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changeset 220:0d30f9074dd9

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Merge
author luisf <luis.figueira@eecs.qmul.ac.uk>
date Wed, 11 Apr 2012 15:56:39 +0100
parents 775caafd5651 (current diff) 4337e28183f1 (diff)
children 29e04ffe742b efe179d9757c
files solvers/CVX_add_const_Audio_declipping.m toolboxes/wrapper_ALPS_toolbox.m
diffstat 42 files changed, 911 insertions(+), 407 deletions(-) [+]
line wrap: on
line diff
--- a/DL/Majorization Minimization DL/wrapper_mm_DL.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/DL/Majorization Minimization DL/wrapper_mm_DL.m	Wed Apr 11 15:56:39 2012 +0100
@@ -1,4 +1,55 @@
 function DL = wrapper_mm_DL(Problem, DL)
+%% SMALL wrapper for Majorization Minimization Dictionary Learning Algorithm
+%
+%   Function gets as input Problem and Dictionary Learning (DL) structures 
+%   and outputs the learned Dictionary.
+%
+%   In Problem structure field b with the training set needs to be defined.
+%   
+%   In DL structure field with name of the Dictionary update method needs 
+%   to be present. For the orignal version of MM algorithm the update 
+%   method should be:
+%       -   'mm_cn'   Regularized DL with column norm contraint
+%       -   'mm_fn'   Regularized DL with Frobenius norm contraint
+%   Alternatively, for comparison purposes the following Dictioanry update
+%   methods (which do not represent the optimised version of the algorithm)
+%   be used:
+%       -   'mod_cn'   Method of Optimized Direction
+%       -   'map-cn'   Maximum a Posteriory Dictionary update
+%       -   'ksvd-cn'  KSVD update
+%
+%   The structure DL.param with parameters is also required. These are:
+%       -   solver           structure with fields toolbox, solver and parameters. 
+%                            For the original version of the algorithm toolbox
+%                            should be 'MMbox' and solver field should be left
+%                            empty ''. Type HELP WRAPPER_MM_SOLVER for more
+%                            details on how to set the parameters.
+%       -   initdict         Initial Dictionary
+%       -   dictsize         Dictionary size (optional)
+%       -   iternum          Number of iterations (default is 40)
+%       -   iterDictUpdate   Number of iterations for Dictionary Update (default is 1000)
+%       -   epsDictUpdate    Stopping criterion for MM dictionary update (default = 1e-7)                         
+%       -   cvset            Dictionary constraint - 0 = Non convex ||d|| = 1, 1 = Convex ||d||<=1
+%                            (default is 0)
+%       -   coherence        Set at 1 if to perform decorrelation in every iteration
+%                            (default is 0)
+%       -   show_dict        Show dictonary every specified number of iterations
+% 
+%
+%   -   MM-DL - Yaghoobi, M.; Blumensath, T,; Davies M.; , "Dictionary
+%   Learning for Sparse Approximation with Majorization Method," IEEE
+%   Transactions on Signal Processing, vol.57, no.6, pp.2178-2191, 2009.
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%%
 
 % determine which solver is used for sparse representation %
 
--- a/DL/Majorization Minimization DL/wrapper_mm_solver.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/DL/Majorization Minimization DL/wrapper_mm_solver.m	Wed Apr 11 15:56:39 2012 +0100
@@ -1,14 +1,27 @@
 function [X , cost] = wrapper_mm_solver(b, A, param)
-%% SMALL wrapper for Majorization Maximization toolbos solver
+%% SMALL wrapper for Majorization Minimization toolbox solver
 %
 %   Function gets as input
 %       b - measurement vector 
 %       A - dictionary 
-%       param - structure containing additional parameters
+%       param - structure containing additional parameters. These are:
+%           - initcoeff   Initial guess for the coefficients
+%                         (optional)
+%           - to          1/(step size). It is larger than spectral norm
+%                         of dictionary A (default is 0.1+(svds(A,1))^2)
+%           - lambda      Lagrangian multiplier. Regulates shrinkage
+%                         (default is 0.4)
+%           - iternum     Inner-loop maximum iteration number 
+%                         (default is 1000)
+%           - epsilon     Stopping criterion for iterative softthresholding
+%                         (default is 1e-7)
+%           - map         Debiasing. 0 = No, 1 = Yes (default is 0)
+%
 %   Output:
 %       x - sparse solution
 %       cost - Objective cost
 
+%
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2011 Ivan Damnjanovic.
 %
--- a/DL/RLS-DLA/SMALL_rlsdla.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/DL/RLS-DLA/SMALL_rlsdla.m	Wed Apr 11 15:56:39 2012 +0100
@@ -26,9 +26,8 @@
 %   -   RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares
 %       Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on,
 %       vol.58, no.4, pp.2121-2130, April 2010
+
 %
-
-
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2011 Ivan Damnjanovic.
 %
--- a/DL/two-step DL/SMALL_two_step_DL.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/DL/two-step DL/SMALL_two_step_DL.m	Wed Apr 11 15:56:39 2012 +0100
@@ -1,10 +1,22 @@
 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;
 
-% 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;
 
@@ -57,14 +69,19 @@
 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.
+% 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
-    learningRate = 0.1;
+    switch typeUpdate
+        case 'ols'
+            learningRate = 0.1;
+        otherwise
+            learningRate = 1;
+    end
 end
 
