diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_lininit.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_lininit.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,285 @@
+function sMap = som_lininit(D, varargin)
+
+%SOM_LININIT Initialize a Self-Organizing Map linearly.
+%
+% sMap = som_lininit(D, [[argID,] value, ...])
+%
+%  sMap = som_lininit(D);
+%  sMap = som_lininit(D,sMap);
+%  sMap = som_lininit(D,'munits',100,'hexa');
+% 
+%  Input and output arguments ([]'s are optional): 
+%   D                 The training data.
+%            (struct) data struct
+%            (matrix) data matrix, size dlen x dim
+%   [argID,  (string) Parameters affecting the map topology are given 
+%    value]  (varies) as argument ID - argument value pairs, listed below.
+%   sMap     (struct) map struct
+%
+% Here are the valid argument IDs and corresponding values. The values 
+% which are unambiguous (marked with '*') can be given without the
+% preceeding argID.
+%  'munits'       (scalar) number of map units
+%  'msize'        (vector) map size
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
+%  'topol'       *(struct) topology struct
+%  'som_topol','sTopol'    = 'topol'
+%  'map'         *(struct) map struct
+%  'som_map','sMap'        = 'map'
+%
+% For more help, try 'type som_lininit' or check out online documentation.
+% See also SOM_MAP_STRUCT, SOM_RANDINIT, SOM_MAKE.
+
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% som_lininit
+%
+% PURPOSE
+%
+% Initializes a SOM linearly along its greatest eigenvectors.
+%
+% SYNTAX
+%
+%  sMap = som_lininit(D);
+%  sMap = som_lininit(D,sMap);
+%  sMap = som_lininit(D,'munits',100,'hexa');
+%
+% DESCRIPTION
+%
+% Initializes a SOM linearly. If necessary, a map struct is created
+% first. The initialization is made by first calculating the eigenvalues
+% and eigenvectors of the training data. Then, the map is initialized
+% along the mdim greatest eigenvectors of the training data, where
+% mdim is the dimension of the map grid.
+%
+% REFERENCES
+%
+% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, 
+%    Berlin, 1995, pp. 106-107.
+%
+% REQUIRED INPUT ARGUMENTS
+%
+%  D                 The training data.
+%           (struct) Data struct. If this is given, its '.comp_names' and 
+%                    '.comp_norm' fields are copied to the map struct.
+%           (matrix) data matrix, size dlen x dim
+%  
+% 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 valid argument IDs and corresponding values. The values 
+%  which are unambiguous (marked with '*') can be given without the 
+%  preceeding argID.
+%  'dlen'         (scalar) length of the training data
+%  'data'         (matrix) the training data
+%                *(struct) the training data
+%  'munits'       (scalar) number of map units
+%  'msize'        (vector) map size
+%  'lattice'     *(string) map lattice: 'hexa' or 'rect'
+%  'shape'       *(string) map shape: 'sheet', 'cyl' or 'toroid'
+%  'topol'       *(struct) topology struct
+%  'som_topol','sTopol'    = 'topol'
+%  'map'         *(struct) map struct
+%  'som_map','sMap'        = 'map'
+%
+% OUTPUT ARGUMENTS
+% 
+%  sMap     (struct) The initialized map struct.
+%
+% EXAMPLES
+%
+%  sMap = som_lininit(D);
+%  sMap = som_lininit(D,sMap);
+%  sMap = som_lininit(D,'msize',[10 10]);
+%  sMap = som_lininit(D,'munits',100,'rect');
+%
+% SEE ALSO
+% 
+%  som_map_struct   Create a map struct.
+%  som_randinit     Initialize a map with random values.
+%  som_make         Initialize and train self-organizing map.
+
+% Copyright (c) 1997-2000 by the SOM toolbox programming team.
