Mercurial > octave-nkf
annotate libinterp/corefcn/inv.cc @ 20612:7374a3a6d594
use new string_value method to handle value extraction errors
* urlwrite.cc: Use new string_value method.
author | John W. Eaton <jwe@octave.org> |
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date | Thu, 08 Oct 2015 17:26:40 -0400 |
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2928 | 1 /* |
2 | |
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3 Copyright (C) 1996-2015 John W. Eaton |
2928 | 4 |
5 This file is part of Octave. | |
6 | |
7 Octave is free software; you can redistribute it and/or modify it | |
8 under the terms of the GNU General Public License as published by the | |
7016 | 9 Free Software Foundation; either version 3 of the License, or (at your |
10 option) any later version. | |
2928 | 11 |
12 Octave is distributed in the hope that it will be useful, but WITHOUT | |
13 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or | |
14 FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License | |
15 for more details. | |
16 | |
17 You should have received a copy of the GNU General Public License | |
7016 | 18 along with Octave; see the file COPYING. If not, see |
19 <http://www.gnu.org/licenses/>. | |
2928 | 20 |
21 */ | |
22 | |
23 #ifdef HAVE_CONFIG_H | |
24 #include <config.h> | |
25 #endif | |
26 | |
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27 #include "defun.h" |
2928 | 28 #include "error.h" |
29 #include "gripes.h" | |
30 #include "oct-obj.h" | |
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31 #include "ops.h" |
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32 #include "ov-re-diag.h" |
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33 #include "ov-cx-diag.h" |
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34 #include "ov-flt-re-diag.h" |
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35 #include "ov-flt-cx-diag.h" |
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36 #include "ov-perm.h" |
2928 | 37 #include "utils.h" |
38 | |
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39 DEFUN (inv, args, nargout, |
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40 "-*- texinfo -*-\n\ |
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41 @deftypefn {Built-in Function} {@var{x} =} inv (@var{A})\n\ |
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42 @deftypefnx {Built-in Function} {[@var{x}, @var{rcond}] =} inv (@var{A})\n\ |
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43 Compute the inverse of the square matrix @var{A}.\n\ |
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44 \n\ |
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45 Return an estimate of the reciprocal condition number if requested,\n\ |
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46 otherwise warn of an ill-conditioned matrix if the reciprocal condition\n\ |
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47 number is small.\n\ |
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48 \n\ |
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49 In general it is best to avoid calculating the inverse of a matrix directly.\n\ |
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50 For example, it is both faster and more accurate to solve systems of\n\ |
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51 equations (@var{A}*@math{x} = @math{b}) with\n\ |
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52 @code{@var{y} = @var{A} \\ @math{b}}, rather than\n\ |
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53 @code{@var{y} = inv (@var{A}) * @math{b}}.\n\ |
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54 \n\ |
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55 If called with a sparse matrix, then in general @var{x} will be a full\n\ |
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56 matrix requiring significantly more storage. Avoid forming the inverse of a\n\ |
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57 sparse matrix if possible.\n\ |
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58 @seealso{ldivide, rdivide}\n\ |
3372 | 59 @end deftypefn") |
2928 | 60 { |
61 octave_value_list retval; | |
62 | |
63 int nargin = args.length (); | |
64 | |
65 if (nargin != 1) | |
66 { | |
5823 | 67 print_usage (); |
2928 | 68 return retval; |
69 } | |
70 | |
71 octave_value arg = args(0); | |
72 | |
5275 | 73 octave_idx_type nr = arg.rows (); |
74 octave_idx_type nc = arg.columns (); | |
2928 | 75 |
76 int arg_is_empty = empty_arg ("inverse", nr, nc); | |
77 | |
78 if (arg_is_empty < 0) | |
79 return retval; | |
80 else if (arg_is_empty > 0) | |
4233 | 81 return octave_value (Matrix ()); |
2928 | 82 |
83 if (nr != nc) | |
84 { | |
85 gripe_square_matrix_required ("inverse"); | |
86 return retval; | |
87 } | |
88 | |
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89 octave_value result; |
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90 octave_idx_type info; |
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91 double rcond = 0.0; |
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92 float frcond = 0.0; |
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93 bool isfloat = arg.is_single_type (); |
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94 |
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95 if (arg.is_diag_matrix ()) |
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96 { |
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97 rcond = 1.0; |
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98 frcond = 1.0f; |
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99 if (arg.is_complex_type ()) |
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100 { |
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101 if (isfloat) |
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102 { |
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103 result = arg.float_complex_diag_matrix_value ().inverse (info); |
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104 if (nargout > 1) |
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105 frcond = arg.float_complex_diag_matrix_value ().rcond (); |
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106 } |
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107 else |
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108 { |
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109 result = arg.complex_diag_matrix_value ().inverse (info); |
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110 if (nargout > 1) |
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111 rcond = arg.complex_diag_matrix_value ().rcond (); |
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112 } |
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113 } |
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114 else |
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115 { |
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116 if (isfloat) |
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117 { |
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118 result = arg.float_diag_matrix_value ().inverse (info); |
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119 if (nargout > 1) |
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120 frcond = arg.float_diag_matrix_value ().rcond (); |
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121 } |
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122 else |
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123 { |
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124 result = arg.diag_matrix_value ().inverse (info); |
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125 if (nargout > 1) |
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126 rcond = arg.diag_matrix_value ().rcond (); |
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127 } |
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128 } |
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129 } |
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130 else if (arg.is_perm_matrix ()) |
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131 { |
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132 rcond = 1.0; |
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133 info = 0; |
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134 result = arg.perm_matrix_value ().inverse (); |
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135 } |
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136 else if (isfloat) |
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138 if (arg.is_real_type ()) |
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139 { |
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140 FloatMatrix m = arg.float_matrix_value (); |
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141 if (! error_state) |
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142 { |
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143 MatrixType mattyp = args(0).matrix_type (); |
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144 result = m.inverse (mattyp, info, frcond, 1); |
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145 args(0).matrix_type (mattyp); |
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146 } |
