def SVM_features_number_ofOpenedChunks(subject,
load_residuals_regression=False,
cleaned=True):
SVM_funcs.SVM_feature_decoding_wrapper(
subject,
'OpenedChunks',
SVM_dec=SVM_funcs.regression_decoder(),
balance_features=False,
distance=False,
load_residuals_regression=load_residuals_regression,
cross_val_func=None,
list_sequences=[3, 4, 5, 6, 7])
def SVM_features_sequence_structure7(subject,
load_residuals_regression=False,
cleaned=True):
cross_val_func = SVM_funcs.leave_one_sequence_out
SVM_funcs.SVM_feature_decoding_wrapper(
subject,
'ChunkDepth',
load_residuals_regression=load_residuals_regression,
cross_val_func=cross_val_func,
list_sequences=[3, 4, 5, 6],
nvalues_feature=4,
SVM_dec=SVM_funcs.regression_decoder(),
balance_features=False,
distance=False,
clean=cleaned)
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})