changeset 85:fd1c32cda22c

Comments to small_rlsdla.m, removed unfinished work.

--- a/DL/RLS-DLA/SMALL_rlsdla 05032011.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,488 +0,0 @@
-function Dictionary = SMALL_rlsdla(X, params)
-
-
-
-
-
-global CODE_SPARSITY CODE_ERROR codemode
-global MEM_LOW MEM_NORMAL MEM_HIGH memusage
-global ompfunc ompparams exactsvd
-
-CODE_SPARSITY = 1;
-CODE_ERROR = 2;
-
-MEM_LOW = 1;
-MEM_NORMAL = 2;
-MEM_HIGH = 3;
-
-
-% p = randperm(size(X,2));
-
-        % coding mode %
-%X_norm=sqrt(sum(X.^2, 1));
-% X_norm_1=sum(abs(X));
-% X_norm_inf=max(abs(X));
-%[X_norm_sort, p]=sort(X_norm);%, 'descend');
-% [X_norm_sort1, p5]=sort(X_norm_1);%, 'descend');
-
-%         if (isfield(params,'codemode'))
-%           switch lower(params.codemode)
-%             case 'sparsity'
-%               codemode = CODE_SPARSITY;
-%               thresh = params.Tdata;
-%             case 'error'
-%               codemode = CODE_ERROR;
-%               thresh = params.Edata;
-%             otherwise
-%               error('Invalid coding mode specified');
-%           end
-%         elseif (isfield(params,'Tdata'))
-%           codemode = CODE_SPARSITY;
-%           thresh = params.Tdata;
-%         elseif (isfield(params,'Edata'))
-%           codemode = CODE_ERROR;
-%           thresh = params.Edata;
-% 
-%         else
-%           error('Data sparse-coding target not specified');
-%         end
-
-thresh = params.Edata;
-% max number of atoms %
-
-% if (codemode==CODE_ERROR && isfield(params,'maxatoms'))
-%   ompparams{end+1} = 'maxatoms';
-%   ompparams{end+1} = params.maxatoms;
-% end
-
-
-% memory usage %
-
-if (isfield(params,'memusage'))
-  switch lower(params.memusage)
-    case 'low'
-      memusage = MEM_LOW;
-    case 'normal'
-      memusage = MEM_NORMAL;
-    case 'high'
-      memusage = MEM_HIGH;
-    otherwise
-      error('Invalid memory usage mode');
-  end
-else
-  memusage = MEM_NORMAL;
-end
-
-
-% iteration count %
-
-if (isfield(params,'iternum'))
-  iternum = params.iternum;
-else
-  iternum = 10;
-end
-
-
-% omp function %
-
-if (codemode == CODE_SPARSITY)
-  ompfunc = @omp;
-else
-  ompfunc = @omp2;
-end
-
-
-% % status messages %
-% 
-% printiter = 0;
-% printreplaced = 0;
-% printerr = 0;
-% printgerr = 0;
-% 
-% verbose = 't';
-% msgdelta = -1;
-% 
-
-% 
-% for i = 1:length(verbose)
-%   switch lower(verbose(i))
-%     case 'i'
-%       printiter = 1;
-%     case 'r'
-%       printiter = 1;
-%       printreplaced = 1;
-%     case 't'
-%       printiter = 1;
-%       printerr = 1;
-%       if (isfield(params,'testdata'))
-%         printgerr = 1;
-%       end
-%   end
-% end
-% 
-% if (msgdelta<=0 || isempty(verbose))
-%   msgdelta = -1; 
-% end
-% 
-% ompparams{end+1} = 'messages';
-% ompparams{end+1} = msgdelta;
-% 
-% 
-% 
-% % compute error flag %
-% 
-% comperr = (nargout>=3 || printerr);
-% 
-% 
-% % validation flag %
-% 
-% testgen = 0;
-% if (isfield(params,'testdata'))
-%   testdata = params.testdata;
-%   if (nargout>=4 || printgerr)
-%     testgen = 1;
-%   end
-% end
-
-% 
-% % data norms %
-% 
-% XtX = []; XtXg = [];
-% if (codemode==CODE_ERROR && memusage==MEM_HIGH)
-%   XtX = colnorms_squared(data);
-%   if (testgen)
-%     XtXg = colnorms_squared(testdata);
-%   end
-% end
-
-
-% mutual incoherence limit %
-
-if (isfield(params,'muthresh'))
-  muthresh = params.muthresh;
-else
-  muthresh = 0.99;
-end
-if (muthresh < 0)
-  error('invalid muthresh value, must be non-negative');
-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);
-% DtD=D'*D;
-
-err = zeros(1,iternum);
-gerr = zeros(1,iternum);
-
-if (codemode == CODE_SPARSITY)
-  errstr = 'RMSE';
-else
-  errstr = 'mean atomnum';
-end
-%X(:,p(X_norm_sort<thresh))=0;
-% if (iternum==4)
-%     X_im=col2imstep(X, [256 256], [8 8]);
-% else
-%     X_im=col2imstep(X, [512 512], [8 8]);
-% end
-% figure(10); imshow(X_im);
-
-%p1=p(cumsum(X_norm_sort)./[1:size(X_norm_sort,2)]>thresh);
-%p1=p(X_norm_sort>thresh);
-% X(:,setxor(p1,1:end))=0;
-% X_im=col2imstep(X, [256 256], [8 8]);
-% figure(10); imshow(X_im);
-% if iternum==2
-% D(:,1)=D(:,2);
-% end
-%p1=p1(p2(1:40000));
-%end-min(40000, end)+1:end));%1:min(40000, end)));
-%p1 = randperm(size(data,2));%size(data,2)
-%data=data(:,p1);
-
-C=(100000*thresh)*eye(dictsize);
-% figure(11);
-w=zeros(dictsize,1);
-replaced=zeros(dictsize,1);
-u=zeros(dictsize,1);
-%   dictimg = showdict(D,[8 8],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-%   figure(11);imshow(imresize(dictimg,2,'nearest'));
-%  pause(1);
-lambda=0.9997%0.99986;%3+0.0001*params.linc;
-for j=1:1
-    %data=X;
-if size(X,2)>40000
-    p2 = randperm(size(X,2));
-    
-    p2=sort(p2(1:40000));%min(floor(size(p1,2)/2),40000)));
-    size(p2,2)
-    data=X(:,p2);
-elseif size(X,2)>0
-    %p2 = randperm(size(p1,2));
-    size(X,2)
-    data=X;
-else
-    break;
-end
-% figure(1);
-% plot(sqrt(sum(data.^2, 1)));
-% a=size(data,2)/4;
-% lambda0=0.99;%1-16/numS+iternum*0.0001-0.0002
-%C(1,1)=0;
-modi=1000;
-for i = 1:size(data,2)
-%     if norm(data(:,i))>thresh
-    %      par.multA= @(x,par)  multMatr(D,x);     % user function   y=Ax
-    %      par.multAt=@(x,par)  multMatrAdj(D,x);  % user function  y=A'*x
-    %      par.y=data(:,i);
-     %  w=SolveFISTA(D,data(:,i),'lambda',0.5*thresh);
-     % w=sesoptn(zeros(dictsize,1),par.func_u, par.func_x, par.multA, par.multAt,options,par);
-   %w = SMALL_chol(D,data(:,i), 256,32, thresh);%
-   %w = sparsecode(data(:,i), D, [], [], thresh);
-   w = omp2mex(D,data(:,i),[],[],[],thresh,0,-1,-1,0);
-
-   %w(find(w<1))=0; 
-   %^2;
-%   lambda(i)=1-0.001/(1+i/a);
-% if i<a
-%     lambda(i)=1-0.001*(1-(i/a));
-% else
-%     lambda(i)=1;
-% end
-%    param.lambda=thresh;
-%    param.mode=2;
-%    param.L=32;
-%    w=mexLasso(data(:,i), D, param);
-    spind=find(w);
-    %replaced(spind)=replaced(spind)+1;
-    %-0.001*(1/2)^(i/a);
-%   w_sp(i)=nnz(w);
-    residual = data(:,i) - D * w;
-   %if ~isempty(spind) 
-    %i
-   if (j==1) 
-    C = C *(1/ lambda);
-   end
-    u = C(:,spind) * w(spind);
-  
-   %spindu=find(u);
-   % v = D' * residual;
-    
-    alfa = 1/(1 + w' * u);
-    
-    D = D + (alfa * residual) * u';
-    
-    %uut=;
-    C = C - (alfa * u)* u';
-   % lambda=(19*lambda+1)/20;
-   % DtD = DtD + alfa * ( v*u' + u*v') + alfa^2 * (residual'*residual) * uut;
-   
-%    if (mod(i,modi)==0)
-%        Ximd=zeros(size(X));
-%        Ximd(:,p1((i-modi+1:i)))=data(:,i-modi+1:i);
-%        
-%        if (iternum==4)
-%             X_ima(:,:,1)=col2imstep(Ximd, [256 256], [8 8]);
-%             X_ima(:,:,2)=col2imstep(X, [256 256], [8 8]);
-%             X_ima(:,:,3)=zeros(256,256);
-%        else
-%             X_ima(:,:,1)=col2imstep(Ximd, [512 512], [8 8]);
-%             X_ima(:,:,2)=col2imstep(X, [512 512], [8 8]);
-%             X_ima(:,:,3)=zeros(512,512);
-%        end
-%        
-%        dictimg1=dictimg;
-%        dictimg = showdict(D,[8 8],...
