diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_supervised.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_supervised.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,337 @@
+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., Mäkivasara, K., Saramäki, 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