from pyFTS.common.transformations.transformation import Transformation
import numpy as np
[docs]class MovingAverage(Transformation):
def __init__(self, **kwargs):
super(MovingAverage, self).__init__(**kwargs)
self.name = 'Moving Average Smoothing'
self.steps = kwargs.get('steps',2)
[docs] def apply(self, data, param=None, **kwargs):
steps = param if param is not None else self.steps
ma = [k for k in data[:steps]]
ma.extend([np.mean(data[k-steps:k]) for k in range(steps, len(data))])
return ma
[docs] def inverse(self, data, param=None, **kwargs):
return data
[docs]class ExponentialSmoothing(Transformation):
def __init__(self, **kwargs):
super(ExponentialSmoothing,self).__init__(**kwargs)
self.name = 'Exponential Moving Average Smoothing'
self.steps = kwargs.get('steps',2)
self.beta = kwargs.get('beta',.5)
[docs] def apply(self, data, param=None, **kwargs):
steps = param if param is not None else self.steps
beta = kwargs.get('beta',None)
beta = beta if beta is not None else self.beta
mm = [k for k in data[:steps]]
for i in range(steps, len(data)):
ret = 0
for k in np.arange(0,steps):
ret += ( beta * (1 - beta) ** k ) * data[i - k]
mm.append(ret)
return mm
[docs] def inverse(self, data, param=None, **kwargs):
return data
[docs]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')
[docs] 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
[docs] def inverse(self, data, param=None, **kwargs):
return data
[docs]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')
[docs] 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
[docs] def inverse(self, data, param=None, **kwargs):
return data