-%         round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-%         dictimg1=(dictimg-dictimg1);
-%         
-%         figure(2);
-%         subplot(2,2,1); imshow(X_ima); title(sprintf('%d',i));
-%         subplot(2,2,3); imshow(imresize(dictimg,2,'nearest'));
-%         subplot(2,2,4);    imshow(imresize(dictimg1,2,'nearest'));
-%         subplot(2,2,2);imshow(C*(255/max(max(C))));
-%         pause(0.02);
-%         if (i>=35000) 
-%             modi=100;
-%             pause 
-%         end;
-%     end
-%    end
-end
-%p1=p1(setxor(p2,1:end));
-%[D,cleared_atoms] = cleardict(D,X,muthresh,p1,replaced);
-%replaced=zeros(dictsize,1);
-% W=sparsecode(data, D, [], [], thresh);
-% data=D*W;
-%lambda=lambda+0.0002
-end
-%Gamma=mexLasso(data, D, param);
-%err=compute_err(D,Gamma, data);
-%[y,i]=max(err);
-%D(:,1)=data(:,i)/norm(data(:,i));
-
-Dictionary = D;%D(:,p);
-% figure(3);
-% plot(lambda);
-% mean(lambda);
-% figure(4+j);plot(w_sp);
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             sparsecode               %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-function Gamma = sparsecode(data,D,XtX,G,thresh)
-
-global CODE_SPARSITY codemode
-global MEM_HIGH memusage
-global ompfunc ompparams
-
-if (memusage < MEM_HIGH)
-  Gamma = ompfunc(D,data,G,thresh,ompparams{:});
-  
-else  % memusage is high
-  
-  if (codemode == CODE_SPARSITY)
-    Gamma = ompfunc(D'*data,G,thresh,ompparams{:});
-    
-  else
-    Gamma = ompfunc(D, data, G, thresh,ompparams{:});
-  end
-  
-end
-
-end
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             compute_err              %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err = compute_err(D,Gamma,data)
-  
-global CODE_SPARSITY codemode
-
-if (codemode == CODE_SPARSITY)
-  err = sqrt(sum(reperror2(data,D,Gamma))/numel(data));
-else
-  err = nnz(Gamma)/size(data,2);
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%           cleardict                  %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function [D,cleared_atoms] = cleardict(D,X,muthresh,unused_sigs,replaced_atoms)
-
-use_thresh = 4;  % at least this number of samples must use the atom to be kept
-
-dictsize = size(D,2);
-
-% compute error in blocks to conserve memory
-% err = zeros(1,size(X,2));
-% blocks = [1:3000:size(X,2) size(X,2)+1];
-% for i = 1:length(blocks)-1
-%   err(blocks(i):blocks(i+1)-1) = sum((X(:,blocks(i):blocks(i+1)-1)-D*Gamma(:,blocks(i):blocks(i+1)-1)).^2);
-% end
-
-cleared_atoms = 0;
-usecount = replaced_atoms;%sum(abs(Gamma)>1e-7, 2);
-
-for j = 1:dictsize
-  
-  % compute G(:,j)
-  Gj = D'*D(:,j);
-  Gj(j) = 0;
-  
-  % replace atom
-  if ( (max(Gj.^2)>muthresh^2 || usecount(j)<use_thresh) && ~replaced_atoms(j) )
-%     [y,i] = max(err(unused_sigs));
-    D(:,j) = X(:,unused_sigs(end)) / norm(X(:,unused_sigs(end)));
-    unused_sigs = unused_sigs([1:end-1]);
-    cleared_atoms = cleared_atoms+1;
-  end
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%            misc functions            %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err2 = reperror2(X,D,Gamma)
-
-% compute in blocks to conserve memory
-err2 = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  err2(blockids) = sum((X(:,blockids) - D*Gamma(:,blockids)).^2);
-end
-
-end
-
-
-function Y = colnorms_squared(X)
-
-% compute in blocks to conserve memory
-Y = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  Y(blockids) = sum(X(:,blockids).^2);
-end
-
-end
-
-

--- a/DL/RLS-DLA/SMALL_rlsdla.m	Fri Apr 01 14:27:44 2011 +0100
+++ b/DL/RLS-DLA/SMALL_rlsdla.m	Tue Apr 05 17:03:26 2011 +0100
@@ -1,9 +1,44 @@
 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;
@@ -45,6 +80,62 @@
 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'))
@@ -64,50 +155,11 @@
      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);
@@ -118,7 +170,7 @@
 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
@@ -140,7 +192,12 @@
     
    
    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;

--- a/DL/RLS-DLA/SMALL_rlsdla1.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,479 +0,0 @@
-function Dictionary = SMALL_rlsdla1(X, params)
-
-
-
-
-
-global CODE_SPARSITY CODE_ERROR codemode
-global MEM_LOW MEM_NORMAL MEM_HIGH memusage
-global ompfunc ompparams exactsvd
-
-CODE_SPARSITY = 1;
-CODE_ERROR = 2;
-
-MEM_LOW = 1;
-MEM_NORMAL = 2;
-MEM_HIGH = 3;
-
-
-% p = randperm(size(X,2));
-
-        % coding mode %
-X_norm=sqrt(sum(X.^2, 1));
-% X_norm_1=sum(abs(X));
-% X_norm_inf=max(abs(X));
-[X_norm_sort, p]=sort(X_norm);%, 'descend');
-% [X_norm_sort1, p5]=sort(X_norm_1);%, 'descend');
-
-%         if (isfield(params,'codemode'))
-%           switch lower(params.codemode)
-%             case 'sparsity'
-%               codemode = CODE_SPARSITY;
-%               thresh = params.Tdata;
-%             case 'error'
-%               codemode = CODE_ERROR;
-%               thresh = params.Edata;
-%             otherwise
-%               error('Invalid coding mode specified');
-%           end
-%         elseif (isfield(params,'Tdata'))
-%           codemode = CODE_SPARSITY;
-%           thresh = params.Tdata;
-%         elseif (isfield(params,'Edata'))
-%           codemode = CODE_ERROR;
-%           thresh = params.Edata;
-% 
-%         else
-%           error('Data sparse-coding target not specified');
-%         end
-
-thresh = params.Edata;
-% max number of atoms %
-
-% if (codemode==CODE_ERROR && isfield(params,'maxatoms'))
-%   ompparams{end+1} = 'maxatoms';
