comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_vs1to2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function sS = som_vs1to2(sS)
+
+%SOM_VS1TO2 Convert version 1 structure to version 2.
+%
+% sSnew = som_vs1to2(sSold)
+%
+%  sMnew = som_vs1to2(sMold);  
+%  sDnew = som_vs1to2(sDold);  
+%
+%  Input and output arguments: 
+%   sSold   (struct) a SOM Toolbox version 1 structure
+%   sSnew   (struct) a SOM Toolbox version 2 structure
+%
+% For more help, try 'type som_vs1to2' or check out online documentation.
+% See also  SOM_SET, SOM_VS2TO1.
+
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% som_vs1to2
+%
+% PURPOSE
+%
+% Transforms SOM Toolbox 1 version structs from to 2 version structs.
+%
+% SYNTAX
+%
+%  sS2 = som_vs1to2(sS1)
+%
+% DESCRIPTION
+%
+% This function is offered to allow the change of old map and data structs
+% to new ones. There are quite a lot of changes between the versions,
+% especially in the map struct, and this function makes it easy to update 
+% the structs.
+%
+% WARNING!
+%
+% 'som_unit_norm' normalization type is not supported by version 2,
+% so this type of normalization will be lost.
+%
+% REQUIRED INPUT ARGUMENTS
+%
+%  sS1       (struct) any SOM Toolbox version 1 struct (map, data, 
+%                     training or normalization struct)
+%
+% OUTPUT ARGUMENTS
+% 
+%  sS2       (struct) the corresponding SOM Toolbox 2 version struct
+%
+% EXAMPLES
+%
+%  sM = som_vs1to2(sMold);
+%  sD = som_vs1to2(sDold);
+%  sT = som_vs1to2(sMold.train_sequence{1});
+%  sN = som_vs1to2(sDold.normalization); 
+%
+% SEE ALSO
+% 
+%  som_set          Set values and create SOM Toolbox structs.
+%  som_vs2to1       Transform structs from version 2.0 to 1.0.
+
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta juuso 101199
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% check arguments
+
+error(nargchk(1, 1, nargin));   % check no. of input arguments is correct
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% set field values
+
+if isfield(sS,'codebook'), type='som_map'; 
+elseif isfield(sS,'data'), type='som_data'; 
+elseif isfield(sS,'algorithm'), type = 'som_train';
+elseif isfield(sS,'inv_params'), type = 'som_norm'; 
+else
+  error('Unrecognized input struct.'); 
+end
+
+switch type, 
+ case 'som_map',
+  msize = sS.msize; munits = prod(msize); dim = prod(size(sS.codebook))/munits; 
+  M = reshape(sS.codebook,[munits dim]);
+
+  % topology
+  if strcmp(sS.shape,'rect'), shape = 'sheet'; else shape = sS.shape; end
+  sTopol = struct('type','som_topol','msize',msize,'lattice',sS.lattice,'shape',shape);
+  
+  % labels
+  labels = cell(munits,1);
+  for i=1:munits, 
+    for j=1:length(sS.labels{i}), labels{i,j} = sS.labels{i}{j}; end
+  end
+  
+  % trainhist
+  tl = length(sS.train_sequence); 
+  if strcmp(sS.init_type,'linear'); alg = 'lininit'; else alg = 'randinit'; end
+  trh = struct('type','som_train');
+  trh.algorithm = alg;
+  trh.neigh = sS.neigh;
+  trh.mask = sS.mask;
+  trh.data_name = sS.data_name; 
+  trh.radius_ini = NaN;
+  trh.radius_fin = NaN;
+  trh.alpha_ini = NaN; 
+  trh.alpha_type = '';
+  trh.trainlen = NaN;
+  trh.time = '';
+  for i=1:tl, 
+    trh(i+1) = som_vs1to2(sS.train_sequence{i});     
+    trh(i+1).mask = sS.mask;
+    trh(i+1).neigh = sS.neigh;
+    trh(i+1).data_name = sS.data_name;
+  end
+  
+  % component normalizations
+  cnorm = som_vs1to2(sS.normalization); 
+  if isempty(cnorm), 
+    cnorm = cell(dim,1);
+  elseif length(cnorm) ~= dim, 
+    warning('Incorrect number of normalizations. Normalizations ignored.\n');	    
+    cnorm = cell(dim,1);
