sparse.cc

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//
// Copyright (C) 1998-2021 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.
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// 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
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// You should have received a copy of the GNU General Public License
// along with Octave; see the file COPYING.  If not, see
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////////////////////////////////////////////////////////////////////////
 
#if defined (HAVE_CONFIG_H)
#  include "config.h"
#endif
 
#include <cstdlib>
#include <string>
 
#include "variables.h"
#include "utils.h"
#include "pager.h"
#include "defun.h"
#include "errwarn.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, ,
       doc: /* -*- texinfo -*-
@deftypefn {} {} issparse (@var{x})
Return true if @var{x} is a sparse matrix.
@seealso{ismatrix}
@end deftypefn */)
{
  if (args.length () != 1)
    print_usage ();
 
  return ovl (args(0).issparse ());
}
 
/*
%!assert (issparse (sparse (1)), true)
%!assert (issparse (1), false)
%!assert (issparse (sparse (false)), true)
%!assert (issparse (true), false)
%!assert (issparse (sparse (single ([1 2]))), true)
%!assert (issparse (single ([1, 2])), false)
%!assert (issparse (sparse ([1+i, 2]')), true)
%!assert (issparse ([1+i, 2]'), false)
 
%!assert (issparse ([]), false)
%!assert (issparse (sparse([])), true)
%!assert (issparse ("test"), false)
%!assert (issparse (struct ("one", {1})), false)
%!assert (issparse (cell (1)), false)
 
## Test input validation
%!error issparse ()
%!error issparse (1,2)
*/
 
DEFUN (sparse, args, ,
       doc: /* -*- texinfo -*-
@deftypefn  {} {@var{s} =} sparse (@var{a})
@deftypefnx {} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n})
@deftypefnx {} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})
@deftypefnx {} {@var{s} =} sparse (@var{m}, @var{n})
@deftypefnx {} {@var{s} =} sparse (@var{i}, @var{j}, @var{s}, @var{m}, @var{n}, "unique")
@deftypefnx {} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})
Create a sparse matrix from a full matrix, or row, column, value triplets.
 
If @var{a} is a full matrix, convert it to a sparse matrix representation,
removing all zero values in the process.
 
Given the integer index vectors @var{i} and @var{j}, and a 1-by-@code{nnz}
vector of real or complex values @var{sv}, construct the sparse matrix
@code{S(@var{i}(@var{k}),@var{j}(@var{k})) = @var{sv}(@var{k})} with overall
dimensions @var{m} and @var{n}.  If any of @var{sv}, @var{i} or @var{j} are
scalars, they are expanded to have a common size.
 
If @var{m} or @var{n} are not specified their values are derived from the
maximum index in the vectors @var{i} and @var{j} as given by
@code{@var{m} = max (@var{i})}, @code{@var{n} = max (@var{j})}.
 
@strong{Note}: if multiple values are specified with the same @var{i},
@var{j} indices, the corresponding value in @var{s} will be the sum of the
values at the repeated location.  See @code{accumarray} for an example of
how to produce different behavior, such as taking the minimum instead.
 
If the option @qcode{"unique"} is given, and more than one value is
specified at the same @var{i}, @var{j} indices, then the last specified
value will be used.
 
@code{sparse (@var{m}, @var{n})} will create an empty @var{m}x@var{n} sparse
matrix and is equivalent to @code{sparse ([], [], [], @var{m}, @var{n})}
 
The argument @var{nzmax} is ignored but accepted for compatibility with
@sc{matlab}.
 
Example 1 (sum at repeated indices):
 
@example
@group
@var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];
sparse (@var{i}, @var{j}, @var{sv}, 3, 4)
@result{}
   Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])
 
     (1, 1) ->  7
     (2, 2) ->  5
@end group
@end example
 
Example 2 ("unique" option):
 
@example
@group
@var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];
sparse (@var{i}, @var{j}, @var{sv}, 3, 4, "unique")
@result{}
   Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])
 
     (1, 1) ->  4
     (2, 2) ->  5
@end group
@end example
@seealso{full, accumarray, spalloc, spdiags, speye, spones, sprand, sprandn, sprandsym, spconvert, spfun}
@end deftypefn */)
{
  int nargin = args.length ();
 
  if (nargin == 0 || nargin > 6)
    print_usage ();
 
  octave_value retval;
 