 % number of iterations (default is 40) %
@@ -113,7 +130,7 @@
     [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))
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/DL/two-step DL/dico_color_separate.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,95 @@
+function [colors nbColors] = dico_color_separate(dico, mu)
+    % DICO_COLOR cluster several dictionaries in pairs of high correlation
+    % atoms. Called by dico_decorr.
+    %
+    % Parameters:
+    % -dico: the dictionaries
+    % -mu: the correlation threshold
+    %
+    % Result:
+    % -colors: a cell array of indices. Two atoms with the same color have
+    % a correlation greater than mu
+    
+    
+    numDico = length(dico);
+    colors = cell(numDico,1);
+    for i = 1:numDico
+        colors{i} = zeros(length(dico{i}),1);
+    end
+    
+    G = cell(numDico);
+    
+    % compute the correlations
+    for i = 1:numDico
+        for j = i+1:numDico
+            G{i,j} = abs(dico{i}'*dico{j});
+        end
+    end
+    
+    % iterate on the correlations higher than mu
+    c = 1;
+    [maxCorr, i, j, m, n] = findMaxCorr(G);
+    while maxCorr > mu
+        % find the highest correlated pair
+        
+        % color them
+        colors{i}(m) = c;
+        colors{j}(n) = c;
+        c = c+1;
+        
+        % make sure these atoms never get selected again
+        % Set to zero relevant lines in the Gram Matrix
+        for j2 = i+1:numDico
+            G{i,j2}(m,:) = 0;
+        end
+        
+        for i2 = 1:i-1
+            G{i2,i}(:,m) = 0;
+        end
+        
+        for j2 = j+1:numDico
+            G{j,j2}(n,:) = 0;
+        end
+        
+        for i2 = 1:j-1
+            G{i2,j}(:,n) = 0;
+        end
+        
+        % find the next correlation
+        [maxCorr, i, j, m, n] = findMaxCorr(G);
+    end
+    
+    % complete the coloring with singletons
+    % index = find(colors==0);
+    % colors(index) = c:c+length(index)-1;
+    nbColors = c-1;
+end
+
+function [val, i, j, m, n] = findMaxCorr(G)
+    %FINDMAXCORR find the maximal correlation in the cellular Gram matrix
+    %
+    %   Input:
+    %   -G: the Gram matrix
+    %
+    %   Output:
+    %   -val: value of the correlation
+    %   -i,j,m,n: indices of the argmax. The maximal correlation is reached
+    %   for the m^th atom of the i^th dictionary and the n^h atom of the
+    %   j^h dictionary
+    
+    val = -1;
+    for tmpI = 1:length(G)
+        for tmpJ = tmpI+1:length(G)
+            [tmpVal tmpM] = max(G{tmpI,tmpJ},[],1);
+            [tmpVal tmpN] = max(tmpVal);
+            if tmpVal > val
+                val = tmpVal; 
+                i = tmpI;
+                j = tmpJ;
+                n = tmpN;
+                m = tmpM(n);
+            end
+        end
+    end
+end
+        
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/DL/two-step DL/dico_decorr_symetric.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,139 @@
+function dico = dico_decorr_symetric(dico, mu)
+    %DICO_DECORR decorrelate a dictionary
+    %   Parameters:
+    %   dico: the dictionary, either a matrix or a cell array of matrices.
+    %   mu: the coherence threshold
+    %
+    %   Result:
+    %   dico: if the input dico was a matrix, then a matrix close to the 
+    %   input one with coherence mu.
+    %   If the input was a cell array, a cell array of the same size
+    %   containing matrices such that the coherence between different cells
+    %   is lower than mu.
+    
+    eps = 1e-3; % define tolerance for normalisation term alpha
+    
+    % convert mu to the to the mean direction
+    theta = acos(mu)/2;
+    ctheta = cos(theta);
+    stheta = sin(theta);
+    
+    % compute atom weights
+    %     if nargin > 2
+    %         rank = sum(amp.*amp, 2);
+    %     else
+    %         rank = randperm(length(dico));
+    %     end
+    
+    % if only one dictionary is provided, then decorrelate it
+    if ~iscell(dico)
+        % several decorrelation iterations might be needed to reach global
+        % coherence mu. niter can be adjusted to needs.
+        niter = 1;
+        while max(max(abs(dico'*dico -eye(length(dico))))) > mu + eps
+            % find pairs of high correlation atoms
+            colors = dico_color(dico, mu);
+            
+            % iterate on all pairs
+            nbColors = max(colors);
+            for c = 1:nbColors
+                index = find(colors==c);
+                if numel(index) == 2
+                    if dico(:,index(1))'*dico(:,index(2)) > 0
+                        %build the basis vectors
+                        v1 = dico(:,index(1))+dico(:,index(2));
+                        v1 = v1/norm(v1);
+                        v2 = dico(:,index(1))-dico(:,index(2));
+                        v2 = v2/norm(v2);
+                        
+                        dico(:,index(1)) = ctheta*v1+stheta*v2;
+                        dico(:,index(2)) = ctheta*v1-stheta*v2;
+                    else
+                        v1 = dico(:,index(1))-dico(:,index(2));
+                        v1 = v1/norm(v1);
+                        v2 = dico(:,index(1))+dico(:,index(2));
+                        v2 = v2/norm(v2);
+                        
+                        dico(:,index(1)) = ctheta*v1+stheta*v2;
+                        dico(:,index(2)) = -ctheta*v1+stheta*v2;
+                    end
+                end
+            end
+            niter = niter+1;
+        end
+        %if a cell array of dictionaries is provided, decorrelate among
+        %different dictionaries only
+    else
+        niter = 1;
+        numDicos = length(dico);
+        G = cell(numDicos);
+        maxCorr = 0;
+        for i = 1:numDicos
+            for j = i+1:numDicos
+                G{i,j} = dico{i}'*dico{j};
+                maxCorr = max(maxCorr,max(max(abs(G{i,j}))));
+            end
+        end
+        
+        while maxCorr > mu + eps
+            % find pairs of high correlation atoms
+            [colors nbColors] = dico_color_separate(dico, mu);
+
+            % iterate on all pairs
+            for c = 1:nbColors
+                for tmpI = 1:numDicos
+                    index = find(colors{tmpI}==c);
+                    if ~isempty(index)
+                        i = tmpI;
+                        m = index;
+                        break;
+                    end
+                end
+                for tmpJ = i+1:numDicos
+                    index = find(colors{tmpJ}==c);
+                    if ~isempty(index)
+                        j = tmpJ;
+                        n = index;
+                        break;
+                    end
+                end
+                
+                if dico{i}(:,m)'*dico{j}(:,n) > 0
+                    %build the basis vectors
+                    v1 = dico{i}(:,m)+dico{j}(:,n);
+                    v1 = v1/norm(v1);
+                    v2 = dico{i}(:,m)-dico{j}(:,n);
+                    v2 = v2/norm(v2);
+                    
+                    dico{i}(:,m) = ctheta*v1+stheta*v2;
+                    dico{j}(:,n) = ctheta*v1-stheta*v2;
+                else
+                    v1 = dico{i}(:,m)-dico{j}(:,n);
+                    v1 = v1/norm(v1);
+                    v2 = dico{i}(:,m)+dico{j}(:,n);
+                    v2 = v2/norm(v2);
+                    
+                    dico{i}(:,m) = ctheta*v1+stheta*v2;
+                    dico{j}(:,n) = -ctheta*v1+stheta*v2;
+                end
+            end
+            niter = niter+1;
+            
+            % Remove noegative components and renormalize
+            for i = 1:length(dico)
+                dico{i} = max(dico{i},0);
+                for m = 1:size(dico{i},2)
+                    dico{i}(:,m) = dico{i}(:,m)/norm(dico{i}(:,m));
+                end
+            end
+            
+            maxCorr = 0;
+            for i = 1:numDicos
+                for j = i+1:numDicos
+                    G{i,j} = dico{i}'*dico{j};
+                    maxCorr = max(maxCorr,max(max(abs(G{i,j}))));
+                end
+            end
+        end
+    end
+end
--- a/DL/two-step DL/dico_update.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/DL/two-step DL/dico_update.m	Wed Apr 11 15:56:39 2012 +0100
@@ -1,5 +1,5 @@
 function [dico, amp] = dico_update(dico, sig, amp, type, flow, rho)
-
+    
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     % [dico, amp] = dico_update(dico, sig, amp, type, flow, rho)
     %
@@ -10,44 +10,56 @@
     % - sig: the training data
     % - amp: the amplitude coefficients as a sparse matrix
     % - type: the algorithm can be one of the following
-    %   - ols: fixed step gradient descent
-    %   - mailhe: optimal step gradient descent (can be implemented as a
-    %   default for ols?)
-    %   - MOD: pseudo-inverse of the coefficients
-    %   - KSVD: already implemented by Elad
+    %   - ols: fixed step gradient descent, as described in Olshausen &
+    %   Field95
+    %   - opt: optimal step gradient descent, as described in Mailhe et
+    %   al.08
+    %   - MOD: pseudo-inverse of the coefficients, as described in Engan99
+    %   - KSVD: PCA update as described in Aharon06. For fast applications,
+    %   use KSVDbox rather than this code.
+    %   - LGD: large step gradient descent. Equivalent to 'opt' with
+    %          rho=2.
     % - flow: 'sequential' or 'parallel'. If sequential, the residual is
     % updated after each atom update. If parallel, the residual is only
-    % updated once the whole dictionary has been computed. Sequential works
-    % better, there may be no need to implement parallel. Not used with
+    % updated once the whole dictionary has been computed.
+    % Default: Sequential (sequential usually works better). Not used with
     % MOD.
     % - rho: learning rate. 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.
+    % the gradient (default: 0.1). If the type is 'opt', the
+    % descent step is the optimal step*rho (default: 1, although 2 works
+    % better. See LGD for more details). Not used for MOD and KSVD.
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-    if ~ exist( 'rho', 'var' ) || isempty(rho)
-        rho = 0.1;
-    end
     
