def combined_dataset(cls, ids, window_length):
dataset = Dataset.empty()
for id in ids:
session = cls.from_api(id)
windows = list(session.window_gen(window_length=window_length))
dataset = dataset + session.dataset(windows)
return dataset
def full_dataset_gen(cls, window_length, count=1, sessions=None):
if sessions is None:
Print.info("Fetching sessions")
sessions = Session.fetch_all(only_real=True,
include_timeframes=True)
for _ in range(count):
dataset = Dataset.empty()
for session in sessions:
windows = list(session.window_gen(window_length=window_length))
dataset = dataset + session.dataset(windows=windows)
yield dataset
def dataset(self, windows, remove_seconds=0):
if len(windows) == 0:
return Dataset.empty()
n_samples = len(windows)
n_channels = len(self.ch_names)
window_length = np.shape(windows)[1]
X = np.empty([n_samples, n_channels, window_length])
y = np.empty([n_samples], dtype=np.int8)
for i, window in enumerate(windows):
X[i] = window[:, 0:n_channels].T
y[i] = int(max(window[:, -1]))
if remove_seconds > 0:
change_points = []
action_labels = []
for i in range(1, len(y), 1):
if y[i] != y[i - 1]:
change_points.append(i)
action_labels.append(np.max(y[i - 1:i + 1]))
remove_distance = (250 * remove_seconds) / window_length
keep_indices = []
for i in range(len(y)):
label = y[i]
if label == 0:
viable = True
for point in change_points:
if np.abs(i - point) <= remove_distance:
viable = False
if viable:
keep_indices.append(i)
else:
keep_indices.append(i)
X = X[keep_indices]
y = y[keep_indices]
return Dataset(X, y, self.person_id, self.id)