  // Temporarily disable sparse_auto_mutate if set (it's obsolete anyway).
  octave::unwind_protect frame;
  frame.protect_var (Vsparse_auto_mutate);
  Vsparse_auto_mutate = false;
 
  if (nargin == 1)
    {
      octave_value arg = args(0);
      if (arg.islogical ())
        retval = arg.sparse_bool_matrix_value ();
      else if (arg.iscomplex ())
        retval = arg.sparse_complex_matrix_value ();
      else if (arg.isnumeric ())
        retval = arg.sparse_matrix_value ();
      else
        err_wrong_type_arg ("sparse", arg);
    }
  else if (nargin == 2)
    {
      octave_idx_type m = 0;
      octave_idx_type n = 0;
 
      octave::get_dimensions (args(0), args(1), "sparse", m, n);
 
      if (m < 0 || n < 0)
        error ("sparse: dimensions must be non-negative");
 
      retval = SparseMatrix (m, n);
    }
  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)
        {
          octave::get_dimensions (args(3), args(4), "sparse", m, n);
 
          if (m < 0 || n < 0)
            error ("sparse: dimensions must be non-negative");
        }
 
      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).islogical ())
            retval = SparseBoolMatrix (args(2).bool_array_value (), i,j,
                                       m, n, summation, nzmax);
          else if (args(2).iscomplex ())
            retval = SparseComplexMatrix (args(2).complex_array_value(),
                                          i, j, m, n, summation, nzmax);
          else if (args(2).isnumeric ())
            retval = SparseMatrix (args(2).array_value (), i, j,
                                   m, n, summation, nzmax);
          else
            err_wrong_type_arg ("sparse", args(2));
        }
      catch (octave::index_exception& e)
        {
          // Rethrow to allow more info to be reported later.
          e.set_pos_if_unset (2, k+1);
          throw;
        }
    }
 
  return retval;
}
 
/*
## Tests for sparse constructor are in test/sparse.tst
%!assert (1);
*/
 
DEFUN (spalloc, args, ,
       doc: /* -*- texinfo -*-
@deftypefn {} {@var{s} =} spalloc (@var{m}, @var{n}, @var{nz})
Create an @var{m}-by-@var{n} sparse matrix with pre-allocated space for at
most @var{nz} nonzero elements.
 
This is useful for building a matrix incrementally by a sequence of indexed
assignments.  Subsequent indexed assignments after @code{spalloc} will reuse
the pre-allocated memory, provided they are of one of the simple forms
 
@itemize
@item @code{@var{s}(I:J) = @var{x}}
 
@item @code{@var{s}(:,I:J) = @var{x}}
 
@item @code{@var{s}(K:L,I:J) = @var{x}}
@end itemize
 
@b{and} that the following conditions are met:
 
@itemize
@item the assignment does not decrease @code{nnz (@var{S})}.
 
@item after the assignment, @code{nnz (@var{S})} does not exceed @var{nz}.
 
@item no index is out of bounds.
@end itemize
 
Partial movement of data may still occur, but in general the assignment will
be more memory and time efficient under these circumstances.  In particular,
it is possible to efficiently build a pre-allocated sparse matrix from a
contiguous block of columns.
 
The amount of pre-allocated memory for a given matrix may be queried using
the function @code{nzmax}.
 
Programming Note: Octave always reserves memory for at least one value,
even if @var{nz} is 0.
@seealso{nzmax, sparse}
@end deftypefn */)
{
  int nargin = args.length ();
 
  if (nargin < 2 || nargin > 3)
    print_usage ();
 
  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)
    error ("spalloc: M, N, and NZ must be non-negative");
 
  return ovl (SparseMatrix (dim_vector (m, n), nz));
}
 
/*
%!assert (issparse (spalloc (1,1)))
%!assert (spalloc (1,1), sparse (1,1))
%!test
%! s = spalloc (1,1,5);
%! assert (s, sparse (1,1));
%! assert (nzmax (s), 5);
%!assert (spalloc (1,2), sparse (1,2))
%!assert (spalloc (1,2,2), sparse (1,2))
%!assert (spalloc (2,1), sparse (2,1))
%!assert (spalloc (2,1,2), sparse (2,1))
 
%!error spalloc ()
%!error spalloc (1)
%!error spalloc (1,2,3,4)
%!error <M, N, and NZ must be non-negative> spalloc (-1, 1, 1)
%!error <M, N, and NZ must be non-negative> spalloc (1, -1, 1)
%!error <M, N, and NZ must be non-negative> spalloc (1, 1, -1)
*/