# imports
import torch
import numpy as np
import torch.nn.functional as F
# classes
[docs]class RemoveOffset(object):
"""
Remove on all events the offset.
:param keys: The keys in the each sample-dict from that we want to remove the offset.
:type keys: list of strings
"""
def __init__(self, keys):
self.keys = keys
def __call__(self, sample):
"""
This gets applied to every sample.
:param sample: Contains the features as 1D numpy arrays.
:type sample: dictionary
:return: Contains the features as 1D arrays with no offset an all keys that are in self.keys.
:rtype: dictionary
"""
for key in self.keys:
sample[key] = sample[key] - np.mean(sample[key][:int(len(sample[key]) / 8)])
return sample
[docs]class Normalize(object):
"""
Normalize Features to given mean and std.
:param norm_vals: Each key corresponds to a key in the sample and is a list of length two: [mean, std],
or if type = 'minmax' then [min, max].
:type norm_vals: dictionary
:param type: 'z' for calculating Z-scores or 'minmax' of scaling from 0 to 1.
:type type: string
"""
def __init__(self, norm_vals, type='z'):
self.norm_vals = norm_vals
self.type = type
def __call__(self, sample):
"""
This gets applied to every sample.
:param sample: Contains the features as 1D numpy arrays.
:type sample: dictionary
:return: Contains the scaled features as 1D arrays.
:rtype: dictionary
"""
if self.type == 'z':
for k in self.norm_vals.keys():
mean, std = self.norm_vals[k]
sample[k] = (sample[k] - mean) / std
elif self.type == 'minmax':
for k in self.norm_vals.keys():
min, max = self.norm_vals[k]
sample[k] = (sample[k] - min) / (max - min)
return sample
[docs]class DownSample(object):
"""
Sample all the time series down.e
:param keys: The keys in each sample-dist we want to downsample.
:type keys: list of strings
"""
def __init__(self, keys, down):
self.keys = keys
self.down = down
def __call__(self, sample):
"""
This gets applied to every sample.
:param sample: Contains the features as 1D numpy arrays.
:type sample: dictionary
:return: Contains the downsampled features as 1D arrays.
:rtype: dictionary
"""
for key in self.keys:
sample[key] = np.mean(sample[key].
reshape(int(len(sample[key]) / self.down), self.down),
axis=1)
return sample
[docs]class ToTensor(object):
"""
Convert numpy arrays in sample to Tensors.
"""
def __call__(self, sample):
"""
This gets applied to every sample.
:param sample: Contains the features as 1D numpy arrays.
:type sample: dictionary
:return: Contains the features as 1D torch tensors.
:rtype: dictionary
"""
for key in sample.keys():
sample[key] = torch.from_numpy(sample[key]).float()
return sample
class OneHotEncode(object):
"""
One Hot Encode the label incides.
:param keys: The keys in each sample-dist we want to one hot encode.
:type keys: list of strings
:param nmbr_classes: The number of classes to that we one hot encode.
:type nmbr_classes: int
"""
def __init__(self, keys, nmbr_classes):
self.keys = keys
self.nmbr_classes = nmbr_classes
def __call__(self, sample):
"""
This gets applied to every sample.
:param sample: Contains the features as 1D numpy arrays.
:type sample: dictionary
:return: Contains the features as 1D arrays with the keys in self.keys one hot encoded.
:rtype: dictionary
"""
for key in self.keys:
sample[key] = F.one_hot(sample[key].long(), num_classes=self.nmbr_classes)
return sample