-%   ompparams{end+1} = params.maxatoms;
-% end
-
-
-% memory usage %
-
-if (isfield(params,'memusage'))
-  switch lower(params.memusage)
-    case 'low'
-      memusage = MEM_LOW;
-    case 'normal'
-      memusage = MEM_NORMAL;
-    case 'high'
-      memusage = MEM_HIGH;
-    otherwise
-      error('Invalid memory usage mode');
-  end
-else
-  memusage = MEM_NORMAL;
-end
-
-
-% iteration count %
-
-if (isfield(params,'iternum'))
-  iternum = params.iternum;
-else
-  iternum = 10;
-end
-
-
-% omp function %
-
-if (codemode == CODE_SPARSITY)
-  ompfunc = @omp;
-else
-  ompfunc = @omp2;
-end
-
-
-% % status messages %
-% 
-% printiter = 0;
-% printreplaced = 0;
-% printerr = 0;
-% printgerr = 0;
-% 
-% verbose = 't';
-% msgdelta = -1;
-% 
-
-% 
-% for i = 1:length(verbose)
-%   switch lower(verbose(i))
-%     case 'i'
-%       printiter = 1;
-%     case 'r'
-%       printiter = 1;
-%       printreplaced = 1;
-%     case 't'
-%       printiter = 1;
-%       printerr = 1;
-%       if (isfield(params,'testdata'))
-%         printgerr = 1;
-%       end
-%   end
-% end
-% 
-% if (msgdelta<=0 || isempty(verbose))
-%   msgdelta = -1; 
-% end
-% 
-% ompparams{end+1} = 'messages';
-% ompparams{end+1} = msgdelta;
-% 
-% 
-% 
-% % compute error flag %
-% 
-% comperr = (nargout>=3 || printerr);
-% 
-% 
-% % validation flag %
-% 
-% testgen = 0;
-% if (isfield(params,'testdata'))
-%   testdata = params.testdata;
-%   if (nargout>=4 || printgerr)
-%     testgen = 1;
-%   end
-% end
-
-% 
-% % data norms %
-% 
-% XtX = []; XtXg = [];
-% if (codemode==CODE_ERROR && memusage==MEM_HIGH)
-%   XtX = colnorms_squared(data);
-%   if (testgen)
-%     XtXg = colnorms_squared(testdata);
-%   end
-% end
-
-
-% mutual incoherence limit %
-
-if (isfield(params,'muthresh'))
-  muthresh = params.muthresh;
-else
-  muthresh = 0.99;
-end
-if (muthresh < 0)
-  error('invalid muthresh value, must be non-negative');
-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(:))))
-    D1 = X(:,params.initdict(1:dictsize));
-     D2 = X(:,params.initdict(1:dictsize));
-  else
-    if (size(params.initdict,1)~=size(X,1) || size(params.initdict,2)<dictsize)
-      error('Invalid initial dictionary');
-    end
-    D1 = params.initdict(:,1:dictsize);
-    D2 = 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);
-% DtD=D'*D;
-
-err = zeros(1,iternum);
-gerr = zeros(1,iternum);
-
-if (codemode == CODE_SPARSITY)
-  errstr = 'RMSE';
-else
-  errstr = 'mean atomnum';
-end
-X(:,p(X_norm_sort<thresh))=0;
-% if (iternum==4)
-%     X_im=col2imstep(X, [256 256], [8 8]);
-% else
-%     X_im=col2imstep(X, [512 512], [8 8]);
-% end
-% figure(10); imshow(X_im);
-p1=p(X_norm_sort>thresh);
-
-%p1=p1(p2(1:40000));
-%end-min(40000, end)+1:end));%1:min(40000, end)));
-%p1 = randperm(size(data,2));%size(data,2)
-%data=data(:,p1);
-
-C1=(100000*thresh)*eye(dictsize);
-C2=(100000*thresh)*eye(dictsize);
-% figure(11);
-w=zeros(dictsize,1);
-replaced=zeros(dictsize,1);
-u=zeros(dictsize,1);
- dictimg = showdict(D1,[8 8],round(sqrt(size(D1,2))),round(sqrt(size(D1,2))),'lines','highcontrast');
-%  h=imshow(imresize(dictimg,2,'nearest'));
-lambda=0.9998
-for j=1:3
-if size(p1,2)>20000
-    p2 = randperm(floor(size(p1,2)/2));
-    p2=sort(p2(1:20000));
-    data1=X(:,p1(p2));
-    data2=X(:,p1(floor(size(p1,2)/2)+p2));
-elseif size(p1,2)>0
-    data=X(:,p1);
-else
-    break;
-end
-% figure(1);
-% plot(sqrt(sum(data.^2, 1)));
-% a=size(data,2)/4;
-% lambda0=0.99;%1-16/numS+iternum*0.0001-0.0002
-C1(1,1)=0;
-C2(1,1)=0;
-for i = 1:size(data1,2)
-%     if norm(data(:,i))>thresh
-    %      par.multA= @(x,par)  multMatr(D,x);     % user function   y=Ax
-    %      par.multAt=@(x,par)  multMatrAdj(D,x);  % user function  y=A'*x
-    %      par.y=data(:,i);
-     %  w=SolveFISTA(D,data(:,i),'lambda',0.5*thresh);
-     % w=sesoptn(zeros(dictsize,1),par.func_u, par.func_x, par.multA, par.multAt,options,par);
-   %w = SMALL_chol(D,data(:,i), 256,32, thresh);%
-   %w = sparsecode(data(:,i), D, [], [], thresh);
-   w1 = omp2mex(D1,data1(:,i),[],[],[],thresh,0,-1,-1,0);
- w2 = omp2mex(D2,data2(:,i),[],[],[],thresh,0,-1,-1,0);
-   %w(find(w<1))=0; 
-   %^2;
-%   lambda(i)=1-0.001/(1+i/a);
-% if i<a
-%     lambda(i)=1-0.001*(1-(i/a));
-% else
-%     lambda(i)=1;
-% end
-%    param.lambda=thresh;
-%    param.mode=2;
-%    param.L=32;
-%    w=mexLasso(data(:,i), D, param);
-    spind1=find(w1);
-    spind2=find(w2);
-    
-    %replaced(spind)=replaced(spind)+1;
-    %-0.001*(1/2)^(i/a);
-%   w_sp(i)=nnz(w);
-    residual1 = data1(:,i) - D1 * w1;
-    residual2 = data2(:,i) - D2 * w2;
-   %if ~isempty(spind) 
-    %i
-    
-   C1 = C1 *(1/ lambda);
-    C2 = C2 *(1/ lambda);
-    u1 = C1(:,spind1) * w1(spind1);
-  u2 = C2(:,spind2) * w2(spind2);
-   %spindu=find(u);
-   % v = D' * residual;
-    
-    alfa1 = 1/(1 + w1' * u1);
-    alfa2 = 1/(1 + w2' * u2);
-    D1 = D1 + (alfa1 * residual1) * u1';
-    D2 = D2 + (alfa2 * residual2) * u2';
-    %uut=;
-    C1 = C1 - (alfa1 * u1)* u1';
-    C2 = C2 - (alfa2 * u2)* u2';
-   % lambda=(19*lambda+1)/20;
-   % DtD = DtD + alfa * ( v*u' + u*v') + alfa^2 * (residual'*residual) * uut;
-%    modi=5000;
-%    if (mod(i,modi)==0)
-%        Ximd=zeros(size(X));
-%        Ximd(:,p((i-modi+1:i)))=data(:,i-modi+1:i);
-%        
-%        if (iternum==4)
-%             X_ima=col2imstep(Ximd, [256 256], [8 8]);
-%        else
-%             X_ima=col2imstep(Ximd, [512 512], [8 8]);
-%        end
-%        dictimg1=dictimg;
-%        dictimg = showdict(D,[8 8],...