     if ~ exist( 'flow', 'var' ) || isempty(flow)
-        flow = sequential;
+        flow = 'sequential';
     end
     
     res = sig - dico*amp;
     nb_pattern = size(dico, 2);
     
+    % if the type is random, then randomly pick another type
     switch type
         case 'rand'
             x = rand();
             if x < 1/3
                 type = 'MOD';
             elseif type < 2/3
-                type = 'mailhe';
+                type = 'opt';
             else
                 type = 'KSVD';
             end
     end
     
+    % set the learning rate to default if not provided
+    if ~ exist( 'rho', 'var' ) || isempty(rho)
+        switch type
+            case 'ols'
+                rho = 0.1;
+            case 'opt'
+                rho = 1;
+        end
+    end
+    
     switch type
         case 'MOD'
             G = amp*amp';
@@ -72,14 +84,30 @@
                     dico(:,p) = pat;
                 end
             end
-        case 'mailhe'
+        case 'opt'
             for p = 1:nb_pattern
-                grad = res*amp(p,:)';
+                index = find(amp(p,:)~=0);
+                vec = amp(p,index);
+                grad = res(:,index)*vec';
                 if norm(grad) > 0
-                    pat = (amp(p,:)*amp(p,:)')*dico(:,p) + rho*grad;
+                    pat = (vec*vec')*dico(:,p) + rho*grad;
                     pat = pat/norm(pat);
                     if nargin >5 && strcmp(flow, 'sequential')
-                        res = res + (dico(:,p)-pat)*amp(p,:);
+                        res(:,index) = res(:,index) + (dico(:,p)-pat)*vec;
+                    end
+                    dico(:,p) = pat;
+                end
+            end
+        case 'LGD'
+            for p = 1:nb_pattern
+                index = find(amp(p,:)~=0);
+                vec = amp(p,index);
+                grad = res(:,index)*vec';
+                if norm(grad) > 0
+                    pat = (vec*vec')*dico(:,p) + 2*grad;
+                    pat = pat/norm(pat);
+                    if nargin >5 && strcmp(flow, 'sequential')
+                        res(:,index) = res(:,index) + (dico(:,p)-pat)*vec;
                     end
                     dico(:,p) = pat;
                 end
@@ -89,7 +117,7 @@
                 index = find(amp(p,:)~=0);
                 if ~isempty(index)
                     patch = res(:,index)+dico(:,p)*amp(p,index);
-                    [U,S,V] = svd(patch);
+                    [U,~,V] = svd(patch);
                     if U(:,1)'*dico(:,p) > 0
                         dico(:,p) = U(:,1);
                     else
--- a/Problems/generateAMTProblem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generateAMTProblem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -40,10 +40,10 @@
 %%
 %ask for file name
 TMPpath=pwd;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'audio']);
 [filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.name=name;
 
 data.notesOriginal=[];
--- a/Problems/generateAMT_Learning_Problem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generateAMT_Learning_Problem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -40,10 +40,10 @@
 %%
 %ask for file name
 TMPpath=pwd;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'audio']);
 [filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.name=name;
 
 data.notesOriginal=[];
--- a/Problems/generateAudioDeclippingProblem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generateAudioDeclippingProblem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -58,10 +58,10 @@
 
 if ~ exist( 'soundfile', 'var' ) || isempty(soundfile)
     %ask for file name 
-    [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+    [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
     cd([pathstr1,FS,'data',FS,'audio']);
     [filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
-    [pathstr, name, ext, versn] = fileparts(filename);
+    [pathstr, name, ext] = fileparts(filename);
     data.name=name;
 
     if strcmp(ext,'.mid')
@@ -92,7 +92,7 @@
     end
 else
     [x.signal, x.fs, x.nbits]=wavread(soundfile);
-    [pathstr, name, ext, versn] = fileparts(soundfile);
+    [pathstr, name, ext] = fileparts(soundfile);
     data.name=name;
 end
 
--- a/Problems/generateAudioDenoiseProblem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generateAudioDenoiseProblem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -57,10 +57,10 @@
 TMPpath=pwd;
 if ~ exist( 'soundfile', 'var' ) || isempty(soundfile)
     %ask for file name 
-    [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+    [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
     cd([pathstr1,FS,'data',FS,'audio']);
     [filename,pathname] = uigetfile({'*.mat; *.mid; *.wav'},'Select a file to transcribe');
-    [pathstr, name, ext, versn] = fileparts(filename);
+    [pathstr, name, ext] = fileparts(filename);
     data.name=name;
 
     if strcmp(ext,'.mid')
@@ -91,7 +91,7 @@
     end
 else
     [x.signal, x.fs, x.nbits]=wavread(soundfile);
-    [pathstr, name, ext, versn] = fileparts(soundfile);
+    [pathstr, name, ext] = fileparts(soundfile);
     data.name=name;
 end
 
