function [xo,Nout]=largestn(xi,N,varargin)
%LARGESTN Keep N largest coefficients
% Usage: xo=largestn(x,N);
% xo=largestn(x,N,mtype);
%
% LARGESTN(x,N) returns an array of the same size as x keeping
% the N largest coefficients.
%
% LARGESTN takes the following flags at the end of the line of input
% arguments:
%
% 'hard' Perform hard thresholding. This is the default.
%
% 'wiener' Perform empirical Wiener shrinkage. This is in between
% soft and hard thresholding.
%
% 'soft' Perform soft thresholding.
%
% 'full' Returns the output as a full matrix. This is the default.
%
% 'sparse' Returns the output as a sparse matrix.
%
% If the coefficients represents a signal expanded in an orthonormal
% basis then this will be the best N-term approximation.
%
% *Note:* If soft- or Wiener thresholding is selected, only N-1
% coefficients will actually be returned. This is caused by the N*'th
% coefficient being set to zero.
%
% See also: largestr
%
% References:
% S. Mallat. A wavelet tour of signal processing. Academic Press, San
% Diego, CA, 1998.
%
%
% Url: http://ltfat.github.io/doc/sigproc/largestn.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 : Peter L. Søndergaard and Bruno Torresani.
% TESTING: OK
% REFERENCE: OK
if nargin<2
error('%s: Too few input parameters.',upper(mfilename));
end;
definput.import={'thresh'};
[flags,keyvals]=ltfatarghelper({},definput,varargin);
if (prod(size(N))~=1 || ~isnumeric(N))
error('N must be a scalar.');
end;
if flags.do_sparse
if ndims(xi)>2
error('Sparse output is only supported for 1D/2D input. This is a limitation of Matlab/Octave.');
end;
end;
% Determine the size of the array.
ss=numel(xi);
% Sort the absolute values of the coefficients.
sxi=sort(abs(xi(:)));
% Find the coeffiecient sitting at position N through the array,
% and use this as a threshing value.
if N<=0
% Choose a thresh value higher than max
lambda=sxi(end)+1;
else
lambda=sxi(ss-N+1);
end;
[xo,Nout]=thresh(xi,lambda,flags.outclass,flags.iofun);