Mercurial > octave-nkf
view libinterp/corefcn/sparse.cc @ 18518:0bdecd41b2dd stable
correctly size fread result (bug #41648)
* oct-stream.cc (octave_base_stream::read): When reading to EOF, don't
add extra column to the result matrix if the number of elements found
is an exact multiple of the number of rows requested.
Avoid mixed signed/unsigned comparisons.
* io.tst: New tests.
author | John W. Eaton <jwe@octave.org> |
---|---|
date | Sat, 22 Feb 2014 13:06:18 -0500 |
parents | 0821a51a9e1b |
children | 3afae1432cd6 2dcc4398950d |
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/* Copyright (C) 2004-2013 David Bateman Copyright (C) 1998-2004 Andy Adler Copyright (C) 2010 VZLU Prague 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/>. */ #ifdef HAVE_CONFIG_H #include <config.h> #endif #include <cstdlib> #include <string> #include "variables.h" #include "utils.h" #include "pager.h" #include "defun.h" #include "gripes.h" #include "quit.h" #include "unwind-prot.h" #include "ov-re-sparse.h" #include "ov-cx-sparse.h" #include "ov-bool-sparse.h" DEFUN (issparse, args, , "-*- texinfo -*-\n\ @deftypefn {Built-in Function} {} issparse (@var{x})\n\ Return true if @var{x} is a sparse matrix.\n\ @seealso{ismatrix}\n\ @end deftypefn") { if (args.length () != 1) { print_usage (); return octave_value (); } else return octave_value (args(0).is_sparse_type ()); } DEFUN (sparse, args, , "-*- texinfo -*-\n\ @deftypefn {Built-in Function} {@var{s} =} sparse (@var{a})\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{s}, @var{m}, @var{n}, \"unique\")\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{m}, @var{n})\n\ Create a sparse matrix from the full matrix or row, column, value triplets.\n\ If @var{a} is a full matrix, convert it to a sparse matrix representation,\n\ removing all zero values in the process.\n\ \n\ Given the integer index vectors @var{i} and @var{j}, a 1-by-@code{nnz} vector\n\ of real of complex values @var{sv}, overall dimensions @var{m} and @var{n}\n\ of the sparse matrix. The argument @code{nzmax} is ignored but accepted for\n\ compatibility with @sc{matlab}. If @var{m} or @var{n} are not specified\n\ their values are derived from the maximum index in the vectors @var{i} and\n\ @var{j} as given by @code{@var{m} = max (@var{i})},\n\ @code{@var{n} = max (@var{j})}.\n\ \n\ @strong{Note}: if multiple values are specified with the same\n\ @var{i}, @var{j} indices, the corresponding values in @var{s} will\n\ be added. See @code{accumarray} for an example of how to produce different\n\ behavior, such as taking the minimum instead.\n\ \n\ The following are all equivalent:\n\ \n\ @example\n\ @group\n\ s = sparse (i, j, s, m, n)\n\ s = sparse (i, j, s, m, n, \"summation\")\n\ s = sparse (i, j, s, m, n, \"sum\")\n\ @end group\n\ @end example\n\ \n\ Given the option @qcode{\"unique\"}, if more than two values are specified\n\ for the same @var{i}, @var{j} indices, the last specified value will be\n\ used.\n\ \n\ @code{sparse (@var{m}, @var{n})} is equivalent to\n\ @code{sparse ([], [], [], @var{m}, @var{n}, 0)}\n\ \n\ If any of @var{sv}, @var{i} or @var{j} are scalars, they are expanded\n\ to have a common size.