comparison DL/two-step DL/SMALL_two_step_DL.m @ 209:dfa795944aae luisf_dev

fixed some bugs in the default parameters of SMALL_two_step_DL

 
 % determine which solver is used for sparse representation %
 
 solver = DL.param.solver;
 
-% determine which type of udate to use ('KSVD', 'MOD', 'ols' or 'mailhe') %
+% determine which type of udate to use ('KSVD', 'MOD', 'ols', 'opt' or 'LGD') %
 
 typeUpdate = DL.name;
 
 sig = Problem.b;
 

     flow = 'sequential';
 end
 
 % learningRate. If the type is 'ols', it is the descent step of
 % the gradient (typical choice: 0.1). If the type is 'mailhe', the 
-% descent step is the optimal step*rho (typical choice: 1, although 2
-% or 3 seems to work better). Not used for MOD and KSVD.
+% descent step is the optimal step*rho (typical choice: 1, although 2 works
+% better). Not used for MOD and KSVD.
 
 if isfield(DL.param,'learningRate')
     learningRate = DL.param.learningRate;
 else
-    learningRate = 0.1;
+    switch typeUpdate
+        case 'ols'
+            learningRate = 0.1;
+        otherwise
+            learningRate = 1;
+    end
 end
 
 % number of iterations (default is 40) %
 
 if isfield(DL.param,'iternum')

     Problem.A = dico;
     solver = SMALL_solve(Problem, solver);
     [dico, solver.solution] = dico_update(dico, sig, solver.solution, ...
         typeUpdate, flow, learningRate);
     if (decorrelate)
-        dico = dico_decorr(dico, mu, solver.solution);
+        dico = dico_decorr_symetric(dico, mu, solver.solution);
     end
     
    if ((show_dictionary)&&(mod(i,show_iter)==0))
        dictimg = SMALL_showdict(dico,[8 8],...
             round(sqrt(size(dico,2))),round(sqrt(size(dico,2))),'lines','highcontrast');