function som_info(sS,level)%SOM_INFO Displays information on the given SOM Toolbox struct.% % som_info(sS,[level])%%  som_info(sMap);%  som_info(sData,3);%  som_info({sMap,sData});%  som_info(sMap.comp_norm{2}); %%  Input and output arguments ([]'s are optional): %   sS       (struct) SOM Toolbox struct %            (cell array of structs) several structs in a cell array%   [level]  (scalar) detail level (1-4), default = 1%% For more help, try 'type som_info' or check out online documentation.% See also SOM_SET.%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% som_info%% PURPOSE%% Display information of the given SOM Toolbox struct(s).%% SYNTAX%%  som_info(sM)%  som_info({sM,sD})%  som_info(...,level)%% DESCRIPTION%% Display the contents of the given SOM Toolbox struct(s). Information% of several structs can be shown if the structs are given in a cell % array. The level of detail can be varied with the second argument.% The number of different levels varies between structs. For map and % data structs, not only the fields, but also some statistics of the % vectors ('.data' and '.codebook' fields) is displayed. %%   map struct%    level 1: name, dimension, topology, dimension, neigborhood function,%             mask and training status%    level 2: ..., training history%    level 3: ..., vector component names, statistics and normalization status%    level 4: ..., vector component normalizations%%   data struct:%    level 1: name, dimension, data set completeness statistics%    level 2: ..., vector component names, statistics and normalization status%    level 3: ..., vector component normalizations%    level 4: ..., label statistics%    %   topology struct: %    level 1: all fields%%   train struct: %    level 1: all fields%%   normalization struct: %    level 1: method, status%    level 2: ..., parameters%    % REQUIRED INPUT ARGUMENTS%%   sS       (struct) SOM Toolbox struct %            (cell array of structs) several structs in a cell array%  % OPTIONAL INPUT ARGUMENTS %%   level    (scalar) detail level (1-4), default = 1%% EXAMPLES%%  som_info(sM)%  som_info(sM,4)%  som_info(sM.trainhist)%  som_info(sM.comp_norm{3})%% SEE ALSO% %  som_set        Set fields and create SOM Toolbox structs.% Copyright (c) 1999-2000 by the SOM toolbox programming team.% http://www.cis.hut.fi/projects/somtoolbox/% Version 1.0beta ecco 110997% Version 2.0beta juuso 101199%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% check argumentserror(nargchk(1, 2, nargin))  % check no. of input args is correctif ~isstruct(sS),  if ~iscell(sS) | ~isstruct(sS{1}),     error('Invalid first input argument.')  end  csS = sS;else  l = length(sS);     csS = cell(l,1);   for i=1:l, csS{i} = sS(i); endendif nargin<2 | isempty(level) | isnan(level), level = 1; end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% print struct informationfor c=1:length(csS),  sS = csS{c}; fprintf(1,'\n');  switch sS.type,  case 'som_map',   mdim = length(sS.topol.msize);  [munits dim] = size(sS.codebook);  t    = length(sS.trainhist);    if t==0, st='uninitialized';   elseif t==1, st = 'initialized';  else st = sprintf('initialized, trained %d times',t-1);  end  % level 1  fprintf(1,'Struct type                           : %s\n', sS.type);  fprintf(1,'Map name                              : %s\n', sS.name);  fprintf(1,'Input dimension                       : %d\n', dim);  fprintf(1,'Map grid size                         : ');  for i = 1:mdim - 1, fprintf(1,'%d x ',sS.topol.msize(i)); end  fprintf(1,'%d\n', sS.topol.msize(mdim));  fprintf(1,'Lattice type (rect/hexa)              : %s\n', sS.topol.lattice);  fprintf(1,'Shape (sheet/cyl/toroid)              : %s\n', sS.topol.shape);  fprintf(1,'Neighborhood type                     : %s\n', sS.neigh);  fprintf(1,'Mask                                  : ');  if dim,    for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end;     fprintf(1,'%d\n',sS.mask(dim));  else fprintf(1,'\n');  end  fprintf(1,'Training status                       : %s\n', st);     % level 1,  status = cell(dim,1);  for i=1:dim,     n = length(sS.comp_norm{i});    if n,       uninit = strcmp('uninit',{sS.comp_norm{i}.status});      done   = strcmp('done',{sS.comp_norm{i}.status});      undone = strcmp('undone',{sS.comp_norm{i}.status});      if sum(uninit)==n, status{i} = 'none';      elseif sum(done)==n, status{i} = 'done';      elseif sum(undone)==n, status{i} = 'undone';      else status{i} = 'partial';      end    else status{i} = 'no normalization'; end  end  if level>1,     fprintf(1,'\nVector components\n');    M = sS.codebook;    fprintf(1,' #   name          mask     min    mean     max     std  normalization\n');    fprintf(1,' --- ------------  ----  ------  ------  ------  ------  -------------\n');    for i = 1:dim,      fprintf(1,' %-3d %-12s  %-4.2f  %6.2g  %6.2g  %6.2g  %6.2g  %s\n', ...              i,sS.comp_names{i}, sS.mask(i), ...              min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i});    end  end  % level 3  if level>2,    fprintf(1,'\nVector component normalizations\n');    fprintf(1,' #   name          method (i=uninit,u=undone,d=done)\n');    fprintf(1,' --- ------------  ---------------------------------------\n');    for i=1:dim,        fprintf(1,' %-3d %-12s  ',i,sS.comp_names{i});      n = length(sS.comp_norm{i});       for j=1:n,         m = sS.comp_norm{i}(j).method;        s = sS.comp_norm{i}(j).status;        if strcmp(s,'uninit'), c='i';         elseif strcmp(s,'undone'), c='u';         else c='d';        end        fprintf(1,'%s[%s] ',m,c);      end      fprintf(1,'\n');    end  end    % level 4  if level>3,    fprintf(1,'\nTraining history\n');    fprintf(1,'Algorithm Data          Trainlen Neigh.f. Radius     Alpha (type)   Date\n');    fprintf(1,'--------- ------------- -------- -------- ---------- -------------- --------------------\n');	           for i=1:t,       sT = sS.trainhist(i);      fprintf(1,'%8s  %13s %8d %8s %4.2f->%4.2f %5.3f (%6s) %s\n',...	      sT.algorithm,sT.data_name,sT.trainlen,...	      sT.neigh,sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type,sT.time);            %for j = 1:length(sT.mask)-1, fprintf(1,'%d ',sT.mask(j)); end;       %if ~isempty(sT.mask), fprintf(1,'%d\n',sT.mask(end)); else fprintf(1,'\n'); end    end  end case 'som_data',  [dlen dim] = size(sS.data);  if dlen*dim    if dim>1, ind = find(~isnan(sum(sS.data,2)));    else ind = find(~isnan(sS.data));    end  else ind = []; end  complete = size(sS.data(ind,:),1);  partial  = dlen - complete;  values   = prod(size(sS.data));  missing  = sum(sum(isnan(sS.data)));   % level 1    fprintf(1,'Struct type             : %s\n', sS.type);  fprintf(1,'Data name               : %s\n', sS.name);  fprintf(1,'Vector dimension        : %d\n', dim);  fprintf(1,'Number of data vectors  : %d\n', dlen);  fprintf(1,'Complete data vectors   : %d\n', complete);  fprintf(1,'Partial data vectors    : %d\n', partial);    if values, r = floor(100 * (values - missing) / values); else r = 0; end  fprintf(1,'Complete values         : %d of %d (%d%%)\n', ...          values-missing, values, r);   % level 2,  status = cell(dim,1);  for i=1:dim,     n = length(sS.comp_norm{i});    if n,       uninit = strcmp('uninit',{sS.comp_norm{i}.status});      done   = strcmp('done',{sS.comp_norm{i}.status});      undone = strcmp('undone',{sS.comp_norm{i}.status});      if sum(uninit)==n, status{i} = 'none';      elseif sum(done)==n, status{i} = 'done';      elseif sum(undone)==n, status{i} = 'undone';      else status{i} = 'partial';      end    else status{i} = 'no normalization'; end  end  if level>1,     fprintf(1,'\nVector components\n');    D = sS.data;    fprintf(1,' #   name            min     mean     max     std  missing      normalization\n');    fprintf(1,' --- ------------  ------  ------  ------  ------  -----------  -------------\n');    for i = 1:dim,      known = find(~isnan(D(:,i)));       miss = dlen-length(known);      switch length(known),        case 0, mi = NaN; me = NaN; ma = NaN; st = NaN;        case 1, mi = D(known,i); me = mi; ma = mi; st = 0;       otherwise, 	mi = min(D(known,i)); ma = max(D(known,i)); 	me = mean(D(known,i)); st = std(D(known,i));       end      fprintf(1,' %-3d %-12s  %6.2g  %6.2g  %6.2g  %6.2g  %5d (%2d%%)  %s\n', ...              