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 1.0beta ecco 100997
+% Version 2.0beta 101199
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% check arguments
+
+% data
+if isstruct(D), 
+  data_name = D.name; 
+  comp_names = D.comp_names; 
+  comp_norm = D.comp_norm; 
+  D = D.data;
+  struct_mode = 1; 
+else 
+  data_name = inputname(1); 
+  struct_mode = 0;
+end
+[dlen dim] = size(D);
+
+% varargin
+sMap = [];
+sTopol = som_topol_struct; 
+sTopol.msize = 0; 
+munits = NaN;
+i=1; 
+while i<=length(varargin), 
+  argok = 1; 
+  if ischar(varargin{i}), 
+    switch varargin{i}, 
+     case 'munits',     i=i+1; munits = varargin{i}; sTopol.msize = 0;
+     case 'msize',      i=i+1; sTopol.msize = varargin{i};
+                               munits = prod(sTopol.msize); 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i}; 
+     case 'shape',      i=i+1; sTopol.shape = varargin{i}; 
+     case {'som_topol','sTopol','topol'}, i=i+1; sTopol = varargin{i}; 
+     case {'som_map','sMap','map'}, i=i+1; sMap = varargin{i}; sTopol = sMap.topol;
+     case {'hexa','rect'}, sTopol.lattice = varargin{i}; 
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i};
+     otherwise argok=0; 
+    end
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), 
+    switch varargin{i}.type, 
+     case 'som_topol',
+      sTopol = varargin{i}; 
+     case 'som_map', 
+      sMap = varargin{i};
+      sTopol = sMap.topol;
+     otherwise argok=0; 
+    end
+  else
+    argok = 0; 
+  end
+  if ~argok, 
+    disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]); 
+  end
+  i = i+1; 
+end
+
+if length(sTopol.msize)==1, sTopol.msize = [sTopol.msize 1]; end
+
+if ~isempty(sMap), 
+  [munits dim2] = size(sMap.codebook);
+  if dim2 ~= dim, error('Map and data must have the same dimension.'); end
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% create map
+
+% map struct
+if ~isempty(sMap), 
+  sMap = som_set(sMap,'topol',sTopol);
+else  
+  if ~prod(sTopol.msize), 
+    if isnan(munits), 
+      sTopol = som_topol_struct('data',D,sTopol);
+    else
+      sTopol = som_topol_struct('data',D,'munits',munits,sTopol);
+    end
+  end  
+  sMap = som_map_struct(dim, sTopol); 
+end
+
+if struct_mode, 
+  sMap = som_set(sMap,'comp_names',comp_names,'comp_norm',comp_norm);
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% initialization
+
+% train struct
+sTrain = som_train_struct('algorithm','lininit');
+sTrain = som_set(sTrain,'data_name',data_name);
+
+msize = sMap.topol.msize;
+mdim = length(msize);
+munits = prod(msize);
+
+[dlen dim] = size(D);
+if dlen<2,  
+  %if dlen==1, sMap.codebook = (sMap.codebook - 0.5)*diag(D); end
+  error(['Linear map initialization requires at least two NaN-free' ...
+	 ' samples.']);
+  return;
+end
+
+% compute principle components
+if dim > 1 & sum(msize > 1) > 1,
+  % calculate mdim largest eigenvalues and their corresponding
+  % eigenvectors
+    
+  % autocorrelation matrix
+  A = zeros(dim);
+  me = zeros(1,dim);
+  for i=1:dim, 
+    me(i) = mean(D(isfinite(D(:,i)),i)); 
+    D(:,i) = D(:,i) - me(i); 
+  end  
+  for i=1:dim, 
+    for j=i:dim, 
+      c = D(:,i).*D(:,j); c = c(isfinite(c));
+      A(i,j) = sum(c)/length(c); A(j,i) = A(i,j); 
+    end
+  end
+  
+  % take mdim first eigenvectors with the greatest eigenvalues
+  [V,S]   = eig(A);
+  eigval  = diag(S);
+  [y,ind] = sort(eigval); 
+  eigval  = eigval(flipud(ind));
+  V       = V(:,flipud(ind)); 
+  V       = V(:,1:mdim);
+  eigval  = eigval(1:mdim);   
+
+  % normalize eigenvectors to unit length and multiply them by 
+  % corresponding (square-root-of-)eigenvalues
+  for i=1:mdim, V(:,i) = (V(:,i) / norm(V(:,i))) * sqrt(eigval(i)); end
+  
+else
+
+  me = zeros(1,dim);
+  V = zeros(1,dim);
+  for i=1:dim, 
+    inds = find(~isnan(D(:,i)));
+    me(i) = mean(D(inds,i),1);
+    V(i) = std(D(inds,i),1);
+  end
+  
+end
+
+% initialize codebook vectors
+if dim>1, 
+  sMap.codebook = me(ones(munits,1),:); 
+  Coords = som_unit_coords(msize,'rect','sheet');
+  cox = Coords(:,1); Coords(:,1) = Coords(:,2); Coords(:,2) = cox;
+  for i=1:mdim,
+    ma = max(Coords(:,i)); mi = min(Coords(:,i)); 
+    if ma>mi, Coords(:,i) = (Coords(:,i)-mi)/(ma-mi); else Coords(:,i) = 0.5; end
+  end
+  Coords = (Coords-0.5)*2;
+  for n = 1:munits,   
+    for d = 1:mdim,    
+      sMap.codebook(n,:) = sMap.codebook(n,:)+Coords(n,d)*V(:, d)';
+    end
+  end
+else  
+  sMap.codebook = [0:(munits-1)]'/(munits-1)*(max(D)-min(D))+min(D);
+end
+
+% training struct
+sTrain = som_set(sTrain,'time',datestr(now,0));
+sMap.trainhist = sTrain;
+
+return;
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%