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annotate libinterp/corefcn/sparse.cc @ 20830:b65888ec820e draft default tip gccjit

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dmalcom gcc jit import
author Stefan Mahr <dac922@gmx.de>
date Fri, 27 Feb 2015 16:59:36 +0100
parents f90c8372b7ba
children
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1 /*
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2
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3 Copyright (C) 2004-2015 David Bateman
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4 Copyright (C) 1998-2004 Andy Adler
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5 Copyright (C) 2010 VZLU Prague
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6
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7 This file is part of Octave.
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8
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9 Octave is free software; you can redistribute it and/or modify it
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10 under the terms of the GNU General Public License as published by the
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11 Free Software Foundation; either version 3 of the License, or (at your
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12 option) any later version.
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13
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14 Octave is distributed in the hope that it will be useful, but WITHOUT
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15 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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16 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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17 for more details.
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18
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19 You should have received a copy of the GNU General Public License
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20 along with Octave; see the file COPYING. If not, see
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21 <http://www.gnu.org/licenses/>.
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22
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23 */
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24
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25 #ifdef HAVE_CONFIG_H
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26 #include <config.h>
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27 #endif
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28
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29 #include <cstdlib>
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30 #include <string>
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31
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32 #include "variables.h"
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33 #include "utils.h"
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34 #include "pager.h"
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35 #include "defun.h"
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36 #include "gripes.h"
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37 #include "quit.h"
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38 #include "unwind-prot.h"
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39
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40 #include "ov-re-sparse.h"
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41 #include "ov-cx-sparse.h"
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42 #include "ov-bool-sparse.h"
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43
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44 DEFUN (issparse, args, ,
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45 "-*- texinfo -*-\n\
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46 @deftypefn {Built-in Function} {} issparse (@var{x})\n\
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47 Return true if @var{x} is a sparse matrix.\n\
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48 @seealso{ismatrix}\n\
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49 @end deftypefn")
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50 {
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51 if (args.length () != 1)
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52 {
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53 print_usage ();
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54 return octave_value ();
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55 }
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56 else
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57 return octave_value (args(0).is_sparse_type ());
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58 }
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59
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60 DEFUN (sparse, args, ,
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61 "-*- texinfo -*-\n\
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62 @deftypefn {Built-in Function} {@var{s} =} sparse (@var{a})\n\
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63 @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n})\n\
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64 @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})\n\
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65 @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{m}, @var{n})\n\
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66 @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{s}, @var{m}, @var{n}, \"unique\")\n\
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67 @deftypefnx {Built-in Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})\n\
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68 Create a sparse matrix from a full matrix, or row, column, value triplets.\n\
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69 \n\
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70 If @var{a} is a full matrix, convert it to a sparse matrix representation,\n\
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71 removing all zero values in the process.\n\
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72 \n\
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73 Given the integer index vectors @var{i} and @var{j}, and a 1-by-@code{nnz}\n\
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74 vector of real or complex values @var{sv}, construct the sparse matrix\n\
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75 @code{S(@var{i}(@var{k}),@var{j}(@var{k})) = @var{sv}(@var{k})} with overall\n\
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76 dimensions @var{m} and @var{n}. If any of @var{sv}, @var{i} or @var{j} are\n\
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77 scalars, they are expanded to have a common size.\n\
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78 \n\
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79 If @var{m} or @var{n} are not specified their values are derived from the\n\
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80 maximum index in the vectors @var{i} and @var{j} as given by\n\
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81 @code{@var{m} = max (@var{i})}, @code{@var{n} = max (@var{j})}.\n\
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82 \n\
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83 @strong{Note}: if multiple values are specified with the same @var{i},\n\
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84 @var{j} indices, the corresponding value in @var{s} will be the sum of the\n\
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85 values at the repeated location. See @code{accumarray} for an example of how\n\
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86 to produce different behavior, such as taking the minimum instead.\n\
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87 \n\
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88 If the option @qcode{\"unique\"} is given, and more than one value is\n\
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89 specified at the same @var{i}, @var{j} indices, then the last specified\n\
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90 value will be used.\n\
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91 \n\
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92 @code{sparse (@var{m}, @var{n})} will create an empty @var{m}x@var{n} sparse\n\
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93 matrix and is equivalent to @code{sparse ([], [], [], @var{m}, @var{n})}\n\
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94 \n\
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95 The argument @code{nzmax} is ignored but accepted for compatibility with\n\
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96 @sc{matlab}.\n\
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97 \n\
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98 Example 1 (sum at repeated indices):\n\
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99 \n\
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100 @example\n\
