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 argumentserror(nargchk(1, 1, nargin));   % check no. of input arguments is correct%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% set field valuesif 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.'); endswitch 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.');endsS = sSnew;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subfunctionsfunction 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