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function sM = som_supervised(sData,varargin)
%SOM_SUPERVISED SOM training which utilizes class information.
%
% sM = som_supervised(sData, [ArgID, value,...]))
%
% Input and output arguments ([]'s are optional)
% sData (struct) data struct, the class information is
% taken from the first column of .labels field
% [argID, (string) See below. These are given as
% value] (varies) 'argID', value -pairs.
%
% sMap (struct) map struct
%
% Here are the argument IDs and corresponding values:
% 'munits' (scalar) the preferred number of map units
% 'msize' (vector) map grid size
% 'mask' (vector) BMU search mask, size dim x 1
% 'name' (string) map name
% 'comp_names' (string array / cellstr) component names, size dim x 1
% 'tracking' (scalar) how much to report, default = 1
% The following values are unambiguous and can therefore
% be given without the preceeding argument ID:
% 'algorithm' (string) training algorithm: 'seq' or 'batch'
% 'mapsize' (string) do you want a 'small', 'normal' or 'big' map
% Any explicit settings of munits or msize override this.
% 'topol' (struct) topology struct
% 'som_topol','sTopol' = 'topol'
% 'lattice' (string) map lattice, 'hexa' or 'rect'
% 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid'
% 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss',
% 'ep' or 'bubble'
%
% For more help, try 'type som_supervised', or check out online documentation.
% See also SOM_MAKE, SOM_AUTOLABEL.
%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% som_supervised
%
% PURPOSE
%
% Creates, initializes and trains a supervised SOM by taking the
% class-identity into account.
%
% SYNTAX
%
% sMap = som_supervised(sData);
% sMap = som_supervised(...,'argID',value,...)
% sMap = som_make(...,value,...);
%
% DESCRIPTION
%
% Creates, initializes and trains a supervised SOM. It constructs the
% training data by adding 1-of-N -coded matrix to the original data
% based on the class information in the .labels field. The dimension
% of vectors after the process is (the old dimension + number of
% different classes). In each vector, one of the new components has
% value '1' (this depends on the class of the vector), and others '0'.
% Calls SOM_MAKE to construct the map. Then the class of each map unit
% is determined by taking maximum over these added components, and a
% label is give accordingly. Finally, the extra components (the
% 1-of-N -coded ones) are removed.
%
% REFERENCES
%
% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag,
% Berlin, 1995, pp. 160-161.
% Kohonen, T., M鋕ivasara, K., Saram鋕i, T., "Phonetic Maps -
% Insightful Representation of Phonological Features For
% Speech Recognition", In proceedings of International
% Conference on Pattern Recognition (ICPR), Montreal, Canada,
% 1984, pp. 182-185.
%
% REQUIRED INPUT ARGUMENTS
%
% sData The data to use in the training.
% (struct) A data struct. '.comp_names' as well as '.name'
% is copied to the map. The class information is
% taken from the first column of '.labels' field.
%
% OPTIONAL INPUT ARGUMENTS
%
% argID (string) Argument identifier string (see below).
% value (varies) Value for the argument (see below).
%
% The optional arguments can be given as 'argID',value -pairs. If an
% argument is given value multiple times, the last one is used.
% Here are the argument IDs and corresponding values:
% 'munits' (scalar) the preferred number of map units - this may
% change a bit, depending on the properties of the data
% 'msize' (vector) map grid size
% 'mask' (vector) BMU search mask, size dim x 1
% 'name' (string) map name
% 'comp_names' (string array / cellstr) component names, size dim x 1
% 'tracking' (scalar) how much to report, default = 1. This parameter
% is also passed to the training functions.
% The following values are unambiguous and can therefore
% be given without the preceeding argument ID:
% 'algorithm' (string) training algorithm: 'seq' or 'batch' (default)
% 'mapsize' (string) do you want a 'small', 'normal' or 'big' map
% Any explicit settings of munits or msize (or topol)
% override this.
% 'topol' (struct) topology struct
% 'som_topol','sTopol' = 'topol'
% 'lattice' (string) map lattice, 'hexa' or 'rect'
% 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid'
% 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss',
% 'ep' or 'bubble'
%
% OUTPUT ARGUMENTS
%
% sMap (struct) SOM -map struct
%
% EXAMPLES
%
% To simply train a map with default parameters:
%
% sMap = som_supervised(sData);
%
% With the optional arguments, the initialization and training can be
% influenced. To change map size, use 'msize', 'munits' or 'mapsize'
% arguments:
%
% sMap = som_supervised(D,'mapsize','big'); or
% sMap = som_supervised(D,'big');
% sMap = som_supervised(D,'munits', 100);
% sMap = som_supervised(D,'msize', [20 10]);
%
% Argument 'algorithm' can be used to switch between 'seq' and 'batch'
% algorithms. 'batch' is the default, so to use 'seq' algorithm:
%
% sMap = som_supervised(D,'algorithm','seq'); or
% sMap = som_supervised(D,'seq');
%
% The 'tracking' argument can be used to control the amout of reporting
% during training. The argument is used in this function, and it is
% passed to the training functions. To make the function work silently
% set it to 0.
