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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 arguments
error(nargchk(1, 2, nargin)) % check no. of input args is correct
if ~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); end
end
if nargin<2 | isempty(level) | isnan(level), level = 1; end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% print struct information
for 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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%