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101 @group\n\
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102 @var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];\n\
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103 sparse (@var{i}, @var{j}, @var{sv}, 3, 4)\n\
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104 @result{}\n\
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105 Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])\n\
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106 \n\
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107 (1, 1) -> 7\n\
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108 (2, 2) -> 5\n\
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109 @end group\n\
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110 @end example\n\
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111 \n\
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112 Example 2 (\"unique\" option):\n\
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113 \n\
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114 @example\n\
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115 @group\n\
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116 @var{i} = [1 1 2]; @var{j} = [1 1 2]; @var{sv} = [3 4 5];\n\
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117 sparse (@var{i}, @var{j}, @var{sv}, 3, 4, \"unique\")\n\
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118 @result{}\n\
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119 Compressed Column Sparse (rows = 3, cols = 4, nnz = 2 [17%])\n\
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120 \n\
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121 (1, 1) -> 4\n\
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122 (2, 2) -> 5\n\
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123 @end group\n\
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124 @end example\n\
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125 @seealso{full, accumarray, spalloc, spdiags, speye, spones, sprand, sprandn, sprandsym, spconvert, spfun}\n\
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126 @end deftypefn")
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127 {
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128 octave_value retval;
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129 int nargin = args.length ();
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130
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131 // Temporarily disable sparse_auto_mutate if set (it's obsolete anyway).
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132 unwind_protect frame;
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133 frame.protect_var (Vsparse_auto_mutate);
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134 Vsparse_auto_mutate = false;
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135
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136 if (nargin == 1)
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137 {
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138 octave_value arg = args(0);
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139 if (arg.is_bool_type ())
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140 retval = arg.sparse_bool_matrix_value ();
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141 else if (arg.is_complex_type ())
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142 retval = arg.sparse_complex_matrix_value ();
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143 else if (arg.is_numeric_type ())
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144 retval = arg.sparse_matrix_value ();
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145 else
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146 gripe_wrong_type_arg ("sparse", arg);
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147 }
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148 else if (nargin == 2)
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149 {
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150 octave_idx_type m = 0;
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151 octave_idx_type n = 0;
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152
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153 get_dimensions (args(0), args(1), "sparse", m, n);
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154
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155 if (m >= 0 && n >= 0)
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156 retval = SparseMatrix (m, n);
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157 else
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158 error ("sparse: dimensions must be non-negative");
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159 }
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160 else if (nargin >= 3)
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161 {
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162 bool summation = true;
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163 if (nargin > 3 && args(nargin-1).is_string ())
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164 {
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165 std::string opt = args(nargin-1).string_value ();
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166 if (opt == "unique")
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167 summation = false;
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168 else if (opt == "sum" || opt == "summation")
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169 summation = true;
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170 else
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171 error ("sparse: invalid option: %s", opt.c_str ());
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172
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173 nargin -= 1;
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174 }
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175
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176 octave_idx_type m, n, nzmax;
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177 m = n = nzmax = -1;
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178 if (nargin == 6)
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179 {
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180 nzmax = args(5).idx_type_value ();
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181 nargin --;
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182 }
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183
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184 if (nargin == 5)
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185 {
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186 get_dimensions (args(3), args(4), "sparse", m, n);
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187
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188 if (! error_state && (m < 0 || n < 0))
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189 error ("sparse: dimensions must be non-negative");
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190 }
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191 else if (nargin != 3)
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192 print_usage ();
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193
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194 int k = 0; // index we're checking when index_vector throws
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195 try
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196 {
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197 idx_vector i = args(0).index_vector ();
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198 k = 1;
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199 idx_vector j = args(1).index_vector ();
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200
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201 if (args(2).is_bool_type ())
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202 retval = SparseBoolMatrix (args(2).bool_array_value (), i,j,
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203 m, n, summation, nzmax);
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204 else if (args(2).is_complex_type ())
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205 retval = SparseComplexMatrix (args(2).complex_array_value(),
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206 i, j, m, n, summation, nzmax);
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207 else if (args(2).is_numeric_type ())
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208 retval = SparseMatrix (args(2).array_value (), i, j,
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209 m, n, summation, nzmax);
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210 else
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211 gripe_wrong_type_arg ("sparse", args(2));
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212 }
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213 catch (index_exception& e)
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214 {
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215 // Rethrow to allow more info to be reported later.
