function [xo]=groupthresh(xi,lambda,varargin)
%GROUPTHRESH Group thresholding
% Usage: xo=groupthresh(xi,lambda);
%
% GROUPTHRESH(x,lambda) performs group thresholding on x, with
% threshold lambda. x must be a two-dimensional array, the first
% dimension labelling groups, and the second one labelling members. This
% means that the groups are the row vectors of the input (the vectors
% along the 2nd dimension).
%
% Several types of grouping behaviour are available:
%
% GROUPTHRESH(x,lambda,'group') shrinks all coefficients within a given
% group according to the value of the l^2 norm of the group in
% comparison to the threshold lambda. This is the default.
%
% GROUPTHRESH(x,lambda,'elite') shrinks all coefficients within a
% given group according to the value of the l^1 norm of the
% group in comparison to the threshold value lambda.
%
% GROUPTHRESH(x,lambda,dim) chooses groups along dimension
% dim. The default value is dim=2.
%
% GROUPTHRESH accepts all the flags of THRESH to choose the
% thresholding type within each group and the output type (full / sparse
% matrix). Please see the help of THRESH for the available
% options. Default is to use soft thresholding and full matrix output.
%
% See also: thresh
%
% Demos: demo_audioshrink
%
% References:
% M. Kowalski. Sparse regression using mixed norms. Appl. Comput. Harmon.
% Anal., 27(3):303--324, 2009.
%
% M. Kowalski and B. Torrésani. Sparsity and persistence: mixed norms
% provide simple signal models with dependent coefficients. Signal, Image
% and Video Processing, 3(3):251--264, 2009.
%
% G. Yu, S. Mallat, and E. Bacry. Audio Denoising by Time-Frequency Block
% Thresholding. IEEE Trans. Signal Process., 56(5):1830--1839, 2008.
%
%
% Url: http://ltfat.github.io/doc/sigproc/groupthresh.html
% Copyright (C) 2005-2023 Peter L. Soendergaard <peter@sonderport.dk> and others.
% This file is part of LTFAT version 2.6.0
%
% 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 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR : Kai Siedenburg, Bruno Torresani.
% REFERENCE: OK
if nargin<2
error('Too few input parameters.');k
end;
if (prod(size(lambda))~=1 || ~isnumeric(lambda))
error('lambda must be a scalar.');
end;
% Define initial value for flags and key/value pairs.
definput.import={'thresh','groupthresh'};
definput.importdefaults={'soft'};
definput.keyvals.dim=2;
[flags,keyvals,dim]=ltfatarghelper({'dim'},definput,varargin);
% kv.dim (the time or frequency selector) is handled by assert_sigreshape_pre
[xi,L,NbMembers,NbGroups,dim,permutedsize,order]=assert_sigreshape_pre(xi,[],dim,'GROUPTHRESH');
if flags.do_sparse
xo = sparse(size(xi));
else
xo = zeros(size(xi));
end;
if flags.do_group
groupnorm = sqrt(sum(abs(xi).^2));
w = thresh(groupnorm, lambda, flags.iofun,flags.outclass)./groupnorm;
% Clean w for NaN. NaN appears if the input has a group with norm
% exactly 0.
w(isnan(w)) = 0;
xo = bsxfun(@times,xi,w);
end
if flags.do_elite
for ii=1:NbGroups,
y = sort(abs(xi(:,ii)),'descend');
rhs = cumsum(y);
rhs = rhs .* lambda ./ (1 + lambda * (1:NbMembers)');
M_ii = find(diff(sign(y-rhs)));
if (M_ii~=0)
tau_ii = lambda * norm(y(1:M_ii),1)/(1+lambda*M_ii);
else
tau_ii = 0;
end
% FIXME: The following line does not work for sparse matrices.
xo(:,ii) = thresh(xi(:,ii),tau_ii,flags.iofun,flags.outclass);
end
end;
xo=assert_sigreshape_post(xo,dim,permutedsize,order);