-%         round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-%         dictimg1=(dictimg-dictimg1)*255;
-%         
-%         figure(2);
-%         subplot(2,2,1); imshow(X_ima);
-%         subplot(2,2,3); imshow(imresize(dictimg,2,'nearest'));
-%         subplot(2,2,4);    imshow(imresize(dictimg1,2,'nearest'));
-%         subplot(2,2,2);imshow(C*(255/max(max(C))));
-%         pause(0.02);
-%     end
-%    end
-end
-%p1=p1(setxor(p2,1:end));
-%[D,cleared_atoms] = cleardict(D,X,muthresh,p1,replaced);
-%replaced=zeros(dictsize,1);
-% W=sparsecode(data, D, [], [], thresh);
-% data=D*W;
-lambda=lambda+0.0001
-end
-%Gamma=mexLasso(data, D, param);
-%err=compute_err(D,Gamma, data);
-%[y,i]=max(err);
-%D(:,1)=data(:,i)/norm(data(:,i));
-% D=normcols(D);
-% D_norm=sqrt(sum(D.^2, 1));
-% D_norm_1=sum(abs(D));
-% X_norm_1=sum(abs(X));
-% X_norm_inf=max(abs(X));
-% [D_norm_sort, p]=sort(D_norm_1, 'descend');
-Dictionary =[D1 D2];
-% figure(3);
-% plot(lambda);
-% mean(lambda);
-% figure(4+j);plot(w_sp);
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             sparsecode               %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-function Gamma = sparsecode(data,D,XtX,G,thresh)
-
-global CODE_SPARSITY codemode
-global MEM_HIGH memusage
-global ompfunc ompparams
-
-if (memusage < MEM_HIGH)
-  Gamma = ompfunc(D,data,G,thresh,ompparams{:});
-  
-else  % memusage is high
-  
-  if (codemode == CODE_SPARSITY)
-    Gamma = ompfunc(D'*data,G,thresh,ompparams{:});
-    
-  else
-    Gamma = ompfunc(D, data, G, thresh,ompparams{:});
-  end
-  
-end
-
-end
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             compute_err              %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err = compute_err(D,Gamma,data)
-  
-global CODE_SPARSITY codemode
-
-if (codemode == CODE_SPARSITY)
-  err = sqrt(sum(reperror2(data,D,Gamma))/numel(data));
-else
-  err = nnz(Gamma)/size(data,2);
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%           cleardict                  %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function [D,cleared_atoms] = cleardict(D,X,muthresh,unused_sigs,replaced_atoms)
-
-use_thresh = 4;  % at least this number of samples must use the atom to be kept
-
-dictsize = size(D,2);
-
-% compute error in blocks to conserve memory
-% err = zeros(1,size(X,2));
-% blocks = [1:3000:size(X,2) size(X,2)+1];
-% for i = 1:length(blocks)-1
-%   err(blocks(i):blocks(i+1)-1) = sum((X(:,blocks(i):blocks(i+1)-1)-D*Gamma(:,blocks(i):blocks(i+1)-1)).^2);
-% end
-
-cleared_atoms = 0;
-usecount = replaced_atoms;%sum(abs(Gamma)>1e-7, 2);
-
-for j = 1:dictsize
-  
-  % compute G(:,j)
-  Gj = D'*D(:,j);
-  Gj(j) = 0;
-  
-  % replace atom
-  if ( (max(Gj.^2)>muthresh^2 || usecount(j)<use_thresh) && ~replaced_atoms(j) )
-%     [y,i] = max(err(unused_sigs));
-    D(:,j) = X(:,unused_sigs(end)) / norm(X(:,unused_sigs(end)));
-    unused_sigs = unused_sigs([1:end-1]);
-    cleared_atoms = cleared_atoms+1;
-  end
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%            misc functions            %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err2 = reperror2(X,D,Gamma)
-
-% compute in blocks to conserve memory
-err2 = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  err2(blockids) = sum((X(:,blockids) - D*Gamma(:,blockids)).^2);
-end
-
-end
-
-
-function Y = colnorms_squared(X)
-
-% compute in blocks to conserve memory
-Y = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  Y(blockids) = sum(X(:,blockids).^2);
-end
-
-end
-
-

--- a/DL/RLS-DLA/SMALL_rlsdlaFirstClustTry.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,494 +0,0 @@
-function Dictionary = SMALL_rlsdla(X, params)
-
-
-
-
-
-global CODE_SPARSITY CODE_ERROR codemode
-global MEM_LOW MEM_NORMAL MEM_HIGH memusage
-global ompfunc ompparams exactsvd
-
-CODE_SPARSITY = 1;
-CODE_ERROR = 2;
-
-MEM_LOW = 1;
-MEM_NORMAL = 2;
-MEM_HIGH = 3;
-
-
-% p = randperm(size(X,2));
-
-        % coding mode %
-X_norm=sqrt(sum(X.^2, 1));
-% X_norm_1=sum(abs(X));
-% X_norm_inf=max(abs(X));
-[X_norm_sort, p]=sort(X_norm);%, 'descend');
-% [X_norm_sort1, p5]=sort(X_norm_1);%, 'descend');
-
-%         if (isfield(params,'codemode'))
-%           switch lower(params.codemode)
-%             case 'sparsity'
-%               codemode = CODE_SPARSITY;
-%               thresh = params.Tdata;
-%             case 'error'
-%               codemode = CODE_ERROR;
-%               thresh = params.Edata;
-%             otherwise
-%               error('Invalid coding mode specified');
-%           end
-%         elseif (isfield(params,'Tdata'))
-%           codemode = CODE_SPARSITY;
-%           thresh = params.Tdata;
-%         elseif (isfield(params,'Edata'))
-%           codemode = CODE_ERROR;
-%           thresh = params.Edata;
-% 
-%         else
-%           error('Data sparse-coding target not specified');
-%         end
-
-thresh = params.Edata;
-% max number of atoms %
-
-% if (codemode==CODE_ERROR && isfield(params,'maxatoms'))
-%   ompparams{end+1} = 'maxatoms';
-%   ompparams{end+1} = params.maxatoms;
-% end
-
-
-% memory usage %
-
-if (isfield(params,'memusage'))
-  switch lower(params.memusage)
-    case 'low'
-      memusage = MEM_LOW;
-    case 'normal'
-      memusage = MEM_NORMAL;
-    case 'high'
-      memusage = MEM_HIGH;
-    otherwise
-      error('Invalid memory usage mode');
-  end
-else
-  memusage = MEM_NORMAL;
-end
-
-
-% iteration count %
-
-if (isfield(params,'iternum'))
-  iternum = params.iternum;
-else
-  iternum = 10;
-end
-
-
-% omp function %
-
-if (codemode == CODE_SPARSITY)
-  ompfunc = @omp;
-else
-  ompfunc = @omp2;
-end
-
-
-% % status messages %
-% 
-% printiter = 0;
-% printreplaced = 0;
-% printerr = 0;
-% printgerr = 0;
-% 
-% verbose = 't';
-% msgdelta = -1;
-% 
-
-% 
-% for i = 1:length(verbose)
-%   switch lower(verbose(i))
-%     case 'i'
-%       printiter = 1;
-%     case 'r'
-%       printiter = 1;
-%       printreplaced = 1;
-%     case 't'
-%       printiter = 1;
-%       printerr = 1;
-%       if (isfield(params,'testdata'))
-%         printgerr = 1;
-%       end
-%   end
-% end
-% 
-% if (msgdelta<=0 || isempty(verbose))
-%   msgdelta = -1; 
-% end
-% 
-% ompparams{end+1} = 'messages';
-% ompparams{end+1} = msgdelta;
-% 
-% 
-% 
-% % compute error flag %
-% 
-% comperr = (nargout>=3 || printerr);
-% 
-% 
-% % validation flag %
-% 
-% testgen = 0;
-% if (isfield(params,'testdata'))
-%   testdata = params.testdata;
-%   if (nargout>=4 || printgerr)
-%     testgen = 1;
-%   end
-% end
-
-% 
-% % data norms %
-% 
-% XtX = []; XtXg = [];
-% if (codemode==CODE_ERROR && memusage==MEM_HIGH)
-%   XtX = colnorms_squared(data);
-%   if (testgen)
-%     XtXg = colnorms_squared(testdata);
-%   end
-% end
-
-
-% mutual incoherence limit %
-
-if (isfield(params,'muthresh'))
-  muthresh = params.muthresh;
-else
-  muthresh = 0.99;
-end
-if (muthresh < 0)
-  error('invalid muthresh value, must be non-negative');
-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);
-% DtD=D'*D;
-
-err = zeros(1,iternum);
-gerr = zeros(1,iternum);
-
-if (codemode == CODE_SPARSITY)
-  errstr = 'RMSE';
-else
-  errstr = 'mean atomnum';
-end
-%X(:,p(X_norm_sort<thresh))=0;
-% if (iternum==4)
-%     X_im=col2imstep(X, [256 256], [8 8]);
-% else
-%     X_im=col2imstep(X, [512 512], [8 8]);
-% end
-% figure(10); imshow(X_im);
-
-%p1=p(cumsum(X_norm_sort)./[1:size(X_norm_sort,2)]>thresh);
