PDS/homework_1/matlab/mergeResultsWithM.m

function [N, D] = mergeResultsWithM(N1, D1, N2, D2, C1, C2, k, m)
    % Merge neighbors from two sources with a limit on candidate neighbors (m)
    
    numQueries = size(N1, 1);  % Number of queries in this subset
    maxCandidates = min(m, size(N1, 2) + size(N2, 2));  % Maximum candidates to consider

    % Combine distances and neighbors
    N_combined = [N1, N2 + size(C1, 1)];  % Adjust indices for C2
    D_combined = [D1, D2];

    % Sort distances and keep only top-m candidates for each query
    [D_sorted, idx] = sort(D_combined, 2, 'ascend');
    D_sorted = D_sorted(:, 1:maxCandidates);  % Keep only top-m distances
    idx = idx(:, 1:maxCandidates);  % Keep indices corresponding to top-m distances

    % Select the corresponding neighbors
    %N_sorted = N_combined(sub2ind(size(N_combined), ...
    %                  repmat((1:numQueries)', 1, maxCandidates), idx));
    N_sorted = zeros(numQueries, maxCandidates);  % Initialize output
    for i = 1:numQueries
        for j = 1:maxCandidates
            N_sorted(i, j) = N_combined(i, idx(i, j));
        end
    end

    % Handle cases where m < k
    if maxCandidates < k
        % Pad with Inf distances and invalid indices
        D_sorted = [D_sorted, Inf(numQueries, k - maxCandidates)];
        N_sorted = [N_sorted, zeros(numQueries, k - maxCandidates)];
    end

    % Extract top-k from the reduced set of candidates
    [D, idx_final] = sort(D_sorted, 2, 'ascend');
    D = D(:, 1:k);  % Final top-k distances
    %N = N_sorted(sub2ind(size(N_sorted), ...
    %                 repmat((1:numQueries)', 1, k), idx_final(:, 1:k)));
    % Extract top-k neighbors using a loop
    N = zeros(numQueries, k);  % Initialize output
    for i = 1:numQueries
        for j = 1:k
            N(i, j) = N_sorted(i, idx_final(i, j));
        end
    end

end