%
% sMap = som_supervised(D,'tracking',0);
%
% SEE ALSO
%
% som_make Create, initialize and train Self-Organizing map.
% som_autolabel Label SOM/data set based on another SOM/data set.
% Contributed to SOM Toolbox vs2, Feb 2nd, 2000 by Juha Parhankangas
% Copyright (c) by Juha Parhankangas
% http://www.cis.hut.fi/projects/somtoolbox/
% Juha Parhankangas 050100
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
D0 = sData.data;
[c,n,classlabels] = class2num(sData.labels(:,1));
%%%%%%%% Checking arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isstruct(sData)
error('Argument ''sData'' must be a ''som_data'' -struct.');
else
data_name = sData.name;
comp_names = sData.comp_names;
comp_norm = sData.comp_norm;
end
[dlen,dim] = size(sData.data);
% defaults
mapsize = '';
sM = som_map_struct(dim+n);
sTopol = sM.topol;
munits = prod(sTopol.msize); % should be zero
mask = sM.mask;
name = sM.name;
neigh = sM.neigh;
tracking = 1;
algorithm = 'batch';
%%%% changes to defaults (checking varargin) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
i=1;
while i <= length(varargin)
argok = 1;
if ischar(varargin{i})
switch varargin{i},
% argument IDs
case 'mask',
i=i+1;
mask = varargin{i};
case 'munits',
i=i+1;
munits = varargin{i};
case 'msize',
i=i+1;
sTopol.msize = varargin{i};
munits = prod(sTopol.msize);
case 'mapsize',
i=i+1;
mapsize = varargin{i};
case 'name',
i=i+1;
name = varargin{i};
case 'comp_names',
i=i+1;
comp_names = varargin{i};
case 'lattice',
i=i+1;
sTopol.lattice = varargin{i};
case 'shape',
i=i+1;
sTopol.shape = varargin{i};
case {'topol','som_topol','sTopol'},
i=i+1;
sTopol = varargin{i};
munits = prod(sTopol.msize);
case 'neigh',
i=i+1;
neigh = varargin{i};
case 'tracking',
i=i+1;
tracking = varargin{i};
case 'algorithm',
i=i+1;
algorithm = varargin{i};
% unambiguous values
case {'hexa','rect'},
sTopol.lattice = varargin{i};
case {'sheet','cyl','toroid'},
sTopol.shape = varargin{i};
case {'gaussian','cutgauss','ep','bubble'},
neigh = varargin{i};
case {'seq','batch'},
algorithm = varargin{i};
case {'small','normal','big'},
mapsize = varargin{i};
otherwise argok=0;
end
elseif isstruct(varargin{i}) & isfield(varargin{i},'type'),
switch varargin{i}(1).type,
case 'som_topol',
sTopol = varargin{i};
otherwise argok=0;
end
else
argok = 0;
end
if ~argok,
disp(['(som_supervised) Ignoring invalid argument #' num2str(i+1)]);
end
i = i+1;
end
%%%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% constructing the training data by adding 1-of-N -coded matrix to the
% original data.
[dlen,dim] = size(D0);
Dc = zeros(dlen,n);
for i=1:dlen
if c(i)
Dc(i,c(i)) = 1;
end
end
D = [D0, Dc];
% initialization and training
sD = som_data_struct(D,...
'name',data_name);
sM = som_make(sD,...
'mask',mask,...
'munits',munits,...
'name',data_name,...
'tracking',tracking,...
'algorithm',algorithm,...
'mapsize',mapsize,...
'topol',sTopol,...
'neigh',neigh);
% add labels
for i=1:prod(sM.topol.msize),
[dummy,class] = max(sM.codebook(i,dim+[1:n]));
sM.labels{i} = classlabels{class};
end
%sD.labels = sData.labels;
%sM = som_autolabel(sM,sD,'vote');
% remove extra components and modify map -struct
sM.codebook = sM.codebook(:,1:dim);
sM.mask = sM.mask(1:dim);
sM.comp_names = sData.comp_names;
sM.comp_norm = sData.comp_norm;
% remove extras from sM.trainhist
for i=1:length(sM.trainhist)
if sM.trainhist(i).mask
sM.trainhist(i).mask = sM.trainhist(i).mask(1:dim);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [numbers, n, names] = class2num(class)
names = {};
numbers = zeros(length(class),1);
for i=1:length(class)
if ~isempty(class{i}) & ~any(strcmp(class{i},names))
names=cat(1,names,class(i));
end
end
n=length(names);
tmp_numbers = (1:n)';
for i=1:length(class)
if ~isempty(class{i})
numbers(i,1) = find(strcmp(class{i},names));
end
end