PDS_kNN/matlab/kNN.m

function [I, D] = kNN(X, Y, k)
%kNN return the k-nearest neighbors Of Y into dataset X
%
% Outputs:
% I : [n x k]   The indexes of X where the nearest neighbors of Y lies
% D : [n x k]   The distances of each neighbor
%
% Inputs:
% X : [m x d]   Corpus data points (d dimensions)
% Y : [n x d]   Query data points (d dimensions)
% k : [scalar]  The number of neighbors

    disMat = distXY(X, Y);
    [m, n] = size(disMat);

    II = repmat([1:k].', 1, n);         % init the min algorithm
    DD = disMat(1:k,:);
    for j = 1:n
        for i = k+1:m
            % calculate candidate and canditate index
            [tail, taili] = maxIdx(DD(:, j));
            if disMat(i,j) < tail
                DD(taili, j) = disMat(i,j);
                II(taili, j) = i;
            end
        end
    end
    I = II.';
    D = DD.';
end