Mercurial > hg > octave-nkf
view scripts/sparse/sprandsym.m @ 6213:0a259ae4375e
[project @ 2006-12-08 22:05:59 by dbateman]
author | dbateman |
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date | Fri, 08 Dec 2006 22:05:59 +0000 |
parents | 34f96dd5441b |
children | 738c97e101eb |
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## Copyright (C) 2004 David Bateman & Andy Adler ## ## 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. ## ## 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, write to the Free Software ## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301 USA ## -*- texinfo -*- ## @deftypefn {Function File} {} sprandsym (@var{n}, @var{d}) ## @deftypefnx {Function File} {} sprandsym (@var{s}) ## Generate a symmetric random sparse matrix. The size of the matrix will be ## @var{n} 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 in its lower ## triangular part. ## @seealso{sprand, sprandn} ## @end deftypefn function S = sprandsym(n,d) if nargin == 1 [i,j,v,nr,nc] = spfind(tril(n)); S = sparse(i,j,randn(size(v)),nr,nc); S = S + tril(S,-1)'; elseif nargin == 2 m1 = floor(n/2); n1 = m1 + 1; mn1 = m1*n1; k1 = round(d*mn1); idx1=unique(fix(rand(min(k1*1.01,k1+10),1)*mn1))+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 k1 = min(length(idx1),k1); # actual number of entries in S j1 = floor((idx1(1:k1)-1)/m1); i1 = idx1(1:k1) - j1*m1; n2 = ceil(n/2); nn2 = n2*n2; k2 = round(d*nn2); idx2=unique(fix(rand(min(k2*1.01,k1+10),1)*nn2))+1; k2 = min(length(idx2),k2); j2 = floor((idx2(1:k2)-1)/n2); i2 = idx2(1:k2) - j2*n2; if isempty(i1) && isempty(i2) S = sparse(n,n); else S1 = sparse(i1,j1+1,randn(k1,1),m1,n1); S = [tril(S1), sparse(m1,m1); ... sparse(i2,j2+1,randn(k2,1),n2,n2), triu(S1,1)']; S = S + tril(S,-1)'; endif else print_usage (); endif endfunction