--- a/Problems/generateImageDenoiseProblem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generateImageDenoiseProblem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -63,10 +63,10 @@
 FS=filesep;
 TMPpath=pwd;
 if ~ exist( 'im', 'var' ) || isempty(im)
-    [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+    [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
     cd([pathstr1,FS,'data',FS,'images']);
     [filename,pathname] = uigetfile({'*.png;'},'Select an image');
-    [pathstr, name, ext, versn] = fileparts(filename);
+    [pathstr, name, ext] = fileparts(filename);
     data.name=name;
     im = imread(filename);
     %im = double(im);
--- a/Problems/generatePierreProblem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generatePierreProblem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -45,10 +45,10 @@
 TMPpath=pwd;
 FS=filesep;
 if ~ exist( 'src', 'var' ) || isempty(src)
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 [filename,pathname] = uigetfile({'*.png;'},'Select a source image');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.srcname=name;
 src = imread(filename);
 src = double(src);
@@ -58,7 +58,7 @@
 
 if ~ exist( 'trg', 'var' ) || isempty(trg)
 [filename,pathname] = uigetfile({'*.png;'},'Select a target image');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.trgname=name;
 trg = imread(filename);
 trg = double(trg);
--- a/Problems/generatePierre_Problem.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/Problems/generatePierre_Problem.m	Wed Apr 11 15:56:39 2012 +0100
@@ -45,10 +45,10 @@
 TMPpath=pwd;
 FS=filesep;
 if ~ exist( 'src', 'var' ) || isempty(src)
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 [filename,pathname] = uigetfile({'*.png;'},'Select a source image');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.srcname=name;
 src = imread(filename);
 src = double(src);
@@ -58,7 +58,7 @@
 
 if ~ exist( 'trg', 'var' ) || isempty(trg)
 [filename,pathname] = uigetfile({'*.png;'},'Select a target image');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 data.trgname=name;
 trg = imread(filename);
 trg = double(trg);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/SMALLboxInit.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,25 @@
+%%  SMALLboxInit
+%
+%   SMALLbox Initialization
+%
+%   Important: If running SMALLBox for the first time, 
+%   please run SMALLboxSetup instead
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 Ivan Damnjanovic, Matthew Davies.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%
+%%
+
+global SMALL_path;
+SMALL_path = fileparts(mfilename('fullpath'));
+
+addpath(genpath(SMALL_path));
+
+
--- a/SMALLboxSetup.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/SMALLboxSetup.m	Wed Apr 11 15:56:39 2012 +0100
@@ -66,14 +66,11 @@
 
 end
 
-
-
-
-SMALL_path=pwd;
+global SMALL_path;
+SMALL_path=fileparts(mfilename('fullpath'));
 SMALL_p=genpath(SMALL_path);
 addpath(SMALL_p);
 
-
 %%
 
 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/config/SMALL_learn_config.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,97 @@
+%% Configuration file used in SMALL_learn
+%
+%   Use this file to change the dictionary learning algorithms in SMALLBox
+%   Please refer to the documentation before editing this file
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%
+%%
+
+if strcmpi(DL.toolbox,'KSVD')
+    param=DL.param;
+    param.data=Problem.b;
+    
+    D = eval([DL.name,'(param)']);%, ''t'', 5);']);
+elseif strcmpi(DL.toolbox,'KSVDS')
+    param=DL.param;
+    param.data=Problem.b;
+    
+    D = eval([DL.name,'(param, ''t'', 5);']);
+elseif strcmpi(DL.toolbox,'SPAMS')
+    
+    X  = Problem.b;
+    param=DL.param;
+    
+    D = eval([DL.name,'(X, param);']);
+    %   As some versions of SPAMS does not produce unit norm column
+    %   dictionaries, we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(D,2)
+        D(:,i)=D(:,i)/norm(D(:,i));
+    end
+elseif strcmpi(DL.toolbox,'SMALL')
+    
+    X  = Problem.b;
+    param=DL.param;
+    
+    D = eval([DL.name,'(X, param);']);
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(D,2)
+        D(:,i)=D(:,i)/norm(D(:,i));
+    end
+    
+elseif strcmpi(DL.toolbox,'TwoStepDL')
+    
+    DL=SMALL_two_step_DL(Problem, DL);
+    
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(DL.D,2)
+        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
+    end
+    D = DL.D;
+    
+elseif strcmpi(DL.toolbox,'MMbox')
+    
+    DL = wrapper_mm_DL(Problem, DL);
+    
+    %   we need to make sure that columns are normalised to
+    %   unit lenght.
+    
+    for i = 1: size(DL.D,2)
+        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
+    end
+    D = DL.D;
+      
+%   To introduce new dictionary learning technique put the files in
+%   your Matlab path. Next, unique name <TolboxID> for your toolbox needs
+%   to be defined and also prefferd API for toolbox functions <Preffered_API>
+%
+% elseif strcmpi(DL.toolbox,'<ToolboxID>')
+%     % This is an example of API that can be used:
+%     % - get training set from Problem part of structure
+%     % - assign parameters defined in the main program
+%
+%     X  = Problem.b;
+%     param=DL.param;
+%
+%     % - Evaluate the function (DL.name - defined in the main) with
+%     %   parameters given above
+%
+%     D = eval([DL.name,'(<Preffered_API>);']);
+
+else
+    printf('\nToolbox has not been registered. Please change SMALL_learn file.\n');
+    return
+end
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/config/SMALL_solve_config.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,80 @@
+%% Configuration file used in SMALL_solve
+%
+%   Use this file to change the solvers in SMALLBox
+%   Please refer to the documentation before editing this file
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%
+%%
+
+if strcmpi(solver.toolbox,'sparselab')
+    y = eval([solver.name,'(SparseLab_A, b, n,',solver.param,');']);
+elseif strcmpi(solver.toolbox,'sparsify')
+    if isa(Problem.A,'float')
+        y = eval([solver.name,'(b, A, n,',solver.param,');']);
+    else
+        y = eval([solver.name,'(b, A, n, ''P_trans'', AT,',solver.param,');']);
+    end
+elseif (strcmpi(solver.toolbox,'spgl1')||strcmpi(solver.toolbox,'gpsr'))
+    y = eval([solver.name,'(b, A,',solver.param,');']);
+elseif (strcmpi(solver.toolbox,'SPAMS'))
+    y = eval([solver.name,'(b, A, solver.param);']);
+elseif (strcmpi(solver.toolbox,'SMALL'))
+    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;
+elseif (strcmpi(solver.toolbox, 'ALPS'))
+    if ~isa(Problem.A,'float')
+        % ALPS does not accept implicit dictionary definition
+        A = opToMatrix(Problem.A, 1);
+    end
+    [y, numiter, time, y_path] = wrapper_ALPS_toolbox(b, A, solver.param);
+elseif (strcmpi(solver.toolbox, 'MMbox'))
+    if ~isa(Problem.A,'float')
+        % MMbox does not accept implicit dictionary definition
+        A = opToMatrix(Problem.A, 1);
+    end
+    
+    [y, cost] = wrapper_mm_solver(b, A, solver.param);
+    
+    %   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>')
+    %
+    %     % - Evaluate the function (solver.name - defined in the main) with
+    %     %   parameters given above
+    %
+    %     y = eval([solver.name,'(<Preffered_API>);']);
+    
+else
+    printf('\nToolbox has not been registered. Please change SMALL_solver file.\n');
+    return
+end
--- a/examples/ALPS solvers tests/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepALPSandMahile.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/ALPS solvers tests/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepALPSandMahile.m	Wed Apr 11 15:56:39 2012 +0100
@@ -36,7 +36,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
--- a/examples/AudioInpainting/Audio_Declipping_Example.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/AudioInpainting/Audio_Declipping_Example.m	Wed Apr 11 15:56:39 2012 +0100
@@ -24,7 +24,7 @@
 %
 %%
 
