- refactored

common/clusters/clusters_ransac.m

@@ -0,0 +1,37 @@
+% 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;