# "+ scaling"
# main_compute([shallow_args, sleepstager_args],
# [dl_dataset_args, dl_dataset_args],
# train_sample, valid_sample, test_sample,
# sample_size_list, saving_params)
train_sample, valid_sample, test_sample = get_epochs_data(
train_subjects=range(0, 10), valid_subjects=range(10, 15),
test_subjects=range(15, 25),
preprocessing=["microvolt_scaling", "filtering"])
dl_dataset_args_with_transforms["transform_list"] = [
["add_noise_to_signal"]]
for magnitude in [0, 0.2, 0.4, 0.6, 0.8, 1, 2, 3]:
transforms_args["magnitude"] = magnitude
dl_dataset_args_with_transforms["transform_type"] = "gaussian noise, "\
"scaling, filtering" \
"+ magnitude : " + str(magnitude)
main_compute([sleepstager_args], [dl_dataset_args_with_transforms],
transforms_args, train_sample, valid_sample, test_sample,
sample_size_list, saving_params)
# dl_dataset_args["transform_type"] = "raw (no transforms)" \
# "+ scaling, filtering"
# dl_dataset_args_with_transforms["transform_type"] = \
# "masking + scaling, filtering"
# run_handcrafted_features(train_sample, test_sample)
def dummy_handcrafted_features(train_sample, valid_sample, test_sample):
main_compute([hf_args], [hf_dataset_args], transforms_args, train_sample,
valid_sample, test_sample, sample_size_list, saving_params)
def dummy_sleepstagernet(train_sample, valid_sample, test_sample):
main_compute([sleepstager_args], [dl_dataset_args], transforms_args,
train_sample, valid_sample, test_sample, sample_size_list,
saving_params)
def dummy_shallownet_with_transf(train_sample, valid_sample, test_sample):
main_compute([shallow_args], [dl_dataset_args_with_transforms],
transforms_args, train_sample, valid_sample, test_sample,
sample_size_list, saving_params)
