# HG changeset patch
# User idamnjanovic
# Date 1300282862 0
# Node ID 55faa9b5d1acc9e3a6475c2f6e375f71eae4bd9e
# Parent  8288a23f041fbe001e328dade6acc1056d9be7b0


diff -r 8288a23f041f -r 55faa9b5d1ac DL/RLS-DLA/SMALL_rlsdla 05032011.m
--- a/DL/RLS-DLA/SMALL_rlsdla 05032011.m	Tue Mar 15 15:30:54 2011 +0000
+++ /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
-
-
diff -r 8288a23f041f -r 55faa9b5d1ac DL/RLS-DLA/SMALL_rlsdla.m
--- a/DL/RLS-DLA/SMALL_rlsdla.m	Tue Mar 15 15:30:54 2011 +0000
+++ b/DL/RLS-DLA/SMALL_rlsdla.m	Wed Mar 16 13:41:02 2011 +0000
@@ -107,7 +107,7 @@
 % Training data
 
 data=X;
-
+cnt=size(data,2);
 %
 
 C=(100000*thresh)*eye(dictsize);
@@ -115,7 +115,7 @@
 u=zeros(dictsize,1);
 
 
-for i = 1:size(data,2)
+for i = 1:cnt
 
    if (codemode == CODE_SPARSITY)
         w = ompmex(D,data(:,i),[],[],thresh,1,-1,0);
diff -r 8288a23f041f -r 55faa9b5d1ac DL/RLS-DLA/SMALL_rlsdla1.m
--- a/DL/RLS-DLA/SMALL_rlsdla1.m	Tue Mar 15 15:30:54 2011 +0000
+++ /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
-
-
diff -r 8288a23f041f -r 55faa9b5d1ac DL/RLS-DLA/SMALL_rlsdlaFirstClustTry.m
--- a/DL/RLS-DLA/SMALL_rlsdlaFirstClustTry.m	Tue Mar 15 15:30:54 2011 +0000
+++ /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
-
-
diff -r 8288a23f041f -r 55faa9b5d1ac Problems/generateImageDenoiseProblem.m
--- a/Problems/generateImageDenoiseProblem.m	Tue Mar 15 15:30:54 2011 +0000
+++ b/Problems/generateImageDenoiseProblem.m	Wed Mar 16 13:41:02 2011 +0000
@@ -156,5 +156,5 @@
 data.maxval = maxval;
 data.initdict= initdict;
 data.signalDim=2;
-
+data.sparse=1;
 end %% end of function
\ No newline at end of file
diff -r 8288a23f041f -r 55faa9b5d1ac To Do list.m
--- a/To Do list.m	Tue Mar 15 15:30:54 2011 +0000
+++ b/To Do list.m	Wed Mar 16 13:41:02 2011 +0000
@@ -10,4 +10,12 @@
     - adds noise sigma*var(signal)
     - do 2d circular fft per image and take random lines as specified by mask
     - mask is made to sample n*szt/fold lines in phase encode and time dimensions
-    - play movies of original, undersampled cardiac images and mask
\ No newline at end of file
+    - play movies of original, undersampled cardiac images and mask
+    
+SMALLboxSetup.m - Installation script:
+
+- Add make call for DL/RLS_DLA/private and Problems/private (still need to 
+    shall these be private directories as in KSVD. Files inside are common
+    common routines that are used all around, so it makes sense to put them
+    in util directory)
+- 
\ No newline at end of file
diff -r 8288a23f041f -r 55faa9b5d1ac examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Tue Mar 15 15:30:54 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Wed Mar 16 13:41:02 2011 +0000
@@ -42,7 +42,7 @@
 % Problem. As an input we set the number of training patches.
 for noise_ind=1:1
 for im_num=4:4
-SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',512, noise_level(noise_ind));
+SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',256, noise_level(noise_ind));
 SMALL.Problem.name=im_num;
 
 results(noise_ind,im_num).noisy_psnr=SMALL.Problem.noisy_psnr;