def SVM_quad_ordpos(subject):
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'WithinChunkPosition',
load_residuals_regression=True,
list_sequences=[4],
crop=[-0.1, 0.4],
cross_val_func=None,
filter_from_metadata="StimPosition > 2 and StimPosition < 15")
save_name = config.SVM_path + subject + '/feature_decoding/' + 'resid_' + 'WithinChunkPosition' + '_quads_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'WithinChunkPosition',
load_residuals_regression=False,
list_sequences=[4],
crop=[-0.1, 0.4],
cross_val_func=None,
filter_from_metadata="StimPosition > 2 and StimPosition < 15")
save_name = config.SVM_path + subject + '/feature_decoding/' + 'full_data_' + 'WithinChunkPosition' + '_quads_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})
def SVM_features_chunkBeg(subject, load_residuals_regression=True):
if load_residuals_regression:
resid_suffix = 'resid_cv_'
else:
resid_suffix = 'full_data_'
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'ChunkBeginning',
load_residuals_regression=load_residuals_regression,
list_sequences=[3, 4, 5, 6, 7],
crop=[-0.1, 0.4],
cross_val_func=None)
save_name = config.SVM_path + subject + '/feature_decoding/' + resid_suffix + 'ChunkBeg' + '_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})
def SVM_features_stimID_eeg(subject, load_residuals_regression=True):
if load_residuals_regression:
resid_suffix = 'resid_cv_'
else:
resid_suffix = 'full_data_'
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'StimID',
load_residuals_regression=load_residuals_regression,
crop=[-0.1, 0.4],
cross_val_func=None,
meg=False)
save_name = config.SVM_path + subject + '/feature_decoding/' + resid_suffix + 'StimID' + '_EEGONLY_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})
def SVM_features_withinchunk_train_quads_test_others(
subject, load_residuals_regression=True):
if load_residuals_regression:
resid_suffix = 'resid_cv_'
else:
resid_suffix = 'full_data_'
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'WithinChunkPosition',
load_residuals_regression=load_residuals_regression,
crop=[-0.1, 0.4],
cross_val_func=SVM_funcs.train_quads_test_others,
balance_features=False,
filter_from_metadata="StimPosition > 2 and StimPosition < 15")
save_name = config.SVM_path + subject + '/feature_decoding/' + resid_suffix + 'WithinChunkPosition_train_Quads_test_others' + '_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})
def SVM_features_number_ofOpenedChunks(subject,
load_residuals_regression=True):
if load_residuals_regression:
resid_suffix = 'resid_cv_'
else:
resid_suffix = 'full_data_'
score, distance, times = SVM_funcs.SVM_decode_feature(
subject,
'OpenedChunks',
SVM_dec=SVM_funcs.regression_decoder(),
load_residuals_regression=load_residuals_regression,
list_sequences=[3, 4, 5, 6, 7],
crop=[-0.1, 0.4],
cross_val_func=None,
balance_features=False,
distance=False)
save_name = config.SVM_path + subject + '/feature_decoding/' + resid_suffix + 'Number_Open_Chunks' + '_score_dict.npy'
np.save(save_name, {'score': score, 'times': times, 'distance': distance})