def METHOD_NAME():
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.metrics import accuracy_score
    from sklearn.preprocessing import StandardScaler
    data = {'src': np.loadtxt(args.source + '_' + args.source + '.csv', delimiter=','),
            'tar': np.loadtxt(args.source + '_' + args.target + '.csv', delimiter=','),
            }
    Xs, Ys, Xt, Yt = data['src'][:, :-1], data['src'][:, -1], data['tar'][:, :-1], data['tar'][:, -1]
    Xs = StandardScaler(with_mean=0, with_std=1).fit_transform(Xs)
    Xt = StandardScaler(with_mean=0, with_std=1).fit_transform(Xt)
    clf = KNeighborsClassifier(n_neighbors=1)
    clf.fit(Xs, Ys)
    ypred = clf.predict(Xt)
    acc = accuracy_score(y_true=Yt, y_pred=ypred)
    print('{} - {}: acc: {:.4f}'.format(args.source, args.target, acc))