comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_vs2to1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function sS = som_vs2to1(sS)
+
+%SOM_VS2TO1 Convert version 2 struct to version 1.
+%
+% sSold = som_vs2to1(sSnew)
+%
+%  sMold = som_vs2to1(sMnew);  
+%  sDold = som_vs2to1(sDnew);  
+%
+%  Input and output arguments: 
+%   sSnew   (struct) a SOM Toolbox version 2 struct
+%   sSold   (struct) a SOM Toolbox version 1 struct
+%
+% For more help, try 'type som_vs2to1' or check out online documentation.
+% See also  SOM_SET, SOM_VS1TO2.
+
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% som_vs2to1
+%
+% PURPOSE
+%
+% Converts SOM Toolbox version 2 structs to version 1 structs.
+%
+% SYNTAX
+%
+%  sS1 = som_vs2to1(sS2)
+%
+% DESCRIPTION
+%
+% This function is offered to allow the change of new map and data structs
+% to old ones. There are quite a lot of changes between the versions,
+% especially in the map struct, and this function makes it possible to 
+% use the old functions with new structs.
+%
+% Note that part of the information is lost in the conversion. Especially, 
+% training history is lost, and the normalization is, except in the simplest
+% cases (like all have 'range' or 'var' normalization) screwed up.
+%
+% REQUIRED INPUT ARGUMENTS
+%
+%  sS2       (struct) som SOM Toolbox version 2.0 struct (map, data, 
+%                     training or normalization struct)
+%
+% OUTPUT ARGUMENTS
+% 
+%  sS1       (struct) the corresponding SOM Toolbox version 2.0 struct
+%
+% EXAMPLES
+%
+%  sM = som_vs2to1(sMnew);
+%  sD = som_vs2to1(sDnew);
+%  sT = som_vs2to1(sMnew.trainhist(1));
+%
+% SEE ALSO
+% 
+%  som_set          Set values and create SOM Toolbox structs.
+%  som_vs1to2       Transform structs from 1.0 version to 2.0.   
+
+% 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
+  
+switch sS.type, 
+ case 'som_map',
+  msize = sS.topol.msize; 
+  [munits dim] = size(sS.codebook);
+  
+  % topology
+  if strcmp(sS.topol.shape,'sheet'), shape = 'rect'; 
+  else shape = sS.shape; 
+  end
+  
+  % labels
+  labels = cell(munits,1);
+  nl = size(sS.labels,2);
+  for i=1:munits, 
+    labels{i} = cell(nl,1);      
+    for j=1:nl, labels{i}{j} = sS.labels{i,j}; end
+  end
+  
+  % trainhist 
+  tl = length(sS.trainhist); 
+  if tl==0 | strcmp(sS.trainhist(1).algorithm,'lininit'), 
+    init_type = 'linear';
+  else
+    init_type = 'random';
+  end
+  if tl>1, 
+    for i=2:tl, 
+      train_seq{i-1} = som_vs2to1(sS.trainhist(i));
+    end
+    train_type = sS.trainhist(tl).algorithm; 
+  else
+    train_seq = [];
+    train_type = 'batch';
+  end 
+  if tl>0, data_name = sS.trainhist(tl).data_name; else data_name = ''; end
+  
+  % component normalizations 
+  sN = convert_normalizations(sS.comp_norm);   
+  if strcmp(sN.name,'som_hist_norm'), 
+    sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
+  end
+  
+  % map 
+  sSnew = struct('init_type', 'linear', 'train_type', 'batch', 'lattice' ,...
+		 'hexa', 'shape', 'rect', 'neigh', 'gaussian', 'msize', msize, ...
+		 'train_sequence', [], 'codebook', [], 'labels', [], ...
+		 'mask', [], 'data_name', 'unnamed', 'normalization', [], ...
+		 'comp_names', [], 'name', 'unnamed');
+  sSnew.init_type = init_type;
+  sSnew.train_type = train_type;
+  sSnew.lattice = sS.topol.lattice;
+  sSnew.shape = shape;
+  sSnew.neigh = sS.neigh;
+  sSnew.msize = sS.topol.msize;
+  sSnew.train_sequence = train_seq;
+  sSnew.codebook = reshape(sS.codebook,[sS.topol.msize dim]);
+  sSnew.labels = labels;
+  sSnew.mask = sS.mask;
+  sSnew.data_name = data_name;
+  sSnew.normalization = sN;
+  sSnew.comp_names = sS.comp_names;
+  sSnew.name = sS.name;
+  
+ case 'som_data',
+  [dlen dim] = size(sS.data);
+  
+  % component normalizations
+  sN = convert_normalizations(sS.comp_norm); 
+  if strcmp(sN.name,'som_hist_norm'), 
+    sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN);
+  end
+  
+  % data
+  sSnew = struct('data', [], 'name', '', 'labels' , [], 'comp_names', ...
+		 [], 'normalization', []);
+  sSnew.data = sS.data;
+  sSnew.name = sS.name;
+  sSnew.labels = sS.labels;
+  sSnew.comp_names = sS.comp_names;
+  sSnew.normalization = sN;
+  
+ case 'som_norm',     
+  sSnew = struct('name','som_var_norm','inv_params',[]);
+  
+  switch sS.method, 
+   case 'var',   sSnew.name = 'som_var_norm';
+   case 'range', sSnew.name = 'som_lin_norm';
+   case 'histD', sSnew.name = 'som_hist_norm';
+   otherwise, 
+    warning(['Method ' method ' does not exist in version 1.'])
