wolffd@0: function sS = som_vs2to1(sS)
wolffd@0: 
wolffd@0: %SOM_VS2TO1 Convert version 2 struct to version 1.
wolffd@0: %
wolffd@0: % sSold = som_vs2to1(sSnew)
wolffd@0: %
wolffd@0: %  sMold = som_vs2to1(sMnew);  
wolffd@0: %  sDold = som_vs2to1(sDnew);  
wolffd@0: %
wolffd@0: %  Input and output arguments: 
wolffd@0: %   sSnew   (struct) a SOM Toolbox version 2 struct
wolffd@0: %   sSold   (struct) a SOM Toolbox version 1 struct
wolffd@0: %
wolffd@0: % For more help, try 'type som_vs2to1' or check out online documentation.
wolffd@0: % See also  SOM_SET, SOM_VS1TO2.
wolffd@0: 
wolffd@0: %%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %
wolffd@0: % som_vs2to1
wolffd@0: %
wolffd@0: % PURPOSE
wolffd@0: %
wolffd@0: % Converts SOM Toolbox version 2 structs to version 1 structs.
wolffd@0: %
wolffd@0: % SYNTAX
wolffd@0: %
wolffd@0: %  sS1 = som_vs2to1(sS2)
wolffd@0: %
wolffd@0: % DESCRIPTION
wolffd@0: %
wolffd@0: % This function is offered to allow the change of new map and data structs
wolffd@0: % to old ones. There are quite a lot of changes between the versions,
wolffd@0: % especially in the map struct, and this function makes it possible to 
wolffd@0: % use the old functions with new structs.
wolffd@0: %
wolffd@0: % Note that part of the information is lost in the conversion. Especially, 
wolffd@0: % training history is lost, and the normalization is, except in the simplest
wolffd@0: % cases (like all have 'range' or 'var' normalization) screwed up.
wolffd@0: %
wolffd@0: % REQUIRED INPUT ARGUMENTS
wolffd@0: %
wolffd@0: %  sS2       (struct) som SOM Toolbox version 2.0 struct (map, data, 
wolffd@0: %                     training or normalization struct)
wolffd@0: %
wolffd@0: % OUTPUT ARGUMENTS
wolffd@0: % 
wolffd@0: %  sS1       (struct) the corresponding SOM Toolbox version 2.0 struct
wolffd@0: %
wolffd@0: % EXAMPLES
wolffd@0: %
wolffd@0: %  sM = som_vs2to1(sMnew);
wolffd@0: %  sD = som_vs2to1(sDnew);
wolffd@0: %  sT = som_vs2to1(sMnew.trainhist(1));
wolffd@0: %
wolffd@0: % SEE ALSO
wolffd@0: % 
wolffd@0: %  som_set          Set values and create SOM Toolbox structs.
wolffd@0: %  som_vs1to2       Transform structs from 1.0 version to 2.0.   
wolffd@0: 
wolffd@0: % Copyright (c) 1999-2000 by the SOM toolbox programming team.
wolffd@0: % http://www.cis.hut.fi/projects/somtoolbox/
wolffd@0: 
wolffd@0: % Version 2.0beta juuso 101199
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% check arguments
wolffd@0: 
wolffd@0: error(nargchk(1, 1, nargin));   % check no. of input arguments is correct
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% set field values
wolffd@0:   
wolffd@0: switch sS.type, 
wolffd@0:  case 'som_map',
wolffd@0:   msize = sS.topol.msize; 
wolffd@0:   [munits dim] = size(sS.codebook);
wolffd@0:   
wolffd@0:   % topology
wolffd@0:   if strcmp(sS.topol.shape,'sheet'), shape = 'rect'; 
wolffd@0:   else shape = sS.shape; 
wolffd@0:   end
wolffd@0:   
wolffd@0:   % labels
wolffd@0:   labels = cell(munits,1);
wolffd@0:   nl = size(sS.labels,2);
wolffd@0:   for i=1:munits, 
wolffd@0:     labels{i} = cell(nl,1);      
wolffd@0:     for j=1:nl, labels{i}{j} = sS.labels{i,j}; end
wolffd@0:   end
wolffd@0:   
wolffd@0:   % trainhist 
wolffd@0:   tl = length(sS.trainhist); 
wolffd@0:   if tl==0 | strcmp(sS.trainhist(1).algorithm,'lininit'), 
wolffd@0:     init_type = 'linear';
wolffd@0:   else
wolffd@0:     init_type = 'random';
wolffd@0:   end
wolffd@0:   if tl>1, 
wolffd@0:     for i=2:tl, 
wolffd@0:       train_seq{i-1} = som_vs2to1(sS.trainhist(i));
wolffd@0:     end
wolffd@0:     train_type = sS.trainhist(tl).algorithm; 
wolffd@0:   else
wolffd@0:     train_seq = [];
wolffd@0:     train_type = 'batch';
wolffd@0:   end 
wolffd@0:   if tl>0, data_name = sS.trainhist(tl).data_name; else data_name = ''; end
wolffd@0:   
wolffd@0:   % component normalizations 
wolffd@0:   sN = convert_normalizations(sS.comp_norm);   
wolffd@0:   if strcmp(sN.name,'som_hist_norm'), 
wolffd@0:     sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
wolffd@0:   end
wolffd@0:   
wolffd@0:   % map 
wolffd@0:   sSnew = struct('init_type', 'linear', 'train_type', 'batch', 'lattice' ,...
