Mercurial > octave
annotate libinterp/corefcn/svd.cc @ 26614:725f3f01f487 stable
doc: Fix typo in svd_driver docstring.
* svd.cc (Fsvd_driver): Change "use" to "used".
author | Rik <rik@octave.org> |
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date | Thu, 24 Jan 2019 10:52:07 -0800 |
parents | 00f796120a6d |
children | 1212568010a8 |
rev | line source |
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2928 | 1 /* |
2 | |
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3 Copyright (C) 1996-2019 John W. Eaton |
2928 | 4 |
5 This file is part of Octave. | |
6 | |
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7 Octave is free software: you can redistribute it and/or modify it |
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8 under the terms of the GNU General Public License as published by |
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9 the Free Software Foundation, either version 3 of the License, or |
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10 (at your option) any later version. |
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12 Octave is distributed in the hope that it will be useful, but |
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13 WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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15 GNU General Public License for more details. |
2928 | 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 |
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19 <https://www.gnu.org/licenses/>. |
2928 | 20 |
21 */ | |
22 | |
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23 #if defined (HAVE_CONFIG_H) |
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24 # include "config.h" |
2928 | 25 #endif |
26 | |
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27 #include "svd.h" |
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29 #include "defun.h" |
2928 | 30 #include "error.h" |
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31 #include "errwarn.h" |
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32 #include "ovl.h" |
2928 | 33 #include "pr-output.h" |
34 #include "utils.h" | |
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35 #include "variables.h" |
2928 | 36 |
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37 static std::string Vsvd_driver = "gesdd"; |
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38 |
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39 template <typename T> |
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40 static typename octave::math::svd<T>::Type |
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41 svd_type (int nargin, int nargout, const octave_value_list & args, const T & A) |
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42 { |
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43 if (nargout == 0 || nargout == 1) |
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44 return octave::math::svd<T>::Type::sigma_only; |
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45 else if (nargin == 1) |
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46 return octave::math::svd<T>::Type::std; |
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47 else if (! args(1).is_real_scalar ()) |
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48 return octave::math::svd<T>::Type::economy; |
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49 else |
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50 { |
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51 if (A.rows () > A.columns ()) |
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52 return octave::math::svd<T>::Type::economy; |
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53 else |
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54 return octave::math::svd<T>::Type::std; |
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55 } |
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56 } |
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57 |
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58 template <typename T> |
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59 static typename octave::math::svd<T>::Driver |
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60 svd_driver (void) |
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61 { |
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62 return (Vsvd_driver == "gesvd" |
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63 ? octave::math::svd<T>::Driver::GESVD |
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64 : octave::math::svd<T>::Driver::GESDD); |
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65 } |
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67 DEFUN (svd, args, nargout, |
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68 classes: double single |
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69 doc: /* -*- texinfo -*- |
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70 @deftypefn {} {@var{s} =} svd (@var{A}) |
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71 @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}) |
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72 @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}, "econ") |
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73 @deftypefnx {} {[@var{U}, @var{S}, @var{V}] =} svd (@var{A}, 0) |
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74 @cindex singular value decomposition |
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75 Compute the singular value decomposition of @var{A}. |
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76 |
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77 The singular value decomposition is defined by the relation |
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78 |
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79 @tex |
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80 $$ |
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81 A = U S V^{\dagger} |
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82 $$ |
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83 @end tex |
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84 @ifnottex |
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85 |
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86 @example |
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87 A = U*S*V' |
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88 @end example |
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89 |
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90 @end ifnottex |
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91 |
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92 The function @code{svd} normally returns only the vector of singular values. |
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93 When called with three return values, it computes |
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94 @tex |
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95 $U$, $S$, and $V$. |
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96 @end tex |
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97 @ifnottex |
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98 @var{U}, @var{S}, and @var{V}. |
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99 @end ifnottex |
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100 For example, |
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101 |
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102 @example |
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103 svd (hilb (3)) |
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104 @end example |
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105 |
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106 @noindent |
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107 returns |
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108 |
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109 @example |
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110 @group |
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111 ans = |
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112 |
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113 1.4083189 |
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114 0.1223271 |
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115 0.0026873 |
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116 @end group |
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117 @end example |
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118 |
