def load_data(path, name_seq, name_track, labels_folder):
"""
Load data from csv file
"""
list_states = []
data_base = pd.read_csv(path + '/data_csv/' + name_seq + '.csv')
ref_data = DataBase(path + '/', name_seq)
list_features = list(data_base.columns.values)
del list_features[0:2]
dim_features = np.ones(len(list_features))
time = data_base['timestamps']
labels, states = ref_data.load_labels_refGT(time, name_track, 'labels_3A')
data = data_base[list_features].as_matrix()
for state in states:
if (state not in list_states):
list_states.append(state)
list_states = sorted(list_states)
return data, labels, time, list_states, list_features
for file in list_files:
name_seq = os.path.splitext(file)[0]
info_participant.append(participant)
info_sequences.append(name_seq)
data_base = pd.read_csv(path_data + file)
ref_data = DataBase(path + '/' + participant, name_seq)
list_features = list(data_base.columns.values)
del list_features[0:2]
dim_features = np.ones(len(list_features))
time = data_base['timestamps']
labels, states = ref_data.load_labels_refGT(
time, name_track, 'labels_3A')
real_labels.append(labels)
data_win2.append(data_base[list_features].as_matrix())
timestamps.append(time)
for state in states:
if (state not in list_states):
list_states.append(state)
list_states = sorted(list_states)
i += 1
####################################################################################
# Feature Selection
####################################################################################
obs = []
