comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_fuzzycolor.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [color,X]=som_fuzzycolor(sM,T,R,mode,initRGB,S)
+
+% SOM_FUZZYCOLOR Heuristic contraction projection/soft cluster color coding for SOM 
+% 
+% function [color,X]=som_fuzzycolor(map,[T],[R],[mode],[initRGB],[S])
+%
+%  sM        (map struct)
+%  [T]       (scalar) parameter that defines the speed of contraction 
+%              T<1: slow contraction, T>1: fast contraction. Default: 1
+%  [R]       (scalar) number of rounds, default: 30
+%  [mode]    (string) 'lin' or 'exp', default: 'lin'  
+%  [initRGB] (string) Strings accepted by SOM_COLORCODE,  default: 'rgb2'
+%  [S]       (matrix) MxM matrix a precalculated similarity matrix 
+%  color     (matrix) of size MxRx3 resulting color codes at each step 
+%  X         (matrix) of size MxRx2 coordiantes for projected unit weight vectors 
+%             at each step of iteration. (Color code C is calculated using this
+%             projection.)
+%
+% The idea of the projection is to use a naive contraction model which
+% pulls the units together. Units that are close to each other in the
+% output space (clusters) contract faster into the same point in the
+% projection. The original position for each unit is its location in
+% the topological grid.
+% 
+% This is an explorative tool to color code the map units so that
+% similar units (in the sense of euclidean norm) have similar coloring
+% (See also SOM_KMEANSCOLOR) The tool gives a series of color codings
+% which start from an initial color coding (see SOM_COLORCODE) and
+% show the how the fuzzy clustering process evolves.
+%
+% The speed of contraction is controlled by the input parameter T. If
+% it is high the projection contracts more slowly and reveals more
+% intermediate stages (hierarchy).  A good value for T must be
+% searched manually. It is probable that the default values do not
+% yield good results.
+%
+% The conatrction process may be slow. In this case the mode can be
+% set to 'exp' instead of 'lin', however, then the computing becomes
+% heavier.
+%
+% EXAMPLE
+%
+%  load iris; % or any other map struct sM 
+%  [color]=som_fuzzycolor(sM,'lin',10);
+%  som_show(sM,'color',color);
+%
+% See also SOM_KMEANSCOLOR, SOM_COLORCODE, SOM_CLUSTERCOLOR
+%
+% REFERENCES
+% 
+% Johan Himberg, "A SOM Based Cluster Visualization and Its
+% Application for False Coloring", in Proceedings of International
+% Joint Conference on Neural Networks (IJCNN2000)},
+% pp. 587--592,Vol. 3, 2000
+% 
+% Esa Alhoniemi, Johan Himberg, and Juha Vesanto, Probabilistic
+% Measures for Responses of Self-Organizing Map Units, pp. 286--290,
+% in Proceedings of the International ICSC Congress on Computational
+% Intelligence Methods and Applications (CIMA '99)}, ICSC Academic
+% Press}, 1999
+%
+% Outline of the heuristic
+%
+% First a matrix D of squared pairwise euclidean distances
+% D(i,j)=d(i,j)^2 between map weight vectors is calculated. This
+% matrix is transformed into a similarity matrix S,
+% s(i,j)=exp(-(D(i,j)/(T.^2*v)), where T is a free input parameter and
+% v the variance of all elements of D v=var(D(:)). The matrix is
+% further normalized so that all rows sum to one. The original
+% topological coordinates X=som_unit_coords(sM) are successively
+% averaged using this matrix. X(:,:,i)=S^i*X(:,:,1); As the process is
+% actually a series of successive weighted averagings of the initial
+% coordinates, all projected points eventually contract into one
+% point.  T is a user defined parameter that defines how fast the
+% projection contracts into this center point. If T is too small, the
+% process will end into the center point at once.
+% 
+% In practise, we don't calculate powers of S, but compute
+% 
+%  X(:,:,i)=S.*X(:,:,i-1); % mode: 'lin'
+%
+% The contraction process may be slow if T is selected to be large,
+% then for each step the similarity matrix is squared
+%
+%  X(:,:,i)=S*X(:,:,1); S=S*S % mode: 'exp'
+%
+% The coloring is done using the function SOM_COLORCODE according to
+% the projections in X, The coordinates are rescaled in order to
+% achieve maximum color resolution.
+
+% Contributed to SOM Toolbox vs2, 2000 by Johan Himberg
+% Copyright (c) by Johan Himberg
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Previously rownorm function normalized the rows of S erroneously
+% into unit length, this major bug was corrected 14042003. Now the
+% rownorm normalizes the rows to have unit sum as it should johan 14042003
+
+%% Check input arguments
+
+if isstruct(sM), 
+   if ~isfield(sM,'topol')
+      error('Topology field missing.');
+   end
+   M=size(sM.codebook,1);
+else
+   error('Requires a map struct.');
+end
+
+if nargin<2 | isempty(T),
+   T=1;
+end
+if ~vis_valuetype(T,{'1x1'})
+   error('Input for T must be a scalar.');
+end
+
+if nargin<3 | isempty(R),
+   R=30;
+end
+if ~vis_valuetype(R,{'1x1'})
+   error('Input for R must be a scalar.');
+end
+
+if nargin < 4 | isempty(mode),
+   mode='lin';
+end
+if ~ischar(mode),
+   error('String input expected for mode.');
+else
+   mode=lower(mode);
+   switch mode
+   case {'lin','exp'}
+      ;   
+   otherwise
+      error('Input for mode must be ''lin'' or ''exp''.');
+   end
+end
+
+if nargin < 5 | isempty(initRGB)
+   initRGB='rgb2';
+end
+
+if ischar(initRGB),   
+   try
+      dummy=som_colorcode(sM,initRGB);
+   catch
+      error(['Color code ''' initRGB ''' not known, see SOM_COLORCODE.']);
+   end
+else
+   error('Invalid color code string');   
+end
+
+if nargin<6 | isempty(S),
+   S=fuzzysimilarity(sM,1./T);
+end
+
+if ~vis_valuetype(S,{[M M]}),
+   error('Similarity matrix must be a MunitsxMunits matrix.')
+end
+
+x = maxnorm(som_unit_coords(sM.topol.msize,sM.topol.lattice,'sheet'));
+
+x = x-repmat(mean(x),size(x,1),1);
+
+X(:,:,1)=x; 
+color(:,:,1)=som_colorcode(x,'rgb2',1);
+
+%%% Actions
+
+for i=1:R,
+   switch mode
+   case 'exp'
+      S=rownorm(S*S);
+      tmpX=S*X(:,:,1);
+   case 'lin'
+      tmpX=S*X(:,:,i);
+   end
+   X(:,:,i+1)=tmpX;
+   color(:,:,i+1)=som_colorcode(X(:,:,i+1),initRGB);
+end
+
+color(isnan(color))=0;
+
+function r=fuzzysimilarity(sM,p)
+  % Calculate a "fuzzy response" similarity matrix
+  % sM: map
+  % p: sharpness factor
+  d=som_eucdist2(sM,sM);
+  v=std(sqrt(d(:))).^2;
+  r=rownorm(exp(-p^2*(d./v)));
+  r(~isfinite(r))=0;
+  return;
+
+
+function X = rownorm(X)
+
+  r = sum(X,2);
+  X = X ./ r(:,ones(size(X,2),1)); 
+  return;
+
+
+function X = maxnorm(X)
+
+  for i=1:size(X,2), r = (max(X(:,i))-min(X(:,i))); if r, X(:,i) = X(:,i) / r; end, end
+  return;