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119 @noindent |
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120 and |
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121 |
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122 @example |
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123 [u, s, v] = svd (hilb (3)) |
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124 @end example |
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125 |
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126 @noindent |
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127 returns |
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128 |
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129 @example |
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130 @group |
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131 u = |
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132 |
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133 -0.82704 0.54745 0.12766 |
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134 -0.45986 -0.52829 -0.71375 |
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135 -0.32330 -0.64901 0.68867 |
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136 |
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137 s = |
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138 |
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139 1.40832 0.00000 0.00000 |
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140 0.00000 0.12233 0.00000 |
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141 0.00000 0.00000 0.00269 |
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142 |
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143 v = |
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144 |
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145 -0.82704 0.54745 0.12766 |
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146 -0.45986 -0.52829 -0.71375 |
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147 -0.32330 -0.64901 0.68867 |
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148 @end group |
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149 @end example |
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150 |
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151 When given a second argument that is not 0, @code{svd} returns an economy-sized |
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152 decomposition, eliminating the unnecessary rows or columns of @var{U} or |
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153 @var{V}. |
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154 |
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155 If the second argument is exactly 0, then the choice of decomposition is based |
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156 on the matrix @var{A}. If @var{A} has more rows than columns then an |
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157 economy-sized decomposition is returned, otherwise a regular decomposition |
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158 is calculated. |
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159 |
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160 Algorithm Notes: When calculating the full decomposition (left and right |
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161 singular matrices in addition to singular values) there is a choice of two |
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162 routines in @sc{lapack}. The default routine used by Octave is @code{gesdd} |
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163 which is 5X faster than the alternative @code{gesvd}, but may use more memory |
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164 and may be less accurate for some matrices. See the documentation for |
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165 @code{svd_driver} for more information. |
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166 @seealso{svd_driver, svds, eig, lu, chol, hess, qr, qz} |
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167 @end deftypefn */) |
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169 int nargin = args.length (); | |
170 | |
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171 if (nargin < 1 || nargin > 2 || nargout > 3) |
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172 print_usage (); |
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174 octave_value arg = args(0); | |
175 | |
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176 if (arg.ndims () != 2) |
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177 error ("svd: A must be a 2-D matrix"); |
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178 |
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179 octave_value_list retval; |
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180 |
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181 bool isfloat = arg.is_single_type (); |
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182 |
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183 if (isfloat) |
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184 { |
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185 if (arg.isreal ()) |
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186 { |
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187 FloatMatrix tmp = arg.float_matrix_value (); |
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188 |
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189 if (tmp.any_element_is_inf_or_nan ()) |
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190 error ("svd: cannot take SVD of matrix containing Inf or NaN values"); |
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191 |
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192 octave::math::svd<FloatMatrix> result |
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193 (tmp, |
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194 svd_type<FloatMatrix> (nargin, nargout, args, tmp), |
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195 svd_driver<FloatMatrix> ()); |
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196 |
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197 FloatDiagMatrix sigma = result.singular_values (); |
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198 |
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199 if (nargout == 0 || nargout == 1) |
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200 retval(0) = sigma.extract_diag (); |
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201 else if (nargout == 2) |
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202 retval = ovl (result.left_singular_matrix (), |
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203 sigma); |
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204 else |
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205 retval = ovl (result.left_singular_matrix (), |
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206 sigma, |
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207 result.right_singular_matrix ()); |
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208 } |
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209 else if (arg.iscomplex ()) |
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210 { |
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211 FloatComplexMatrix ctmp = arg.float_complex_matrix_value (); |
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212 |
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213 if (ctmp.any_element_is_inf_or_nan ()) |
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214 error ("svd: cannot take SVD of matrix containing Inf or NaN values"); |
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215 |
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216 octave::math::svd<FloatComplexMatrix> result |
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217 (ctmp, |
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218 svd_type<FloatComplexMatrix> (nargin, nargout, args, ctmp), |
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219 svd_driver<FloatComplexMatrix> ()); |
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220 |
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221 FloatDiagMatrix sigma = result.singular_values (); |
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222 |
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223 if (nargout == 0 || nargout == 1) |
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224 retval(0) = sigma.extract_diag (); |
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225 else if (nargout == 2) |
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226 retval = ovl (result.left_singular_matrix (), |
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227 sigma); |
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228 else |
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229 retval = ovl (result.left_singular_matrix (), |
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230 sigma, |
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231 result.right_singular_matrix ()); |
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232 } |
2928 | 233 } |
234 else | |
235 { | |
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236 if (arg.isreal ()) |
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237 { |
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238 Matrix tmp = arg.matrix_value (); |
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239 |
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240 if (tmp.any_element_is_inf_or_nan ()) |
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241 error ("svd: cannot take SVD of matrix containing Inf or NaN values"); |
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243 octave::math::svd<Matrix> result |
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244 (tmp, |
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245 svd_type<Matrix> (nargin, nargout, args, tmp), |
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246 svd_driver<Matrix> ()); |
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247 |
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248 DiagMatrix sigma = result.singular_values (); |
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250 if (nargout == 0 || nargout == 1) |
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251 retval(0) = sigma.extract_diag (); |
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252 else if (nargout == 2) |
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253 retval = ovl (result.left_singular_matrix (), |
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254 sigma); |
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255 else |
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256 retval = ovl (result.left_singular_matrix (), |
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257 sigma, |
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258 result.right_singular_matrix ()); |
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259 } |
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260 else if (arg.iscomplex ()) |
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261 { |
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262 ComplexMatrix ctmp = arg.complex_matrix_value (); |
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264 if (ctmp.any_element_is_inf_or_nan ()) |
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265 error ("svd: cannot take SVD of matrix containing Inf or NaN values"); |
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267 octave::math::svd<ComplexMatrix> result |
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268 (ctmp, |
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269 svd_type<ComplexMatrix> (nargin, nargout, args, ctmp), |
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270 svd_driver<ComplexMatrix> ()); |
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272 DiagMatrix sigma = result.singular_values (); |
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273 |
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274 if (nargout == 0 || nargout == 1) |
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275 retval(0) = sigma.extract_diag (); |
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276 else if (nargout == 2) |
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277 retval = ovl (result.left_singular_matrix (), |
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278 sigma); |
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279 else |
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280 retval = ovl (result.left_singular_matrix (), |
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281 sigma, |
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282 result.right_singular_matrix ()); |
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283 } |
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285 err_wrong_type_arg ("svd", arg); |
2928 | 286 } |
287 | |
288 return retval; | |
289 } | |
290 | |
291 /* | |
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292 %!assert (svd ([1, 2; 2, 1]), [3; 1], sqrt (eps)) |
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293 |
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294 %!test |
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295 %! a = [1, 2; 3, 4] + [5, 6; 7, 8]*i; |
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296 %! [u,s,v] = svd (a); |
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297 %! assert (a, u * s * v', 128 * eps); |
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298 |
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299 %!test |
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300 %! [u, s, v] = svd ([1, 2; 2, 1]); |
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301 %! x = 1 / sqrt (2); |
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302 %! assert (u, [-x, -x; -x, x], sqrt (eps)); |
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303 %! assert (s, [3, 0; 0, 1], sqrt (eps)); |
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304 %! assert (v, [-x, x; -x, -x], sqrt (eps)); |
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305 |
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306 %!test |
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307 %! a = [1, 2, 3; 4, 5, 6]; |
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308 %! [u, s, v] = svd (a); |
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309 %! assert (u * s * v', a, sqrt (eps)); |
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310 |
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311 %!test |
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312 %! a = [1, 2; 3, 4; 5, 6]; |
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313 %! [u, s, v] = svd (a); |
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314 %! assert (u * s * v', a, sqrt (eps)); |
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315 |
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316 %!test |
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317 %! a = [1, 2, 3; 4, 5, 6]; |
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318 %! [u, s, v] = svd (a, 1); |
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319 %! assert (u * s * v', a, sqrt (eps)); |
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320 |
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321 %!test |
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322 %! a = [1, 2; 3, 4; 5, 6]; |
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323 %! [u, s, v] = svd (a, 1); |
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324 %! assert (u * s * v', a, sqrt (eps)); |
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325 |
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326 %!assert (svd (single ([1, 2; 2, 1])), single ([3; 1]), sqrt (eps ("single"))) |
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327 |
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328 %!test |
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329 %! [u, s, v] = svd (single ([1, 2; 2, 1])); |
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330 %! x = single (1 / sqrt (2)); |
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331 %! assert (u, [-x, -x; -x, x], sqrt (eps ("single"))); |
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332 %! assert (s, single ([3, 0; 0, 1]), sqrt (eps ("single"))); |
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333 %! assert (v, [-x, x; -x, -x], sqrt (eps ("single"))); |
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334 |
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335 %!test |
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336 %! a = single ([1, 2, 3; 4, 5, 6]); |
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337 %! [u, s, v] = svd (a); |
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338 %! assert (u * s * v', a, sqrt (eps ("single"))); |
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339 |
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340 %!test |