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216 e.set_pos_if_unset (2, k+1);
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217 throw;
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218 }
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219 }
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220
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221 return retval;
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222 }
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223
11097
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diff changeset
224 DEFUN (spalloc, args, ,
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diff changeset
225 "-*- texinfo -*-\n\
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226 @deftypefn {Built-in Function} {@var{s} =} spalloc (@var{m}, @var{n}, @var{nz})\n\
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227 Create an @var{m}-by-@var{n} sparse matrix with pre-allocated space for at\n\
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228 most @var{nz} nonzero elements.\n\
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229 \n\
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230 This is useful for building a matrix incrementally by a sequence of indexed\n\
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231 assignments. Subsequent indexed assignments after @code{spalloc} will reuse\n\
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232 the pre-allocated memory, provided they are of one of the simple forms\n\
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233 \n\
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234 @itemize\n\
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235 @item @code{@var{s}(I:J) = @var{x}}\n\
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diff changeset
236 \n\
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diff changeset
237 @item @code{@var{s}(:,I:J) = @var{x}}\n\
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diff changeset
238 \n\
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diff changeset
239 @item @code{@var{s}(K:L,I:J) = @var{x}}\n\
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diff changeset
240 @end itemize\n\
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241 \n\
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diff changeset
242 @b{and} that the following conditions are met:\n\
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243 \n\
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244 @itemize\n\
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245 @item the assignment does not decrease nnz (@var{S}).\n\
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246 \n\
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diff changeset
247 @item after the assignment, nnz (@var{S}) does not exceed @var{nz}.\n\
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248 \n\
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249 @item no index is out of bounds.\n\
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250 @end itemize\n\
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251 \n\
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252 Partial movement of data may still occur, but in general the assignment will\n\
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253 be more memory and time efficient under these circumstances. In particular,\n\
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254 it is possible to efficiently build a pre-allocated sparse matrix from a\n\
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255 contiguous block of columns.\n\
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256 \n\
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257 The amount of pre-allocated memory for a given matrix may be queried using\n\
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258 the function @code{nzmax}.\n\
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diff changeset
259 @seealso{nzmax, sparse}\n\
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diff changeset
260 @end deftypefn")
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261 {
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262 octave_value retval;
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263 int nargin = args.length ();
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diff changeset
264
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265 if (nargin == 2 || nargin == 3)
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266 {
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267 octave_idx_type m = args(0).idx_type_value ();
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268 octave_idx_type n = args(1).idx_type_value ();
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269 octave_idx_type nz = 0;
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270
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271 if (nargin == 3)
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272 nz = args(2).idx_type_value ();
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273
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274 if (m >= 0 && n >= 0 && nz >= 0)
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275 retval = SparseMatrix (dim_vector (m, n), nz);
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276 else
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277 error ("spalloc: M,N,NZ must be non-negative");
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278 }
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parents: 17744
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279 else
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parents: 17744
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280 print_usage ();
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parents: 10479
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281
17787
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parents: 17744
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282 return retval;
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Jaroslav Hajek <highegg@gmail.com>
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283 }