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function [sC,old2new,newi] = som_clset(sC,action,par1,par2)
% SOM_CLSET Create and/or set values in the som_clustering struct.
%
% first argument
% sC (struct) a som_clustering struct
% Z (matrix) size nb-1 x 3, as given by LINKAGE function
% base (vector) size dlen x 1, a partitioning of the data
%
% actions
% 'remove' removes the indicated clusters (par1: vector)
% 'add' add a cluster by making a combination of the indicated
% clusters (par1: vector)
% %'move' moves a child cluster (par1: scalar) from a parent to another
% % (par2: vector 1 x 2)
% 'merge' like 'add', followed by removing the indicated clusters (par1: vector)
% %'split' the indicated cluster (par1: scalar) is partitioned into indicated
% % parts (par2: vector), which are then added, while the indicated cluster
% % (par1) is removed
% 'coord' sets the coordinates of base clusters (par1: matrix nb x *), and
% recalculates coordinates of the derived clusters (by averaging base cluster
% coordinates)
% 'color' sets the colors of base clusters (par1: matrix nb x 3), and recalculates
% colors of the derived clusters (as averages of base cluster colors)
%
% sC
% .type (string) 'som_clustering'
% .name (string) Identifier for the clustering.
% .nb (scalar) Number of base clusters in the clustering.
% .base (vector) Size dlen x 1, the basic groups of data
% forming the base clusters, e.g. as a result
% of partitive clustering. Allowed values are
% 1:nb indicating the base cluster
% to which the data belongs to.
% NaN indicating that the data has
% been ignored in the clustering
% .nc (scalar) Number of clusters in the clustering (nb + derived clusters).
% .children (cellarray) size nc x 1, each cell gives the list of indeces
% of child clusters for the cluster
% .parent (vector) size nc x 1, the index of parent of each cluster
% (or zero if the cluster does not have a parent)
% .coord (matrix) size nc x *, visualization coordinates for each cluster
% By default the coordinates are set so that
% the base clusters are ordered on a line, and the
% position of each combined cluster is average of
% the base clusters that constitute it.
% .color (matrix) size nc x 3, color for each cluster.
% By default the colors are set so that the
% base clusters are ordered on a line,
% and then colors are assigned from the 'hsv'
% colormap to the base clusters. The color
% of each combined cluster is average as above.
% .cldist (string) Default cluster distance function.
inew = [];
if isstruct(sC),
% it should be a som_clustering struct
old2new = [1:sC.nc];
elseif size(sC,2)==3,
% assume it is a cluster hierarchy matrix Z
sC = Z2sC(sC);
old2new = [1:sC.nc];
else
% assume it is a partitioning vector
base = sC;
u = unique(base(isfinite(base)));
old2new = sparse(u,1,1:length(u));
base = old2new(base);
sC = part2sC(base);
end
switch action,
case 'remove',
for i=1:length(par1),
[sC,o2n] = removecluster(sC,old2new(par1(i)));
old2new = o2n(old2new);
end
case 'add',
[sC,old2new,inew] = addmergedcluster(sC,par1);
case 'move',
% not implemented yet
case 'split',
% not implemented yet
case 'merge',
[sC,old2new,inew] = addmergedcluster(sC,par1);
for i=1:length(par1),
[sC,o2n] = removecluster(sC,old2new(par1(i)));
old2new = o2n(old2new);
end
case 'color',
sC.color = derivative_average(sC,par1);
case 'coord',
sC.coord = derivative_average(sC,par1);
end
return;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% subfunctions
function sC = clstruct(nb,nc)
sC = struct('type','som_clustering',...
'name','','base',[],'nb',nb,'nc',nc,...
