view solvers/SMALL_ompGabor/omp2Gabor.m @ 229:ae71e160f80c luisf_dev

removed duplicate file.

function gamma = omp2Gabor(varargin)
%% omp2Gabor Error-constrained Orthogonal Matching Pursuit.
%
%  This file is part of SMALLbox [2] and it is adaptation of Ron
%  Rubenstein omp solver [1] for Gabor dictionary as defined in 
%  Audio Inpainting by Adler et al [3]. The dictionary is presented as
%  DCT+DST and solver pics two atoms per iteration (given the frequency one
%  from DCT and one from DST). For more information look in [3].
%  
%  GAMMA = OMP2(D,X,G,EPSILON) solves the optimization problem
%
%       min  |GAMMA|_0     s.t.  |X - D*GAMMA|_2 <= EPSILON
%      gamma
%
%  for each of the signals in X, using Batch Orthogonal Matching Pursuit.
%  Here, D is a dictionary with normalized columns, X is a matrix
%  containing column signals, EPSILON is the error target for each signal,
%  and G is the Gramm matrix D'*D. The output GAMMA is a matrix containing
%  the sparse representations as its columns. 
%
%  GAMMA = OMP2(D,X,[],EPSILON) performs the same operation, but without
%  the matrix G, using OMP-Cholesky. This call produces the same output as
%  Batch-OMP, but is significantly slower. Using this syntax is only
%  recommended when available memory is too small to store G.
%
%  GAMMA = OMP2(DtX,XtX,G,EPSILON) is the fastest implementation of OMP2,
%  but also requires the most memory. Here, DtX stores the projections
%  D'*X, and XtX is a row vector containing the squared norms of the
%  signals, sum(X.*X). In this case Batch-OMP is used, but without having
%  to compute D'*X and XtX in advance, which slightly improves runtime.
%  Note that in general, the call
%
%    GAMMA = OMP2(D'*X, sum(X.*X), G, EPSILON);
%
%  will be faster than the call
%
%    GAMMA = OMP2(D,X,G,EPSILON);
%
%  due to optimized matrix multiplications in Matlab. However, when the
%  entire matrix D'*X cannot be stored in memory, one of the other two
%  versions can be used. Both compute D'*X for just one signal at a time,
%  and thus require much less memory.
%
%  GAMMA = OMP2(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies additional
%  parameters for OMP2. Available parameters are:
%
%    'gammamode' - Specifies the representation mode for GAMMA. Can be
%                  either 'full' or 'sparse', corresponding to a full or
%                  sparse matrix, respectively. By default, GAMMA is
%                  returned as a sparse matrix.
%    'maxatoms' -  Limits the number of atoms in the representation of each
%                  signal. If specified, the number of atoms in each
%                  representation does not exceed this number, even if the
%                  error target is not met. Specifying maxatoms<0 implies
%                  no limit (default).
%    'messages'  - Specifies whether progress messages should be displayed.
%                  When positive, this is the number of seconds between
%                  status prints. When negative, indicates that no messages
%                  should be displayed (this is the default).
%    'checkdict' - Specifies whether dictionary normalization should be
%                  verified. When set to 'on' (default) the dictionary
%                  atoms are verified to be of unit L2-norm. Setting this
%                  parameter to 'off' disables verification and accelerates
%                  function performance. Note that an unnormalized
%                  dictionary will produce invalid results.
%    'profile'   - Can be either 'on' or 'off'. When 'on', profiling
%                  information is displayed at the end of the funciton
%                  execution.
%
%
%  Summary of OMP2 versions:
%
%    version                 |   speed     |   memory
%  -------------------------------------------------------------
%   OMP2(DtX,XtX,G,EPSILON)  |  very fast  |  very large
%   OMP2(D,X,G,EPSILON)      |  fast       |  moderate
%   OMP2(D,X,[],EPSILON)     |  very slow  |  small
%  -------------------------------------------------------------
%
%
%  References:
%  [1] M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient Implementation
%      of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit",
%      Technical Report - CS, Technion, April 2008.
%
%  See also OMP.


%  Ron Rubinstein
%  Computer Science Department
%  Technion, Haifa 32000 Israel
%  ronrubin@cs
%
%  April 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.
%%

% default options

sparse_gamma = 1;
msgdelta = -1;
maxatoms = -1;
checkdict = 1;
profile = 0;


% determine number of parameters

paramnum = 1;
while (paramnum<=nargin && ~ischar(varargin{paramnum}))
  paramnum = paramnum+1;
end
paramnum = paramnum-1;


% parse options

for i = paramnum+1:2:length(varargin)
  paramname = varargin{i};
  paramval = varargin{i+1};

  switch lower(paramname)

    case 'gammamode'
      if (strcmpi(paramval,'sparse'))
        sparse_gamma = 1;
      elseif (strcmpi(paramval,'full'))
        sparse_gamma = 0;
      else
        error('Invalid GAMMA mode');
      end
      
    case 'maxatoms'
      maxatoms = paramval;
      
    case 'messages'
      msgdelta = paramval;

    case 'checkdict'
      if (strcmpi(paramval,'on'))
        checkdict = 1;
      elseif (strcmpi(paramval,'off'))
        checkdict = 0;
      else
        error('Invalid checkdict option');
      end

    case 'profile'
      if (strcmpi(paramval,'on'))
        profile = 1;
      elseif (strcmpi(paramval,'off'))
        profile = 0;
      else
        error('Invalid profile mode');
      end

    otherwise
      error(['Unknown option: ' paramname]);
  end
  
end


% determine call type

if (paramnum==4)
  
  n1 = size(varargin{1},1);
  n2 = size(varargin{2},1);
  n3 = size(varargin{3},1);
  
  if ( (n1>1 && n2==1) || (n1==1 && n2==1 && n3==1) )  %  DtX,XtX,G,EPSILON
    
    DtX = varargin{1};
    XtX = varargin{2};
    G = varargin{3};
    epsilon = varargin{4};
    D = [];
    X = [];
    
  else  % D,X,G,EPSILON
    
    D = varargin{1};
    X = varargin{2};
    G = varargin{3};
    epsilon = varargin{4};
    DtX = [];
    XtX = [];
    
  end
  
else
  error('Invalid number of parameters');
end

G=[];

% verify dictionary normalization

if (checkdict)
  if (isempty(G))
    atomnorms = sum(D.*D);
  else
    atomnorms = diag(G);
  end
  if (any(abs(atomnorms-1) > 1e-2))
    error('Dictionary columns must be normalized to unit length');
  end
end


% omp

gamma = omp2mexGabor(D,X,DtX,XtX,G,epsilon,sparse_gamma,msgdelta,maxatoms,profile);