comparison DL/RLS-DLA/SMALL_rlsdla.m @ 40:6416fc12f2b8

(none)

+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;
+
+%
+
+C=(100000*thresh)*eye(dictsize);
+w=zeros(dictsize,1);
+u=zeros(dictsize,1);
+
+
+for i = 1:size(data,2)
+
+   if (codemode == CODE_SPARSITY)
+        w = ompmex(D,data(:,i),[],[],thresh,1,-1,0);
+   else
+        w = omp2mex(D,data(:,i),[],[],[],thresh,0,-1,maxatoms,0);
+   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