'parent',[],'children',[],'coord',[],'color',[],'cldist','centroid');
sC.base = [1:nb];
sC.parent = zeros(nc,1);
sC.children = cell(nc,1); sC.children(:) = {[]};
sC.coord = zeros(nc,2);
sC.color = zeros(nc,3);
return;
function Z = sC2Z(sC,height)
if nargin<2, height = 'level'; end
root = find(sC.parent==0);
order = [root];
ch = sC.children(root);
while any(ch), i = ch(1); order = [ch(1), order]; ch = [ch(2:end), sC.children{i}]; end
he = zeros(sC.nc,1);
if strcmp(height,'level'),
ch = sC.children{root};
while any(ch),
i = ch(1); he(i) = he(sC.parent(i))+1;
ch = [ch(2:end), sC.children{i}];
end
he = max(he)-he;
elseif strcmp(height,'level2'),
for i=order, if any(sC.children{i}), he(i) = max(he(sC.children{i}))+1; end, end
else
%he = som_cldist ( between children )
end
Z = zeros(sC.nb-1,3);
i = sC.nb-1;
inds = root;
while i>0,
ch = sC.children{inds(1)}; h = he(inds(1)); inds = [inds(2:end), ch];
if length(ch)>=2,
for k=1:length(ch)-2, Z(i,:) = [i-1, ch(k), h]; i = i - 1; end
Z(i,:) = [ch(end-1) ch(end) h]; i = i - 1;
end
end
return;
function sC = Z2sC(Z)
nb = size(Z,1)+1;
nc = 2*nb-1;
sC = clstruct(nb,nc);
sC.base = [1:nb];
for i=1:nc,
j = find(Z(:,1)==i | Z(:,2)==i);
sC.parent(i) = nb+j;
sC.children{sC.parent(i)}(end+1) = i;
end
% coords and color
order = nc;
nonleaves = 1;
while any(nonleaves),
j = nonleaves(1);
ch = sC.children{order(j)};
if j==1, oleft = []; else oleft = order(1:(j-1)); end
if j==length(order), oright = []; else oright = order((j+1):length(order)); end
order = [oleft, ch, oright];
nonleaves = find(order>nb);
end
[dummy,co] = sort(order);
sC.coord = derivative_average(sC,co');
H = hsv(nb+1);
sC.color = derivative_average(sC,H(co,:));
return;
function sC = part2sC(part)
nb = max(part);
nc = nb+1;
sC = clstruct(nb,nc);
sC.base = part;
sC.parent(1:nb) = nc;
sC.children{nc} = [1:nb];
co = [1:nb]';
sC.coord = derivative_average(sC,co);
H = hsv(nb+1);
sC.color = derivative_average(sC,H(1:nb,:));
return;
function [sC,old2new] = removecluster(sC,ind)
old2new = [1:sC.nc];
parent_ind = sC.parent(ind);
ch = sC.children{ind};
if ~parent_ind,
% trying to remove root cluster - no go
return;
elseif ~any(ch),
% trying to remove a base cluster - no go
return;
else
% ok, proceed
old2new = [1:ind-1 0 ind:sC.nc-1];
% update parent and child fields
sC.parent(ch) = parent_ind;
sC.children{parent_ind} = setdiff([sC.children{parent_ind}, ch],ind);
% remove old cluster
j = [1:ind-1, ind+1:sC.nc];
sC.parent = sC.parent(j);
sC.children = sC.children(j);
sC.color = sC.color(j,:);
sC.coord = sC.coord(j,:);
sC.nc = sC.nc-1;
% update old indeces to new indices
sC.parent = old2new(sC.parent);
for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
end
return;
function [sC,old2new,inew] = addmergedcluster(sC,inds)
old2new = [1:sC.nc];
inew = 0;
p_inds = sC.parent(inds);
if ~all(p_inds(1)==p_inds),
% clusters are not siblings - no go
return;
end
parent_ind = p_inds(1);
if isempty(setdiff(sC.children{parent_ind},inds)),
% such a merged cluster exists already
return;
else
% ok, proceed
inew = parent_ind;
old2new = [1:inew-1,inew+1:sC.nc+1];
% add the new cluster (=copy of parent_ind)
j = [1:inew,inew:sC.nc];
sC.parent = sC.parent(j);
sC.children = sC.children(j);
sC.color = sC.color(j,:);
sC.coord = sC.coord(j,:);
sC.nc = sC.nc+1;
% update old indeces to new indices
sC.parent = old2new(sC.parent);
for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end
inds = old2new(inds);
parent_ind = old2new(parent_ind);
% update parent, child, color and coord fields
sC.parent(inds) = inew;
sC.parent(inew) = parent_ind;
sC.children{inew} = inds;
sC.children{parent_ind} = [setdiff(sC.children{parent_ind}, inds), inew];
b = baseind(sC,inew);
sC.color(inew,:) = mean(sC.color(b,:));
sC.coord(inew,:) = mean(sC.coord(b,:));
end
return;
function C = derivative_average(sC,Cbase)
[n dim] = size(Cbase);
if n ~= sC.nb, error('Color / Coord matrix should have nb rows'); end
C = zeros(sC.nc,dim);
for i=1:sC.nc, C(i,:) = mean(Cbase(baseind(sC,i),:)); end
return;
function bi = baseind(sC,ind)
bi = [ind];
i = 1;
while i<=length(bi), bi = [bi, sC.children{bi(i)}]; end
bi = bi(bi<=sC.nb);
return;