# # predicting
start = timeit.default_timer()
y_targets = ocsvm.predict(clf, pseudo_targets)
stop = timeit.default_timer()
final_target = stop - start
start = timeit.default_timer()
y_outliers = ocsvm.predict(clf, pseudo_outliers)
stop = timeit.default_timer()
final_outlier = stop - start
targets = np.column_stack((pseudo_targets, y_targets))
outliers = np.column_stack((pseudo_outliers, y_outliers))
pred_testset = np.concatenate((targets, outliers))
ocsvm.visualization(clf, new_data, pred_testset, pseudo_targets.shape[0])
writer = csv.writer(open("result_ocsvm.csv", 'w'))
writer.writerow(['Best param:'])
writer.writerow(best_param)
writer.writerow(['Smallest error:'])
writer.writerow([err_min])
writer.writerow(['Best error:'])
writer.writerow(best_err)
writer.writerow(['Error array:'])
for row in error_array:
writer.writerow(row)
writer.writerow(['Full error array:'])
for row in full_err_array:
writer.writerow(row)
writer.writerow(['Average fitting time:'])
