def METHOD_NAME(self, Xs, Ys, Xt, Yt):
    '''
    Transform and Predict using 1NN as JDA paper did
    :param Xs: ns * n_feature, source feature
    :param Ys: ns * 1, source label
    :param Xt: nt * n_feature, target feature
    :param Yt: nt * 1, target label
    :return: acc, y_pred, list_acc
    '''
    list_acc = []
    X = np.hstack((Xs.T, Xt.T))
    X /= np.linalg.norm(X, axis=0)
    m, n = X.shape
    ns, nt = len(Xs), len(Xt)
    e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
    C = len(np.unique(Ys))
    H = np.eye(n) - 1 / n * np.ones((n, n))
    M = 0
    Y_tar_pseudo = None
    for t in range(self.T):
        N = 0
        M0 = e * e.T * C
        if Y_tar_pseudo is not None and len(Y_tar_pseudo) == nt:
            for c in range(1, C + 1):
                e = np.zeros((n, 1))
                tt = Ys == c
                e[np.where(tt == True)] = 1 / len(Ys[np.where(Ys == c)])
                yy = Y_tar_pseudo == c
                ind = np.where(yy == True)
                inds = [item + ns for item in ind]
                e[tuple(inds)] = -1 / len(Y_tar_pseudo[np.where(Y_tar_pseudo == c)])
                e[np.isinf(e)] = 0
                N = N + np.dot(e, e.T)
        M = M0 + N
        M = M / np.linalg.norm(M, 'fro')
        K = kernel(self.kernel_type, X, None, gamma=self.gamma)
        n_eye = m if self.kernel_type == 'primal' else n
        a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T])
        w, V = scipy.linalg.eig(a, b)
        ind = np.argsort(w)
        A = V[:, ind[:self.dim]]
        Z = np.dot(A.T, K)
        Z /= np.linalg.norm(Z, axis=0)
        Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T
        clf = KNeighborsClassifier(n_neighbors=1)
        clf.fit(Xs_new, Ys.ravel())
        Y_tar_pseudo = clf.predict(Xt_new)
        acc = sklearn.metrics.accuracy_score(Yt, Y_tar_pseudo)
        list_acc.append(acc)
        print('JDA iteration [{}/{}]: Acc: {:.4f}'.format(t + 1, self.T, acc))
    return acc, Y_tar_pseudo, list_acc