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Grammarcheck .cc files
author Rik <octave@nomad.inbox5.com>
date Sat, 31 Jul 2010 11:18:11 -0700
parents 3140cb7a05a1
children a4f482e66b65
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1 /*
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2
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3 Copyright (C) 2004, 2005, 2006, 2007, 2008, 2009 David Bateman
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4 Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 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-dld.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_DLD (issparse, args, ,
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45 "-*- texinfo -*-\n\
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46 @deftypefn {Loadable Function} {} issparse (@var{expr})\n\
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47 Return 1 if the value of the expression @var{expr} is a sparse matrix.\n\
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48 @end deftypefn")
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49 {
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50 if (args.length() != 1)
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51 {
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52 print_usage ();
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53 return octave_value ();
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54 }
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55 else
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56 return octave_value (args(0).is_sparse_type ());
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57 }
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58
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59 DEFUN_DLD (sparse, args, ,
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60 "-*- texinfo -*-\n\
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61 @deftypefn {Loadable Function} {@var{s} =} sparse (@var{a})\n\
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62 @deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv}, @var{m}, @var{n}, @var{nzmax})\n\
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63 @deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{sv})\n\
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64 @deftypefnx {Loadable Function} {@var{s} =} sparse (@var{i}, @var{j}, @var{s}, @var{m}, @var{n}, \"unique\")\n\
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65 @deftypefnx {Loadable Function} {@var{s} =} sparse (@var{m}, @var{n})\n\
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66 Create a sparse matrix from the full matrix or row, column, value triplets.\n\
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67 If @var{a} is a full matrix, convert it to a sparse matrix representation,\n\
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68 removing all zero values in the process.\n\
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69 \n\
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70 Given the integer index vectors @var{i} and @var{j}, a 1-by-@code{nnz} vector\n\
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71 of real of complex values @var{sv}, overall dimensions @var{m} and @var{n}\n\
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72 of the sparse matrix. The argument @code{nzmax} is ignored but accepted for\n\
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73 compatibility with @sc{matlab}. If @var{m} or @var{n} are not specified their\n\
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74 values are derived from the maximum index in the vectors @var{i} and @var{j} as\n\
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75 given by @code{@var{m} = max (@var{i})}, @code{@var{n} = max (@var{j})}.\n\
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76 \n\
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77 @strong{Note}: if multiple values are specified with the same\n\
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78 @var{i}, @var{j} indices, the corresponding values in @var{s} will\n\
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79 be added.\n\
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80 \n\
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81 The following are all equivalent:\n\
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82 \n\
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83 @example\n\
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84 @group\n\
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85 s = sparse (i, j, s, m, n)\n\
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86 s = sparse (i, j, s, m, n, \"summation\")\n\
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87 s = sparse (i, j, s, m, n, \"sum\")\n\
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88 @end group\n\
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89 @end example\n\
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90 \n\
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91 Given the option \"unique\". if more than two values are specified for the\n\
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92 same @var{i}, @var{j} indices, the last specified value will be used.\n\
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93 \n\
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94 @code{sparse(@var{m}, @var{n})} is equivalent to\n\
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95 @code{sparse ([], [], [], @var{m}, @var{n}, 0)}\n\
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96 \n\
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97 If any of @var{sv}, @var{i} or @var{j} are scalars, they are expanded\n\
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98 to have a common size.\n\
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99 @seealso{full}\n\
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100 @end deftypefn")
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101 {
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102 octave_value retval;
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103 int nargin = args.length ();
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104
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105 // Temporarily disable sparse_auto_mutate if set (it's obsolete anyway).
