wolffd@0: function [color,best,kmeans]=som_kmeanscolor(sM,C,initRGB,contrast)
wolffd@0: 
wolffd@0: % SOM_KMEANSCOLOR Map unit color code according to K-means clustering
wolffd@0: %
wolffd@0: % [color, best, kmeans] = som_kmeanscolor(sM, C, [initRGB],[contrast])
wolffd@0: %
wolffd@0: %  color        = som_kmeanscolor(sM,15,som_colorcode(sM,'rgb1'),'enhance');
wolffd@0: %  [color,best] = som_kmeanscolor(sM,15,[],'normal');
wolffd@0: %  
wolffd@0: %  Input and output arguments ([]'s are optional):
wolffd@0: %   sM       (struct) map struct
wolffd@0: %   C        (scalar) maximum number of clusters
wolffd@0: %   initRGB  (string, matrix) color code string accepted by SOM_COLORCODE
wolffd@0: %                     or an Mx3 matrix of RGB triples, where M is the number
wolffd@0: %                     of map units. Default: SOM_COLORCODEs default
wolffd@0: %   contrast (string) 'flat', 'enhanced' color contrast mode, default:
wolffd@0: %                     'enhanced'
wolffd@0: %
wolffd@0: %   color    (matrix) MxCx3 of RGB triples
wolffd@0: %   best     (scalar) index for "best" clustering according to 
wolffd@0: %                     Davies-Boulding index; color(:,:,best) includes the 
wolffd@0: %                     corresponding color code.
wolffd@0: %   kmeans   (cell)   output of KMEANS_CLUSTERS in a cell array.
wolffd@0: % 
wolffd@0: % The function gives a set of color codings according to K-means 
wolffd@0: % clustering. For clustering, it uses function KMEANS_CLUSTERS for map units, 
wolffd@0: % and it calculates color codings for 1,2,...,C clusters. 
wolffd@0: % The idea of coloring is that the color of a cluster is the mean of the 
wolffd@0: % original colors (RGB values) of the map units belonging to that cluster, 
wolffd@0: % see SOM_CLUSTERCOLOR. The original colors are defined by  SOM_COLORCODE
wolffd@0: % by default. Input 'contrast' simply specifies whether or not 
wolffd@0: % to linearly redistribute R,G, and B values so that minimum is 0 and 
wolffd@0: % maximum 1 ('enahanced')  or to use directly the output of 
wolffd@0: % SOM_CLUSTERCOLOR ('flat'). KMEANS_CLUSTERS uses certain heuristics to 
wolffd@0: % select the best of 5 trials for each  number of clusters. Evaluating the 
wolffd@0: % clustering multiple times may take some time. 
wolffd@0: %
wolffd@0: % EXAMPLE
wolffd@0: % 
wolffd@0: %  load iris; % or any other map struct sM 
wolffd@0: %  [color,b]=som_kmeanscolor(sM,10);
wolffd@0: %  som_show(sM,'color',color,'color',{color(:,:,b),'"Best clustering"');
wolffd@0: % 
wolffd@0: % See also SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, KMEANS_CLUSTERS
wolffd@0: 
wolffd@0: % Contributed to SOM Toolbox 2.0, April 1st, 2000 by Johan Himberg
wolffd@0: % Copyright (c) by Johan Himberg
wolffd@0: % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0: 
wolffd@0: % corrected help text 11032005 johan
wolffd@0: 
wolffd@0: %%% Check number of inputs
wolffd@0: 
wolffd@0: error(nargchk(2, 4, nargin));  % check no. of input args
wolffd@0: 
wolffd@0: %%% Check input args & set defaults
wolffd@0: 
wolffd@0: if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
wolffd@0:    [tmp,lattice,msize]=vis_planeGetArgs(sM);
wolffd@0:    munits=prod(msize);
wolffd@0:    if length(msize)>2 
wolffd@0:       error('Does not work with 3D maps.')
wolffd@0:    end
wolffd@0: else
wolffd@0:    error('Map struct requires for first input argument!');
wolffd@0: end
wolffd@0: 
wolffd@0: if ~vis_valuetype(C,{'1x1'}),
wolffd@0:    error('Scalar value expect for maximum number of clusters.');
wolffd@0: end
wolffd@0: 
wolffd@0: % check initial color coding
wolffd@0: if nargin<3 | isempty(initRGB)
wolffd@0:    initRGB=som_colorcode(sM);
wolffd@0: end
wolffd@0: 
wolffd@0: % check contrast checking
wolffd@0: if nargin<4 | isempty(contrast),
wolffd@0:    contrast='enhanced';
wolffd@0: end
wolffd@0: 
wolffd@0: if ~ischar(contrast),
wolffd@0:    error('String input expected for input arg. ''contrast''.');
wolffd@0: else
wolffd@0:    switch lower(contrast)
wolffd@0:    case {'flat','enhanced'}
wolffd@0:       ;
wolffd@0:    otherwise 
wolffd@0:       error(['''flat'' or ''enhanced'' expected for '...
wolffd@0:             'input argument ''contrast''.']);
wolffd@0:    end
wolffd@0: end
wolffd@0: 
wolffd@0: if ischar(initRGB),
wolffd@0:    try 
wolffd@0:       initRGB=som_colorcode(sM,initRGB);
wolffd@0:    catch
wolffd@0:       error(['Color code ' initRGB ...
wolffd@0:             'was not recognized by SOM_COLORCODE.']);
wolffd@0:    end
wolffd@0: elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
wolffd@0:    ;
wolffd@0: else
wolffd@0:    error(['The initial color code must be a string '...
wolffd@0:          'or an Mx3 matrix of RGB triples.']);
wolffd@0: end
wolffd@0: 
wolffd@0: %%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: 
wolffd@0: disp('Wait...');
wolffd@0: [c,p,err,ind]=kmeans_clusters(sM,C,5,0); % use 5 trials, verbose off
wolffd@0: 
wolffd@0: % Store outputs to kmeans
wolffd@0: kmeans{1}=c; 
wolffd@0: kmeans{2}=p; 
wolffd@0: kmeans{3}=err; 
wolffd@0: kmeans{4}=ind;
wolffd@0: 
wolffd@0: %%% Build output
wolffd@0: color=som_clustercolor(sM,cat(2,p{:}),initRGB);
wolffd@0: [tmp,best]=min(ind);
wolffd@0: 
wolffd@0: switch contrast
wolffd@0: case 'flat'
wolffd@0:    ;
wolffd@0: case 'enhanced'
wolffd@0:    warning off;
wolffd@0:    ncolor=maxnorm(color);
wolffd@0:    ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
wolffd@0:    color=ncolor;
wolffd@0:    warning on;
wolffd@0: end
wolffd@0: 
wolffd@0: %%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: function X=maxnorm(x)
wolffd@0: % normalize columns of x between [0,1]
wolffd@0: 
wolffd@0: x=x-repmat(min(x),[size(x,1) 1 1]);
wolffd@0: X=x./repmat(max(x),[size(x,1) 1 1]);