def main_EIV_single_from_template(myfilename, template_path):
# x_test,y_test,e_test,t_test = make_epochs_EIV(myfilename, chnames, bandpass = (1.0,20.0), filtre_order = 2 ,delta=0.6, target=[2], nontarget=[1])
centroids_train = np.load(template_path + 'Centroids_List.npy')
Covmats_Dict = np.load(template_path + 'Covmats_Dict.npy')
Covmats_Dict = Covmats_Dict.item()
chnames = Covmats_Dict['channel names']
ERP_train = np.load(template_path + 'ERP_Array.npy')
erp_train = ERPCovariances(estimator='cov')
erp_train.P = ERP_train
# X_test = erp_train.transform(x_test)
r_TNT_mu_List = np.load(template_path + 'rTNT_mu.npy')
r_TNT_var_List = np.load(template_path + 'rTNT_var.npy')
data, labels, event, target = get_data_from_csv_EIV(myfilename=myfilename,
chnames=chnames)
mean, var = generic_test_loop(data,
labels,
event,
ERP_train,
centroids_train,
r_TNT_mu_List,
r_TNT_var_List,
column_number=7,
nb_repetitions=4,
items_list=[1, 2, 3, 4, 5, 6, 7],
visu=False,
flashmode='EIV')
return mean, var
def distribution_single_from_template(test_sub, test_sess, template_path,
database):
x_test, y_test, e_test, t_test, _, _ = database.subjects[
test_sub].get_data([test_sess])
ERP_train = np.load(template_path + 'ERP_Array.npy')
erp_train = ERPCovariances(estimator='cov')
erp_train.P = ERP_train
X_test = erp_train.transform(x_test)
centroids_train = np.load(template_path + 'Centroids_List.npy')
erp_train = ERPCovariances(estimator='cov')
erp_train.P = ERP_train
r_TNT_test = predict_R_TNT(X=X_test, centroids_list=centroids_train)
return r_TNT_test, y_test
def test_from_cross_template_P300(template_path,
subject_path,
test_chnames,
flashmode='RoCo',
nb_targets=180,
visu=False):
T = 0
NT = 1
ERP = np.load(template_path + 'ERP_Array.npy')
Centroids_List = np.load(template_path + 'Centroids_List.npy')
mu_TNT = np.load(template_path + 'rTNT_mu.npy')
sigma_TNT = np.load(template_path + 'rTNT_var.npy')
data, labels, event = get_data_from_csv_EIV(
myfilename=subject_path + '-signals.csv',
markersfile=subject_path + 'markers.csv',
chnames=test_chnames)
erp = ERPCovariances()
erp.P = ERP
erp.estimator = 'cov'
X = erp.transform(data)
train_NaiveBayes = R_TNT_NaiveBayes(targets=[
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '1', '2',
'3', '4', '5', '6', '7', '8', '9', '_'
],
mu_TNT=mu_TNT,
sigma_TNT=sigma_TNT,
class_prior=None)
dist = [
np.array([distance(x, Centroids_List[l]) for i, x in enumerate(X)])
for l in [T, NT]
]
r_TNT = np.array(np.log(dist[0] / dist[1]))
mean, var = test_loop_P300(r_TNT_test=r_TNT,
y_test=labels,
e_test=event,
train_NaiveBayes=train_NaiveBayes,
T=0,
NT=1,
flashmode=flashmode,
visu=visu,
nb_targets=nb_targets)
return mean, var