Mercurial > octave-nkf
view libinterp/corefcn/sparse.cc @ 20620:e5f36a7854a5
Remove fuzzy matching from odeset/odeget.
* levenshtein.cc: Deleted file.
* libinterp/corefcn/module.mk: Remove levenshtein.cc from build system.
* fuzzy_compare.m: Deleted file.
* scripts/ode/module.mk: Remove fuzzy_compare.m from build system
* odeget.m: Reword docstring. Use a persistent cellstr variable to keep track
of all options. Replace fuzzy_compare() calls with combination of strcmpi and
strncmpi. Report errors relative to function odeget rather than OdePkg.
Rewrite and extend BIST tests. Add input validation BIST tests.
* odeset.m: Reword docstring. Use a persistent cellstr variable to keep track
of all options. Replace fuzzy_compare() calls with combination of strcmpi and
strncmpi. Report errors relative to function odeset rather than OdePkg.
Use more meaningful variables names and create intermediate variables with
logical names to help make code readable. Remove interactive input when
multiple property names match and just issue an error. Rewrite BIST tests.
* ode_struct_value_check.m: Remove input checking for private function which
must always be invoked correctly by caller. Use intermediate variables opt and
val to make the code more understandable. Consolidate checks on values into
single if statements. Use 'val == fix (val)' to check for integer.
* __unimplemented__.m: Removed odeset, odeget, ode45 from list.
author | Rik <rik@octave.org> |
---|---|
date | Fri, 09 Oct 2015 12:03:23 -0700 |
parents | f90c8372b7ba |
children |
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/* Copyright (C) 2004-2015 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})\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})\n\ @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{m}, @var{n})\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{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})\n\ Create a sparse matrix from a full matrix, or row, column, value triplets.\n\ \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}, and a 1-by-@code{nnz}\n\ vector of real or complex values @var{sv}, construct the sparse matrix\n\ @code{S(@var{i}(@var{k}),@var{j}(@var{k})) = @var{sv}(@var{k})} with overall\n\ dimensions @var{m} and @var{n}. If any of @var{sv}, @var{i} or @var{j} are\n\ scalars, they are expanded to have a common size.\n\ \n\ If @var{m} or @var{n} are not specified their values are derived from the\n\ maximum index in the vectors @var{i} and @var{j} as given by\n\ @code{@var{m} = max (@var{i})}, @code{@var{n} = max (@var{j})}.\n\ \n\ @strong{Note}: if multiple values are specified with the same @var{i},\n\ @var{j} indices, the corresponding value in @var{s} will be the sum of the\n\ values at the repeated location. See @code{accumarray} for an example of how\n\ to produce different behavior, such as taking the minimum instead.\n\ \n\ If the option @qcode{\"unique\"} is given, and more than one value is\n\ specified at the same @var{i}, @var{j} indices, then the last specified\n\ value will be used.\n\ \n\ @code{sparse (@var{m}, @var{n})} will create an empty @var{m}x@var{n} sparse\n\ matrix and is equivalent to @code{sparse ([], [], [], @var{m}, @var{n})}\n\ \n\ The argument @code{nzmax} is ignored but accepted for compatibility with\n\ @sc{matlab}.\n\ \n\ Example 1 (sum at repeated indices):\n\ \n\ @example\n\ @group\n\ @var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];\n\ sparse (@var{i}, @var{j}, @var{sv}, 3, 4)\n\ @result{}\n\ Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])\n\ \n\ (1, 1) -> 7\n\ (2, 2) -> 5\n\ @end group\n\ @end example\n\ \n\ Example 2 (\"unique\" option):\n\ \n\ @example\n\ @group\n\ @var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];\n\ sparse (@var{i}, @var{j}, @var{sv}, 3, 4, \"unique\")\n\ @result{}\n\ Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])\n\ \n\ (1, 1) -> 4\n\ (2, 2) -> 5\n\ @end group\n\ @end example\n\ @seealso{full, accumarray, spalloc, spdiags, speye, spones, sprand, sprandn, sprandsym, spconvert, spfun}\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 (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; } octave_idx_type m, n, nzmax; m = n = 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 (); int k = 0; // index we're checking when index_vector throws try { idx_vector i = args(0).index_vector (); k = 1; 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)); } catch (index_exception& e) { // Rethrow to allow more info to be reported later. e.set_pos_if_unset (2, k+1); throw; } } 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.\n\ \n\ This is useful for building a matrix incrementally by a sequence of indexed\n\ assignments. Subsequent indexed assignments after @code{spalloc} will reuse\n\ the pre-allocated memory, provided they are of one of 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 a\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 (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; }