diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeanscolor2.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeanscolor2.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,193 @@
+function [color,centroids]=som_kmeanscolor2(mode,sM,C,initRGB,contrast,R)
+
+% SOM_KMEANSCOLOR2 Color codes a SOM according to averaged or best K-means clustering
+%
+% color = som_kmeanscolor2('average',sM, C, [initRGB], [contrast],[R]) 
+%
+%  color=som_kmeanscolor2('average',sM,[2 4 8 16],som_colorcode(sM,'rgb1'),'enhanced');
+%  [color,centroid]=som_kmeanscolor2('best',sM,15,[],'flat',R);
+%  
+%  Input and output arguments ([]'s are optional):
+%
+%   mode       (string) 'average' or 'best', defalut: 'average'
+%   sM         (struct) a map struct
+%   C          (vector) number of clusters
+%   [initRGB]  (string, matrix) a color code string accepted by SOM_COLORCODE
+%               or an Mx3 matrix of RGB triples, where M is the number
+%               of map units. Default: SOM_COLORCODEs default
+%   [contrast] (string) 'flat', 'enhanced' color contrast mode, default:
+%               'enhanced'.
+%   [R]        (scalar) number of K-means trials, default: 30.
+%   color      (matrix) Mx3xC of RGB triples
+%   centroid   (array of matrices) centroid{i} includes codebook for the best
+%               k-means for C(i) clusters, i.e. the cluster centroids corresponding to
+%               the color code color(:,:,i).
+% 
+% The function gives a set of color codes for the SOM according to K-means 
+% clustering. It has two operation modes: 
+% 
+% 'average': The idea of coloring is that the color of the units belonging to the same 
+%   cluster is the  mean of the original RGB values (see SOM_COLORCODE) of the map units 
+%   belonging to the cluster (see SOM_CLUSTERCOLOR). The K-means clustering is made,
+%   by default, 30 times and the resulting color codes are averaged for
+%   each specified number of clusters C(i), i=1,...,k. In a way, the resulting averaged color 
+%   codes reflect the stability of the K-means clustering made on the map units.
+%
+% 'best': runs the k-means R times for C(i), i=1,...,n clusters as in previous mode, 
+%   but instead of averaging all the R color codes, it picks the one that corresponds to the 
+%   best k-means clustering for each C(i). The 'best' is the one with the lowest 
+%   quantization error. The result may differ from run to run.
+%
+% EXAMPLE
+% 
+%  load iris; % or any other map struct sM 
+%  color=som_kmeanscolor2('average',sM,[2:6]);
+%  som_show(sM,'umat','all','color',color);
+% 
+% See also SOM_KMEANS, SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR
+
+% Contributed to SOM Toolbox 2.0, 2001 February by Johan Himberg
+% Copyright (c) by Johan Himberg
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+%%% Check number of inputs
+
+error(nargchk(3, 6, nargin));  % check no. of input args
+
+%%% Check input args & set defaults
+
+if ~vis_valuetype(mode,{'string'}),
+   error('Mode must be a string.');
+end
+switch lower(mode),
+case{'average','best'}
+   ;
+otherwise
+   error('Mode must be string ''average'' or ''best''.');
+end
+
+if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'),
+   [tmp,lattice,msize]=vis_planeGetArgs(sM);
+   munits=prod(msize);
+   if length(msize)>2 
+      error('Does not work with 3D maps.')
+   end
+else
+   error('Map struct required for the second input argument!');
+end
+
+if ~vis_valuetype(C,{'1xn','nx1'}),
+   error('Vector value expected for cluster number.');
+end
+
+% Round C and check
+C=round(C(:)');
+
+if any(C<2),
+   error('Cluster number must be 2 or more.');
+end
+
+% check initial color coding
+if nargin<4 | isempty(initRGB)
+   initRGB=som_colorcode(sM);
+end
+
+% check contrast checking
+if nargin<5 | isempty(contrast),
+   contrast='enhanced';
+end
+
+if ~ischar(contrast),
+   error('String input expected for input arg. ''contrast''.');
+else
+   switch lower(contrast)
+   case {'flat','enhanced'}
+      ;
+   otherwise 
+      error(['''flat'' or ''enhanced'' expected for '...
+            'input argument ''contrast''.']);
+   end
+end
+
+if ischar(initRGB),
+   try 
+      initRGB=som_colorcode(sM,initRGB);
+   catch
+      error(['Color code ' initRGB ...
+            'was not recognized by SOM_COLORCODE.']);
+   end
+elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'),
+   ;
+else
+   error(['The initial color code must be a string '...
+         'or an Mx3 matrix of RGB triples.']);
+end
+
+if nargin<6|isempty(R),
+   R=30;
+end
+
+if ~vis_valuetype(R,{'1x1'}),
+   error('''R'' must be scalar.');
+end
+
+%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+disp('Wait...');
+index=0; hit_=zeros(munits,munits);
+
+switch mode,
+   %% Averaged k-means coloring
+case 'average'
+   for k=C,
+      disp(['Running K-means for ' num2str(k) ' clusters...']); 
+      color_=zeros(munits,3);
+      colord_=color_;
+      % Average R k-means colorings for C clusters
+      for j=1:R,
+         [dummy,c]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off 
+         color_=color_+som_clustercolor(sM,c,initRGB);
+      end
+      index=index+1;
+      color(:,:,index)=color_./R;
+   end
+   
+   %% coloring for 'best' k-means coloring
+case 'best'
+   for k=C,
+      disp(['Running K-means for ' num2str(k) ' clusters...']);
+      c=[];err=Inf; div=[];
+      %% look for the best k-means among R trials
+      for i=1:R,
+         [c_,div_,err_(i)]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off
+         if err_(i)<err, 
+            err=err_(i); c=c_; div=div_; 
+         end
+      end
+      % record the 'best' k-means for C clusters
+      index=index+1;
+      color(:,:,index)=som_clustercolor(sM,div,initRGB);
+      centroid{index}=c;   
+   end
+end
+
+%%% Build output
+
+switch contrast
+case 'flat'
+   ;
+case 'enhanced'
+   warning off;
+   ncolor=maxnorm(color);
+   ncolor(~isfinite(ncolor))=color(~isfinite(ncolor));
+   color=ncolor;
+   warning on;
+end
+
+
+%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function X=maxnorm(x)
+% normalize columns of x between [0,1]
+
+x=x-repmat(min(x),[size(x,1) 1 1]);
+X=x./repmat(max(x),[size(x,1) 1 1]);