-p1=p(X_norm_sort>thresh);
-tic; idx=kmeans(X(:,p1)',4, 'Start', 'cluster','MaxIter',200); toc
-D=[D D D D];
-dictsize1=4*dictsize;
-% X(:,setxor(p1,1:end))=0;
-% X_im=col2imstep(X, [256 256], [8 8]);
-% figure(10); imshow(X_im);
-% if iternum==2
-% D(:,1)=D(:,2);
-% end
-%p1=p1(p2(1:40000));
-%end-min(40000, end)+1:end));%1:min(40000, end)));
-%p1 = randperm(size(data,2));%size(data,2)
-%data=data(:,p1);
-
-C=(100000*thresh)*eye(dictsize1);
-% figure(11);
-w=zeros(dictsize,1);
-replaced=zeros(dictsize,1);
-u=zeros(dictsize,1);
-%   dictimg = showdict(D,[8 8],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-%   figure(11);imshow(imresize(dictimg,2,'nearest'));
-%  pause(1);
-lambda=0.99986;%3+0.0001*params.linc;
-for j=1:1
-if size(p1,2)>60000
-    p2 = randperm(size(p1,2));
-    
-    p2=sort(p2(1:60000));%min(floor(size(p1,2)/2),40000)));
-    size(p2,2)
-    data=X(:,p1(p2));
-elseif size(p1,2)>0
-    p2 = randperm(size(p1,2));
-    size(p2,2)
-    data=X(:,p1);
-else
-    break;
-end
-% figure(1);
-% plot(sqrt(sum(data.^2, 1)));
-% a=size(data,2)/4;
-% lambda0=0.99;%1-16/numS+iternum*0.0001-0.0002
-%C(1,1)=0;
-modi=1000;
-for i = 1:size(data,2)
-%     if norm(data(:,i))>thresh
-    %      par.multA= @(x,par)  multMatr(D,x);     % user function   y=Ax
-    %      par.multAt=@(x,par)  multMatrAdj(D,x);  % user function  y=A'*x
-    %      par.y=data(:,i);
-     %  w=SolveFISTA(D,data(:,i),'lambda',0.5*thresh);
-     % w=sesoptn(zeros(dictsize,1),par.func_u, par.func_x, par.multA, par.multAt,options,par);
-   %w = SMALL_chol(D,data(:,i), 256,32, thresh);%
-   %w = sparsecode(data(:,i), D, [], [], thresh);
-   w = omp2mex(D(:,((idx(i)-1)*dictsize+1):idx(i)*dictsize),data(:,i),[],[],[],thresh,0,-1,-1,0);
-
-   %w(find(w<1))=0; 
-   %^2;
-%   lambda(i)=1-0.001/(1+i/a);
-% if i<a
-%     lambda(i)=1-0.001*(1-(i/a));
-% else
-%     lambda(i)=1;
-% end
-%    param.lambda=thresh;
-%    param.mode=2;
-%    param.L=32;
-%    w=mexLasso(data(:,i), D, param);
-    spind=find(w);
-    %replaced(spind)=replaced(spind)+1;
-    %-0.001*(1/2)^(i/a);
-%   w_sp(i)=nnz(w);
-    residual = data(:,i) - D (:,((idx(i)-1)*dictsize+1):idx(i)*dictsize)* w;
-   %if ~isempty(spind) 
-    %i
-   if (j==1) 
-    C = C *(1/ lambda);
-   end
-    u = C(((idx(i)-1)*dictsize+1):idx(i)*dictsize,((idx(i)-1)*dictsize)+spind) * w(spind);
-  
-   %spindu=find(u);
-   % v = D' * residual;
-    
-    alfa = 1/(1 + w' * u);
-    
-    D(:,((idx(i)-1)*dictsize+1):idx(i)*dictsize) = D (:,((idx(i)-1)*dictsize+1):idx(i)*dictsize)+ (alfa * residual) * u';
-    
-    %uut=;
-    C (((idx(i)-1)*dictsize+1):idx(i)*dictsize,((idx(i)-1)*dictsize+1):idx(i)*dictsize)= C(((idx(i)-1)*dictsize+1):idx(i)*dictsize,((idx(i)-1)*dictsize+1):idx(i)*dictsize) - (alfa * u)* u';
-   % lambda=(19*lambda+1)/20;
-   % DtD = DtD + alfa * ( v*u' + u*v') + alfa^2 * (residual'*residual) * uut;
-   
-%    if (mod(i,modi)==0)
-%        Ximd=zeros(size(X));
-%        Ximd(:,p1((i-modi+1:i)))=data(:,i-modi+1:i);
-%        
-%        if (iternum==4)
-%             X_ima(:,:,1)=col2imstep(Ximd, [256 256], [8 8]);
-%             X_ima(:,:,2)=col2imstep(X, [256 256], [8 8]);
-%             X_ima(:,:,3)=zeros(256,256);
-%        else
-%             X_ima(:,:,1)=col2imstep(Ximd, [512 512], [8 8]);
-%             X_ima(:,:,2)=col2imstep(X, [512 512], [8 8]);
-%             X_ima(:,:,3)=zeros(512,512);
-%        end
-%        
-%        dictimg1=dictimg;
-%        dictimg = showdict(D,[8 8],...
-%         round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-%         dictimg1=(dictimg-dictimg1);
-%         
-%         figure(2);
-%         subplot(2,2,1); imshow(X_ima); title(sprintf('%d',i));
-%         subplot(2,2,3); imshow(imresize(dictimg,2,'nearest'));
-%         subplot(2,2,4);    imshow(imresize(dictimg1,2,'nearest'));
-%         subplot(2,2,2);imshow(C*(255/max(max(C))));
-%         pause(0.02);
-%         if (i>=35000) 
-%             modi=100;
-%             pause 
-%         end;
-%     end
-%    end
-end
-%p1=p1(setxor(p2,1:end));
-%[D,cleared_atoms] = cleardict(D,X,muthresh,p1,replaced);
-%replaced=zeros(dictsize,1);
-% W=sparsecode(data, D, [], [], thresh);
-% data=D*W;
-lambda=lambda+0.0002
-end
-%Gamma=mexLasso(data, D, param);
-%err=compute_err(D,Gamma, data);
-%[y,i]=max(err);
-%D(:,1)=data(:,i)/norm(data(:,i));
-D=normcols(D);
-D_norm=sqrt(sum(D.^2, 1));
-D_norm_1=sum(abs(D));
-% X_norm_1=sum(abs(X));
-% X_norm_inf=max(abs(X));
-[D_norm_sort, p]=sort(D_norm_1, 'descend');
-Dictionary = D;%D(:,p);
-% figure(3);
-% plot(lambda);
-% mean(lambda);
-% figure(4+j);plot(w_sp);
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             sparsecode               %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-function Gamma = sparsecode(data,D,XtX,G,thresh)
-
-global CODE_SPARSITY codemode
-global MEM_HIGH memusage
-global ompfunc ompparams
-
-if (memusage < MEM_HIGH)
-  Gamma = ompfunc(D,data,G,thresh,ompparams{:});
-  
-else  % memusage is high
-  
-  if (codemode == CODE_SPARSITY)
-    Gamma = ompfunc(D'*data,G,thresh,ompparams{:});
-    
-  else
-    Gamma = ompfunc(D, data, G, thresh,ompparams{:});
-  end
-  
-end
-
-end
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%             compute_err              %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err = compute_err(D,Gamma,data)
-  
-global CODE_SPARSITY codemode
-
-if (codemode == CODE_SPARSITY)
-  err = sqrt(sum(reperror2(data,D,Gamma))/numel(data));
-else
-  err = nnz(Gamma)/size(data,2);
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%           cleardict                  %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function [D,cleared_atoms] = cleardict(D,X,muthresh,unused_sigs,replaced_atoms)
-
-use_thresh = 4;  % at least this number of samples must use the atom to be kept
-
-dictsize = size(D,2);
-
-% compute error in blocks to conserve memory
-% err = zeros(1,size(X,2));
-% blocks = [1:3000:size(X,2) size(X,2)+1];
-% for i = 1:length(blocks)-1
-%   err(blocks(i):blocks(i+1)-1) = sum((X(:,blocks(i):blocks(i+1)-1)-D*Gamma(:,blocks(i):blocks(i+1)-1)).^2);
-% end
-
-cleared_atoms = 0;
-usecount = replaced_atoms;%sum(abs(Gamma)>1e-7, 2);
-
-for j = 1:dictsize
-  
-  % compute G(:,j)
-  Gj = D'*D(:,j);
-  Gj(j) = 0;
-  
-  % replace atom
-  if ( (max(Gj.^2)>muthresh^2 || usecount(j)<use_thresh) && ~replaced_atoms(j) )
-%     [y,i] = max(err(unused_sigs));
-    D(:,j) = X(:,unused_sigs(end)) / norm(X(:,unused_sigs(end)));
-    unused_sigs = unused_sigs([1:end-1]);
-    cleared_atoms = cleared_atoms+1;
-  end
-end
-
-end
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%            misc functions            %
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-
-function err2 = reperror2(X,D,Gamma)
-
-% compute in blocks to conserve memory
-err2 = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  err2(blockids) = sum((X(:,blockids) - D*Gamma(:,blockids)).^2);
-end
-
-end
-
-
-function Y = colnorms_squared(X)
-
-% compute in blocks to conserve memory
-Y = zeros(1,size(X,2));
-blocksize = 2000;
-for i = 1:blocksize:size(X,2)
-  blockids = i : min(i+blocksize-1,size(X,2));
-  Y(blockids) = sum(X(:,blockids).^2);
-end
-
-end
-
-

--- a/DL/RLS-DLA/SolveFISTA.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,211 +0,0 @@
-% Copyright ©2010. The Regents of the University of California (Regents). 
