comparison DL/two-step DL/SMALL_two_step_DL.m @ 210:f12a476a4977 luisf_dev

Added help comments to SMALL_two_step_DL.m

 function DL=SMALL_two_step_DL(Problem, DL)
+    
+    %%  DL=SMALL_two_step_DL(Problem, DL) learn a dictionary using two_step_DL
+    %   The specific parameters of the DL structure are:
+    %   -name: can be either 'ols', 'opt', 'MOD', KSVD' or 'LGD'.
+    %   -param.learningRate: a step size used by 'ols' and 'opt'. Default: 0.1
+    %   for 'ols', 1 for 'opt'.
+    %   -param.flow: can be either 'sequential' or 'parallel'. De fault:
+    %   'sequential'. Not used by MOD.
+    %   -param.coherence: a real number between 0 and 1. If present, then
+    %   a low-coherence constraint is added to the learning.
+    %
+    %   See dico_update.m for more details.
 
 % determine which solver is used for sparse representation %
 
 solver = DL.param.solver;
 

 else
     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 works
+% the gradient (default: 0.1). If the type is 'mailhe', the 
+% descent step is the optimal step*rho (default: 1, although 2 works
 % better). Not used for MOD and KSVD.
 
 if isfield(DL.param,'learningRate')
     learningRate = DL.param.learningRate;
 else