test_t_sne.py 文件源码

def test_barnes_hut_angle():
    # When Barnes-Hut's angle=0 this corresponds to the exact method.
    angle = 0.0
    perplexity = 10
    n_samples = 100
    for n_components in [2, 3]:
        n_features = 5
        degrees_of_freedom = float(n_components - 1.0)

        random_state = check_random_state(0)
        distances = random_state.randn(n_samples, n_features)
        distances = distances.astype(np.float32)
        distances = distances.dot(distances.T)
        np.fill_diagonal(distances, 0.0)
        params = random_state.randn(n_samples, n_components)
        P = _joint_probabilities(distances, perplexity, False)
        kl, gradex = _kl_divergence(params, P, degrees_of_freedom, n_samples,
                                    n_components)

        k = n_samples - 1
        bt = BallTree(distances)
        distances_nn, neighbors_nn = bt.query(distances, k=k + 1)
        neighbors_nn = neighbors_nn[:, 1:]
        Pbh = _joint_probabilities_nn(distances, neighbors_nn,
                                      perplexity, False)
        kl, gradbh = _kl_divergence_bh(params, Pbh, neighbors_nn,
                                       degrees_of_freedom, n_samples,
                                       n_components, angle=angle,
                                       skip_num_points=0, verbose=False)
        assert_array_almost_equal(Pbh, P, decimal=5)
        assert_array_almost_equal(gradex, gradbh, decimal=5)