-clear all;
+clear;
 %   Defining the solvers to test in Audio declipping scenario
 
 % First solver omp2 - DCT+DST dictionary  with no additional constraints
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/AudioInpainting/CVX_add_const_Audio_declipping.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,85 @@
+function solver=CVX_add_const_Audio_declipping(Problem, solver, idxFrame)
+
+%% CVX additional constrains 
+    % Clipping level: take the analysis window into account
+    % clipping level detection
+    
+    signal = Problem.b1;
+    signalFull = Problem.b(:,idxFrame);
+    Dict = Problem.A;
+    DictFull = Problem.B;
+    Clipped = ~Problem.M(:,idxFrame);
+    W=1./sqrt(diag(Dict'*Dict));
+    
+    idxCoeff = find(solver.solution~=0);
+    
+    solution = solver.solution;
+    
+    wa = Problem.wa(Problem.windowSize); % analysis window
+    
+    
+    clippingLevelEst = max(abs(signal./wa(~Clipped)'));
+
+    idxPos = find(signalFull>=0 & Clipped);
+    idxNeg = find(signalFull<0  & Clipped);
+
+    DictPos=DictFull(idxPos,:);
+    DictNeg=DictFull(idxNeg,:);
+    
+    
+    wa_pos = wa(idxPos);
+    wa_neg = wa(idxNeg);
+    b_ineq_pos = wa_pos(:)*clippingLevelEst;
+    b_ineq_neg = -wa_neg(:)*clippingLevelEst;
+    if isfield(Problem,'Upper_Limit') && ~isempty(Problem.Upper_Limit)
+        b_ineq_pos_upper_limit = wa_pos(:)*Problem.Upper_Limit;
+        b_ineq_neg_upper_limit = -wa_neg(:)*Problem.Upper_Limit;
+    else
+        b_ineq_pos_upper_limit = Inf;
+        b_ineq_neg_upper_limit = -Inf;
+    end
+    
+    
+    j = length(idxCoeff);
+    
+    if isinf(b_ineq_pos_upper_limit)
+        %% CVX code
+        cvx_begin
+        cvx_quiet(true)
+        variable solution(j)
+        minimize(norm(Dict(:,idxCoeff)*solution-signal))
+        subject to
+        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_pos
+        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_neg
+        cvx_end
+        if cvx_optval>1e3
+            cvx_begin
+            cvx_quiet(true)
+            variable solution(j)
+            minimize(norm(Dict(:,idxCoeff)*solution-xObs))
+            cvx_end
+        end
+    else
+        %% CVX code
+        cvx_begin
+        cvx_quiet(true)
+        variable solution(j)
+        minimize(norm(Dict(:,idxCoeff)*solution-signal))
+        subject to
+        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_pos
+        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_neg
+        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_pos_upper_limit
+        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_neg_upper_limit
+        cvx_end
+        if cvx_optval>1e3
+            cvx_begin
+            cvx_quiet(true)
+            variable solution(j)
+            minimize(norm(Dict(:,idxCoeff)*solution-xObs))
+            cvx_end
+        end
+    end
+ 
+   solver.solution(idxCoeff) = solution;
+    
+   
\ No newline at end of file
--- a/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Wed Apr 11 15:56:39 2012 +0100
@@ -210,7 +210,7 @@
 
 FS=filesep;
 
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'results']);
 
 [filename,pathname] = uiputfile({' *.mid;' },'Save KSVD result midi');
--- a/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Err_test.m	Wed Apr 11 15:56:39 2012 +0100
@@ -130,7 +130,7 @@
 title('False Positives vs Edata');
 
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'results']);
 [filename,pathname] = uiputfile({' *.mid;' },'Save midi');
 if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
--- a/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_KSVD_Sparsity_test.m	Wed Apr 11 15:56:39 2012 +0100
@@ -141,7 +141,7 @@
 title('False Positives vs Tdata');
 
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'results']);
 [filename,pathname] = uiputfile({' *.mid;' },'Save midi');
 if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
--- a/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Wed Apr 11 15:56:39 2012 +0100
@@ -142,7 +142,7 @@
 title('False Positives vs lambda');
 
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'results']);
 [filename,pathname] = uiputfile({' *.mid;' },'Save midi');
 if filename~=0 writemidi(BLmidi, [pathname,FS,filename]);end
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Wed Apr 11 15:56:39 2012 +0100
@@ -36,7 +36,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLAvsTwoStepMOD.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLAvsTwoStepMOD.m	Wed Apr 11 15:56:39 2012 +0100
@@ -33,7 +33,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Wed Apr 11 15:56:39 2012 +0100
@@ -41,10 +41,10 @@
 
 % TMPpath=pwd;
 % FS=filesep;
-% [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+% [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 % cd([pathstr1,FS,'data',FS,'images']);
 % [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
-% [pathstr, name, ext, versn] = fileparts(filename);
+% [pathstr, name, ext] = fileparts(filename);
 % test_image = imread(filename);
 % test_image = double(test_image);
 % cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepKSVD.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepKSVD.m	Wed Apr 11 15:56:39 2012 +0100
@@ -36,7 +36,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Wed Apr 11 15:56:39 2012 +0100
@@ -22,15 +22,15 @@
 %   COPYING included with this distribution for more information.
 %%
 
-clear all;
+clear;
 
 %% Load an image
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 test_image = imread(filename);
 test_image = double(test_image);
 cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Wed Apr 11 15:56:39 2012 +0100
@@ -33,15 +33,15 @@
 %   COPYING included with this distribution for more information.%%
 %%
 
-clear all;
+clear;
 
 %% Load an image
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
-[pathstr, name, ext, versn] = fileparts(filename);
+[pathstr, name, ext] = fileparts(filename);
 test_image = imread(filename);
 test_image = double(test_image);
 cd(TMPpath);
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_TwoStep_KSVD_MOD_OLS_Mailhe.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_TwoStep_KSVD_MOD_OLS_Mailhe.m	Wed Apr 11 15:56:39 2012 +0100
@@ -36,7 +36,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
@@ -262,7 +262,7 @@
 %   Setting Dictionary structure fields (toolbox, name, param, D and time)
 %   to zero values
 
-SMALL.DL(4)=SMALL_init_DL('TwoStepDL', 'mailhe', '', 1);
+SMALL.DL(4)=SMALL_init_DL('TwoStepDL', 'opt', '', 1);
 
 
 %   Defining the parameters for KSVD
--- a/examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/MajorizationMinimization tests/SMALL_AMT_DL_test_KSVD_MM.m	Wed Apr 11 15:56:39 2012 +0100
@@ -254,7 +254,7 @@
 
 FS=filesep;
 
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'results']);
 