+  end
+
+  if strcmp(sS.status,'done'),   
+    switch sS.method, 
+     case 'var', 
+      sSnew.inv_params = zeros(2,1);
+      sSnew.inv_params(1) = sS.params(1);
+      sSnew.inv_params(2) = sS.params(2);
+     case 'range', 
+      sSnew.inv_params = zeros(2,1);
+      sSnew.inv_params(1) = sS.params(1);
+      sSnew.inv_params(2) = sS.params(2) + sS.params(1);;
+     case 'histD',
+      bins = length(sS.params);
+      sSnew.inv_params = zeros(bins+1,1) + Inf;
+      sSnew.inv_params(1:bins,i) = sS.params;
+      sSnew.inv_params(end,i) = bins; 
+    end
+  end
+  
+ case 'som_train', 
+  sSnew = struct('algorithm', sS.algorithm, 'radius_ini', ...
+		 sS.radius_ini, 'radius_fin', sS.radius_fin, 'alpha_ini', ...
+		 sS.alpha_ini, 'alpha_type', sS.alpha_type, 'trainlen', sS.trainlen, ...
+		 'qerror', NaN, 'time', sS.time);
+  
+ case 'som_topol', 
+  disp('Version 1 of SOM Toolbox did not have topology structure.\n');
+  
+ otherwise, 
+  
+  error('Unrecognized struct.');
+end
+
+sS = sSnew;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% subfunctions
+
+function sN = convert_normalizations(cnorm)
+
+  dim = length(cnorm);
+  sN = struct('name','som_var_norm','inv_params',[]);
+    
+  % check that there is exactly one normalization per component
+  % and that their status and method is the same
+  ok = 1;
+  nof = zeros(dim,1);
+  for i=1:dim, nof(i) = length(cnorm{i}); end
+  if any(nof>1), ok=0; 
+  elseif any(nof==1) & any(nof==0), ok=0;
+  elseif any(nof>0), 
+    status = cnorm{1}.status;
+    method = cnorm{1}.method;
+    for i=2:dim, 
+      if ~strcmp(cnorm{i}.status,status) | ~strcmp(cnorm{i}.method,method), 
+	ok = 0; 
+      end
+    end    
+  elseif all(nof==0), 
+    return;
+  end
+  if ~ok, 
+    warning(['Normalization could not be converted. All variables can' ...
+	     ' only be normalized with a single, and same, method.']);
+    return;
+  end  
+  
+  % method name
+  switch method, 
+   case 'var', sN.name = 'som_var_norm';
+   case 'range', sN.name = 'som_lin_norm';
+   case 'histD', sN.name = 'som_hist_norm';
+   otherwise, 
+    warning(['Normalization could not be converted. Method ' method ...
+	     'does not exist in version 1.']);
+    return;
+  end
+
+  % if not done, inv_params is empty
+  if ~strcmp(status,'done'), return; end  
+   
+  % ok, make the conversion  
+  switch method, 
+   case 'var',   
+    sN.inv_params = zeros(2,dim);
+    for i=1:dim, 
+      sN.inv_params(1,i) = cnorm{i}.params(1);
+      sN.inv_params(2,i) = cnorm{i}.params(2);
+    end
+   case 'range',
+    sN.inv_params = zeros(2,dim);
+    for i=1:dim, 
+      sN.inv_params(1,i) = cnorm{i}.params(1);
+      sN.inv_params(2,i) = cnorm{i}.params(2) + cnorm{i}.params(1);
+    end
+   case 'histD',     
+    bins = zeros(dim,1); 
+    for i=1:dim, bins(i) = length(cnorm{i}.params); end
+    m = max(bins); 
+    sN.inv_params = zeros(m+1,dim) + Inf;
+    for i=1:dim, 
+      sN.inv_params(1:bins(i),i) = cnorm{i}.params;
+      if bins(i)<m, sN.inv_params(bins(i)+1,i) = NaN; end
+      sN.inv_params(end,i) = bins(i); 
+    end
+  end
+
+function D = redo_hist_norm(D,cnorm,sN)
+
+  dim = size(D,2);
+
+  % first - undo the new way
+  for i=1:dim, 
+    bins = length(cnorm{i}.params);
+    D(:,i) = round(D(:,i)*(bins-1)+1);
+    inds = find(~isnan(D(:,i)) & ~isinf(D(:,i)));
+    D(inds,i) = cnorm{i}.params(D(inds,i));
+  end  
+  % then - redo the old way
+  n_bins = sN.inv_params(size(sN.inv_params,1),:);
+  for j = 1:dim,        
+    for i = 1:size(D, 1)
+      if ~isnan(D(i, j)),
+	[d ind] = min(abs(D(i, j) - sN.inv_params(1:n_bins(j), j)));
+	if (D(i, j) - sN.inv_params(ind, j)) > 0 & ind < n_bins(j),
+	  D(i, j) = ind + 1;   
+	else                   
+	  D(i, j) = ind;
+	end
+      end
+    end
+  end
+  D = D * sparse(diag(1 ./ n_bins));
+
+