-% All Rights Reserved. Contact The Office of Technology Licensing, 
-% UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, 
-% [phone], for commercial licensing opportunities.
-
-% Authors: Arvind Ganesh, Allen Y. Yang, Zihan Zhou.
-% Contact: Allen Y. Yang, Department of EECS, University of California,
-% Berkeley. <[e-mail]>
-
-% IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, 
-% SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, 
-% ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF 
-% REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-% REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED
-% TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A 
-% PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, 
-% PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO 
-% PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
-%% This function is modified from Matlab code proximal_gradient_bp
-
-function [x_hat,nIter] = SolveFISTA(A,b, varargin)
-
-% b - m x 1 vector of observations/data (required input)
-% A - m x n measurement matrix (required input)
-%
-% tol - tolerance for stopping criterion.
-%     - DEFAULT 1e-7 if omitted or -1.
-% maxIter - maxilambdam number of iterations
-%         - DEFAULT 10000, if omitted or -1.
-% lineSearchFlag - 1 if line search is to be done every iteration
-%                - DEFAULT 0, if omitted or -1.
-% continuationFlag - 1 if a continuation is to be done on the parameter lambda
-%                  - DEFAULT 1, if omitted or -1.
-% eta - line search parameter, should be in (0,1)
-%     - ignored if lineSearchFlag is 0.
-%     - DEFAULT 0.9, if omitted or -1.
-% lambda - relaxation parameter
-%    - ignored if continuationFlag is 1.
-%    - DEFAULT 1e-3, if omitted or -1.
-% outputFileName - Details of each iteration are dumped here, if provided.
-%
-% x_hat - estimate of coeeficient vector
-% numIter - number of iterations until convergence
-%
-%
-% References
-% "Robust PCA: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization", J. Wright et al., preprint 2009.
-% "An Accelerated Proximal Gradient Algorithm for Nuclear Norm Regularized Least Squares problems", K.-C. Toh and S. Yun, preprint 2009.
-%
-% Arvind Ganesh, Summer 2009. Questions? [e-mail]
-
-DEBUG = 0 ;
-
-STOPPING_GROUND_TRUTH = -1;
-STOPPING_DUALITY_GAP = 1;
-STOPPING_SPARSE_SUPPORT = 2;
-STOPPING_OBJECTIVE_VALUE = 3;
-STOPPING_SUBGRADIENT = 4;
-STOPPING_DEFAULT = STOPPING_SUBGRADIENT;
-
-stoppingCriterion = STOPPING_DEFAULT;
-maxIter = 1000 ;
-tolerance = 1e-3;
-[m,n] = size(A) ;
-x0 = zeros(n,1) ;
-xG = [];
-
-%% Initializing optimization variables
-t_k = 1 ; 
-t_km1 = 1 ;
-L0 = 1 ;
-G = A'*A ;
-nIter = 0 ;
-c = A'*b ;
-lambda0 = 0.99*L0*norm(c,inf) ;
-eta = 0.6 ;
-lambda_bar = 1e-4*lambda0 ;
-xk = zeros(n,1) ;
-lambda = lambda0 ;
-L = L0 ;
-beta = 1.5 ;
-
-% Parse the optional inputs.
-if (mod(length(varargin), 2) ~= 0 ),
-    error(['Extra Parameters passed to the function ''' mfilename ''' lambdast be passed in pairs.']);
-end
-parameterCount = length(varargin)/2;
-
-for parameterIndex = 1:parameterCount,
-    parameterName = varargin{parameterIndex*2 - 1};
-    parameterValue = varargin{parameterIndex*2};
-    switch lower(parameterName)
-        case 'stoppingcriterion'
-            stoppingCriterion = parameterValue;
-        case 'groundtruth'
-            xG = parameterValue;
-        case 'tolerance'
-            tolerance = parameterValue;
-        case 'linesearchflag'
-            lineSearchFlag = parameterValue;
-        case 'lambda'
-            lambda_bar = parameterValue;
-        case 'maxiteration'
-            maxIter = parameterValue;
-        case 'isnonnegative'
-            isNonnegative = parameterValue;
-        case 'continuationflag'
-            continuationFlag = parameterValue;
-        case 'initialization'
-            xk = parameterValue;
-            if ~all(size(xk)==[n,1])
-                error('The dimension of the initial xk does not match.');
-            end
-        case 'eta'
-            eta = parameterValue;
-            if ( eta <= 0 || eta >= 1 )
-                disp('Line search parameter out of bounds, switching to default 0.9') ;
-                eta = 0.9 ;
-            end
-        otherwise
-            error(['The parameter ''' parameterName ''' is not recognized by the function ''' mfilename '''.']);
-    end
-end
-clear varargin
-
-if stoppingCriterion==STOPPING_GROUND_TRUTH && isempty(xG)
-    error('The stopping criterion must provide the ground truth value of x.');
-end
-
-keep_going = 1 ;
-nz_x = (abs(xk)> eps*10);
-f = 0.5*norm(b-A*xk)^2 + lambda_bar * norm(xk,1);
-xkm1 = xk;
-while keep_going && (nIter < maxIter)
-    nIter = nIter + 1 ;
-    
-    yk = xk + ((t_km1-1)/t_k)*(xk-xkm1) ;
-    
-    stop_backtrack = 0 ;
-    
-    temp = G*yk - c ; % gradient of f at yk
-    
-    while ~stop_backtrack
-        
-        gk = yk - (1/L)*temp ;
-        
-        xkp1 = soft(gk,lambda/L) ;
-        
-        temp1 = 0.5*norm(b-A*xkp1)^2 ;
-        temp2 = 0.5*norm(b-A*yk)^2 + (xkp1-yk)'*temp + (L/2)*norm(xkp1-yk)^2 ;
-        
-        if temp1 <= temp2
-            stop_backtrack = 1 ;
-        else
-            L = L*beta ;
-        end
-        
-    end
-    
-    switch stoppingCriterion
-        case STOPPING_GROUND_TRUTH
-            keep_going = norm(xG-xkp1)>tolerance;
-        case STOPPING_SUBGRADIENT
-            sk = L*(yk-xkp1) + G*(xkp1-yk) ;
-            keep_going = norm(sk) > tolerance*L*max(1,norm(xkp1));
-        case STOPPING_SPARSE_SUPPORT
-            % compute the stopping criterion based on the change
-            % of the number of non-zero components of the estimate
-            nz_x_prev = nz_x;
-            nz_x = (abs(xkp1)>eps*10);
-            num_nz_x = sum(nz_x(:));
-            num_changes_active = (sum(nz_x(:)~=nz_x_prev(:)));
-            if num_nz_x >= 1
-                criterionActiveSet = num_changes_active / num_nz_x;
-                keep_going = (criterionActiveSet > tolerance);
-            end
-        case STOPPING_OBJECTIVE_VALUE
-            % compute the stopping criterion based on the relative
-            % variation of the objective function.