 [filename,pathname] = uiputfile({' *.mid;' },'Save KSVD result midi');
--- a/examples/MajorizationMinimization tests/SMALL_AudioDenoise_DL_test_KSVDvsSPAMS.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/MajorizationMinimization tests/SMALL_AudioDenoise_DL_test_KSVDvsSPAMS.m	Wed Apr 11 15:56:39 2012 +0100
@@ -4,7 +4,7 @@
 % It calls generateAudioDenoiseProblem that will let you to choose audio file,
 % add noise and use noisy audio to generate training set for dictionary
 % learning.
-%   
+   
 %
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2011 Ivan Damnjanovic.
--- a/examples/MajorizationMinimization tests/SMALL_ImgDenoise_DL_test_KSVDvsMajorizationMinimization.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/MajorizationMinimization tests/SMALL_ImgDenoise_DL_test_KSVDvsMajorizationMinimization.m	Wed Apr 11 15:56:39 2012 +0100
@@ -37,7 +37,7 @@
 clear;
 TMPpath=pwd;
 FS=filesep;
-[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+[pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
 cd(TMPpath);
--- a/examples/Pierre Villars/Pierre_Villars_Example.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/examples/Pierre Villars/Pierre_Villars_Example.m	Wed Apr 11 15:56:39 2012 +0100
@@ -19,7 +19,7 @@
 %
 %%
 
-clear all;
+clear;
 
 %   Defining the Problem structure
 
@@ -44,14 +44,10 @@
 psnr = zeros(1,n);
 
 for i=1:n
-    
-    
     %   Set reconstruction function
     
     SMALL.Problem.reconstruct=@(x) Pierre_reconstruct(x, SMALL.Problem);
     
