Mercurial > octave-nkf
view test/test_diag_perm.m @ 8964:f4f4d65faaa0
Implement sparse * diagonal and diagonal * sparse operations, double-prec only.
Date: Sun, 8 Mar 2009 16:28:18 -0400
These preserve sparsity, so eye(5) * sprand (5, 5, .2) is *sparse*
and not dense. This may affect people who use multiplication by
eye() rather than full().
The liboctave routines do *not* check if arguments are scalars in
disguise. There is a type problem with checking at that level. I
suspect we want diag * "sparse scalar" to stay diagonal, but we have
to return a sparse matrix at the liboctave. Rather than worrying
about that in liboctave, we cope with it when binding to Octave and
return the correct higher-level type.
The implementation is in Sparse-diag-op-defs.h rather than
Sparse-op-defs.h to limit recompilation. And the implementations
are templates rather than macros to produce better compiler errors
and debugging information.
author | Jason Riedy <jason@acm.org> |
---|---|
date | Mon, 09 Mar 2009 17:49:13 -0400 |
parents | 43aec7c168eb |
children | 42aff15e059b |
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## Copyright (C) 2009 E. Jason Riedy ## ## 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/>. ######################################## ## Permutation matrices ## row permutation %!test %! n = 5; %! A = rand (n); %! perm = randperm (n); %! Prow = eye (n) (perm, :); %! assert (A(perm, :), Prow * A); %! invperm(perm) = 1:n; %! assert (Prow \ A, A(invperm, :)); %! assert (Prow' * A, A(invperm, :)); ## column permutation %!test %! n = 7; %! A = rand (n); %! perm = randperm (n); %! Pcol = eye (n) (:, perm); %! assert (A(:, perm), A * Pcol); %! invperm(perm) = 1:n; %! assert (A / Pcol, A(:, invperm)); %! assert (A * Pcol.', A(:, invperm)); ## fall back to a matrix in addition %!test %! n = 4; %! P1 = eye (n) (:, randperm (n)); %! A = zeros (n) + P1; %! assert (sum (A), ones (1, n)); %! assert (sum (A, 2), ones (n, 1)); ## preserve dense matrix structure %!test %! n = 7; %! Pc = eye (n) (:, randperm (n)); %! Pr = eye (n) (randperm (n), :); %! assert (typeinfo (rand (n) * Pc), "matrix"); %! assert (typeinfo (Pr * rand (n)), "matrix"); ## permuting a matrix with exceptional values does not introduce new ones. %!test %! n = 5; %! pc = randperm (n); %! Pc = eye (n) (:, pc); %! pr = randperm (n); %! Pr = eye (n) (pr, :); %! A = rand (n); %! A(n, n-2) = NaN; %! A(3, 1) = Inf; %! assert (Pr * A * Pc, A(pr, pc)); ## conversion to sparse form %!test %! n = 7; %! P = eye (n) (:, randperm (n)); %! sP = sparse (P); %! assert (full (sP), full (P)); %! assert (size (find (sP), 1), n); %! [I, J, V] = find (sP); %! assert (all (V == 1)); ######################################## ## Diagonal matrices ## square row scaling %!test %! m = 7; %! n = 11; %! A = rand (m, n); %! scalefact = rand (m, 1); %! Dr = diag (scalefact); %! assert (Dr * A, repmat (scalefact, 1, n) .* A); %! assert (Dr \ A, A ./ repmat (scalefact, 1, n)); %! scalefact(m-1) = Inf; %! Dr(m-1, m-1) = 0; %! assert (Dr \ A, A ./ repmat (scalefact, 1, n)); ## square column scaling %!test %! m = 13; %! n = 11; %! A = rand (m, n); %! scalefact = rand (1, n); %! Dc = diag (scalefact); %! assert (A * Dc, repmat (scalefact, m, 1) .* A); %! assert (A / Dc, A ./ repmat (scalefact, m, 1)); %! scalefact(n-1) = Inf; %! Dc(n-1, n-1) = 0; %! assert (A / Dc, A ./ repmat (scalefact, m, 1)); ## arithmetic %!test %! m = 9; %! n = 7; %! mn = min (m, n); %! d1 = rand (mn, 1) + I () * rand (mn, 1); %! D1 = diag (d1, m, n); %! d2 = rand (mn, 1); %! D2 = diag (d2, m, n); %! D1D2 = D1 + D2; %! assert (typeinfo (D1D2), "complex diagonal matrix"); %! assert (diag (D1D2), d1 + d2); %! D1D2 = D2.' * D1; %! assert (typeinfo (D1D2), "complex diagonal matrix"); %! assert (diag (D1D2), d1 .* d2); ## slicing %!test %! m = 13; %! n = 6; %! mn = min (m, n); %! d = rand (mn, 1); %! D = diag (d, m, n); %! Dslice = D (1:(m-3), 1:(n-2)); %! assert (typeinfo (Dslice), "diagonal matrix"); ## preserve dense matrix structure when scaling %!assert (typeinfo (rand (8) * (3 * eye (8))), "matrix"); %!assert (typeinfo ((3 * eye (8)) * rand (8)), "matrix"); ## preserve sparse matrix structure when scaling %!assert (typeinfo (sprand (8, 8, .5) * (3 * eye (8))), "sparse matrix"); %!assert (typeinfo (sprand (8, 8, .5) * (3 * eye (8))'), "sparse matrix"); %!assert (typeinfo (((3 + 2 * I ()) * eye (8)) * sprand (8, 8, .5)), "sparse complex matrix"); %!assert (typeinfo (((3 + 2 * I ()) * eye (8))' * sprand (8, 8, .5)), "sparse complex matrix"); %!assert (typeinfo (sprand (8, 8, .5) * ((3 + 2 * I ()) * eye (8)).'), "sparse complex matrix"); ## scaling a matrix with exceptional values does not introduce new ones. %!test %! n = 6; %! dr = rand (n, 1); %! Dr = diag (dr); %! dc = rand (1, n); %! Dc = diag (dc); %! A = rand (n); %! A(n, n-2) = NaN; %! A(4, 1) = Inf; %! assert (Dr * A * Dc, A .* kron (dr, dc), eps);