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147 } |
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148 else if (arg.is_complex_type ()) |
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149 { |
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150 FloatComplexMatrix m = arg.float_complex_matrix_value (); |
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151 if (! error_state) |
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152 { |
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153 MatrixType mattyp = args(0).matrix_type (); |
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154 result = m.inverse (mattyp, info, frcond, 1); |
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155 args(0).matrix_type (mattyp); |
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156 } |
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157 } |
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159 else |
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161 if (arg.is_real_type ()) |
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162 { |
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163 if (arg.is_sparse_type ()) |
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164 { |
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165 SparseMatrix m = arg.sparse_matrix_value (); |
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166 if (! error_state) |
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167 { |
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168 MatrixType mattyp = args(0).matrix_type (); |
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169 result = m.inverse (mattyp, info, rcond, 1); |
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170 args(0).matrix_type (mattyp); |
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171 } |
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172 } |
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173 else |
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174 { |
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175 Matrix m = arg.matrix_value (); |
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176 if (! error_state) |
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177 { |
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178 MatrixType mattyp = args(0).matrix_type (); |
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179 result = m.inverse (mattyp, info, rcond, 1); |
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180 args(0).matrix_type (mattyp); |
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181 } |
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182 } |
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183 } |
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184 else if (arg.is_complex_type ()) |
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185 { |
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186 if (arg.is_sparse_type ()) |
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187 { |
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188 SparseComplexMatrix m = arg.sparse_complex_matrix_value (); |
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189 if (! error_state) |
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190 { |
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191 MatrixType mattyp = args(0).matrix_type (); |
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192 result = m.inverse (mattyp, info, rcond, 1); |
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193 args(0).matrix_type (mattyp); |
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194 } |
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195 } |
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196 else |
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197 { |
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198 ComplexMatrix m = arg.complex_matrix_value (); |
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199 if (! error_state) |
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200 { |
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201 MatrixType mattyp = args(0).matrix_type (); |
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202 result = m.inverse (mattyp, info, rcond, 1); |
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203 args(0).matrix_type (mattyp); |
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204 } |
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205 } |
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206 } |
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207 else |
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208 gripe_wrong_type_arg ("inv", arg); |
2928 | 209 } |
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210 |
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211 if (! error_state) |
2928 | 212 { |
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213 if (nargout > 1) |
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214 retval(1) = isfloat ? octave_value (frcond) : octave_value (rcond); |
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215 |
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216 retval(0) = result; |
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217 |
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218 bool rcond_plus_one_eq_one = false; |
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219 |
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220 if (isfloat) |
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221 { |
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222 volatile float xrcond = frcond; |
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223 rcond_plus_one_eq_one = xrcond + 1.0F == 1.0F; |
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224 } |
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225 else |
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226 { |
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227 volatile double xrcond = rcond; |
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228 rcond_plus_one_eq_one = xrcond + 1.0 == 1.0; |
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229 } |
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230 |
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231 if (nargout < 2 && (info == -1 || rcond_plus_one_eq_one)) |
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232 gripe_singular_matrix (isfloat ? frcond : rcond); |
2928 | 233 } |
234 | |
235 return retval; | |
236 } | |
237 | |
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238 /* |
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239 %!assert (inv ([1, 2; 3, 4]), [-2, 1; 1.5, -0.5], sqrt (eps)) |
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240 %!assert (inv (single ([1, 2; 3, 4])), single ([-2, 1; 1.5, -0.5]), sqrt (eps ("single"))) |
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241 |
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242 %!error inv () |
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243 %!error inv ([1, 2; 3, 4], 2) |
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244 %!error <argument must be a square matrix> inv ([1, 2; 3, 4; 5, 6]) |
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245 |
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246 %!test |
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247 %! [xinv, rcond] = inv (single ([1,2;3,4])); |
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248 %! assert (isa (xinv, 'single')); |
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249 %! assert (isa (rcond, 'single')); |
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250 |
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251 %!test |
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252 %! [xinv, rcond] = inv ([1,2;3,4]); |
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253 %! assert (isa (xinv, 'double')); |
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254 %! assert (isa (rcond, 'double')); |
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255 */ |
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256 |
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257 // FIXME: this should really be done with an alias, but |
2928 | 258 // alias_builtin() won't do the right thing if we are actually using |
259 // dynamic linking. | |
260 | |
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261 DEFUN (inverse, args, nargout, |
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262 "-*- texinfo -*-\n\ |
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263 @deftypefn {Built-in Function} {@var{x} =} inverse (@var{A})\n\ |
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264 @deftypefnx {Built-in Function} {[@var{x}, @var{rcond}] =} inverse (@var{A})\n\ |
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265 Compute the inverse of the square matrix @var{A}.\n\ |
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266 \n\ |
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267 This is an alias for @code{inv}.\n\ |
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268 @seealso{inv}\n\ |
3458 | 269 @end deftypefn") |
2928 | 270 { |
271 return Finv (args, nargout); | |
272 } |