d6fe384f6763c09da358e70375db034ec0e442f8,pynndescent/utils.py,,build_candidates,#Any#Any#Any#Any#Any#,499

Before Change

            idx = current_graph[0][i, j]
            isn = current_graph[2][i, j]
            d = tau_rand(rng_state)
            heap_push(candidate_neighbors, i, d, idx, isn)
            heap_push(candidate_neighbors, idx, d, i, isn)
            current_graph[2][i, j] = 0

    return candidate_neighbors

After Change

    candidate_neighbors: A heap with an array of (randomly sorted) candidate
    neighbors for each vertex in the graph.
    
    candidate_neighbors = make_heap(n_vertices, max_candidates)
    for i in range(n_vertices):
        for j in range(n_neighbors):
            if current_graph[0][i, j] < 0:
                continue
            idx = current_graph[0][i, j]
            isn = current_graph[2][i, j]
            d = tau_rand(rng_state)
            // heap_push(candidate_neighbors, i, d, idx, isn)
            // heap_push(candidate_neighbors, idx, d, i, isn)
            checked_flagged_heap_push(
                candidate_neighbors[1][i],
                candidate_neighbors[0][i],
                candidate_neighbors[2][i],
                d,
                idx,
                isn,
            )
            checked_flagged_heap_push(
                candidate_neighbors[1][idx],
                candidate_neighbors[0][idx],
                candidate_neighbors[2][idx],
                d,
                i,
                isn,
            )
            current_graph[2][i, j] = 0

    return candidate_neighbors

Instances