a434c63df31f3b7d25f77da7c64e32f86d5230dd,umap/umap_.py,,fuzzy_simplicial_set,#Any#Any#Any#Any#Any#Any#Any#,574

Before Change

                                                    rp_tree_init=True,
                                                    leaf_array=leaf_array,
                                                    verbose=verbose)
        knn_indices = tmp_indices.astype(np.int64)

        if np.any(knn_indices < 0):
            warn("Failed to correctly find n_neighbors for some samples."
                "Results may be less than ideal. Try re-running with"
                "different parameters.")

        for i in range(knn_indices.shape[0]):
            order = np.argsort(knn_dists[i])
            knn_dists[i] = knn_dists[i][order]
            knn_indices[i] = knn_indices[i][order]

    sigmas, rhos = smooth_knn_dist(knn_dists, n_neighbors)

    for i in range(knn_indices.shape[0]):
        for j in range(n_neighbors):

After Change

            metric_nn_descent = make_nn_descent(distance_func,
                                                tuple(metric_kwds.values()))
            // TODO: Hacked values for now
            n_trees = 5 + int(round((X.shape[0]) ** 0.5 / 20.0))
            n_iters = max(5, int(round(np.log2(X.shape[0]))))

            leaf_array = rptree_leaf_array(X, n_neighbors,
                                           rng_state, n_trees=n_trees,

Instances