i,sS.comp_names{i},mi,me,ma,st,miss,floor(100*miss/dlen),status{i});    end  end  % level 3  if level>2,    fprintf(1,'\nVector component normalizations\n');    fprintf(1,' #   name          method (i=uninit,u=undone,d=done)\n');    fprintf(1,' --- ------------  ---------------------------------------\n');    for i=1:dim,        fprintf(1,' %-3d %-12s  ',i,sS.comp_names{i});      n = length(sS.comp_norm{i});               for j=1:n,         m = sS.comp_norm{i}(j).method;        s = sS.comp_norm{i}(j).status;        if strcmp(s,'uninit'), c='i';         elseif strcmp(s,'undone'), c='u';         else c='d';        end        fprintf(1,'%s[%s] ',m,c);      end      fprintf(1,'\n');    end  end  % level 4  if level>3,    m = size(sS.labels,2);    fprintf(1,'\nLabels\n');       if isempty(sS.label_names),             labs = {''}; freq = 0;       for i=1:dlen*m, 	l = sS.labels{i}; 	if isempty(l), freq(1) = freq(1)+1; 	else 	  k = find(strcmp(labs,l)); 	  if isempty(k), labs{end+1} = l; freq(end+1) = 1; 	  else freq(k)=freq(k)+1;	  end	end      end      emp = freq(1);       uni = length(freq)-1;      if uni>0, tot = sum(freq(2:end)); else tot = 0; end      fprintf(1,' Total: %d\n Empty: %d\n Unique: %d\n',tot,emp,uni);    else      for j=1:m, 	labs = {''}; freq = 0; 	for i=1:dlen, 	  l = sS.labels{i,j}; 	  if isempty(l), freq(1) = freq(1)+1; 	  else 	    k = find(strcmp(labs,l)); 	    if isempty(k), labs{end+1} = l; freq(end+1) = 1; 	    else freq(k)=freq(k)+1;	    end	  end	end	emp = freq(1); 	uni = length(freq)-1;	if uni>0, tot = sum(freq(2:end)); else tot = 0; end	fprintf(1,' [%s] Total / empty / unique: %d / %d / %d\n', ...		sS.label_names{j},tot,emp,uni);       end    end  end   case 'som_topol',   mdim = length(sS.msize);   % level 1  fprintf(1,'Struct type                           : %s\n',sS.type);  fprintf(1,'Map grid size                         : ');  for i = 1:mdim - 1, fprintf(1,'%d x ',sS.msize(i)); end  fprintf(1,'%d\n', sS.msize(mdim));  fprintf(1,'Lattice type (rect/hexa)              : %s\n', sS.lattice);  fprintf(1,'Shape (sheet/cyl/toroid)              : %s\n', sS.shape); case 'som_train',   % level 1  fprintf(1,'Struct type                           : %s\n',sS.type);  fprintf(1,'Training algorithm                    : %s\n',sS.algorithm);  fprintf(1,'Training data                         : %s\n',sS.data_name);  fprintf(1,'Neighborhood function                 : %s\n',sS.neigh);  fprintf(1,'Mask                                  : ');  dim = length(sS.mask);  if dim,     for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end;     fprintf(1,'%d\n',sS.mask(end));  else fprintf(1,'\n'); end  fprintf(1,'Initial radius                        : %-6.1f\n',sS.radius_ini);  fprintf(1,'Final radius                          : %-6.1f\n',sS.radius_fin);  fprintf(1,'Initial learning rate (alpha)         : %-6.1f\n',sS.alpha_ini);  fprintf(1,'Alpha function type (linear/inv)      : %s\n',sS.alpha_type);  fprintf(1,'Training length                       : %d\n',sS.trainlen);  fprintf(1,'When training was done                : %s\n',sS.time);  case 'som_norm',       % level 1   fprintf(1,'Struct type                           : %s\n',sS.type);   fprintf(1,'Normalization method                  : %s\n',sS.method);   fprintf(1,'Status                                : %s\n',sS.status);      % level 2   if level>1,      fprintf(1,'Parameters:\n');     sS.params   end     case 'som_grid',       % level 1   fprintf(1,'Struct type                           : %s\n',sS.type);   if ischar(sS.neigh),      fprintf(1,'Connections                           : [%d %d], %s, %s\n',...	     sS.msize(1),sS.msize(2),sS.neigh,sS.shape);   else     fprintf(1,'Connections                           : [%d %d] %d lines\n',...	     sS.msize(1),sS.msize(2),sum(sS.neigh));   end   fprintf(1,'Line                                  : %s\n',sS.line);   if length(sS.marker)==1,      fprintf(1,'Marker                                : %s\n',sS.marker);   else     fprintf(1,'Marker                                : varies\n');   end   fprintf(1,'Surf                                  : ');   if isempty(sS.surf), fprintf(1,'off\n'); else fprintf(1,'on\n'); end   fprintf(1,'Labels                                : ');   if isempty(sS.label), fprintf(1,'off\n');    else fprintf(1,'on (%d)\n',sS.labelsize); end    end fprintf(1,'\n');end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%