This commit is contained in:
Petrônio Cândido 2021-02-22 14:48:24 -03:00
commit 22de024b8f
2 changed files with 58 additions and 5 deletions

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@ -1,10 +1,9 @@
from pyFTS.common.transformations.transformation import Transformation from pyFTS.common.transformations.transformation import Transformation
import numpy as np import numpy as np
class MovingAverage(Transformation): class MovingAverage(Transformation):
def __init__(self, **kwargs): def __init__(self, **kwargs):
super(MovingAverage, self).__init__() super(MovingAverage, self).__init__(**kwargs)
self.name = 'Moving Average Smoothing' self.name = 'Moving Average Smoothing'
self.steps = kwargs.get('steps',2) self.steps = kwargs.get('steps',2)
@ -20,8 +19,8 @@ class MovingAverage(Transformation):
class ExponentialSmoothing(Transformation): class ExponentialSmoothing(Transformation):
def __init__(self, **kwargs): def __init__(self, **kwargs):
super(MovingAverage, self).__init__() super(ExponentialSmoothing,self).__init__(**kwargs)
self.name = 'Moving Average Smoothing' self.name = 'Exponential Moving Average Smoothing'
self.steps = kwargs.get('steps',2) self.steps = kwargs.get('steps',2)
self.beta = kwargs.get('beta',.5) self.beta = kwargs.get('beta',.5)
@ -39,3 +38,53 @@ class ExponentialSmoothing(Transformation):
def inverse(self, data, param=None, **kwargs): def inverse(self, data, param=None, **kwargs):
return data 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

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@ -5,6 +5,10 @@ import setuptools
setuptools.setup( setuptools.setup(
name='pyFTS', name='pyFTS',
install_requires=[
'matplotlib',
'numpy',
'pandas'],
packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.common.transformations', 'pyFTS.data', packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.common.transformations', 'pyFTS.data',
'pyFTS.models.ensemble', 'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.models.ensemble', 'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners',
'pyFTS.probabilistic', 'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate', 'pyFTS.probabilistic', 'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate',