Mercurial > octave
annotate libinterp/corefcn/matrix_type.cc @ 20443:610c74748518
maint: Clean up code based on static analysis suggestions.
* __dispatch__.cc (F__dispatch__): Eliminate unnecessary double check on
(nargin > 0).
* cellfun.cc (Fnum2cell): Eliminate duplicate else-if branch for
array.is_object().
* load-save.cc: Eliminate unreachable else branch. Simplify else-if to simple
else since condition is always true at this point.
* matrix_type.cc (Fmatrix_type): Eliminate unnecessary check on 'str_type ==
upper' and just mark matrix as permuted when permutation matrix was given as
input.
* kpse.cc (set_end): remove unused variable and its usage in conditional.
author | Rik <rik@octave.org> |
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date | Fri, 14 Aug 2015 13:28:30 -0700 |
parents | a9574e3c6e9e |
children | b10432a40432 |
rev | line source |
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5323 | 1 /* |
2 | |
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3 Copyright (C) 2005-2015 David Bateman |
5323 | 4 |
5720 | 5 This file is part of Octave. |
6 | |
5323 | 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. | |
5323 | 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/>. | |
5323 | 20 |
21 */ | |
22 | |
23 #ifdef HAVE_CONFIG_H | |
24 #include <config.h> | |
25 #endif | |
26 | |
5403 | 27 #include <algorithm> |
28 | |
5323 | 29 #include "ov.h" |
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30 #include "defun.h" |
5323 | 31 #include "error.h" |
5785 | 32 #include "ov-re-mat.h" |
33 #include "ov-cx-mat.h" | |
5323 | 34 #include "ov-re-sparse.h" |
35 #include "ov-cx-sparse.h" | |
5785 | 36 #include "MatrixType.h" |
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37 #include "oct-locbuf.h" |
5323 | 38 |
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39 DEFUN (matrix_type, args, , |
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40 "-*- texinfo -*-\n\ |
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41 @deftypefn {Built-in Function} {@var{type} =} matrix_type (@var{A})\n\ |
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42 @deftypefnx {Built-in Function} {@var{type} =} matrix_type (@var{A}, \"nocompute\")\n\ |
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43 @deftypefnx {Built-in Function} {@var{A} =} matrix_type (@var{A}, @var{type})\n\ |
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44 @deftypefnx {Built-in Function} {@var{A} =} matrix_type (@var{A}, \"upper\", @var{perm})\n\ |
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45 @deftypefnx {Built-in Function} {@var{A} =} matrix_type (@var{A}, \"lower\", @var{perm})\n\ |
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46 @deftypefnx {Built-in Function} {@var{A} =} matrix_type (@var{A}, \"banded\", @var{nl}, @var{nu})\n\ |
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47 Identify the matrix type or mark a matrix as a particular type.\n\ |
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48 \n\ |
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49 This allows more rapid solutions of linear equations involving @var{A} to be\n\ |
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50 performed.\n\ |
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51 \n\ |
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52 Called with a single argument, @code{matrix_type} returns the type of the\n\ |
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53 matrix and caches it for future use.\n\ |
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54 \n\ |
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55 Called with more than one argument, @code{matrix_type} allows the type of\n\ |
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56 the matrix to be defined.\n\ |
5323 | 57 \n\ |
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58 If the option @qcode{\"nocompute\"} is given, the function will not attempt\n\ |
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59 to guess the type if it is still unknown. This is useful for debugging\n\ |
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60 purposes.\n\ |
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61 \n\ |
10840 | 62 The possible matrix types depend on whether the matrix is full or sparse, and\n\ |
63 can be one of the following\n\ | |
5323 | 64 \n\ |
65 @table @asis\n\ | |
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66 @item @qcode{\"unknown\"}\n\ |
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67 Remove any previously cached matrix type, and mark type as unknown.\n\ |
5323 | 68 \n\ |
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69 @item @qcode{\"full\"}\n\ |
5323 | 70 Mark the matrix as full.\n\ |
71 \n\ | |
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72 @item @qcode{\"positive definite\"}\n\ |
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73 Probable full positive definite matrix.\n\ |
5323 | 74 \n\ |
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75 @item @qcode{\"diagonal\"}\n\ |
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76 Diagonal matrix. (Sparse matrices only)\n\ |
5323 | 77 \n\ |
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78 @item @qcode{\"permuted diagonal\"}\n\ |
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79 Permuted Diagonal matrix. The permutation does not need to be specifically\n\ |
10840 | 80 indicated, as the structure of the matrix explicitly gives this. (Sparse\n\ |
81 matrices only)\n\ | |
5323 | 82 \n\ |
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83 @item @qcode{\"upper\"}\n\ |
10840 | 84 Upper triangular. If the optional third argument @var{perm} is given, the\n\ |
85 matrix is assumed to be a permuted upper triangular with the permutations\n\ | |
86 defined by the vector @var{perm}.\n\ | |
5323 | 87 \n\ |
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88 @item @qcode{\"lower\"}\n\ |
10840 | 89 Lower triangular. If the optional third argument @var{perm} is given, the\n\ |
90 matrix is assumed to be a permuted lower triangular with the permutations\n\ | |
91 defined by the vector @var{perm}.\n\ | |
5323 | 92 \n\ |
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93 @item @qcode{\"banded\"}\n\ |
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94 @itemx @qcode{\"banded positive definite\"}\n\ |
10840 | 95 Banded matrix with the band size of @var{nl} below the diagonal and @var{nu}\n\ |
96 above it. If @var{nl} and @var{nu} are 1, then the matrix is tridiagonal and\n\ | |
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97 treated with specialized code. In addition the matrix can be marked as\n\ |
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98 probably a positive definite. (Sparse matrices only)\n\ |
5323 | 99 \n\ |
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100 @item @qcode{\"singular\"}\n\ |
10840 | 101 The matrix is assumed to be singular and will be treated with a minimum norm\n\ |
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102 solution.\n\ |
5323 | 103 \n\ |
104 @end table\n\ | |
105 \n\ | |
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106 Note that the matrix type will be discovered automatically on the first\n\ |
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107 attempt to solve a linear equation involving @var{A}. Therefore\n\ |
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108 @code{matrix_type} is only useful to give Octave hints of the matrix type.\n\ |
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109 Incorrectly defining the matrix type will result in incorrect results from\n\ |
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110 solutions of linear equations; it is entirely @strong{the responsibility of\n\ |
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111 the user} to correctly identify the matrix type.\n\ |
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112 \n\ |
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113 Also, the test for positive definiteness is a low-cost test for a Hermitian\n\ |
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114 matrix with a real positive diagonal. This does not guarantee that the\n\ |
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115 matrix is positive definite, but only that it is a probable candidate. When\n\ |
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116 such a matrix is factorized, a Cholesky@tie{}factorization is first\n\ |
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117 attempted, and if that fails the matrix is then treated with an\n\ |
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118 LU@tie{}factorization. Once the matrix has been factorized,\n\ |
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119 @code{matrix_type} will return the correct classification of the matrix.\n\ |
5323 | 120 @end deftypefn") |
121 { | |
122 int nargin = args.length (); | |
123 octave_value retval; | |
124 | |
125 if (nargin == 0) | |
5823 | 126 print_usage (); |
5323 | 127 else if (nargin > 4) |
128 error ("matrix_type: incorrect number of arguments"); | |
129 else | |
130 { | |
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131 bool autocomp = true; |
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132 if (nargin == 2 && args(1).is_string () |
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133 && args(1).string_value () == "nocompute") |
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134 { |
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135 nargin = 1; |
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136 autocomp = false; |
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137 } |
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138 |
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139 if (args(0).is_scalar_type ()) |
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140 { |
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141 if (nargin == 1) |
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142 retval = octave_value ("Diagonal"); |
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143 else |
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144 retval = args(0); |
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145 } |
6371 | 146 else if (args(0).is_sparse_type ()) |
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147 { |
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148 if (nargin == 1) |
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149 { |
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150 MatrixType mattyp; |
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152 if (args(0).is_complex_type ()) |
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153 { |
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154 mattyp = args(0).matrix_type (); |
5323 | 155 |
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156 if (mattyp.is_unknown () && autocomp) |
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157 { |
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158 SparseComplexMatrix m = |
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159 args(0).sparse_complex_matrix_value (); |
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160 if (!error_state) |
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161 { |
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162 mattyp = MatrixType (m); |
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163 args(0).matrix_type (mattyp); |
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164 } |
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165 } |
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166 } |
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167 else |
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168 { |
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169 mattyp = args(0).matrix_type (); |
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171 if (mattyp.is_unknown () && autocomp) |
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172 { |
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173 SparseMatrix m = args(0).sparse_matrix_value (); |
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174 if (!error_state) |
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175 { |
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176 mattyp = MatrixType (m); |
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177 args(0).matrix_type (mattyp); |
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178 } |
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179 } |
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180 } |
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182 int typ = mattyp.type (); |
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184 if (typ == MatrixType::Diagonal) |
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185 retval = octave_value ("Diagonal"); |
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186 else if (typ == MatrixType::Permuted_Diagonal) |
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187 retval = octave_value ("Permuted Diagonal"); |
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188 else if (typ == MatrixType::Upper) |
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189 retval = octave_value ("Upper"); |
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190 else if (typ == MatrixType::Permuted_Upper) |
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191 retval = octave_value ("Permuted Upper"); |
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192 else if (typ == MatrixType::Lower) |
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193 retval = octave_value ("Lower"); |
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194 else if (typ == MatrixType::Permuted_Lower) |
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195 retval = octave_value ("Permuted Lower"); |
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196 else if (typ == MatrixType::Banded) |
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197 retval = octave_value ("Banded"); |
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198 else if (typ == MatrixType::Banded_Hermitian) |
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199 retval = octave_value ("Banded Positive Definite"); |
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200 else if (typ == MatrixType::Tridiagonal) |
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201 retval = octave_value ("Tridiagonal"); |
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202 else if (typ == MatrixType::Tridiagonal_Hermitian) |
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203 retval = octave_value ("Tridiagonal Positive Definite"); |
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204 else if (typ == MatrixType::Hermitian) |
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205 retval = octave_value ("Positive Definite"); |
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206 else if (typ == MatrixType::Rectangular) |
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207 { |
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208 if (args(0).rows () == args(0).columns ()) |
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209 retval = octave_value ("Singular"); |
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210 else |
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211 retval = octave_value ("Rectangular"); |
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212 } |
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213 else if (typ == MatrixType::Full) |
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214 retval = octave_value ("Full"); |
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215 else |
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216 retval = octave_value ("Unknown"); |
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217 } |
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218 else |
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219 { |
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220 // Ok, we're changing the matrix type |
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221 if (! args(1).is_string ()) |
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222 error ("matrix_type: TYPE must be a string"); |
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223 else |
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224 { |
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225 std::string str_typ = args(1).string_value (); |
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226 |
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227 // FIXME: why do I have to explicitly call the constructor? |
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228 MatrixType mattyp = MatrixType (); |
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229 |
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230 octave_idx_type nl = 0; |
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231 octave_idx_type nu = 0; |
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232 |
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233 // Use STL function to convert to lower case |
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234 std::transform (str_typ.begin (), str_typ.end (), |
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235 str_typ.begin (), tolower); |
5323 | 236 |
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237 if (str_typ == "diagonal") |
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238 mattyp.mark_as_diagonal (); |
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239 if (str_typ == "permuted diagonal") |
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240 mattyp.mark_as_permuted_diagonal (); |
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241 else if (str_typ == "upper") |
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242 mattyp.mark_as_upper_triangular (); |
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243 else if (str_typ == "lower") |
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244 mattyp.mark_as_lower_triangular (); |
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245 else if (str_typ == "banded" |
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246 || str_typ == "banded positive definite") |
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247 { |
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248 if (nargin != 4) |
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249 error ("matrix_type: banded matrix type requires 4 arguments"); |
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250 else |
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251 { |
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|
252 nl = args(2).nint_value (); |
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253 nu = args(3).nint_value (); |
5323 | 254 |
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255 if (error_state) |
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|
256 error ("matrix_type: band size NL, NU must be integers"); |
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257 else |
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258 { |
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259 if (nl == 1 && nu == 1) |
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260 mattyp.mark_as_tridiagonal (); |
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261 else |
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|
262 mattyp.mark_as_banded (nu, nl); |
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263 |
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264 if (str_typ == "banded positive definite") |
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265 mattyp.mark_as_symmetric (); |
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266 } |
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267 } |
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268 } |
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269 else if (str_typ == "positive definite") |
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270 { |
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271 mattyp.mark_as_full (); |
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272 mattyp.mark_as_symmetric (); |
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273 } |
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274 else if (str_typ == "singular") |
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275 mattyp.mark_as_rectangular (); |
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276 else if (str_typ == "full") |
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277 mattyp.mark_as_full (); |
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278 else if (str_typ == "unknown") |
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279 mattyp.invalidate_type (); |
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280 else |
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281 error ("matrix_type: Unknown matrix type %s", str_typ.c_str ()); |
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283 if (! error_state) |
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284 { |
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285 if (nargin == 3 |
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286 && (str_typ == "upper" || str_typ == "lower")) |
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287 { |
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288 const ColumnVector perm = |
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289 ColumnVector (args(2).vector_value ()); |
5323 | 290 |
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291 if (error_state) |
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292 error ("matrix_type: Invalid permutation vector PERM"); |
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293 else |
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294 { |
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295 octave_idx_type len = perm.numel (); |
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296 dim_vector dv = args(0).dims (); |
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297 |
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298 if (len != dv(0)) |
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299 error ("matrix_type: Invalid permutation vector PERM"); |
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300 else |
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301 { |
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302 OCTAVE_LOCAL_BUFFER (octave_idx_type, p, len); |
5323 | 303 |
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304 for (octave_idx_type i = 0; i < len; i++) |
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305 p[i] = static_cast<octave_idx_type> |
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306 (perm (i)) |
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307 - 1; |
5323 | 308 |
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309 mattyp.mark_as_permuted (len, p); |
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310 } |
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311 } |
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312 } |
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313 else if (nargin != 2 |
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314 && str_typ != "banded positive definite" |
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315 && str_typ != "banded") |
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316 error ("matrix_type: Invalid number of arguments"); |
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318 if (! error_state) |
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319 { |
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320 // Set the matrix type |
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321 if (args(0).is_complex_type ()) |
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322 retval = |
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323 octave_value (args(0).sparse_complex_matrix_value (), |
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324 mattyp); |
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325 else |
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326 retval |
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327 = octave_value (args(0).sparse_matrix_value (), |
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328 mattyp); |
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329 } |
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330 } |
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331 } |
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332 } |
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333 } |
5323 | 334 else |
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335 { |
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336 if (nargin == 1) |
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337 { |
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338 MatrixType mattyp; |
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340 if (args(0).is_complex_type ()) |
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341 { |
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342 mattyp = args(0).matrix_type (); |
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344 if (mattyp.is_unknown () && autocomp) |
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345 { |
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346 if (args(0).is_single_type ()) |
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347 { |
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348 FloatComplexMatrix m; |
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349 m = args(0).float_complex_matrix_value (); |
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350 if (!error_state) |
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351 { |
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352 mattyp = MatrixType (m); |
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353 args(0).matrix_type (mattyp); |
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354 } |
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355 } |
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356 else |
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357 { |
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358 ComplexMatrix m = args(0).complex_matrix_value (); |
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359 if (!error_state) |
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360 { |
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361 mattyp = MatrixType (m); |
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362 args(0).matrix_type (mattyp); |
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363 } |
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364 } |
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365 } |
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366 } |
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367 else |
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368 { |
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369 mattyp = args(0).matrix_type (); |
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371 if (mattyp.is_unknown () && autocomp) |
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372 { |
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373 if (args(0).is_single_type ()) |
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374 { |
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375 FloatMatrix m = args(0).float_matrix_value (); |
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376 if (!error_state) |
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377 { |
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378 mattyp = MatrixType (m); |
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379 args(0).matrix_type (mattyp); |
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|
380 } |
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|
381 } |
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|
382 else |
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|
383 { |
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|
384 Matrix m = args(0).matrix_value (); |
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385 if (!error_state) |
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386 { |
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387 mattyp = MatrixType (m); |
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388 args(0).matrix_type (mattyp); |
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389 } |
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|
390 } |
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|
391 } |
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|
392 } |
5785 | 393 |
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394 int typ = mattyp.type (); |
5785 | 395 |
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396 if (typ == MatrixType::Upper) |
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397 retval = octave_value ("Upper"); |
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398 else if (typ == MatrixType::Permuted_Upper) |
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399 retval = octave_value ("Permuted Upper"); |
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400 else if (typ == MatrixType::Lower) |
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|
401 retval = octave_value ("Lower"); |
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402 else if (typ == MatrixType::Permuted_Lower) |
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403 retval = octave_value ("Permuted Lower"); |
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404 else if (typ == MatrixType::Hermitian) |
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405 retval = octave_value ("Positive Definite"); |
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406 else if (typ == MatrixType::Rectangular) |
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407 { |
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|
408 if (args(0).rows () == args(0).columns ()) |
10154
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|
409 retval = octave_value ("Singular"); |
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410 else |
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411 retval = octave_value ("Rectangular"); |
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412 } |
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413 else if (typ == MatrixType::Full) |
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414 retval = octave_value ("Full"); |
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|
415 else |
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|
416 retval = octave_value ("Unknown"); |
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|
417 } |
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|
418 else |
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|
419 { |
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420 // Ok, we're changing the matrix type |
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421 if (! args(1).is_string ()) |
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422 error ("matrix_type: TYPE must be a string"); |
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423 else |
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424 { |
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425 std::string str_typ = args(1).string_value (); |
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426 |
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|
427 // FIXME: why do I have to explicitly call the constructor? |
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428 MatrixType mattyp = MatrixType (MatrixType::Unknown, true); |
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429 |
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430 // Use STL function to convert to lower case |
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431 std::transform (str_typ.begin (), str_typ.end (), |
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432 str_typ.begin (), tolower); |
5785 | 433 |
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434 if (str_typ == "upper") |
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435 mattyp.mark_as_upper_triangular (); |
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436 else if (str_typ == "lower") |
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437 mattyp.mark_as_lower_triangular (); |
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438 else if (str_typ == "positive definite") |
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439 { |
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440 mattyp.mark_as_full (); |
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441 mattyp.mark_as_symmetric (); |
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442 } |
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443 else if (str_typ == "singular") |
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444 mattyp.mark_as_rectangular (); |
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445 else if (str_typ == "full") |
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446 mattyp.mark_as_full (); |
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447 else if (str_typ == "unknown") |
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448 mattyp.invalidate_type (); |
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449 else |
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450 error ("matrix_type: Unknown matrix type %s", |
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451 str_typ.c_str ()); |
5785 | 452 |
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453 if (! error_state) |
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454 { |
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455 if (nargin == 3 && (str_typ == "upper" |
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456 || str_typ == "lower")) |
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457 { |
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458 const ColumnVector perm = |
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459 ColumnVector (args(2).vector_value ()); |
5785 | 460 |
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461 if (error_state) |
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462 error ("matrix_type: Invalid permutation vector PERM"); |
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463 else |
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464 { |
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Carnë Draug <carandraug@octave.org>
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465 octave_idx_type len = perm.numel (); |
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466 dim_vector dv = args(0).dims (); |
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467 |
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468 if (len != dv(0)) |
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469 error ("matrix_type: Invalid permutation vector PERM"); |
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470 else |
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471 { |
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472 OCTAVE_LOCAL_BUFFER (octave_idx_type, p, len); |
5785 | 473 |
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474 for (octave_idx_type i = 0; i < len; i++) |
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475 p[i] = static_cast<octave_idx_type> |
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476 (perm (i)) |
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477 - 1; |
5785 | 478 |
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479 mattyp.mark_as_permuted (len, p); |
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480 } |
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481 } |
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482 } |
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483 else if (nargin != 2) |
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484 error ("matrix_type: Invalid number of arguments"); |
5785 | 485 |
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486 if (! error_state) |
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487 { |
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|
488 // Set the matrix type |
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489 if (args(0).is_single_type ()) |
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490 { |
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|
491 if (args(0).is_complex_type ()) |
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492 retval = octave_value |
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|
493 (args(0).float_complex_matrix_value (), |
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494 mattyp); |
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495 else |
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496 retval = octave_value |
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|
497 (args(0).float_matrix_value (), |
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|
498 mattyp); |
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|
499 } |
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|
500 else |
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|
501 { |
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502 if (args(0).is_complex_type ()) |
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503 retval = octave_value |
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|
504 (args(0).complex_matrix_value (), |
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505 mattyp); |
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506 else |
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507 retval = octave_value |
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508 (args(0).matrix_value (), |
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509 mattyp); |
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510 } |
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511 } |
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512 } |
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513 } |
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514 } |
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515 } |
5323 | 516 } |
517 | |
518 return retval; | |
519 } | |
520 | |
521 /* | |
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522 ## FIXME: |
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523 ## Disable tests for lower under-determined and upper over-determined |
7789
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524 ## matrices as this detection is disabled in MatrixType due to issues |
5681 | 525 ## of non minimum norm solution being found. |
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526 |
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527 %!assert (matrix_type (speye (10,10)), "Diagonal") |
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528 %!assert (matrix_type (speye (10,10)([2:10,1],:)), "Permuted Diagonal") |
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529 %!assert (matrix_type ([[speye(10,10);sparse(1,10)],[1;sparse(9,1);1]]), "Upper") |
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530 %!assert (matrix_type ([[speye(10,10);sparse(1,10)],[1;sparse(9,1);1]](:,[2,1,3:11])), "Permuted Upper") |
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531 %!assert (matrix_type ([speye(10,10),sparse(10,1);1,sparse(1,9),1]), "Lower") |
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532 %!assert (matrix_type ([speye(10,10),sparse(10,1);1,sparse(1,9),1]([2,1,3:11],:)), "Permuted Lower") |
5610 | 533 |
534 %!test | |
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535 %! bnd = spparms ("bandden"); |
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|
536 %! spparms ("bandden", 0.5); |
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537 %! a = spdiags (rand (10,3)-0.5,[-1,0,1],10,10); |
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|
538 %! assert (matrix_type (a), "Tridiagonal"); |
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|
539 %! assert (matrix_type (a'+a+2*speye (10)), "Tridiagonal Positive Definite"); |
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|
540 %! spparms ("bandden", bnd); |
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541 %!test |
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542 %! bnd=spparms ("bandden"); |
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|
543 %! spparms ("bandden", 0.5); |
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544 %! a = spdiags (randn (10,4),[-2:1],10,10); |
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|
545 %! assert (matrix_type (a), "Banded"); |
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546 %! assert (matrix_type (a'*a), "Banded Positive Definite"); |
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547 %! spparms ("bandden", bnd); |
5610 | 548 %!test |
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549 %! a = [speye(10,10),[sparse(9,1);1];-1,sparse(1,9),1]; |
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550 %! assert (matrix_type (a), "Full"); |
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551 %! assert (matrix_type (a'*a), "Positive Definite"); |
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552 |
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553 %!assert (matrix_type (speye (10,11)), "Diagonal") |
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554 %!assert (matrix_type (speye (10,11)([2:10,1],:)), "Permuted Diagonal") |
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555 %!assert (matrix_type (speye (11,10)), "Diagonal") |
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556 %!assert (matrix_type (speye (11,10)([2:11,1],:)), "Permuted Diagonal") |
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557 %#!assert (matrix_type ([[speye(10,10);sparse(1,10)],[[1,1];sparse(9,2);[1,1]]]), "Upper") |
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558 %#!assert (matrix_type ([[speye(10,10);sparse(1,10)],[[1,1];sparse(9,2);[1,1]]](:,[2,1,3:12])), "Permuted Upper") |
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559 %!assert (matrix_type ([speye(11,9),[1;sparse(8,1);1;0]]), "Upper") |
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560 %!assert (matrix_type ([speye(11,9),[1;sparse(8,1);1;0]](:,[2,1,3:10])), "Permuted Upper") |
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561 %#!assert (matrix_type ([speye(10,10),sparse(10,1);[1;1],sparse(2,9),[1;1]]), "Lower") |
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562 %#!assert (matrix_type ([speye(10,10),sparse(10,1);[1;1],sparse(2,9),[1;1]]([2,1,3:12],:)), "Permuted Lower") |
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563 %!assert (matrix_type ([speye(9,11);[1,sparse(1,8),1,0]]), "Lower") |
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564 %!assert (matrix_type ([speye(9,11);[1,sparse(1,8),1,0]]([2,1,3:10],:)), "Permuted Lower") |
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565 %!assert (matrix_type (spdiags (randn (10,4),[-2:1],10,9)), "Rectangular") |
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566 |
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567 %!assert (matrix_type (1i*speye (10,10)), "Diagonal") |
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568 %!assert (matrix_type (1i*speye (10,10)([2:10,1],:)), "Permuted Diagonal") |
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569 %!assert (matrix_type ([[speye(10,10);sparse(1,10)],[1i;sparse(9,1);1]]), "Upper") |
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570 %!assert (matrix_type ([[speye(10,10);sparse(1,10)],[1i;sparse(9,1);1]](:,[2,1,3:11])), "Permuted Upper") |
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571 %!assert (matrix_type ([speye(10,10),sparse(10,1);1i,sparse(1,9),1]), "Lower") |
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572 %!assert (matrix_type ([speye(10,10),sparse(10,1);1i,sparse(1,9),1]([2,1,3:11],:)), "Permuted Lower") |
5610 | 573 |
574 %!test | |
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575 %! bnd = spparms ("bandden"); |
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576 %! spparms ("bandden", 0.5); |
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577 %! assert (matrix_type (spdiags (1i*randn (10,3),[-1,0,1],10,10)), "Tridiagonal"); |
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578 %! a = 1i*(rand (9,1)-0.5); |
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579 %! a = [[a;0],ones(10,1),[0;-a]]; |
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580 %! assert (matrix_type (spdiags (a,[-1,0,1],10,10)), "Tridiagonal Positive Definite"); |
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581 %! spparms ("bandden", bnd); |
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582 %!test |
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583 %! bnd = spparms ("bandden"); |
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584 %! spparms ("bandden", 0.5); |
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585 %! assert (matrix_type (spdiags (1i*randn (10,4),[-2:1],10,10)), "Banded"); |
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586 %! a = 1i*(rand (9,2)-0.5); |
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587 %! a = [[a;[0,0]],ones(10,1),[[0;-a(:,2)],[0;0;-a(1:8,1)]]]; |
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588 %! assert (matrix_type (spdiags (a,[-2:2],10,10)), "Banded Positive Definite"); |
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589 %! spparms ("bandden", bnd); |
5785 | 590 %!test |
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591 %! a = [speye(10,10),[sparse(9,1);1i];-1,sparse(1,9),1]; |
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592 %! assert (matrix_type (a), "Full"); |
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593 %! assert (matrix_type (a'*a), "Positive Definite"); |
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594 |
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595 %!assert (matrix_type (1i*speye (10,11)), "Diagonal") |
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596 %!assert (matrix_type (1i*speye (10,11)([2:10,1],:)), "Permuted Diagonal") |
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597 %!assert (matrix_type (1i*speye (11,10)), "Diagonal") |
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598 %!assert (matrix_type (1i*speye (11,10)([2:11,1],:)), "Permuted Diagonal") |
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599 %#!assert (matrix_type ([[speye(10,10);sparse(1,10)],[[1i,1i];sparse(9,2);[1i,1i]]]), "Upper") |
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600 %#!assert (matrix_type ([[speye(10,10);sparse(1,10)],[[1i,1i];sparse(9,2);[1i,1i]]](:,[2,1,3:12])), "Permuted Upper") |
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601 %!assert (matrix_type ([speye(11,9),[1i;sparse(8,1);1i;0]]), "Upper") |
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602 %!assert (matrix_type ([speye(11,9),[1i;sparse(8,1);1i;0]](:,[2,1,3:10])), "Permuted Upper") |
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603 %#!assert (matrix_type ([speye(10,10),sparse(10,1);[1i;1i],sparse(2,9),[1i;1i]]), "Lower") |
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604 %#!assert (matrix_type ([speye(10,10),sparse(10,1);[1i;1i],sparse(2,9),[1i;1i]]([2,1,3:12],:)), "Permuted Lower") |
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605 %!assert (matrix_type ([speye(9,11);[1i,sparse(1,8),1i,0]]), "Lower") |
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606 %!assert (matrix_type ([speye(9,11);[1i,sparse(1,8),1i,0]]([2,1,3:10],:)), "Permuted Lower") |
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607 %!assert (matrix_type (1i*spdiags(randn(10,4),[-2:1],10,9)), "Rectangular") |
5785 | 608 |
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609 %!test |
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610 %! a = matrix_type (spdiags (randn (10,3),[-1,0,1],10,10), "Singular"); |
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611 %! assert (matrix_type (a), "Singular"); |
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612 |
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613 %!assert (matrix_type (triu (ones(10,10))), "Upper") |
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614 %!assert (matrix_type (triu (ones(10,10),-1)), "Full") |
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615 %!assert (matrix_type (tril (ones(10,10))), "Lower") |
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616 %!assert (matrix_type (tril (ones(10,10),1)), "Full") |
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617 %!assert (matrix_type (10*eye (10,10) + ones (10,10)), "Positive Definite") |
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618 %!assert (matrix_type (ones (11,10)), "Rectangular") |
5785 | 619 %!test |
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620 %! a = matrix_type (ones (10,10), "Singular"); |
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621 %! assert (matrix_type (a), "Singular"); |
5785 | 622 |
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623 %!assert (matrix_type (triu (1i*ones (10,10))), "Upper") |
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624 %!assert (matrix_type (triu (1i*ones (10,10),-1)), "Full") |
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625 %!assert (matrix_type (tril (1i*ones (10,10))), "Lower") |
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626 %!assert (matrix_type (tril (1i*ones (10,10),1)), "Full") |
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627 %!assert (matrix_type (10*eye (10,10) + 1i*triu (ones (10,10),1) -1i*tril (ones (10,10),-1)), "Positive Definite") |
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628 %!assert (matrix_type (ones (11,10)), "Rectangular") |
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629 %!test |
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630 %! a = matrix_type (ones (10,10), "Singular"); |
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631 %! assert (matrix_type (a), "Singular"); |
5610 | 632 */ |