-            prev_f = f;
-            f = 0.5*norm(b-A*xkp1)^2 + lambda_bar * norm(xk,1);
-            criterionObjective = abs(f-prev_f)/(prev_f);
-            keep_going =  (criterionObjective > tolerance);
-        case STOPPING_DUALITY_GAP
-            error('Duality gap is not a valid stopping criterion for PGBP.');
-        otherwise
-            error('Undefined stopping criterion.');
-    end
-    
-    lambda = max(eta*lambda,lambda_bar) ;
-    
-    
-    t_kp1 = 0.5*(1+sqrt(1+4*t_k*t_k)) ;
-    
-    t_km1 = t_k ;
-    t_k = t_kp1 ;
-    xkm1 = xk ;
-    xk = xkp1 ;
-end
-
-x_hat = xk ;
-
-function y = soft(x,T)
-if sum(abs(T(:)))==0
-    y = x;
-else
-    y = max(abs(x) - T, 0);
-    y = sign(x).*y;
-end

--- a/Problems/Cardiac_MRI_problem.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,155 +0,0 @@
-function data = Cardiac_MRI_Problem(varargin)
-% CHANGE!!!!PROB503  Shepp-Logan phantom, partial Fourier with sample mask,
-%         complex domain, total variation.
-%
-%   PROB503 creates a problem structure.  The generated signal will
-%   consist of a N = 256 by N Shepp-Logan phantom. The signal is
-%   sampled at random locations in frequency domain generated
-%   according to a probability density function.
-%
-%   The following optional arguments are supported:
-%
-%   PROB503('n',N,flags) is the same as above, but with a
-%   phantom of size N by N. The 'noseed' flag can be specified to
-%   suppress initialization of the random number generators. Both
-%   the parameter pair and flags can be omitted.
-%
-%   Examples:
-%   P = prob503;  % Creates the default 503 problem.
-%
-%   References:
-%
-%   [LustDonoPaul:2007] M. Lustig, D.L. Donoho and J.M. Pauly,
-%     Sparse MRI: The application of compressed sensing for rapid MR
-%     imaging, Submitted to Magnetic Resonance in Medicine, 2007.
-%
-%   [sparsemri] M. Lustig, SparseMRI,
-%     http://www.stanford.edu/~mlustig/SparseMRI.html
-%
-%   See also GENERATEPROBLEM.
-%
-%MATLAB SPARCO Toolbox.
-
-%   Copyright 2008, Ewout van den Berg and Michael P. Friedlander
-%   http://www.cs.ubc.ca/labs/scl/sparco
-%   $Id: prob503.m 1040 2008-06-26 20:29:02Z ewout78 $
-
-% Parse parameters and set problem name
-
-[opts,varg] = parseDefaultOpts(varargin{:});
-[parm,varg] = parseOptions(varg,{'noseed'},{'n','fold','sigma','slice'});
-n           = getOption(parm,'n',256);
-info.name   = 'Cardiac_MRI';
-opts.show   =   1;
-
-
-fold        = getOption(parm,'fold', 6);  % undersampling level
-sigma       = getOption(parm,'sigma', 0.05);;  % noise level 
-z           = getOption(parm,'slice', 5);;  % slice number (1-10) 
-szt         = 20; % number of time samples
-
-% Return problem name if requested
-if opts.getname, data = info.name; return; end;
-
-% Initialize random number generators
-if (~parm.noseed), randn('state',0); rand('twister',2000); end;
-
-% Set up the data
-% if allowed use variable density
-%pdf  = genPDF([n,n],5,0.1,2,0.1,0);
-
-
-
-%load heart images
-FS=filesep;
-TMPpath=pwd;
-    [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
-    cd([pathstr1,FS,'data',FS,'images',FS,'Cardiac_MRI_dataset',FS,'Images']);
-    [filename,pathname] = uigetfile({'*.mat;'},'Select a patient MRI image set');
-    [pathstr, name, ext, versn] = fileparts(filename);
-load(filename);
-data.name=name;
-cd(TMPpath);
-
-% Set up the problem
-
-% Get 3D matrix of heart images (size 256x256, 20 frames) and stack them to
-% 2D matrix (256 x 256*20)
-data.signal          = reshape(sol_yxzt(:,:,z,:), [n n*szt]);
-
-% make a noise matrix 
-
-noise_var=sqrt(sigma*var(reshape(data.signal, [n*n*szt 1])));
-data.noise           = randn(n,n*szt)*noise_var + sqrt(-1)*randn(n,n*szt)*noise_var;
-
-% make a mask of random lines in phase encode and time domain random - vector
-% of 0 and 1 of size n*szt multiplied with vector of 1 of size n
-
-mask = rand(n*szt,1);
-mask(mask>(1-1/fold))=1;
-mask(mask<=(1-1/fold))=0;
-mask=(mask*ones(1,n))'; 
-data.op.mask         = opMask(mask);
-data.op.padding      = opPadding([n,n*szt],[n,n*szt]);
-
-% make an fft 2D dictionary. It will do 2D fft on evry image in the stack
-data.op.fft2d        = opKron(opDiag(szt,1), opFFT2C(n,n));
-
-% make measurement operator mask*padding*fft2d
-data.M               = opFoG(data.op.mask, data.op.padding, ...
-                             data.op.fft2d);
-
-% make a mesurement vector b = M* (signal + noise) where s+n is stack to 1d vector                     
-data.b               = data.M(reshape(data.signal + data.noise,[n*n*szt,1]),1);
-
-
-data                 = completeOps(data);
-
-% Additional information
-info.title           = 'Cardiac-MRI';
-info.thumb           = 'figcardiacProblem';
-info.citations       = {'LustDonoPaul:2007','sparsemri'};
-info.fig{1}.title    = 'Cardiac MRI';
-% info.fig{1}.filename = 'figProblemCardiac';
-% info.fig{2}.title    = 'Probability density function';
-% info.fig{2}.filename = 'figProblem503PDF';
-% info.fig{3}.title    = 'Sampling mask';
-% info.fig{3}.filename = 'figProblem503Mask';
-
-% Set the info field in data
-data.info = info;
-opts.figinc=1;
-% Plot figures
-if opts.update || opts.show
-  
-    %figure(opts.figno); opts.figno = opts.figno + opts.figinc;
-    
-    mov=reshape(data.signal/500, [n n szt]);
-  
-    implay(mov);
-    clear mov;
-  
-    %updateFigure(opts, info.fig{1}.title, info.fig{1}.filename);
-  
-    movMeas=reshape(abs(data.A(data.b,2))/500, [n n szt]);
-    implay(movMeas);
-    clear movMeas;
-%   figure(opts.figno); opts.figno = opts.figno + opts.figinc;
-%   imagesc(pdf), colormap gray;
-%   updateFigure(opts, info.fig{2}.title, info.fig{2}.filename)
-
-    implay(reshape(mask, [n n szt]));
-
-%   figure(opts.figno); opts.figno = opts.figno + opts.figinc;
-%   imagesc(mask), colormap gray
-%   updateFigure(opts, info.fig{3}.title, info.fig{3}.filename)
-%   
-%   if opts.update
-%      mn = min(min(data.signal + real(data.noise)));
-%      mx = max(max(data.signal + real(data.noise)));
-%      P = (data.signal + real(data.noise) - mn) / (mx - mn);
-%      P = scaleImage(P,128,128);
-%      P = P(1:2:end,1:2:end,:);
-%      thumbwrite(P, info.thumb, opts);
-%   end
-end

--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Fri Apr 01 14:27:44 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Tue Apr 05 17:03:26 2011 +0100
@@ -205,7 +205,8 @@
     'initdict', SMALL.Problem.initdict,...
