function [indices, distances] = brute(points, refind, nnr, past)% [indices, distances] = brute(points, refind, nnr, past)%% Brute force implementation of nearest neighbor search%% Input arguments :% % points - N by D matrix of N points of dimension D% refind - integer reference indices% nnr - number of neighbor to find% past - number of points to exclude before and after the actual reference index%% Output arguments :%% indices - R by nnr integer matrix containing the indices of the nearest neighbors, R = length(refind)% dimension - R by nnr double matrix containing the distances to the nearest neighbors %% Using euclidian normerror(nargchk(2,4,nargin))if nnr < 1	error('At least one neighbor must be requested')end[N, dim] =  size(points);R = length(refind);indices = zeros(R, nnr);distances = zeros(R, nnr);for i=1:R	actual = refind(i);	d = euclnorm(points - repmat(points(actual,:), N, 1));	d(max(1, actual-past):min(N, actual+past)) = inf;	[d2,ind] = sort(d);	indices(i,:) = ind(1:nnr)';	distances(i,:) = d2(1:nnr)';endfunction y = euclnorm(x)y = sqrt(sum(x.*x, 2));