\n\ @seealso{full, accumarray}\n\ @end deftypefn") { octave_value retval; int nargin = args.length (); // Temporarily disable sparse_auto_mutate if set (it's obsolete anyway). unwind_protect frame; frame.protect_var (Vsparse_auto_mutate); Vsparse_auto_mutate = false; if (nargin == 1) { octave_value arg = args (0); if (arg.is_bool_type ()) retval = arg.sparse_bool_matrix_value (); else if (arg.is_complex_type ()) retval = arg.sparse_complex_matrix_value (); else if (arg.is_numeric_type ()) retval = arg.sparse_matrix_value (); else gripe_wrong_type_arg ("sparse", arg); } else if (nargin == 2) { octave_idx_type m = 0; octave_idx_type n = 0; get_dimensions (args(0), args(1), "sparse", m, n); if (! error_state) { if (m >= 0 && n >= 0) retval = SparseMatrix (m, n); else error ("sparse: dimensions must be non-negative"); } } else if (nargin >= 3) { bool summation = true; if (nargin > 3 && args(nargin-1).is_string ()) { std::string opt = args(nargin-1).string_value (); if (opt == "unique") summation = false; else if (opt == "sum" || opt == "summation") summation = true; else error ("sparse: invalid option: %s", opt.c_str ()); nargin -= 1; } if (! error_state) { octave_idx_type m = -1, n = -1, nzmax = -1; if (nargin == 6) { nzmax = args(5).idx_type_value (); nargin --; } if (nargin == 5) { get_dimensions (args(3), args(4), "sparse", m, n); if (! error_state && (m < 0 || n < 0)) error ("sparse: dimensions must be non-negative"); } else if (nargin != 3) print_usage (); if (! error_state) { idx_vector i = args(0).index_vector (); idx_vector j = args(1).index_vector (); if (args(2).is_bool_type ()) retval = SparseBoolMatrix (args(2).bool_array_value (), i, j, m, n, summation, nzmax); else if (args(2).is_complex_type ()) retval = SparseComplexMatrix (args(2).complex_array_value (), i, j, m, n, summation, nzmax); else if (args(2).is_numeric_type ()) retval = SparseMatrix (args(2).array_value (), i, j, m, n, summation, nzmax); else gripe_wrong_type_arg ("sparse", args(2)); } } } return retval; } DEFUN (spalloc, args, , "-*- texinfo -*-\n\ @deftypefn {Built-in Function} {@var{s} =} spalloc (@var{m}, @var{n}, @var{nz})\n\ Create an @var{m}-by-@var{n} sparse matrix with pre-allocated space for at\n\ most @var{nz} nonzero elements. This is useful for building the matrix\n\ incrementally by a sequence of indexed assignments. Subsequent indexed\n\ assignments will reuse the pre-allocated memory, provided they are of one of\n\ the simple forms\n\ \n\ @itemize\n\ @item @code{@var{s}(I:J) = @var{x}}\n\ \n\ @item @code{@var{s}(:,I:J) = @var{x}}\n\ \n\ @item @code{@var{s}(K:L,I:J) = @var{x}}\n\ @end itemize\n\ \n\ @b{and} that the following conditions are met:\n\ \n\ @itemize\n\ @item the assignment does not decrease nnz (@var{S}).\n\ \n\ @item after the assignment, nnz (@var{S}) does not exceed @var{nz}.\n\ \n\ @item no index is out of bounds.\n\ @end itemize\n\ \n\ Partial movement of data may still occur, but in general the assignment will\n\ be more memory and time-efficient under these circumstances. In particular,\n\ it is possible to efficiently build a pre-allocated sparse matrix from\n\ contiguous block of columns.\n\ \n\ The amount of pre-allocated memory for a given matrix may be queried using\n\ the function @code{nzmax}.\n\ @seealso{nzmax, sparse}\n\ @end deftypefn") { octave_value retval; int nargin = args.length (); if (nargin == 2 || nargin == 3) { octave_idx_type m = args(0).idx_type_value (); octave_idx_type n = args(1).idx_type_value (); octave_idx_type nz = 0; if (nargin == 3) nz = args(2).idx_type_value (); if (error_state) ; else if (m >= 0 && n >= 0 && nz >= 0) retval = SparseMatrix (dim_vector (m, n), nz); else error ("spalloc: M,N,NZ must be non-negative"); } else print_usage (); return retval; }