def preprocess_data(self, x, y=None, **kwargs):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have lie on the interval [-1, 1]'''
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
min_, max_, = -1, 1
mins, maxes = preprocessing.scale_min_max(x, min_, max_)
if 'channel' in kwargs:
channel = kwargs['channel']
else:
channel = None
preprocessing.standardize_cylinder_rotation(x, channel)
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
preprocessing.standardize_cylinder_rotation(other, channel)
return repeat_transformation
def preprocess_data(self, x, y=None, **kwargs):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have lie on the interval [-1, 1]'''
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
min_, max_, = -1, 1
mins, maxes = preprocessing.scale_min_max(x, min_, max_)
if 'channel' in kwargs:
channel = kwargs['channel']
else:
channel = None
preprocessing.standardize_cylinder_rotation(x, channel)
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
preprocessing.standardize_cylinder_rotation(other, channel)
return repeat_transformation
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other /= stds/std
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other /= stds / std
def repeat_transformation(other):
if len(other) == 0:
return
else:
if not getattr(self, 'only_charge', False):
preprocessing.fix_time_zeros(other)
other -= means
other /= stds / std
def repeat_transformation(other):
if len(other) == 0:
return
else:
if not getattr(self, 'only_charge', False):
preprocessing.fix_time_zeros(other)
other -= means
other /= stds/std
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
preprocessing.standardize_cylinder_rotation(other, channel)
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
preprocessing.standardize_cylinder_rotation(other, channel)
def preprocess_data(self, x, y=None):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have a standard deviation of 1'''
std = 1
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
stds = preprocessing.scale(x, std, mode='standardize')
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other /= stds/std
return repeat_transformation
def preprocess_data(self, x, y=None):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have a standard deviation of 1'''
std = 1
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
stds = preprocessing.scale(x, std, mode='standardize')
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other /= stds / std
return repeat_transformation
def preprocess_data(self, x, y=None):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have lie on the interval [-1, 1]'''
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
min_, max_, = -1, 1
mins, maxes = preprocessing.scale_min_max(x, min_, max_)
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
return repeat_transformation
def preprocess_data(self, x, y=None):
'''Prepare the data for the neural network.
- Remove 0's from the time channels
- Center the data on 0
- Scale it to have lie on the interval [-1, 1]'''
preprocessing.fix_time_zeros(x)
means = preprocessing.center(x)
min_, max_, = -1, 1
mins, maxes = preprocessing.scale_min_max(x, min_, max_)
def repeat_transformation(other):
if len(other) == 0:
return
else:
preprocessing.fix_time_zeros(other)
other -= means
other -= mins
other /= maxes - mins
other *= max_ - min_
other += min_
return repeat_transformation
