Mercurial > hg > octave-lyh
view scripts/sparse/sprandn.m @ 7505:f5005d9510f4
Remove dispatched sparse functions and treat in the generic versions of the functions
author | David Bateman <dbateman@free.fr> |
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date | Wed, 20 Feb 2008 15:52:11 -0500 |
parents | a1dbe9d80eee |
children | eb63fbe60fab |
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## Copyright (C) 2004, 2005, 2006, 2007 Paul Kienzle ## ## This file is part of Octave. ## ## Octave 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. ## ## Octave 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 Octave; see the file COPYING. If not, see ## <http://www.gnu.org/licenses/>. ## ## Original version by Paul Kienzle distributed as free software in the ## public domain. ## -*- texinfo -*- ## @deftypefn {Function File} {} sprandn (@var{m}, @var{n}, @var{d}) ## @deftypefnx {Function File} {} sprandn (@var{s}) ## Generate a random sparse matrix. The size of the matrix will be ## @var{m} by @var{n}, with a density of values given by @var{d}. ## @var{d} should be between 0 and 1. Values will be normally ## distributed with mean of zero and variance 1. ## ## Note: sometimes the actual density may be a bit smaller than @var{d}. ## This is unlikely to happen for large really sparse matrices. ## ## If called with a single matrix argument, a random sparse matrix is ## generated wherever the matrix @var{S} is non-zero. ## @seealso{sprand} ## @end deftypefn ## Author: Paul Kienzle <pkienzle@users.sf.net> function S = sprandn (m, n, d) if (nargin == 1) [i, j, v] = find (m); [nr, nc] = size (m); S = sparse (i, j, randn (size (v)), nr, nc); elseif (nargin == 3) mn = m*n; k = round (d*mn); idx = unique (fix (rand (min (k*1.01, k+10), 1) * mn)) + 1; ## idx contains random numbers in [1,mn] ## generate 1% or 10 more random values than necessary in order to ## reduce the probability that there are less than k distinct ## values; maybe a better strategy could be used but I don't think ## it's worth the price. ## actual number of entries in S k = min (length (idx), k); j = floor ((idx(1:k)-1)/m); i = idx(1:k) - j*m; if (isempty (i)) S = sparse (m, n); else S = sparse (i, j+1, randn (k, 1), m, n); endif else print_usage (); endif endfunction