function base = som_clspread(sM,base,cldist,Ne,verbosity)% SOM_CLSPREAD Partition the given data by flooding.%%  part = som_clspread(sM,part,cldist,[Ne],[verbos])%%  Input and output arguments ([]'s are optional):%   sM       (struct) map or data struct%            (matrix) size dlen x dim, the data set            %   base     (vector) initial partition, where if base(i) is%                      0         i should be assigned to some cluster%                      NaN       i should not be assigned to any cluster%                      otherwise i belongs to cluster base(i)%   cldist   (string) cluster distance measure: 'single', 'average',%                     'complete', 'neighf', 'ward', 'centroid', 'BMU'  %   [Ne]     (scalar) 0 = not constrined to neighborhood%                     1 = constrained   %            (matrix) size dlen x dlen, indicating possible connections%   [verbos] (scalar) 1 (default) = show status bar%                     0  = don't%% See also SOM_CLDIST. % Copyright (c) 2000 by Juha Vesanto% Contributed to SOM Toolbox on XXX by Juha Vesanto% http://www.cis.hut.fi/projects/somtoolbox/ % Version 2.0beta juuso 220800%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% input argumentsq = 2; % map/dataif isstruct(sM),   switch sM.type,    case 'som_map',  M = sM.codebook; mask = sM.mask; sT = sM.topol;    case 'som_data', M = sM.data; mask = []; sT = [];   endelse M = sM; mask = []; sT = []; end[dlen dim] = size(M); if isempty(mask), mask = ones(dim,1); end% simple optionif any(strcmp(cldist,{'closest','BMU'})),   i0 = find(base==0);  i1 = find(base>0);  bmus = som_bmus(M(i1,:),M(i0,:));  base(i0) = base(i1(bmus));  return; end% constrained clusteringif nargin<4, Ne = []; endif prod(size(Ne))==1,    if Ne & isempty(sT),    warning('Cannot use constrained clustering.'); Ne = 0;   end  if Ne, Ne = som_unit_neighs(sT); else Ne = []; endendif ~isempty(Ne),   Ne([0:dlen-1]*dlen+[1:dlen]) = 1; % set diagonal elements = 1  if all(Ne(:)>0), Ne = []; endendif nargin<5, verbosity = 1; end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% initializeif size(base,1)==1, base = base'; endcid = unique(base(isfinite(base) & base~=0)); % cluster IDsnc = length(cid);    uind = find(base==0); % unclustered pointsnu = length(uind); if nu==0, return; end% initial clustersclinds = cell(nc,1); for i=1:nc, clinds{i} = find(base==i); endclinds2 = cell(nu,1); for i=1:nu, clinds2{i} = uind(i); end% neighborhood function valuesif strcmp(cldist,'neighf')     if isempty(sT), error('Cannot use neighf linkage.'); end  q = som_unit_dists(sT).^2;   r = sM.trainhist(end).radius_fin^2;   if isnan(r) | isempty(r), r = 1; end   switch sM.neigh,   case 'bubble',   q = (q <= r);   case 'gaussian', q = exp(-q/(2*r));   case 'cutgauss', q = exp(-q/(2*r)) .* (q <= r);   case 'ep',       q = (1-q/r) .* (q <= r);  endend% distance of each cluster to the unclustered pointsif any(strcmp(cldist,{'single','average','complete','neighf'})),   M = som_mdist(M,2,mask,Ne); end Cd = som_cldist(M,clinds,clinds2,cldist,q,mask); 			      % check out from Ne which of the clusters are not connectedif ~isempty(Ne) & any(strcmp(cldist,{'centroid','ward'})),  Clconn = sparse(nc,nu);     for i=1:nc, for j=1:nu, Clconn(i,j) = any(any(Ne(clinds{i},uind(j)))); end, end  Cd(Clconn==0) = Inf; else  Clconn = []; end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% actionif verbosity,  nu0 = nu;   h = waitbar(1-nu/nu0,'Assigning unclustered points'); % trackingendwhile 1,   % find closest unclustered point  [dk,k] = min(Cd,[],2);  % min distance from each unclustered point  [d,c]  = min(dk);       % cluster to which it is assigned    k = k(c);   if ~isfinite(d),     break;   end  % add k to cluster c  base(uind(k)) = cid(c);     clinds{c} = [clinds{c}; uind(k)];    % remove point k  notk = [1:k-1,k+1:nu];   nu = nu-1; if nu<=0, break; end    Cd = Cd(:,notk);   uind = uind(notk);   clinds2 = clinds2(notk);   if ~isempty(Clconn), Clconn = Clconn(:,notk); end  % update cluster distances to c  Cd(c,:) = som_cldist(M,clinds(c),clinds2,cldist,q,mask);   if ~isempty(Clconn),     for j=1:nu, Clconn(c,j) = any(any(Ne(clinds{c},uind(j)))); end    Cd(c,find(Clconn(c,:)==0)) = Inf;   end    if verbosity, waitbar(1-nu/nu0,h); end % trackingendif verbosity, close(h); endreturn; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%