clf_model = SVC(C=1)
elif args.clf_model == 'ELMK':
clf_model = elm.ELMKernel()
elif args.clf_model == 'ELMR':
clf_model = elm.ELMRandom()
if args.clf_model in ['ELMK', 'ELMR']:
train_elm_data = np.concatenate(
(train_label[:, np.newaxis], train_data), axis=1)
test_elm_data = np.concatenate(
(test_label[:, np.newaxis], test_data), axis=1)
clf_model.search_param(train_elm_data,
cv="kfold",
of="accuracy",
eval=10)
train_acc = clf_model.train(train_elm_data).get_accuracy()
test_acc = clf_model.test(test_elm_data).get_accuracy()
elif args.clf_model in ['pcafc', 'pcafc_sd']:
train_acc, test_acc = deep_classify(train_data, test_data,
train_label, test_label,
num_signal_features, i_exp)
else:
train_pred, test_pred = classify(clf_model, train_data,
train_label, test_data)
# Calculate error
train_acc = np.sum(train_pred == train_label) / len(train_pred)
test_acc = np.sum(test_pred == test_label) / len(test_pred)
dict_error['train_acc'].update(train_acc)
dict_error['test_acc'].update(test_acc)
