e52e9c8d7536b6315da655164951060642a52707,sklearn/manifold/tests/test_t_sne.py,,test_barnes_hut_angle,#,609

Before Change

        k = n_samples - 1
        bt = BallTree(distances)
        distances_nn, neighbors_nn = bt.query(distances, k=k + 1)
        neighbors_nn = neighbors_nn[:, 1:]
        distances_nn = np.array([distances[i, neighbors_nn[i]]
                                 for i in range(n_samples)])

After Change

                                              n_samples, n_components)

        n_neighbors = n_samples - 1
        distances_csr = NearestNeighbors().fit(data).kneighbors_graph(
            n_neighbors=n_neighbors, mode="distance")
        P_bh = _joint_probabilities_nn(distances_csr, perplexity, verbose=0)
        kl_bh, grad_bh = _kl_divergence_bh(params, P_bh, degrees_of_freedom,
                                           n_samples, n_components,
                                           angle=angle, skip_num_points=0,

Instances