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From: <fpoto@us...>  20081116 19:49:13

Revision: 5439 http://octave.svn.sourceforge.net/octave/?rev=5439&view=rev Author: fpoto Date: 20081116 19:49:09 +0000 (Sun, 16 Nov 2008) Log Message:  Corrected a typo: now "complete" works. Added more tests. Performance improvements. Modified Paths:  trunk/octaveforge/main/statistics/inst/linkage.m Modified: trunk/octaveforge/main/statistics/inst/linkage.m ===================================================================  trunk/octaveforge/main/statistics/inst/linkage.m 20081114 18:40:34 UTC (rev 5438) +++ trunk/octaveforge/main/statistics/inst/linkage.m 20081116 19:49:09 UTC (rev 5439) @@ 22,6 +22,17 @@ ## Return clusters generated from a distance vector created by the pdist ## function. ## +## @var{x} is the dissimilarity matrix relative to @var{n} observations, +## formatted as a @math{(n1)*n/2}x1 vector as produced by @code{pdist}. +## @code{linkage} starts by putting each observation into a singleton +## cluster and numbering those from 1 to @var{n}. Then it merges two +## clusters, chosen according to @var{method}, to create a new cluster +## numbered @var{n+1}, and so on until all observations are grouped into +## a single cluster numbered @var{2*n1}. Row @var{m} of the +## @math{m1}x3 output matrix relates to cluster @math{n+m}: the first +## two columns are the numbers of the two component clusters and column +## 3 contains their distance. +## ## Methods can be: ## ## @table @samp @@ 51,16 +62,6 @@ ## ## @end table ## ## @var{x} is the dissimilarity matrix relative to @var{n} observations, ## formatted as a @math{(n1)*n/2}x1 vector as produced by @code{pdist}. ## @code{linkage} starts by putting each observation into a singleton ## cluster and numbering those from 1 to @var{n}. Then it merges two ## clusters to create a new cluster numbered @var{n+1}, and so on ## until all observations are grouped into a single cluster numbered ## @var{2*n1}. Row @var{m} of the @math{m1}x3 output matrix relates ## to cluster @math{n+m}: the first two columns are the numbers of the ## two component clusters and column 3 contains their distance. ## ## @seealso{cluster,pdist} ## @end deftypefn @@ 90,43 +91,46 @@ ## this is just a minimal spanning tree findfxn = @min; case "complete"  error ("linkage: %s is not yet implemented", method); findfxn = @max;  case { "median", "weighted" }  findfxn = @mean;  case { "average", "centroid", "ward" } + case { "median", "weighted", "average", "centroid", "ward" } error ("linkage: %s is not yet implemented", method); otherwise error ("linkage: %s: unknown method", method); endswitch  dissim = squareform (x, "tomatrix");  startsize = size (dissim, 1);  y = zeros (startsize  1, 3);  cnameidx = 1:startsize;  for yidx = 1:startsize1  ## Find the two nearest clusters.  available = logical(tril (ones(size(dissim)))  eye(size(dissim)));  [r, c] = find (min (dissim(available)) == dissim, 1);  ## Here is the new cluster.  y(yidx, :) = [cnameidx(r) cnameidx(c) dissim(r, c)];  ## Add it as a new cluster index and remove the old ones.  cnameidx(r) = yidx + startsize;  cnameidx(c) = [];  ## Update the dissimilarity matrix + dissim = squareform (x, "tomatrix"); # dissimilarity matrix in square format + n = rows (dissim); # the number of observations + diagidx = sub2ind ([n,n], 1:n, 1:n); # indices of diagonal elements + dissim(diagidx) = Inf; # consider a cluster as far from itself + cname = 1:n; # cluster names in dissim + y = zeros (n1, 3); # clusters from n+1 to 2*n1 + for yidx = 1:n1 + ## Find the two nearest clusters + [m midx] = min (dissim(:)); # the min distance and its first index + [r, c] = ind2sub (size (dissim), midx); # row and col number + ## Here is the new cluster + y(yidx, :) = [cname(r) cname(c) dissim(r, c)]; + ## Add it as a new cluster index and remove the old ones + cname(r) = yidx + n; + cname(c) = []; + ## Add the new dissimilarities and remove the old ones newdissim = findfxn (dissim([r c], :)); + newdissim(r) = Inf; dissim(r,:) = newdissim; dissim(:,r) = newdissim';  dissim(r,r) = 0; dissim(c,:) = []; dissim(:,c) = []; endfor endfunction %!shared xy, t %! xy = [3 1.7; 1 1; 2 3; 2 2.5; 1.2 1; 1.1 1.5; 3 1]; +%!shared x, y, t +%! x = [3 1.7; 1 1; 2 3; 2 2.5; 1.2 1; 1.1 1.5; 3 1]; +%! y = reshape(mod(magic(6),5),[],3); %! t = 1e6; %!assert (cond (linkage (pdist (xy))), 66.534612, t); %!assert (cond (linkage (pdist (xy), "single")), 66.534612, t); %!assert (cond (linkage (pdist (xy), "complete")), 27.071750, t); +%!assert (cond (linkage (pdist (x))), 66.534612, t); +%!assert (cond (linkage (pdist (y))), 34.945071, t); +%!assert (cond (linkage (pdist (x), "single")), 66.534612, t); +%!assert (cond (linkage (pdist (y), "single")), 34.945071, t); +%!assert (cond (linkage (pdist (x), "complete")), 27.071750, t); +%!assert (cond (linkage (pdist (y), "complete")), 20.296516, t); This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. 