     'dictsize', SMALL.Problem.p,...
     'forgettingMode', 'FIX',...
-    'forgettingFactor', lambda);
+    'forgettingFactor', lambda,...
+    'show_dict', 500);
 
 
 SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));

--- a/examples/Image Denoising/SMALL_ImgDenoise_dic_ODCT_solvers_OMP_BPDN_etc_test.m	Fri Apr 01 14:27:44 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,163 +0,0 @@
-%% DICTIONARY LEARNING FOR IMAGE DENOISING
-%   This file contains an example of how SMALLbox can be used to test different
-%   dictionary learning techniques in Image Denoising problem.
-%   It calls generateImageDenoiseProblem that will let you to choose image,
-%   add noise and use noisy image to generate training set for dictionary
-%   learning.
-%   Three dictionary learning techniques were compared:
-%   -   KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient
-%              Implementation of the K-SVD Algorithm using Batch Orthogonal
-%              Matching Pursuit", Technical Report - CS, Technion, April 2008.
-%   -   KSVDS - R. Rubinstein, M. Zibulevsky, and M. Elad, "Learning Sparse
-%               Dictionaries for Sparse Signal Approximation", Technical
-%               Report - CS, Technion, June 2009.
-%   -   SPAMS - J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online
-%               Dictionary Learning for Sparse Coding. International
-%               Conference on Machine Learning,Montreal, Canada, 2009
-%
-%
-% Ivan Damnjanovic 2010
-%%
-
-clear;
-
-%   If you want to load the image outside of generateImageDenoiseProblem
-%   function uncomment following lines. This can be useful if you want to
-%   denoise more then one image for example.
-
-% TMPpath=pwd;
-% FS=filesep;
-% [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
-% cd([pathstr1,FS,'data',FS,'images']);
-% [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
-% [pathstr, name, ext, versn] = fileparts(filename);
-% test_image = imread(filename);
-% test_image = double(test_image);
-% cd(TMPpath);
-% SMALL.Problem.name=name;
-
-
-% Defining Image Denoising Problem as Dictionary Learning
-% Problem. As an input we set the number of training patches.
-
-SMALL.Problem = generateImageDenoiseProblem('', 40000, '','', 20);
-
-Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
-maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
-%%   Use KSVD Dictionary Learning Algorithm to Learn overcomplete dictionary
-% 
-% %   Initialising Dictionary structure
-% %   Setting Dictionary structure fields (toolbox, name, param, D and time)
-% %   to zero values
-% 
-% SMALL.DL(1)=SMALL_init_DL();
-% 
-% % Defining the parameters needed for dictionary learning
-% 
-% SMALL.DL(1).toolbox = 'KSVD';
-% SMALL.DL(1).name = 'ksvd';
-% 
-% %   Defining the parameters for KSVD
-% %   In this example we are learning 256 atoms in 20 iterations, so that
-% %   every patch in the training set can be represented with target error in
-% %   L2-norm (EData)
-% %   Type help ksvd in MATLAB prompt for more options.
-% 
-% 
-% SMALL.DL(1).param=struct(...
-%     'Edata', Edata,...
-%     'initdict', SMALL.Problem.initdict,...
-%     'dictsize', SMALL.Problem.p,...
-%     'iternum', 20,...
-%     'memusage', 'high');
-% 
-% %   Learn the dictionary
-% 
-% SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
-%%   Initialising Dictionary structure
-%   Setting Dictionary structure fields (toolbox, name, param, D and time)
-%   to zero values 
-% 
-
-SMALL.DL(1)=SMALL_init_DL();
-%   Take initial dictonary (overcomplete DCT) to be a final dictionary for
-%   reconstruction
-
-SMALL.DL(1).D=SMALL.Problem.initdict;
-%%
-
-%   Set SMALL.Problem.A dictionary
-%   (backward compatiblity with SPARCO: solver structure communicate
-%   only with Problem structure, ie no direct communication between DL and
-%   solver structures)
-SMALL.Problem.A = SMALL.DL(1).D;
-
-SparseDict=0;
-SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem, SparseDict);
-
-%%
-%   Initialising solver structure
-%   Setting solver structure fields (toolbox, name, param, solution,
-%   reconstructed and time) to zero values
-
-
-SMALL.solver(1)=SMALL_init_solver;
-
-% Defining the parameters needed for image denoising
-
-SMALL.solver(1).toolbox='ompbox';
-SMALL.solver(1).name='omp2';
-SMALL.solver(1).param=struct(...
-    'epsilon',Edata,...
-    'maxatoms', maxatoms); 
-
-%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-%   but backward compatible with KSVD definition of denoising
-%   Pay attention that since implicit base dictionary is used, denoising
-%   can be much faster then using explicit dictionary in KSVD example.
-
-SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
-
-%%
-%   Initialising solver structure
-%   Setting solver structure fields (toolbox, name, param, solution,
-%   reconstructed and time) to zero values
-lam=2*SMALL.Problem.sigma;%*sqrt(2*log2(size(SMALL.Problem.A,1)))
-for i=1:11
-    lambda(i)=lam+5-(i-1);
-SMALL.DL(2)=SMALL_init_DL();
-i
-%SMALL.Problem.A = SMALL.Problem.initdict;
-SMALL.DL(2).D=SMALL.Problem.initdict;
-SMALL.solver(2)=SMALL_init_solver;
-
-% Defining the parameters needed for image denoising
-
-SMALL.solver(2).toolbox='SPAMS';
-SMALL.solver(2).name='mexLasso';
-SMALL.solver(2).param=struct(...
-    'mode', 2, ...
-     'lambda',lambda(i),...
-    'L', maxatoms); 
-
-%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-%   but backward compatible with KSVD definition of denoising
-%   Pay attention that since implicit base dictionary is used, denoising
-%   can be much faster then using explicit dictionary in KSVD example.
-
-SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
-
-
-% show results %
-
-%SMALL_ImgDeNoiseResult(SMALL);
-
-    time(1,i) = SMALL.solver(2).time;
-    psnr(1,i) = SMALL.solver(2).reconstructed.psnr;
-end%% show time and psnr %%
-figure('Name', 'SPAMS LAMBDA TEST');
-
-subplot(1,2,1); plot(lambda, time(1,:), 'ro-');
-title('time vs lambda');
-subplot(1,2,2); plot(lambda, psnr(1,:), 'b*-');
-title('PSNR vs lambda');
\ No newline at end of file

--- a/util/SMALL_solve.m	Fri Apr 01 14:27:44 2011 +0100
+++ b/util/SMALL_solve.m	Tue Apr 05 17:03:26 2011 +0100
@@ -1,6 +1,12 @@
 function solver = SMALL_solve(Problem, solver)
 %%% SMALL sparse solver
 %
+%   Function gets as input SMALL structure that contains SPARCO problem to
+%   be solved, name of the toolbox and solver, and parameters file for
+%   particular solver.
+%
+%   Outputs are solution, reconstructed signal and time spent
+
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2009 Ivan Damnjanovic.
 %
@@ -10,11 +16,6 @@
 %   License, or (at your option) any later version.  See the file
 %   COPYING included with this distribution for more information.
 %   
-%   Function gets as input SMALL structure that contains SPARCO problem to
-%   be solved, name of the toolbox and solver, and parameters file for
-%   particular solver.
-%
-%   Outputs are solution, reconstructed signal and time spent
 %%
 
 if isa(Problem.A,'float')