luis@190: 
luis@190: 
luis@190:   if strcmpi(DL.toolbox,'KSVD')
luis@190:     param=DL.param; 
luis@190:     param.data=Problem.b;
luis@190:  
luis@190:     D = eval([DL.name,'(param)']);%, ''t'', 5);']);
luis@190:   elseif strcmpi(DL.toolbox,'KSVDS')
luis@190:     param=DL.param; 
luis@190:     param.data=Problem.b;
luis@190:     
luis@190:     D = eval([DL.name,'(param, ''t'', 5);']);
luis@190:   elseif strcmpi(DL.toolbox,'SPAMS')
luis@190:     
luis@190:     X  = Problem.b; 
luis@190:     param=DL.param;
luis@190:     
luis@190:     D = eval([DL.name,'(X, param);']);
luis@190:     %   As some versions of SPAMS does not produce unit norm column
luis@190:     %   dictionaries, we need to make sure that columns are normalised to
luis@190:     %   unit lenght.
luis@190:     
luis@190:     for i = 1: size(D,2)
luis@190:         D(:,i)=D(:,i)/norm(D(:,i));
luis@190:     end
luis@190:   elseif strcmpi(DL.toolbox,'SMALL')
luis@190:     
luis@190:     X  = Problem.b; 
luis@190:     param=DL.param;
luis@190:     
luis@190:     D = eval([DL.name,'(X, param);']);
luis@190:     %   we need to make sure that columns are normalised to
luis@190:     %   unit lenght.
luis@190:     
luis@190:     for i = 1: size(D,2)
luis@190:         D(:,i)=D(:,i)/norm(D(:,i));
luis@190:     end
luis@190:     
luis@190:    elseif strcmpi(DL.toolbox,'TwoStepDL')
luis@190:         
luis@190:     DL=SMALL_two_step_DL(Problem, DL);
luis@190:     
luis@190:     %   we need to make sure that columns are normalised to
luis@190:     %   unit lenght.
luis@190:     
luis@190:     for i = 1: size(DL.D,2)
luis@190:         DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
luis@190:     end
luis@190:     D = DL.D;
luis@190:     
luis@190: elseif strcmpi(DL.toolbox,'MMbox')
luis@190:         
luis@190:     DL = wrapper_mm_DL(Problem, DL);
luis@190:     
luis@190:     %   we need to make sure that columns are normalised to
luis@190:     %   unit lenght.
luis@190:     
luis@190:     for i = 1: size(DL.D,2)
luis@190:         DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
luis@190:     end
luis@190:     D = DL.D; 
luis@190:     
luis@190: %   To introduce new dictionary learning technique put the files in
luis@190: %   your Matlab path. Next, unique name <TolboxID> for your toolbox needs 
luis@190: %   to be defined and also prefferd API for toolbox functions <Preffered_API>
luis@190: %   
luis@190: % elseif strcmpi(DL.toolbox,'<ToolboxID>')
luis@190: %     % This is an example of API that can be used:
luis@190: %     % - get training set from Problem part of structure
luis@190: %     % - assign parameters defined in the main program
luis@190: %
luis@190: %     X  = Problem.b; 
luis@190: %     param=DL.param;
luis@190: %
luis@190: %     % - Evaluate the function (DL.name - defined in the main) with
luis@190: %     %   parameters given above
luis@190: %
luis@190: %     D = eval([DL.name,'(<Preffered_API>);']);
luis@190: 
luis@190:   else
luis@190:     printf('\nToolbox has not been registered. Please change SMALL_learn file.\n');
luis@190:     return
luis@190:   end