% clusters.m
% function cb = clusters(input,ref,maxIt)
% clusters(randn(2,400)/sqrt(12),randn(2,1)/sqrt(12));
% 17.01.2003, Jens Ahrensfeld

function [cb, idx, output] =clusters_ransac(input,max_distance,move_thresh)
cl_count = 0;
[nDim, nData] = size(input);
points_remain = input;

while length(points_remain) > 0,
	
    [nDim,nRem] = size(points_remain);
    new_idx = round(rand(1,1)*(nRem-1))+1;
    ref = points_remain(:,new_idx);
	
	ref_last = zeros(nDim,1);
	d_max = 2*move_thresh;
	while d_max > move_thresh
		d = sqrt(sum((points_remain-repmat(ref,1,nRem)).^2));			% Calc distances between references and input
		points_assigned_idx = find(d <= max_distance);
		num_assigned = length(points_assigned_idx);
		if (num_assigned > 0);
			ref = mean(points_remain(:,points_assigned_idx),2);
			d_max = max(sqrt(sum((ref_last-ref).^2)));
		    ref_last = ref;
		else
			break;
		end;
	end;
	cl_count = cl_count +1;
	output{cl_count} = points_remain(:,points_assigned_idx);
	idx{cl_count} = points_assigned_idx;
	points_remain(:,points_assigned_idx) = [];
	cb(:,cl_count) = ref;
	
end;