wolffd@0: 		 'hexa', 'shape', 'rect', 'neigh', 'gaussian', 'msize', msize, ...
wolffd@0: 		 'train_sequence', [], 'codebook', [], 'labels', [], ...
wolffd@0: 		 'mask', [], 'data_name', 'unnamed', 'normalization', [], ...
wolffd@0: 		 'comp_names', [], 'name', 'unnamed');
wolffd@0:   sSnew.init_type = init_type;
wolffd@0:   sSnew.train_type = train_type;
wolffd@0:   sSnew.lattice = sS.topol.lattice;
wolffd@0:   sSnew.shape = shape;
wolffd@0:   sSnew.neigh = sS.neigh;
wolffd@0:   sSnew.msize = sS.topol.msize;
wolffd@0:   sSnew.train_sequence = train_seq;
wolffd@0:   sSnew.codebook = reshape(sS.codebook,[sS.topol.msize dim]);
wolffd@0:   sSnew.labels = labels;
wolffd@0:   sSnew.mask = sS.mask;
wolffd@0:   sSnew.data_name = data_name;
wolffd@0:   sSnew.normalization = sN;
wolffd@0:   sSnew.comp_names = sS.comp_names;
wolffd@0:   sSnew.name = sS.name;
wolffd@0:   
wolffd@0:  case 'som_data',
wolffd@0:   [dlen dim] = size(sS.data);
wolffd@0:   
wolffd@0:   % component normalizations
wolffd@0:   sN = convert_normalizations(sS.comp_norm); 
wolffd@0:   if strcmp(sN.name,'som_hist_norm'), 
wolffd@0:     sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
wolffd@0:   end
wolffd@0:   
wolffd@0:   % data
wolffd@0:   sSnew = struct('data', [], 'name', '', 'labels' , [], 'comp_names', ...
wolffd@0: 		 [], 'normalization', []);
wolffd@0:   sSnew.data = sS.data;
wolffd@0:   sSnew.name = sS.name;
wolffd@0:   sSnew.labels = sS.labels;
wolffd@0:   sSnew.comp_names = sS.comp_names;
wolffd@0:   sSnew.normalization = sN;
wolffd@0:   
wolffd@0:  case 'som_norm',     
wolffd@0:   sSnew = struct('name','som_var_norm','inv_params',[]);
wolffd@0:   
wolffd@0:   switch sS.method, 
wolffd@0:    case 'var',   sSnew.name = 'som_var_norm';
wolffd@0:    case 'range', sSnew.name = 'som_lin_norm';
wolffd@0:    case 'histD', sSnew.name = 'som_hist_norm';
wolffd@0:    otherwise, 
wolffd@0:     warning(['Method ' method ' does not exist in version 1.'])
wolffd@0:   end
wolffd@0: 
wolffd@0:   if strcmp(sS.status,'done'),   
wolffd@0:     switch sS.method, 
wolffd@0:      case 'var', 
wolffd@0:       sSnew.inv_params = zeros(2,1);
wolffd@0:       sSnew.inv_params(1) = sS.params(1);
wolffd@0:       sSnew.inv_params(2) = sS.params(2);
wolffd@0:      case 'range', 
wolffd@0:       sSnew.inv_params = zeros(2,1);
wolffd@0:       sSnew.inv_params(1) = sS.params(1);
wolffd@0:       sSnew.inv_params(2) = sS.params(2) + sS.params(1);;
wolffd@0:      case 'histD',
wolffd@0:       bins = length(sS.params);
wolffd@0:       sSnew.inv_params = zeros(bins+1,1) + Inf;
wolffd@0:       sSnew.inv_params(1:bins,i) = sS.params;
wolffd@0:       sSnew.inv_params(end,i) = bins; 
wolffd@0:     end
wolffd@0:   end
wolffd@0:   
wolffd@0:  case 'som_train', 
wolffd@0:   sSnew = struct('algorithm', sS.algorithm, 'radius_ini', ...
wolffd@0: 		 sS.radius_ini, 'radius_fin', sS.radius_fin, 'alpha_ini', ...
wolffd@0: 		 sS.alpha_ini, 'alpha_type', sS.alpha_type, 'trainlen', sS.trainlen, ...