+  else
+    if strcmp(cnorm{1}.method,'histD'),
+      M = redo_hist_norm(M,sS.normalization.inv_params,cnorm);
+    end
+  end     
+  
+  % map
+  sSnew = struct('type','som_map');
+  sSnew.codebook = M;
+  sSnew.topol = sTopol;
+  sSnew.labels = labels;
+  sSnew.neigh = sS.neigh;
+  sSnew.mask = sS.mask;
+  sSnew.trainhist = trh;
+  sSnew.name = sS.name;
+  sSnew.comp_norm = cnorm;
+  sSnew.comp_names = sS.comp_names;
+  
+ case 'som_data',
+  [dlen dim] = size(sS.data);
+  
+  % component normalizations
+  cnorm = som_vs1to2(sS.normalization);
+  if isempty(cnorm), 
+    cnorm = cell(dim,1);
+  elseif length(cnorm) ~= dim, 
+    warning('Incorrect number of normalizations. Normalizations ignored.\n');
+    cnorm = cell(dim,1);
+  else
+    if strcmp(cnorm{1}.method,'histD'),
+      sS.data = redo_hist_norm(sS.data,sS.normalization.inv_params,cnorm);
+    end     
+  end
+
+  % data
+  sSnew = struct('type','som_data');
+  sSnew.data = sS.data;
+  sSnew.name = sS.name;
+  sSnew.labels = sS.labels;
+  sSnew.comp_names = sS.comp_names;
+  sSnew.comp_norm = cnorm;
+  sSnew.label_names = []; 
+  
+ case 'som_norm',       
+  if isempty(sS.inv_params), 
+    sSnew = []; 
+  else 
+    dim = size(sS.inv_params,2);      
+    sSnew = cell(dim,1);
+    switch sS.name, 
+     case 'som_var_norm',  method = 'var'; 
+     case 'som_lin_norm',  method = 'range'; 
+     case 'som_hist_norm', method = 'histD'; 
+     case 'som_unit_norm', method = '';
+      warning(['Normalization method ''som_unit_norm'' is not available' ...
+	       ' in version 2 of SOM Toolbox.\n']);
+    end
+    if ~isempty(method), 
+      for i=1:dim, 
+	sSnew{i} = struct('type','som_norm');
+	sSnew{i}.method = method;
+	sSnew{i}.params = [];
+	sSnew{i}.status = 'done';
+	switch method, 
+	 case 'var',   
+	  me = sS.inv_params(1,i); st = sS.inv_params(2,i);
+	  sSnew{i}.params = [me, st];
+	 case 'range', 
+	  mi = sS.inv_params(1,i); ma = sS.inv_params(2,i); 
+	  sSnew{i}.params = [mi, ma-mi]; 
+	 case 'histD',
+	  vals = sS.inv_params(1:(end-1),i);
+	  bins = sum(isfinite(vals));
+	  vals = vals(1:bins);
+	  sSnew{i}.params = vals;
+	end	  
+      end
+    end
+  end
+  
+ case 'som_train', 
+  sSnew = struct('type','som_train');
+  sSnew.algorithm = sS.algorithm;
+  sSnew.neigh = 'gaussian';
+  sSnew.mask = [];
+  sSnew.data_name = 'unknown'; 
+  sSnew.radius_ini = sS.radius_ini;
+  sSnew.radius_fin = sS.radius_fin;
+  sSnew.alpha_ini = sS.alpha_ini;
+  sSnew.alpha_type = sS.alpha_type;
+  sSnew.trainlen = sS.trainlen;
+  sSnew.time = sS.time;
+  
+ case 'som_topol', 
+  disp('Version 1.0 of SOM Toolbox did not have topology structure.\n');
+ 
+ case {'som_grid','som_vis'}
+  disp('Version 1.0 of SOM Toolbox did not have visualization structs.\n');
+  
+ otherwise, 
+  
+  error('Unrecognized struct.');
+end
+
+sS = sSnew;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% subfunctions
+
+function D = redo_hist_norm(D,inv_params,cnorm)
+
+  dim = size(D,2);
+
+  % first - undo the old way
+  n_bins = inv_params(end,:);
+  D   = round(D * sparse(diag(n_bins)));
+  for i = 1:dim,
+    if any(isnan(D(:, i))), D(isnan(D(:, i)), i) = n_bins(i); end
+    D(:, i) = inv_params(D(:, i), i);
+  end
+  % then - redo the new way
+  for i=1:dim, 
+    bins = length(cnorm{i}.params);
+    x = D(:,i);
+    inds = find(~isnan(x) & ~isinf(x))';
+    for j = inds, 
+      [dummy ind] = min(abs(x(j) - cnorm{i}.params));
+      if x(j) > cnorm{i}.params(ind) & ind < bins, x(j) = ind + 1;  
+      else x(j) = ind;
+      end
+    end
+    D(:,i) = (x-1)/(bins-1);
+  end
+
+
+
+