comparison DL/RLS-DLA/SMALL_rlsdla.m @ 85:fd1c32cda22c

Comments to small_rlsdla.m, removed unfinished work.

 function Dictionary = SMALL_rlsdla(X, params)
-
-
-
-
-
+%% Recursive Least Squares Dictionary Learning Algorithm
+%
+%   D = SMALL_rlsdla(X, params) - runs RLS-DLA algorithm for
+%   training signals specified as columns of matrix X with parameters 
+%   specified in params structure returning the learned dictionary D.
+%   
+%   Fields in params structure:
+%       Required:
+%               'Tdata' / 'Edata'        sparse-coding target
+%               'initdict' / 'dictsize'  initial dictionary / dictionary size
+%
+%       Optional (default values in parentheses):
+%               'codemode'               'sparsity' or 'error' ('sparsity')
+%               'maxatoms'               max # of atoms in error sparse-coding (none)
+%               'forgettingMode'         'fix' - fix forgetting factor,
+%                                        other modes are not implemented in
+%                                        this version(exponential etc.)
+%               'forgettingFactor'      for 'fix' mode (default is 1)
+%               'show_dict'             shows dictionary after # of
+%                                       iterations specified (less then 100
+%                                       can make it running slow). In this
+%                                       version it assumes that it is image
+%                                       dictionary and atoms size is 8x8
+%                                       
+%   -   RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares
+%       Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on,
+%       vol.58, no.4, pp.2121-2130, April 2010
+%
+
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%   
+%%
 
 CODE_SPARSITY = 1;
 CODE_ERROR = 2;
 
 

 else
   maxatoms = -1;
 end
 
 
+
+
+% determine dictionary size %
+
+if (isfield(params,'initdict'))
+  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
+    dictsize = length(params.initdict);
+  else
+    dictsize = size(params.initdict,2);
+  end
+end
+if (isfield(params,'dictsize'))    % this superceedes the size determined by initdict
+  dictsize = params.dictsize;
+end
+
+if (size(X,2) < dictsize)
+  error('Number of training signals is smaller than number of atoms to train');
+end
+
+
+% initialize the dictionary %
+
+if (isfield(params,'initdict'))
+  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
+    D = X(:,params.initdict(1:dictsize));
+  else
+    if (size(params.initdict,1)~=size(X,1) || size(params.initdict,2)<dictsize)
+      error('Invalid initial dictionary');
+    end
+    D = params.initdict(:,1:dictsize);
+  end
+else
+  data_ids = find(colnorms_squared(X) > 1e-6);   % ensure no zero data elements are chosen
+  perm = randperm(length(data_ids));
+  D = X(:,data_ids(perm(1:dictsize)));
+end
+
+
+% normalize the dictionary %
+
+D = normcols(D);
+
+% show dictonary every specified number of iterations
+
+if (isfield(params,'show_dict'))
+    show_dictionary=1;
+    show_iter=params.show_dict;
+else
+    show_dictionary=0;
+    show_iter=0;
+end
+
+if (show_dictionary)
+    dictimg = showdict(D,[8 8],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
+    figure(2); imshow(imresize(dictimg,2,'nearest'));
+end
 % Forgetting factor
 
 if (isfield(params,'forgettingMode'))
      switch lower(params.forgettingMode)
        case 'fix'

      lambda=params.forgettingFactor;
 else
      lambda=1;
 end
 
-% determine dictionary size %
-
-if (isfield(params,'initdict'))
-  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
-    dictsize = length(params.initdict);
-  else
-    dictsize = size(params.initdict,2);
-  end
-end
-if (isfield(params,'dictsize'))    % this superceedes the size determined by initdict
-  dictsize = params.dictsize;
-end
-
-if (size(X,2) < dictsize)
-  error('Number of training signals is smaller than number of atoms to train');
-end
-
-
-% initialize the dictionary %
-
-if (isfield(params,'initdict'))
-  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
-    D = X(:,params.initdict(1:dictsize));
-  else
-    if (size(params.initdict,1)~=size(X,1) || size(params.initdict,2)<dictsize)
-      error('Invalid initial dictionary');
-    end
-    D = params.initdict(:,1:dictsize);
-  end
-else
-  data_ids = find(colnorms_squared(X) > 1e-6);   % ensure no zero data elements are chosen
-  perm = randperm(length(data_ids));
-  D = X(:,data_ids(perm(1:dictsize)));
-end
-
-
-% normalize the dictionary %
-
-D = normcols(D);
-
 % Training data
 
 data=X;
 cnt=size(data,2);
+
 %
 
 C=(100000*thresh)*eye(dictsize);
 w=zeros(dictsize,1);
 u=zeros(dictsize,1);
 
 
 for i = 1:cnt
 
    if (codemode == CODE_SPARSITY)
-        w = ompmex(D,data(:,i),[],thresh,'checkdict','off');
+        w = omp2(D,data(:,i),[],thresh,'checkdict','off');
    else
         w = omp2(D,data(:,i),[],thresh,'maxatoms',maxatoms, 'checkdict','off');
    end
    
    spind=find(w);

     
    D = D + (alfa * residual) * u';
     
    
    C = C - (alfa * u)* u';
-   
+   if (show_dictionary &&(mod(i,show_iter)==0))
+       dictimg = showdict(D,[8 8],...
+            round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');  
+       figure(2); imshow(imresize(dictimg,2,'nearest'));
+       pause(0.02);
+   end
 end
 
 Dictionary = D;
 
 end