wolffd@0: 		 'qerror', NaN, 'time', sS.time);
wolffd@0:   
wolffd@0:  case 'som_topol', 
wolffd@0:   disp('Version 1 of SOM Toolbox did not have topology structure.\n');
wolffd@0:   
wolffd@0:  otherwise, 
wolffd@0:   
wolffd@0:   error('Unrecognized struct.');
wolffd@0: end
wolffd@0: 
wolffd@0: sS = sSnew;
wolffd@0: 
wolffd@0: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
wolffd@0: %% subfunctions
wolffd@0: 
wolffd@0: function sN = convert_normalizations(cnorm)
wolffd@0: 
wolffd@0:   dim = length(cnorm);
wolffd@0:   sN = struct('name','som_var_norm','inv_params',[]);
wolffd@0:     
wolffd@0:   % check that there is exactly one normalization per component
wolffd@0:   % and that their status and method is the same
wolffd@0:   ok = 1;
wolffd@0:   nof = zeros(dim,1);
wolffd@0:   for i=1:dim, nof(i) = length(cnorm{i}); end
wolffd@0:   if any(nof>1), ok=0; 
wolffd@0:   elseif any(nof==1) & any(nof==0), ok=0;
wolffd@0:   elseif any(nof>0), 
wolffd@0:     status = cnorm{1}.status;
wolffd@0:     method = cnorm{1}.method;
wolffd@0:     for i=2:dim, 
wolffd@0:       if ~strcmp(cnorm{i}.status,status) | ~strcmp(cnorm{i}.method,method), 
wolffd@0: 	ok = 0; 
wolffd@0:       end
wolffd@0:     end    
wolffd@0:   elseif all(nof==0), 
wolffd@0:     return;
wolffd@0:   end
wolffd@0:   if ~ok, 
wolffd@0:     warning(['Normalization could not be converted. All variables can' ...
wolffd@0: 	     ' only be normalized with a single, and same, method.']);
wolffd@0:     return;
wolffd@0:   end  
wolffd@0:   
wolffd@0:   % method name
wolffd@0:   switch method, 
wolffd@0:    case 'var', sN.name = 'som_var_norm';
wolffd@0:    case 'range', sN.name = 'som_lin_norm';
wolffd@0:    case 'histD', sN.name = 'som_hist_norm';
wolffd@0:    otherwise, 
wolffd@0:     warning(['Normalization could not be converted. Method ' method ...
wolffd@0: 	     'does not exist in version 1.']);
wolffd@0:     return;
wolffd@0:   end
wolffd@0: 
wolffd@0:   % if not done, inv_params is empty
wolffd@0:   if ~strcmp(status,'done'), return; end  
wolffd@0:    
wolffd@0:   % ok, make the conversion  
wolffd@0:   switch method, 
wolffd@0:    case 'var',   
wolffd@0:     sN.inv_params = zeros(2,dim);
wolffd@0:     for i=1:dim, 
wolffd@0:       sN.inv_params(1,i) = cnorm{i}.params(1);
wolffd@0:       sN.inv_params(2,i) = cnorm{i}.params(2);
wolffd@0:     end
wolffd@0:    case 'range',
wolffd@0:     sN.inv_params = zeros(2,dim);
wolffd@0:     for i=1:dim, 
wolffd@0:       sN.inv_params(1,i) = cnorm{i}.params(1);
wolffd@0:       sN.inv_params(2,i) = cnorm{i}.params(2) + cnorm{i}.params(1);
wolffd@0:     end
wolffd@0:    case 'histD',     
wolffd@0:     bins = zeros(dim,1); 
wolffd@0:     for i=1:dim, bins(i) = length(cnorm{i}.params); end
wolffd@0:     m = max(bins); 
wolffd@0:     sN.inv_params = zeros(m+1,dim) + Inf;
wolffd@0:     for i=1:dim, 
wolffd@0:       sN.inv_params(1:bins(i),i) = cnorm{i}.params;
wolffd@0:       if bins(i)<m, sN.inv_params(bins(i)+1,i) = NaN; end
wolffd@0:       sN.inv_params(end,i) = bins(i); 
wolffd@0:     end
wolffd@0:   end
wolffd@0: 
wolffd@0: function D = redo_hist_norm(D,cnorm,sN)
wolffd@0: 
wolffd@0:   dim = size(D,2);
wolffd@0: 
wolffd@0:   % first - undo the new way
wolffd@0:   for i=1:dim, 
wolffd@0:     bins = length(cnorm{i}.params);
wolffd@0:     D(:,i) = round(D(:,i)*(bins-1)+1);
wolffd@0:     inds = find(~isnan(D(:,i)) & ~isinf(D(:,i)));
wolffd@0:     D(inds,i) = cnorm{i}.params(D(inds,i));
wolffd@0:   end  
wolffd@0:   % then - redo the old way
wolffd@0:   n_bins = sN.inv_params(size(sN.inv_params,1),:);
wolffd@0:   for j = 1:dim,        
wolffd@0:     for i = 1:size(D, 1)
wolffd@0:       if ~isnan(D(i, j)),
wolffd@0: 	[d ind] = min(abs(D(i, j) - sN.inv_params(1:n_bins(j), j)));
wolffd@0: 	if (D(i, j) - sN.inv_params(ind, j)) > 0 & ind < n_bins(j),
wolffd@0: 	  D(i, j) = ind + 1;   
wolffd@0: 	else                   
wolffd@0: 	  D(i, j) = ind;
wolffd@0: 	end
wolffd@0:       end
wolffd@0:     end
wolffd@0:   end
wolffd@0:   D = D * sparse(diag(1 ./ n_bins));
wolffd@0: 
wolffd@0: