Mercurial > octave
view libinterp/corefcn/svd.cc @ 23581:c3075ae020e1
maint: Deprecate is_complex_type and replace with iscomplex.
* ov.h (is_complex_type): Use OCTAVE_DEPRECATED macro around function.
* ov.h (iscomplex): New function.
* __ichol__.cc, __ilu__.cc, balance.cc, bsxfun.cc, cellfun.cc, conv2.cc,
daspk.cc, dasrt.cc, dassl.cc, data.cc, det.cc, dot.cc, fft.cc, fft2.cc,
fftn.cc, filter.cc, find.cc, givens.cc, graphics.cc, gsvd.cc, hess.cc,
hex2num.cc, inv.cc, kron.cc, lookup.cc, ls-mat-ascii.cc, ls-mat4.cc,
ls-mat5.cc, lsode.cc, lu.cc, matrix_type.cc, mex.cc, mgorth.cc, ordschur.cc,
pinv.cc, psi.cc, quad.cc, qz.cc, rcond.cc, schur.cc, sparse-xpow.cc, sparse.cc,
sqrtm.cc, svd.cc, sylvester.cc, symtab.cc, typecast.cc, variables.cc, xnorm.cc,
__eigs__.cc, amd.cc, ccolamd.cc, chol.cc, colamd.cc, qr.cc, symbfact.cc,
ov-base.h, ov-complex.h, ov-cx-diag.h, ov-cx-mat.h, ov-cx-sparse.h,
ov-flt-complex.h, ov-flt-cx-diag.h, ov-flt-cx-mat.h, jit-typeinfo.cc,
pt-tm-const.cc: Replace instances of is_complex_type with iscomplex.
author | Rik <rik@octave.org> |
---|---|
date | Mon, 12 Jun 2017 21:18:23 -0700 |
parents | 1b4f4ec53b4a |
children | 0cc2011d800e |
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/* Copyright (C) 1996-2017 John W. Eaton 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/>. */ #if defined (HAVE_CONFIG_H) # include "config.h" #endif #include "svd.h" #include "defun.h" #include "error.h" #include "errwarn.h" #include "ovl.h" #include "pr-output.h" #include "utils.h" #include "variables.h" static std::string Vsvd_driver = "gesdd"; template <typename T> static typename octave::math::svd<T>::Type svd_type (int nargin, int nargout, const octave_value_list & args, const T & A) { if (nargout == 0 || nargout == 1) return octave::math::svd<T>::Type::sigma_only; else if (nargin == 1) return octave::math::svd<T>::Type::std; else if (! args(1).is_real_scalar ()) return octave::math::svd<T>::Type::economy; else { if (A.rows () > A.columns ()) return octave::math::svd<T>::Type::economy; else return octave::math::svd<T>::Type::std; } } template <typename T> static typename octave::math::svd<T>::Driver svd_driver (void) { return (Vsvd_driver == "gesvd" ? octave::math::svd<T>::Driver::GESVD : octave::math::svd<T>::Driver::GESDD); } DEFUN (svd, args, nargout, classes: double single doc: /* -*- texinfo -*- @deftypefn {} {@var{s} =} svd (@var{A}) @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}) @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}, "econ") @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}, 0) @cindex singular value decomposition Compute the singular value decomposition of @var{A} @tex $$ A = U S V^{\dagger} $$ @end tex @ifnottex @example A = U*S*V' @end example @end ifnottex The function @code{svd} normally returns only the vector of singular values. When called with three return values, it computes @tex $U$, $S$, and $V$. @end tex @ifnottex @var{U}, @var{S}, and @var{V}. @end ifnottex For example, @example svd (hilb (3)) @end example @noindent returns @example @group ans = 1.4083189 0.1223271 0.0026873 @end group @end example @noindent and @example [u, s, v] = svd (hilb (3)) @end example @noindent returns @example @group u = -0.82704 0.54745 0.12766 -0.45986 -0.52829 -0.71375 -0.32330 -0.64901 0.68867 s = 1.40832 0.00000 0.00000 0.00000 0.12233 0.00000 0.00000 0.00000 0.00269 v = -0.82704 0.54745 0.12766 -0.45986 -0.52829 -0.71375 -0.32330 -0.64901 0.68867 @end group @end example When given a second argument that is not 0, @code{svd} returns an economy-sized decomposition, eliminating the unnecessary rows or columns of @var{U} or @var{V}. If the second argument is exactly 0, then the choice of decomposition is based on the matrix @var{A}. If @var{A} has more rows than columns then an economy-sized decomposition is returned, otherwise a regular decomposition is calculated. Algorithm Notes: When calculating the full decomposition (left and right singular matrices in addition to singular values) there is a choice of two routines in @sc{lapack}. The default routine used by Octave is @code{gesdd} which is 5X faster than the alternative @code{gesvd}, but may use more memory and may be less accurate for some matrices. See the documentation for @code{svd_driver} for more information. @seealso{svd_driver, svds, eig, lu, chol, hess, qr, qz} @end deftypefn */) { int nargin = args.length (); if (nargin < 1 || nargin > 2 || nargout == 2 || nargout > 3) print_usage (); octave_value arg = args(0); if (arg.ndims () != 2) error ("svd: A must be a 2-D matrix"); octave_value_list retval; bool isfloat = arg.is_single_type (); if (isfloat) { if (arg.is_real_type ()) { FloatMatrix tmp = arg.float_matrix_value (); if (tmp.any_element_is_inf_or_nan ()) error ("svd: cannot take SVD of matrix containing Inf or NaN values"); octave::math::svd<FloatMatrix> result (tmp, svd_type<FloatMatrix> (nargin, nargout, args, tmp), svd_driver<FloatMatrix> ()); FloatDiagMatrix sigma = result.singular_values (); if (nargout == 0 || nargout == 1) retval(0) = sigma.extract_diag (); else retval = ovl (result.left_singular_matrix (), sigma, result.right_singular_matrix ()); } else if (arg.iscomplex ()) { FloatComplexMatrix ctmp = arg.float_complex_matrix_value (); if (ctmp.any_element_is_inf_or_nan ()) error ("svd: cannot take SVD of matrix containing Inf or NaN values"); octave::math::svd<FloatComplexMatrix> result (ctmp, svd_type<FloatComplexMatrix> (nargin, nargout, args, ctmp), svd_driver<FloatComplexMatrix> ()); FloatDiagMatrix sigma = result.singular_values (); if (nargout == 0 || nargout == 1) retval(0) = sigma.extract_diag (); else retval = ovl (result.left_singular_matrix (), sigma, result.right_singular_matrix ()); } } else { if (arg.is_real_type ()) { Matrix tmp = arg.matrix_value (); if (tmp.any_element_is_inf_or_nan ()) error ("svd: cannot take SVD of matrix containing Inf or NaN values"); octave::math::svd<Matrix> result (tmp, svd_type<Matrix> (nargin, nargout, args, tmp), svd_driver<Matrix> ()); DiagMatrix sigma = result.singular_values (); if (nargout == 0 || nargout == 1) retval(0) = sigma.extract_diag (); else retval = ovl (result.left_singular_matrix (), sigma, result.right_singular_matrix ()); } else if (arg.iscomplex ()) { ComplexMatrix ctmp = arg.complex_matrix_value (); if (ctmp.any_element_is_inf_or_nan ()) error ("svd: cannot take SVD of matrix containing Inf or NaN values"); octave::math::svd<ComplexMatrix> result (ctmp, svd_type<ComplexMatrix> (nargin, nargout, args, ctmp), svd_driver<ComplexMatrix> ()); DiagMatrix sigma = result.singular_values (); if (nargout == 0 || nargout == 1) retval(0) = sigma.extract_diag (); else retval = ovl (result.left_singular_matrix (), sigma, result.right_singular_matrix ()); } else err_wrong_type_arg ("svd", arg); } return retval; } /* %!assert (svd ([1, 2; 2, 1]), [3; 1], sqrt (eps)) %!test a = [1, 2; 3, 4] + [5, 6; 7, 8]*i; [u,s,v] = svd (a); assert (a, u * s * v', 128 * eps); %!test %! [u, s, v] = svd ([1, 2; 2, 1]); %! x = 1 / sqrt (2); %! assert (u, [-x, -x; -x, x], sqrt (eps)); %! assert (s, [3, 0; 0, 1], sqrt (eps)); %! assert (v, [-x, x; -x, -x], sqrt (eps)); %!test %! a = [1, 2, 3; 4, 5, 6]; %! [u, s, v] = svd (a); %! assert (u * s * v', a, sqrt (eps)); %!test %! a = [1, 2; 3, 4; 5, 6]; %! [u, s, v] = svd (a); %! assert (u * s * v', a, sqrt (eps)); %!test %! a = [1, 2, 3; 4, 5, 6]; %! [u, s, v] = svd (a, 1); %! assert (u * s * v', a, sqrt (eps)); %!test %! a = [1, 2; 3, 4; 5, 6]; %! [u, s, v] = svd (a, 1); %! assert (u * s * v', a, sqrt (eps)); %!assert (svd (single ([1, 2; 2, 1])), single ([3; 1]), sqrt (eps ("single"))) %!test %! [u, s, v] = svd (single ([1, 2; 2, 1])); %! x = single (1 / sqrt (2)); %! assert (u, [-x, -x; -x, x], sqrt (eps ("single"))); %! assert (s, single ([3, 0; 0, 1]), sqrt (eps ("single"))); %! assert (v, [-x, x; -x, -x], sqrt (eps ("single"))); %!test %! a = single ([1, 2, 3; 4, 5, 6]); %! [u, s, v] = svd (a); %! assert (u * s * v', a, sqrt (eps ("single"))); %!test %! a = single ([1, 2; 3, 4; 5, 6]); %! [u, s, v] = svd (a); %! assert (u * s * v', a, sqrt (eps ("single"))); %!test %! a = single ([1, 2, 3; 4, 5, 6]); %! [u, s, v] = svd (a, 1); %! assert (u * s * v', a, sqrt (eps ("single"))); %!test %! a = single ([1, 2; 3, 4; 5, 6]); %! [u, s, v] = svd (a, 1); %! assert (u * s * v', a, sqrt (eps ("single"))); %!test %! a = zeros (0, 5); %! [u, s, v] = svd (a); %! assert (size (u), [0, 0]); %! assert (size (s), [0, 5]); %! assert (size (v), [5, 5]); %!test %! a = zeros (5, 0); %! [u, s, v] = svd (a, 1); %! assert (size (u), [5, 0]); %! assert (size (s), [0, 0]); %! assert (size (v), [0, 0]); %!test <*49309> %! [~,~,v] = svd ([1, 1, 1], 0); %! assert (size (v), [3 3]); %! [~,~,v] = svd ([1, 1, 1], "econ"); %! assert (size (v), [3 1]); %!error svd () %!error svd ([1, 2; 4, 5], 2, 3) %!error [u, v] = svd ([1, 2; 3, 4]) */ DEFUN (svd_driver, args, nargout, doc: /* -*- texinfo -*- @deftypefn {} {@var{val} =} svd_driver () @deftypefnx {} {@var{old_val} =} svd_driver (@var{new_val}) @deftypefnx {} {} svd_driver (@var{new_val}, "local") Query or set the underlying @sc{lapack} driver used by @code{svd}. Currently recognized values are @qcode{"gesdd"} and @qcode{"gesvd"}. The default is @qcode{"gesdd"}. When called from inside a function with the @qcode{"local"} option, the variable is changed locally for the function and any subroutines it calls. The original variable value is restored when exiting the function. Algorithm Notes: The @sc{lapack} library provides two routines for calculating the full singular value decomposition (left and right singular matrices as well as singular values). When calculating just the singular values the following discussion is not relevant. The default routine use by Octave is the newer @code{gesdd} which is based on a Divide-and-Conquer algorithm that is 5X faster than the alternative @code{gesvd}, which is based on QR factorization. However, the new algorithm can use significantly more memory. For an @nospell{MxN} input matrix the memory usage is of order O(min(M,N) ^ 2), whereas the alternative is of order O(max(M,N)). In general, modern computers have abundant memory so Octave has chosen to prioritize speed. In addition, there have been instances in the past where some input matrices were not accurately decomposed by @code{gesdd}. This appears to have been resolved with modern versions of @sc{lapack}. However, if certainty is required the accuracy of the decomposition can always be tested after the fact with @example @group [@var{u}, @var{s}, @var{v}] = svd (@var{x}); norm (@var{x} - @var{u}*@var{s}*@var{v'}, "fro") @end group @end example @seealso{svd} @end deftypefn */) { static const char *driver_names[] = { "gesvd", "gesdd", 0 }; return SET_INTERNAL_VARIABLE_CHOICES (svd_driver, driver_names); } /* %!test %! A = [1+1i, 1-1i, 0; 0, 2, 0; 1i, 1i, 1+2i]; %! old_driver = svd_driver ("gesvd"); %! [U1, S1, V1] = svd (A); %! svd_driver ("gesdd"); %! [U2, S2, V2] = svd (A); %! svd_driver (old_driver); %! assert (U1, U2, 5*eps); %! assert (S1, S2, 5*eps); %! assert (V1, V2, 5*eps); */