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106 unwind_protect frame;
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107 frame.protect_var (Vsparse_auto_mutate);
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108 Vsparse_auto_mutate = false;
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109
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110 if (nargin == 1)
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111 {
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112 octave_value arg = args (0);
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113 if (arg.is_bool_type ())
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114 retval = arg.sparse_bool_matrix_value ();
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115 else if (arg.is_complex_type ())
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116 retval = arg.sparse_complex_matrix_value ();
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117 else if (arg.is_numeric_type ())
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118 retval = arg.sparse_matrix_value ();
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119 else
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120 gripe_wrong_type_arg ("sparse", arg);
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121 }
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122 else if (nargin == 2)
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123 {
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124 octave_idx_type m = 0, n = 0;
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125 if (args(0).is_scalar_type () && args(1).is_scalar_type ())
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126 {
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127 m = args(0).idx_type_value ();
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128 n = args(1).idx_type_value ();
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129 }
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130 else
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131 error ("sparse: expecting scalar dimensions");
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132
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133 if (! error_state)
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134 {
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135 if (m >= 0 && n >= 0)
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136 retval = SparseMatrix (m, n);
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137 else
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138 error ("sparse: dimensions must be nonnegative");
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139 }
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140 }
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141 else if (nargin >= 3)
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142 {
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143 bool summation = true;
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144 if (nargin > 3 && args(nargin-1).is_string ())
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145 {
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146 std::string opt = args(nargin-1).string_value ();
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147 if (opt == "unique")
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148 summation = false;
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149 else if (opt == "sum" || opt == "summation")
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150 summation = true;
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151 else
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152 error ("sparse: invalid option: %s", opt.c_str ());
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153
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154 nargin -= 1;
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155 }
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156
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157 if (! error_state)
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158 {
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159 octave_idx_type m = -1, n = -1, nzmax = -1;
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160 if (nargin == 6)
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161 {
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162 nzmax = args(5).idx_type_value ();
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163 nargin --;
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164 }
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165
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166 if (nargin == 5)
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167 {
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168 if (args(3).is_scalar_type () && args(4).is_scalar_type ())
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169 {
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170 m = args(3).idx_type_value ();
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171 n = args(4).idx_type_value ();
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172 }
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173 else
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174 error ("sparse: expecting scalar dimensions");
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175
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176
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177 if (! error_state && (m < 0 || n < 0))
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178 error ("sparse: dimensions must be nonnegative");
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179 }
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180 else if (nargin != 3)
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181 print_usage ();
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182
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183 if (! error_state)
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184 {
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185 idx_vector i = args(0).index_vector ();
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186 idx_vector j = args(1).index_vector ();
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187
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188 if (args(2).is_bool_type ())
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189 retval = SparseBoolMatrix (args(2).bool_array_value (), i, j,
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190 m, n, summation, nzmax);
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191 else if (args(2).is_complex_type ())
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192 retval = SparseComplexMatrix (args(2).complex_array_value (),
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193 i, j, m, n, summation, nzmax);
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194 else if (args(2).is_numeric_type ())
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195 retval = SparseMatrix (args(2).array_value (), i, j,
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196 m, n, summation, nzmax);
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197 else
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198 gripe_wrong_type_arg ("sparse", args(2));
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199 }
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200
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201 }
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202 }
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203
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204 return retval;
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205 }
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206
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207 DEFUN_DLD (spalloc, args, ,
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208 "-*- texinfo -*-\n\
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209 @deftypefn {Loadable Function} {@var{s} =} spalloc (@var{m}, @var{n}, @var{nz})\n\
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210 Creates a @var{m}-by-@var{n} sparse matrix with pre-allocated space for at most\n\
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211 @var{nz} nonzero elements. This is useful for building the matrix incrementally\n\
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212 by a sequence of indexed assignments. Subsequent indexed assignments will reuse\n\
10513
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213 the pre-allocated memory, provided they are of one of the simple forms\n\
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214 \n\
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215 @itemize\n\
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216 @item @code{@var{s}(I:J) = @var{x}}\n\
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217 \n\
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218 @item @code{@var{s}(:,I:J) = @var{x}}\n\
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219 \n\
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220 @item @code{@var{s}(K:L,I:J) = @var{x}}\n\
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221 @end itemize\n\
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222 \n\
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223 @b{and} that the following conditions are met:\n\
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224 \n\
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225 @itemize\n\
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226 @item the assignment does not decrease nnz(@var{S}).\n\
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227 \n\
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228 @item after the assignment, nnz(@var{S}) does not exceed @var{nz}.\n\
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229 \n\
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230 @item no index is out of bounds.\n\
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231 @end itemize\n\
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232 \n\
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233 Partial movement of data may still occur, but in general the assignment will be\n\
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234 more memory and time-efficient under these circumstances. In particular, it is\n\
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235 possible to efficiently build a pre-allocated sparse matrix from contiguous\n\
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236 block of columns.\n\
10513
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237 \n\
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238 The amount of pre-allocated memory for a given matrix may be queried using the\n\
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239 function\n\
10513
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240 @code{nzmax}.\n\
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241 @seealso{nzmax, sparse}\n\
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242 @end deftypefn")
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243 {
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244 octave_value retval;
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245 int nargin = args.length ();
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246
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247 if (nargin == 2 || nargin == 3)
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248 {
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249 octave_idx_type m = args(0).idx_type_value ();
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250 octave_idx_type n = args(1).idx_type_value ();
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251 octave_idx_type nz = 0;
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252 if (nargin == 3)
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253 nz = args(2).idx_type_value ();
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254 if (error_state)
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255 ;
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256 else if (m >= 0 && n >= 0 && nz >= 0)
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257 retval = SparseMatrix (dim_vector (m, n), nz);
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258 else
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259 error ("spalloc: m,n,nz must be non-negative");
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260 }
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261 else
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262 print_usage ();
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263
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264 return retval;
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265 }