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341 %! a = single ([1, 2; 3, 4; 5, 6]); |
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342 %! [u, s, v] = svd (a); |
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343 %! assert (u * s * v', a, sqrt (eps ("single"))); |
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344 |
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345 %!test |
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346 %! a = single ([1, 2, 3; 4, 5, 6]); |
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347 %! [u, s, v] = svd (a, 1); |
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348 %! assert (u * s * v', a, sqrt (eps ("single"))); |
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349 |
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350 %!test |
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351 %! a = single ([1, 2; 3, 4; 5, 6]); |
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352 %! [u, s, v] = svd (a, 1); |
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353 %! assert (u * s * v', a, sqrt (eps ("single"))); |
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354 |
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355 %!test |
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356 %! a = zeros (0, 5); |
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357 %! [u, s, v] = svd (a); |
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358 %! assert (size (u), [0, 0]); |
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359 %! assert (size (s), [0, 5]); |
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360 %! assert (size (v), [5, 5]); |
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361 |
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362 %!test |
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363 %! a = zeros (5, 0); |
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364 %! [u, s, v] = svd (a, 1); |
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365 %! assert (size (u), [5, 0]); |
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366 %! assert (size (s), [0, 0]); |
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367 %! assert (size (v), [0, 0]); |
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368 |
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369 %!test <*49309> |
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370 %! [~,~,v] = svd ([1, 1, 1], 0); |
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371 %! assert (size (v), [3 3]); |
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372 %! [~,~,v] = svd ([1, 1, 1], "econ"); |
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373 %! assert (size (v), [3 1]); |
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374 |
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375 %!error svd () |
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376 %!error svd ([1, 2; 4, 5], 2, 3) |
2928 | 377 */ |
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379 DEFUN (svd_driver, args, nargout, |
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380 doc: /* -*- texinfo -*- |
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381 @deftypefn {} {@var{val} =} svd_driver () |
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382 @deftypefnx {} {@var{old_val} =} svd_driver (@var{new_val}) |
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383 @deftypefnx {} {} svd_driver (@var{new_val}, "local") |
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384 Query or set the underlying @sc{lapack} driver used by @code{svd}. |
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385 |
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386 Currently recognized values are @qcode{"gesdd"} and @qcode{"gesvd"}. |
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387 The default is @qcode{"gesdd"}. |
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388 |
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389 When called from inside a function with the @qcode{"local"} option, the |
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390 variable is changed locally for the function and any subroutines it calls. |
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391 The original variable value is restored when exiting the function. |
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392 |
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393 Algorithm Notes: The @sc{lapack} library provides two routines for calculating |
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394 the full singular value decomposition (left and right singular matrices as |
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395 well as singular values). When calculating just the singular values the |
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396 following discussion is not relevant. |
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397 |
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398 The default routine used by Octave is the newer @code{gesdd} which is based on |
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399 a Divide-and-Conquer algorithm that is 5X faster than the alternative |
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400 @code{gesvd}, which is based on QR factorization. However, the new algorithm |
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401 can use significantly more memory. For an @nospell{MxN} input matrix the |
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402 memory usage is of order O(min(M,N) ^ 2), whereas the alternative is of order |
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403 O(max(M,N)). In general, modern computers have abundant memory so Octave has |
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404 chosen to prioritize speed. |
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405 |
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406 In addition, there have been instances in the past where some input matrices |
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407 were not accurately decomposed by @code{gesdd}. This appears to have been |
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408 resolved with modern versions of @sc{lapack}. However, if certainty is |
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409 required the accuracy of the decomposition can always be tested after the fact |
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410 with |
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411 |
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412 @example |
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413 @group |
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414 [@var{u}, @var{s}, @var{v}] = svd (@var{x}); |
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415 norm (@var{x} - @var{u}*@var{s}*@var{v'}, "fro") |
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416 @end group |
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417 @end example |
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418 |
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419 @seealso{svd} |
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420 @end deftypefn */) |
10601 | 421 { |
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422 static const char *driver_names[] = { "gesvd", "gesdd", nullptr }; |
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424 return SET_INTERNAL_VARIABLE_CHOICES (svd_driver, driver_names); |
10601 | 425 } |
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426 |
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427 /* |
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428 %!test |
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429 %! A = [1+1i, 1-1i, 0; 0, 2, 0; 1i, 1i, 1+2i]; |
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430 %! old_driver = svd_driver ("gesvd"); |
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431 %! [U1, S1, V1] = svd (A); |
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432 %! svd_driver ("gesdd"); |
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433 %! [U2, S2, V2] = svd (A); |
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434 %! svd_driver (old_driver); |
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435 %! assert (U1, U2, 5*eps); |
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436 %! assert (S1, S2, 5*eps); |
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437 %! assert (V1, V2, 5*eps); |
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438 */ |