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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