)
 
@@ -20,8 +21,8 @@ class MovingAverage(Transformation):
 
 class ExponentialSmoothing(Transformation):
     def __init__(self, **kwargs):
-        super(MovingAverage, self).__init__()
-        self.name = 'Moving Average Smoothing'
+        super(ExponentialSmoothing,self).__init__(**kwargs)
+        self.name = 'Exponential Moving Average Smoothing'
         self.steps = kwargs.get('steps',2)
         self.beta = kwargs.get('beta',.5)
 
@@ -39,3 +40,53 @@ class ExponentialSmoothing(Transformation):
 
     def inverse(self, data, param=None, **kwargs):
         return data
+
+
+class AveragePooling(Transformation):
+    def __init__(self, **kwargs):
+        super(AveragePooling,self).__init__(**kwargs)
+        self.name = 'Exponential Average Smoothing'
+        self.kernel = kwargs.get('kernel',5)
+        self.stride = kwargs.get('stride',1)
+        self.padding = kwargs.get('padding','same')
+
+    def apply(self, data):
+        result = []
+        if self.padding == 'same':
+            for i in range(int(self.kernel/2), len(data)+int(self.kernel/2), self.stride):
+                result.append(np.mean(data[np.max([0,i-self.kernel]):np.min([i, len(data)])]))
+
+        elif self.padding == 'valid':
+            for i in range(self.kernel, len(data), self.stride):
+                result.append(np.mean(data[i-self.kernel:i]))
+        else:
+            raise ValueError('Invalid padding schema')
+        return result
+
+    def inverse(self, data, param=None, **kwargs):
+        return data
+
+
+class MaxPooling(Transformation):
+    def __init__(self, **kwargs):
+        super(MaxPooling,self).__init__(**kwargs)
+        self.name = 'Exponential Average Smoothing'
+        self.kernel = kwargs.get('kernel',5)
+        self.stride = kwargs.get('stride',1)
+        self.padding = kwargs.get('padding','same')
+
+    def apply(self, data):
+        result = []
+        if self.padding == 'same':
+            for i in range(int(self.kernel/2), len(data)+int(self.kernel/2), self.stride):
+                result.append(np.max(data[np.max([0,i-self.kernel]):np.min([i, len(data)])]))
+
+        elif self.padding == 'valid':
+            for i in range(self.kernel - 1, len(data), self.stride):
+                result.append(np.max(data[i-self.kernel:i]))
+        else:
+            raise ValueError('Invalid padding schema')
+        return result
+
+    def inverse(self, data, param=None, **kwargs):
+        return data