-   
-    
     %   Defining the parameters sparse representation
     SMALL.solver(i)=SMALL_init_solver;
     SMALL.solver(i).toolbox='SMALL';
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/SMALL_DL_test.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,76 @@
+function SMALL_DL_test
+clear, clc, close all
+% Create a 2-dimensional dataset of points that are oriented in 3
+% directions on a x/y plane
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2012 Daniele Barchiesi.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+
+nData = 10000;						%number of data
+theta = [pi/6 pi/3 4*pi/6];			%angles
+nAngles = length(theta);			%number of angles
+Q	  = [cos(theta); sin(theta)];	%rotation matrix
+X	  = Q*randmog(nAngles,nData);	%training data
+
+% find principal directions using PCA
+XXt = X*X';							%cross correlation matrix
+[U ~] = svd(XXt);					%svd of XXt
+
+scale = 3;							%scale factor for plots
+subplot(1,2,1), hold on
+title('Principal Component Analysis')
+scatter(X(1,:), X(2,:),'.');		%scatter training data
+O = zeros(size(U));					%origin
+quiver(O(1,1:2),O(2,1:2),scale*U(1,:),scale*U(2,:),...
+	'LineWidth',2,'Color','k')		%plot atoms
+axis equal							%scale axis
+
+subplot(1,2,2), hold on
+title('K-SVD Dictionary')
+scatter(X(1,:), X(2,:),'.');
+axis equal
+
+nAtoms  = 3;						%number of atoms in the dictionary
+nIter   = 1;						%number of dictionary learning iterations
+initDict = normc(randn(2,nAtoms));	%random initial dictionary
+O = zeros(size(initDict));			%origin
+
+% apply dictionary learning algorithm
+ksvd_params = struct('data',X,...			%training data
+					 'Tdata',1,...			%sparsity level
+					 'dictsize',nAtoms,...	%number of atoms
+					 'initdict',initDict,...%initial dictionary
+					 'iternum',10);			%number of iterations
+DL = SMALL_init_DL('ksvd','ksvd',ksvd_params); %dictionary learning structure
+DL.D = initDict;							%copy initial dictionary in solution variable
+problem = struct('b',X);					%copy training data in problem structure
+
+xdata = DL.D(1,:);
+ydata = DL.D(2,:);
+qPlot = quiver(O(1,:),O(2,:),scale*initDict(1,:),scale*initDict(2,:),...
+	'LineWidth',2,'Color','k','UDataSource','xdata','VDataSource','ydata');
+
+for iIter=1:nIter
+	DL.ksvd_params.initdict = DL.D;
+	pause
+	DL = SMALL_learn(problem,DL);			%learn dictionary
+	xdata = scale*DL.D(1,:);	
+	ydata = scale*DL.D(2,:);
+	refreshdata(gcf,'caller');
+end
+
+
+function X = randmog(m, n)
+% RANDMOG - Generate mixture of Gaussians
+s = [0.2 2];
+% Choose which Gaussian
+G1 = (rand(m, n) < 0.9);
+% Make them
+X = (G1.*s(1) + (1-G1).*s(2)) .* randn(m,n);
--- a/solvers/CVX_add_const_Audio_declipping.m	Wed Sep 07 14:17:55 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,85 +0,0 @@
-function solver=CVX_add_const_Audio_declipping(Problem, solver, idxFrame)
-
-%% CVX additional constrains 
-    % Clipping level: take the analysis window into account
-    % clipping level detection
-    
-    signal = Problem.b1;
-    signalFull = Problem.b(:,idxFrame);
-    Dict = Problem.A;
-    DictFull = Problem.B;
-    Clipped = ~Problem.M(:,idxFrame);
-    W=1./sqrt(diag(Dict'*Dict));
-    
-    idxCoeff = find(solver.solution~=0);
-    
-    solution = solver.solution;
-    
-    wa = Problem.wa(Problem.windowSize); % analysis window
-    
-    
-    clippingLevelEst = max(abs(signal./wa(~Clipped)'));
-
-    idxPos = find(signalFull>=0 & Clipped);
-    idxNeg = find(signalFull<0  & Clipped);
-
-    DictPos=DictFull(idxPos,:);
-    DictNeg=DictFull(idxNeg,:);
-    
-    
-    wa_pos = wa(idxPos);
-    wa_neg = wa(idxNeg);
-    b_ineq_pos = wa_pos(:)*clippingLevelEst;
-    b_ineq_neg = -wa_neg(:)*clippingLevelEst;
-    if isfield(Problem,'Upper_Limit') && ~isempty(Problem.Upper_Limit)
-        b_ineq_pos_upper_limit = wa_pos(:)*Problem.Upper_Limit;
-        b_ineq_neg_upper_limit = -wa_neg(:)*Problem.Upper_Limit;
-    else
-        b_ineq_pos_upper_limit = Inf;
-        b_ineq_neg_upper_limit = -Inf;
-    end
-    
-    
-    j = length(idxCoeff);
-    
-    if isinf(b_ineq_pos_upper_limit)
-        %% CVX code
-        cvx_begin
-        cvx_quiet(true)
-        variable solution(j)
-        minimize(norm(Dict(:,idxCoeff)*solution-signal))
-        subject to
-        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_pos
-        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_neg
-        cvx_end
-        if cvx_optval>1e3
-            cvx_begin
-            cvx_quiet(true)
-            variable solution(j)
-            minimize(norm(Dict(:,idxCoeff)*solution-xObs))
-            cvx_end
-        end
-    else
-        %% CVX code
-        cvx_begin
-        cvx_quiet(true)
-        variable solution(j)
-        minimize(norm(Dict(:,idxCoeff)*solution-signal))
-        subject to
-        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_pos
-        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_neg
-        DictPos(:,idxCoeff)*(W(idxCoeff).*solution) <= b_ineq_pos_upper_limit
-        DictNeg(:,idxCoeff)*(W(idxCoeff).*solution) >= b_ineq_neg_upper_limit
-        cvx_end
-        if cvx_optval>1e3
-            cvx_begin
-            cvx_quiet(true)
-            variable solution(j)
-            minimize(norm(Dict(:,idxCoeff)*solution-xObs))
-            cvx_end
-        end
-    end
- 
-   solver.solution(idxCoeff) = solution;
-    
-   
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/alps/wrapper_ALPS_toolbox.m	Wed Apr 11 15:56:39 2012 +0100
@@ -0,0 +1,106 @@
+function [x , numiter, time, x_path] = wrapper_ALPS_toolbox(b, A, param)
+%% SMALL wrapper for ALPS toolbox
+%
+%   Function gets as input
+%       b - measurement vector 
+%       A - dictionary 
+%       K - desired sparsity level
+%       param - structure containing additional parameters. These are:
+%           - memory        Memory (momentum) of proposed algorithm. 
+%                           Possible values are 0,1,'infty' for memoryless,
+%                           one memory and infinity memory ALPS,
+%                           respectively. Default value: memory = 0.
+%           - tol           Early stopping tolerance. Default value: tol =
+%                           1-e5.
+%           - ALPSiters     Maximum number of algorithm iterations. Default
+%                           value: 300.  
+%           - mod           According to [1], possible values are
+%                           [0,1,2,4,5,6]. This value comes from the binary 
+%                           representation of the parameters:
+%                           (solveNewtob, gradientDescentx, solveNewtonx), 
+%                           which are explained next. Default value = 0.
+%           - mu            Variable that controls the step size selection. 
+%                           When mu = 0, step size is computed adaptively 
+%                           per iteration. Default value: mu = 0. 
+%           - tau           Variable that controls the momentum in
+%                           non-memoryless case. Ignored in memoryless
+%                           case. User can insert as value a function handle on tau.
+%                           Description given below. Default value: tau = -1. 
+%           - CGiters       Maximum iterations for Conjugate-Gradients method.
+%           - CGtol         Tolerance variable for Conjugate-Gradients method.
+%           - verbose       verbose = 1 prints out execution infromation.
+%   Output:
+%       x - sparse solution
+%       numiter - number of iterations
+%       time - time needed to solve the problem#
+%       x_path - matrix containing x after every iteration
+%
+%   For more details see AlgebraicPursuit.m.
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%   
+%%
+
+if isfield(param, 'sparsity')
+   sparsity = param.sparsity;
+else
+   printf('\nAlebraic Pursuit algorithms require desired sparsity level.\n("sparsity" field in solver parameters structure)\n ');
+   return
+end
+
+if isfield(param, 'memory')
+    memory = param.memory;
+else
+    memory = 0;
+end
+
+if isfield(param, 'mode')
+    mode = param.mode;
+else
+    mode = 0;
+end
+if isfield(param, 'tolerance')
+    tolerance = param.tolerance;
+else
+    tolerance = 1e-5;
+end
+if isfield(param, 'iternum')
+    iternum = param.iternum;
+else
+    iternum = 300;
+end
+if isfield(param, 'verbose')
+    verbose = param.verbose;
+else
+    verbose = 0;
+end
+if isfield(param, 'tau')
+    tau = param.tau;
+else
+    tau = 1/2;
+end
+if isfield(param, 'useCG')
+    useCG = param.useCG;
+else
+    useCG = 0;
+end
+if isfield(param, 'mu')
+    mu = param.mu;
+else
+    mu = 0;
+end
+training_size = size(b,2);
+x=zeros(size(A,2),training_size);
+for i = 1:training_size
+    [x(:,i), numiter, time, x_path] = AlgebraicPursuit(b(:,i), A, sparsity, 'memory', memory,...
+        'mode', mode, 'tolerance', tolerance, 'ALPSiterations', iternum, ...
+        'verbose', verbose, 'tau', tau, 'useCG', useCG, 'mu', mu);
+end
\ No newline at end of file
--- a/toolboxes/wrapper_ALPS_toolbox.m	Wed Sep 07 14:17:55 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,80 +0,0 @@
-function [x , numiter, time, x_path] = wrapper_ALPS_toolbox(b, A, param)
-%% SMALL wrapper for ALPS toolbox
-%
-%   Function gets as input
-%       b - measurement vector 
-%       A - dictionary 
-%       K - desired sparsity level
-%       param - structure containing additional parameters
-%   Output:
-%       x - sparse solution
-%       numiter - number of iterations
-%       time - time needed to solve the problem#
-%       x_path - matrix containing x after every iteration
-
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2011 Ivan Damnjanovic.
-%
-%   This program is free software; you can redistribute it and/or
-%   modify it under the terms of the GNU General Public License as
-%   published by the Free Software Foundation; either version 2 of the
-%   License, or (at your option) any later version.  See the file
-%   COPYING included with this distribution for more information.
-%   
-%%
-
-if isfield(param, 'sparsity')
-   sparsity = param.sparsity;
-else
-   printf('\nAlebraic Pursuit algorithms require desired sparsity level.\n("sparsity" field in solver parameters structure)\n ');
-   return
-end
-
-if isfield(param, 'memory')
-    memory = param.memory;
-else
-    memory = 0;
-end
-
-if isfield(param, 'mode')
-    mode = param.mode;
-else
-    mode = 0;
-end
-if isfield(param, 'tolerance')
-    tolerance = param.tolerance;
-else
-    tolerance = 1e-5;
-end
-if isfield(param, 'iternum')
-    iternum = param.iternum;
-else
-    iternum = 300;
-end
-if isfield(param, 'verbose')
-    verbose = param.verbose;
-else
-    verbose = 0;
-end
-if isfield(param, 'tau')
-    tau = param.tau;
-else
-    tau = 1/2;
-end
-if isfield(param, 'useCG')
-    useCG = param.useCG;
-else
-    useCG = 0;
-end
-if isfield(param, 'mu')
-    mu = param.mu;
-else
-    mu = 0;
-end
-training_size = size(b,2);
-x=zeros(size(A,2),training_size);
-for i = 1:training_size
-    [x(:,i), numiter, time, x_path] = AlgebraicPursuit(b(:,i), A, sparsity, 'memory', memory,...
-        'mode', mode, 'tolerance', tolerance, 'ALPSiterations', iternum, ...
-        'verbose', verbose, 'tau', tau, 'useCG', useCG, 'mu', mu);
-end
\ No newline at end of file
--- a/util/SMALL_learn.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/util/SMALL_learn.m	Wed Apr 11 15:56:39 2012 +0100
@@ -1,9 +1,9 @@
 function DL = SMALL_learn(Problem,DL)
-%% SMALL Dictionary Learning   
-%   
-%   Function gets as input Problem and Dictionary Learning (DL) structures 
+%% SMALL Dictionary Learning
+%
+%   Function gets as input Problem and Dictionary Learning (DL) structures
 %   In Problem structure field b with the training set needs to be defined
-%   In DL fields with name of the toolbox and solver, and parameters file 
+%   In DL fields with name of the toolbox and solver, and parameters file
 %   for particular dictionary learning technique needs to be present.
 %
 %   Outputs are Learned dictionary and time spent as a part of DL structure
@@ -18,105 +18,31 @@
 %   License, or (at your option) any later version.  See the file
 %   COPYING included with this distribution for more information.
 %%
+
+SMALLboxInit
+
 if (DL.profile)
-  fprintf('\nStarting Dictionary Learning %s... \n', DL.name);
+    fprintf('\nStarting Dictionary Learning %s... \n', DL.name);
 end
-  start=cputime;
-  tStart=tic;
-  if strcmpi(DL.toolbox,'KSVD')
-    param=DL.param; 
-    param.data=Problem.b;
- 
-    D = eval([DL.name,'(param)']);%, ''t'', 5);']);
-  elseif strcmpi(DL.toolbox,'KSVDS')
-    param=DL.param; 
-    param.data=Problem.b;
-    
-    D = eval([DL.name,'(param, ''t'', 5);']);
-  elseif strcmpi(DL.toolbox,'SPAMS')
-    
-    X  = Problem.b; 
-    param=DL.param;
-    
-    D = eval([DL.name,'(X, param);']);
-    %   As some versions of SPAMS does not produce unit norm column
-    %   dictionaries, we need to make sure that columns are normalised to
-    %   unit lenght.
-    
-    for i = 1: size(D,2)
-        D(:,i)=D(:,i)/norm(D(:,i));
-    end
-  elseif strcmpi(DL.toolbox,'SMALL')
-    
-    X  = Problem.b; 
-    param=DL.param;
-    
-    D = eval([DL.name,'(X, param);']);
-    %   we need to make sure that columns are normalised to
-    %   unit lenght.
-    
-    for i = 1: size(D,2)
-        D(:,i)=D(:,i)/norm(D(:,i));
-    end
-    
-   elseif strcmpi(DL.toolbox,'TwoStepDL')
-        
-    DL=SMALL_two_step_DL(Problem, DL);
-    
-    %   we need to make sure that columns are normalised to
-    %   unit lenght.
-    
-    for i = 1: size(DL.D,2)
-        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
-    end
-    D = DL.D;
-    
-elseif strcmpi(DL.toolbox,'MMbox')
-        
-    DL = wrapper_mm_DL(Problem, DL);
-    
-    %   we need to make sure that columns are normalised to
-    %   unit lenght.
-    
-    for i = 1: size(DL.D,2)
-        DL.D(:,i)=DL.D(:,i)/norm(DL.D(:,i));
-    end
-    D = DL.D; 
-    
-%   To introduce new dictionary learning technique put the files in
-%   your Matlab path. Next, unique name <TolboxID> for your toolbox needs 
-%   to be defined and also prefferd API for toolbox functions <Preffered_API>
-%   
-% elseif strcmpi(DL.toolbox,'<ToolboxID>')
-%     % This is an example of API that can be used:
-%     % - get training set from Problem part of structure
-%     % - assign parameters defined in the main program
-%
-%     X  = Problem.b; 
-%     param=DL.param;
-%
-%     % - Evaluate the function (DL.name - defined in the main) with
-%     %   parameters given above
-%
-%     D = eval([DL.name,'(<Preffered_API>);']);
 
