annotate DL/RLS-DLA/SMALL_rlsdla.m @ 77:62f20b91d870

add routines from sparco problems privite folder to {root}\util
some changes to ksvd vs rlsdla image denoising example

function Dictionary = SMALL_rlsdla(X, params)






CODE_SPARSITY = 1;
CODE_ERROR = 2;


% Determine which method will be used for sparse representation step -
% Sparsity or Error mode

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


% max number of atoms %

if (codemode==CODE_ERROR && isfield(params,'maxatoms'))
  maxatoms = params.maxatoms;
else
  maxatoms = -1;
end


% Forgetting factor

if (isfield(params,'forgettingMode'))
     switch lower(params.forgettingMode)
       case 'fix'
                if (isfield(params,'forgettingFactor'))
                    lambda=params.forgettingFactor;
                else
                    lambda=1;
                end
       otherwise
              error('This mode is still not implemented');
     end
elseif (isfield(params,'forgettingFactor'))
     lambda=params.forgettingFactor;
else
     lambda=1;
end

% determine dictionary size %

if (isfield(params,'initdict'))
  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
    dictsize = length(params.initdict);
  else
    dictsize = size(params.initdict,2);
  end
end
if (isfield(params,'dictsize'))    % this superceedes the size determined by initdict
  dictsize = params.dictsize;
end

if (size(X,2) < dictsize)
  error('Number of training signals is smaller than number of atoms to train');
end


% initialize the dictionary %

if (isfield(params,'initdict'))
  if (any(size(params.initdict)==1) && all(iswhole(params.initdict(:))))
    D = X(:,params.initdict(1:dictsize));
  else
    if (size(params.initdict,1)~=size(X,1) || size(params.initdict,2)<dictsize)
      error('Invalid initial dictionary');
    end
    D = params.initdict(:,1:dictsize);
  end
else
  data_ids = find(colnorms_squared(X) > 1e-6);   % ensure no zero data elements are chosen
  perm = randperm(length(data_ids));
  D = X(:,data_ids(perm(1:dictsize)));
end


% normalize the dictionary %

D = normcols(D);

% Training data

data=X;
cnt=size(data,2);
%

C=(100000*thresh)*eye(dictsize);
w=zeros(dictsize,1);
u=zeros(dictsize,1);


for i = 1:cnt

   if (codemode == CODE_SPARSITY)
        w = ompmex(D,data(:,i),[],thresh,'checkdict','off');
   else
        w = omp2(D,data(:,i),[],thresh,'maxatoms',maxatoms, 'checkdict','off');
   end
   
   spind=find(w);
   
   residual = data(:,i) - D * w;
   
   if (lambda~=1) 
       C = C *(1/ lambda);
   end
   
   u = C(:,spind) * w(spind);
  
    
   alfa = 1/(1 + w' * u);
    
   D = D + (alfa * residual) * u';
    
   
   C = C - (alfa * u)* u';
   
end

Dictionary = D;

end