comparison util/SMALL_solve.m @ 37:d80c103d9876

(none)

     SparseLab_A =@(mode, m, n, x, I, dim) SL_A(Problem.A, mode, m, n, x, I, dim);
     m = Problem.sizeA(1);      % m is the no. of rows.
     n = Problem.sizeA(2);      % n is the no. of columns.
     
 end
-b  = Problem.b;            % The right-hand-side vector.
+% if signal that needs to be represented is different then training set for
+% dictionary learning it should be stored in Problem.b1 matix
+if isfield(Problem, 'b1')
+    b = Problem.b1;
+else
+    b = Problem.b;            % The right-hand-side vector.
+end
 %%
 fprintf('\nStarting solver %s... \n', solver.name);
 start=cputime;
-
+tStart=tic;
 if strcmpi(solver.toolbox,'sparselab')
     y = eval([solver.name,'(SparseLab_A, b, n,',solver.param,');']);
 elseif strcmpi(solver.toolbox,'sparsify')
     y = eval([solver.name,'(b, A, n, ''P_trans'', AT,',solver.param,');']);
 elseif (strcmpi(solver.toolbox,'spgl1')||strcmpi(solver.toolbox,'gpsr'))

     if isa(Problem.A,'float')
         y = eval([solver.name,'(A, b, n,',solver.param,');']);
     else
         y = eval([solver.name,'(A, b, n,',solver.param,',AT);']);
     end
-    
+elseif (strcmpi(solver.toolbox, 'ompbox'))
+    G=A'*A;
+    epsilon=solver.param.epsilon;
+    maxatoms=solver.param.maxatoms;
+    y = eval([solver.name,'(A, b, G,epsilon,''maxatoms'',maxatoms,''checkdict'',''off'');']);
+elseif (strcmpi(solver.toolbox, 'ompsbox'))
+    basedict = Problem.basedict;
+     if issparse(Problem.A)
+        A = Problem.A;
+      else
+        A = sparse(Problem.A);
+      end
+    G = dicttsep(basedict,A,dictsep(basedict,A,speye(size(A,2))));
+    epsilon=solver.param.epsilon;
+    maxatoms=solver.param.maxatoms;
+    y = eval([solver.name,'(basedict, A, b, G,epsilon,''maxatoms'',maxatoms,''checkdict'',''off'');']);
+    Problem.sparse=1;
 %   To introduce new sparse representation algorithm put the files in
 %   your Matlab path. Next, unique name <TolboxID> for your toolbox and
 %   prefferd API <Preffered_API> needs to be defined.
 %
 % elseif strcmpi(solver.toolbox,'<ToolboxID>')

     return
 end
 
 %%
 %   Sparse representation time
-
+tElapsed=toc(tStart);
 solver.time = cputime - start;
 fprintf('Solver %s finished task in %2f seconds. \n', solver.name, solver.time);
-
+fprintf('Solver %s finished task in %2f seconds. \n', solver.name, tElapsed);
+solver.time=tElapsed;
 % geting around out of memory problem when converting big matrix from
 % sparse to full...
 
 if isfield(Problem, 'sparse')&&(Problem.sparse==1)
     solver.solution = y;
 else
     solver.solution = full(y);
 end
-
+if isfield(Problem,'reconstruct')
 %   Reconstruct the signal from the solution
 solver.reconstructed  = Problem.reconstruct(solver.solution);
 end
+end