-  else
-    printf('\nToolbox has not been registered. Please change SMALL_learn file.\n');
-    return
-  end
-  
+start=cputime;
+tStart=tic;
+
+% toolboxes configuration file
+run(fullfile(SMALL_path, 'config/SMALL_learn_config.m'));
+
 %%
 %   Dictionary Learning time
 tElapsed=toc(tStart);
 DL.time = cputime - start;
 if (DL.profile)
-  fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time);
-  fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed);
+    fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time);
+    fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed);
 end
 DL.time=tElapsed;
-%   If dictionary is given as a sparse matrix change it to full  
+%   If dictionary is given as a sparse matrix change it to full
 
-  DL.D = full(D);
-  
+DL.D = full(D);
+
 end
-  
\ No newline at end of file
+
--- a/util/SMALL_solve.m	Wed Sep 07 14:17:55 2011 +0100
+++ b/util/SMALL_solve.m	Wed Apr 11 15:56:39 2012 +0100
@@ -15,9 +15,11 @@
 %   published by the Free Software Foundation; either version 2 of the
 %   License, or (at your option) any later version.  See the file
 %   COPYING included with this distribution for more information.
-%   
+%
 %%
 
+SMALLboxInit
+
 if isa(Problem.A,'float')
     A = Problem.A;
     SparseLab_A=Problem.A;
@@ -45,72 +47,9 @@
 
 start=cputime;
 tStart=tic;
-if strcmpi(solver.toolbox,'sparselab')
-    y = eval([solver.name,'(SparseLab_A, b, n,',solver.param,');']);
-elseif strcmpi(solver.toolbox,'sparsify')
-    if isa(Problem.A,'float')
-        y = eval([solver.name,'(b, A, n,',solver.param,');']);
-    else
-        y = eval([solver.name,'(b, A, n, ''P_trans'', AT,',solver.param,');']);
-    end
-elseif (strcmpi(solver.toolbox,'spgl1')||strcmpi(solver.toolbox,'gpsr'))
-    y = eval([solver.name,'(b, A,',solver.param,');']);
-elseif (strcmpi(solver.toolbox,'SPAMS'))
-    y = eval([solver.name,'(b, A, solver.param);']);
-elseif (strcmpi(solver.toolbox,'SMALL'))
-    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;
-elseif (strcmpi(solver.toolbox, 'ALPS'))
-    if ~isa(Problem.A,'float')
-        % ALPS does not accept implicit dictionary definition
-        A = opToMatrix(Problem.A, 1);
-    end
-    [y, numiter, time, y_path] = wrapper_ALPS_toolbox(b, A, solver.param);
-elseif (strcmpi(solver.toolbox, 'MMbox'))
-    if ~isa(Problem.A,'float')
-        % MMbox does not accept implicit dictionary definition
-        A = opToMatrix(Problem.A, 1);
-    end
-    
-    [y, cost] = wrapper_mm_solver(b, A, solver.param);
-    
-     
-    
-    %   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>')
-    %
-    %     % - Evaluate the function (solver.name - defined in the main) with
-    %     %   parameters given above
-    %
-    %     y = eval([solver.name,'(<Preffered_API>);']);
-    
-else
-    printf('\nToolbox has not been registered. Please change SMALL_solver file.\n');
-    return
-end
+
+% solvers configuration file
+run(fullfile(SMALL_path, 'config/SMALL_solve_config.m'));
 
 %%
 %   Sparse representation time