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########################################################################
##
## Copyright (C) 2004-2024 The Octave Project Developers
##
## See the file COPYRIGHT.md in the top-level directory of this
## distribution or <https://octave.org/copyright/>.
##
## 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
## <https://www.gnu.org/licenses/>.
##
########################################################################
## -*- texinfo -*-
## @deftypefn {} {@var{S} =} sprandsym (@var{n}, @var{d})
## @deftypefnx {} {@var{S} =} sprandsym (@var{s})
## Generate a symmetric random sparse matrix.
##
## The size of the matrix will be @var{n}x@var{n}, with a density of values
## given by @var{d}. @var{d} must be between 0 and 1 inclusive. Values will
## be normally distributed with a mean of zero and a variance of 1.
##
## If called with a single matrix argument, a random sparse matrix is generated
## wherever the matrix @var{s} is nonzero in its lower triangular part.
## @seealso{sprand, sprandn, spones, sparse}
## @end deftypefn
function S = sprandsym (n, d)
if (nargin < 1)
print_usage ();
endif
if (nargin == 1)
[i, j] = find (tril (n));
[nr, nc] = size (n);
S = sparse (i, j, randn (size (i)), nr, nc);
S += tril (S, -1)';
return;
endif
if (!(isscalar (n) && n == fix (n) && n >= 0))
error ("sprandsym: N must be a non-negative integer 0");
endif
if (n == 0)
S = sparse (n, n);
return;
endif
if (d < 0 || d > 1)
error ("sprandsym: density D must be between 0 and 1");
endif
## Actual number of nonzero entries
k = round (n^2*d);
## Diagonal nonzero entries, same parity as k
r = pick_rand_diag (n, k);
## Off diagonal nonzero entries
m = (k - r)/2;
ondiag = randperm (n, r);
offdiag = randperm (n*(n - 1)/2, m);
## Row index
i = lookup (cumsum (0:n), offdiag - 1) + 1;
## Column index
j = offdiag - (i - 1).*(i - 2)/2;
diagvals = randn (1, r);
offdiagvals = randn (1, m);
S = sparse ([ondiag, i, j], [ondiag, j, i],
[diagvals, offdiagvals, offdiagvals], n, n);
endfunction
function r = pick_rand_diag (n, k)
## Pick a random number R of entries for the diagonal of a sparse NxN
## symmetric square matrix with exactly K nonzero entries, ensuring
## that this R is chosen uniformly over all such matrices.
##
## Let D be the number of diagonal entries and M the number of
## off-diagonal entries. Then K = D + 2*M. Let A = N*(N-1)/2 be the
## number of available entries in the upper triangle of the matrix.
## Then, by a simple counting argument, there is a total of
##
## T = nchoosek (N, D) * nchoosek (A, M)
##
## symmetric NxN matrices with a total of K nonzero entries and D on
## the diagonal. Letting D range from mod (K,2) through min (N,K), and
## dividing by this sum, we obtain the probability P for D to be each
## of those values.
##
## However, we cannot use this form for computation, as the binomial
## coefficients become unmanageably large. Instead, we use the
## successive quotients Q(i) = T(i+1)/T(i), which we easily compute to
## be
##
## (N - D)*(N - D - 1)*M
## Q = -------------------------------
## (D + 2)*(D + 1)*(A - M + 1)
##
## Then, after prepending 1, the cumprod of these quotients is
##
## C = [ T(1)/T(1), T(2)/T(1), T(3)/T(1), ..., T(N)/T(1) ]
##
## Their sum is thus S = sum (T)/T(1), and then C(i)/S is the desired
## probability P(i) for i=1:N. The cumsum will finally give the
## distribution function for computing the random number of entries on
## the diagonal R.
##
## Thanks to Zsbán Ambrus <ambrus@math.bme.hu> for most of the ideas
## of the implementation here, especially how to do the computation
## numerically to avoid overflow.
## Degenerate case
if (k == 1)
r = 1;
return;
endif
## Compute the stuff described above
a = n*(n - 1)/2;
d = [mod(k,2):2:min(n,k)-2];
m = (k - d)/2;
q = (n - d).*(n - d - 1).*m ./ (d + 2)./(d + 1)./(a - m + 1);
## Slight modification from discussion above: pivot around the max in
## order to avoid overflow (underflow is fine, just means effectively
## zero probabilities).
[~, midx] = max (cumsum (log (q)));
midx += 1;
lc = fliplr (cumprod (1./q(midx-1:-1:1)));
rc = cumprod (q(midx:end));
## Now c = t(i)/t(midx), so c > 1 == [].
c = [lc, 1, rc];
s = sum (c);
p = c/s;
## Add final d
d(end+1) = d(end) + 2;
## Pick a random r using this distribution
r = d(sum (cumsum (p) < rand) + 1);
endfunction
%!test
%! s = sprandsym (10, 0.1);
%! assert (issparse (s));
%! assert (issymmetric (s));
%! assert (size (s), [10, 10]);
%! assert (nnz (s) / numel (s), 0.1, .01);
## Test 1-input calling form
%!test
%! s = sprandsym (sparse ([1 2 3], [3 2 3], [2 2 2]));
%! [i, j] = find (s);
%! assert (sort (i), [2 3]');
%! assert (sort (j), [2 3]');
## Test empty array creation
%!assert (size (sprandsym (0, 0.5)), [0, 0])
## Test input validation
%!error <Invalid call> sprandsym ()
%!error sprandsym (ones (3), 0.5)
%!error sprandsym (3.5, 0.5)
%!error sprandsym (-1, 0.5)
%!error sprandsym (3, -1)
%!error sprandsym (3, 2)
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