Mercurial > octave
annotate liboctave/numeric/oct-norm.cc @ 31608:23664317f0d3
maint: merge stable to default
author | Rik <rik@octave.org> |
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date | Thu, 01 Dec 2022 20:05:44 -0800 |
parents | dfa5d9c3ae72 aac27ad79be6 |
children | 5f11de0e7440 |
rev | line source |
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1 //////////////////////////////////////////////////////////////////////// |
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2 // |
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3 // Copyright (C) 2008-2022 The Octave Project Developers |
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4 // |
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5 // See the file COPYRIGHT.md in the top-level directory of this |
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6 // distribution or <https://octave.org/copyright/>. |
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7 // |
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8 // This file is part of Octave. |
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9 // |
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10 // Octave is free software: you can redistribute it and/or modify it |
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11 // under the terms of the GNU General Public License as published by |
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12 // the Free Software Foundation, either version 3 of the License, or |
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13 // (at your option) any later version. |
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14 // |
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15 // Octave is distributed in the hope that it will be useful, but |
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16 // WITHOUT ANY WARRANTY; without even the implied warranty of |
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17 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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18 // GNU General Public License for more details. |
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19 // |
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20 // You should have received a copy of the GNU General Public License |
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21 // along with Octave; see the file COPYING. If not, see |
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22 // <https://www.gnu.org/licenses/>. |
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23 // |
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24 //////////////////////////////////////////////////////////////////////// |
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25 |
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26 #if defined (HAVE_CONFIG_H) |
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27 # include "config.h" |
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28 #endif |
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29 |
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30 #include <cmath> |
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31 |
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32 #include <algorithm> |
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33 #include <limits> |
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34 #include <vector> |
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35 |
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36 #include "Array.h" |
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37 #include "CColVector.h" |
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38 #include "CMatrix.h" |
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39 #include "CRowVector.h" |
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40 #include "CSparse.h" |
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41 #include "MArray.h" |
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42 #include "dColVector.h" |
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43 #include "dDiagMatrix.h" |
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44 #include "dMatrix.h" |
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45 #include "dRowVector.h" |
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46 #include "dSparse.h" |
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47 #include "fCColVector.h" |
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48 #include "fCMatrix.h" |
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49 #include "fCRowVector.h" |
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50 #include "fColVector.h" |
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51 #include "fDiagMatrix.h" |
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52 #include "fMatrix.h" |
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53 #include "fRowVector.h" |
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54 #include "lo-error.h" |
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55 #include "lo-ieee.h" |
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56 #include "lo-mappers.h" |
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57 #include "mx-cm-s.h" |
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58 #include "mx-fcm-fs.h" |
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59 #include "mx-fs-fcm.h" |
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60 #include "mx-s-cm.h" |
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61 #include "oct-cmplx.h" |
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62 #include "oct-norm.h" |
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63 #include "quit.h" |
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64 #include "svd.h" |
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65 |
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66 OCTAVE_BEGIN_NAMESPACE(octave) |
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67 |
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68 // Theory: norm accumulator is an object that has an accum method able |
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69 // to handle both real and complex element, and a cast operator |
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70 // returning the intermediate norm. Reference: Higham, N. "Estimating |
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71 // the Matrix p-Norm." Numer. Math. 62, 539-555, 1992. |
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72 |
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73 // norm accumulator for the p-norm |
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74 template <typename R> |
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75 class norm_accumulator_p |
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76 { |
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77 public: |
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78 norm_accumulator_p () { } // we need this one for Array |
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79 norm_accumulator_p (R pp) : m_p(pp), m_scl(0), m_sum(1) { } |
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80 |
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81 template <typename U> |
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82 void accum (U val) |
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83 { |
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84 octave_quit (); |
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85 R t = std::abs (val); |
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86 if (m_scl == t) // we need this to handle Infs properly |
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87 m_sum += 1; |
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88 else if (m_scl < t) |
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89 { |
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90 m_sum *= std::pow (m_scl/t, m_p); |
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91 m_sum += 1; |
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92 m_scl = t; |
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93 } |
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94 else if (t != 0) |
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95 m_sum += std::pow (t/m_scl, m_p); |
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96 } |
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97 |
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98 operator R () { return m_scl * std::pow (m_sum, 1/m_p); } |
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99 |
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100 private: |
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101 R m_p, m_scl, m_sum; |
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102 }; |
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103 |
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104 // norm accumulator for the minus p-pseudonorm |
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105 template <typename R> |
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106 class norm_accumulator_mp |
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107 { |
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108 public: |
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109 norm_accumulator_mp () { } // we need this one for Array |
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110 norm_accumulator_mp (R pp) : m_p(pp), m_scl(0), m_sum(1) { } |
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111 |
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112 template <typename U> |
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113 void accum (U val) |
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114 { |
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115 octave_quit (); |
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116 R t = 1 / std::abs (val); |
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117 if (m_scl == t) |
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118 m_sum += 1; |
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119 else if (m_scl < t) |
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120 { |
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121 m_sum *= std::pow (m_scl/t, m_p); |
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122 m_sum += 1; |
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123 m_scl = t; |
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124 } |
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125 else if (t != 0) |
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126 m_sum += std::pow (t/m_scl, m_p); |
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127 } |
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128 |
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129 operator R () { return m_scl * std::pow (m_sum, -1/m_p); } |
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130 |
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131 private: |
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132 R m_p, m_scl, m_sum; |
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133 }; |
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134 |
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135 // norm accumulator for the 2-norm (euclidean) |
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136 template <typename R> |
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137 class norm_accumulator_2 |
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138 { |
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139 public: |
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140 norm_accumulator_2 () : m_scl(0), m_sum(1) { } |
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141 |
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142 void accum (R val) |
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143 { |
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144 R t = std::abs (val); |
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145 if (m_scl == t) |
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146 m_sum += 1; |
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147 else if (m_scl < t) |
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148 { |
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149 m_sum *= pow2 (m_scl/t); |
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150 m_sum += 1; |
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151 m_scl = t; |
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152 } |
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153 else if (t != 0) |
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154 m_sum += pow2 (t/m_scl); |
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155 } |
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156 |
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157 void accum (std::complex<R> val) |
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158 { |
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159 accum (val.real ()); |
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160 accum (val.imag ()); |
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161 } |
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162 |
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163 operator R () { return m_scl * std::sqrt (m_sum); } |
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164 |
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165 private: |
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166 static inline R pow2 (R x) { return x*x; } |
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167 |
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168 //-------- |
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169 |
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170 R m_scl, m_sum; |
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171 }; |
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172 |
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173 // norm accumulator for the 1-norm (city metric) |
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174 template <typename R> |
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175 class norm_accumulator_1 |
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176 { |
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177 public: |
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178 norm_accumulator_1 () : m_sum (0) { } |
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179 template <typename U> |
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180 void accum (U val) |
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181 { |
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182 m_sum += std::abs (val); |
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183 } |
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184 |
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185 operator R () { return m_sum; } |
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186 |
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187 private: |
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188 R m_sum; |
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189 }; |
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190 |
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191 // norm accumulator for the inf-norm (max metric) |
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192 template <typename R> |
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193 class norm_accumulator_inf |
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194 { |
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195 public: |
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196 norm_accumulator_inf () : m_max (0) { } |
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197 template <typename U> |
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198 void accum (U val) |
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199 { |
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200 if (math::isnan (val)) |
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201 m_max = numeric_limits<R>::NaN (); |
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202 else |
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203 m_max = std::max (m_max, std::abs (val)); |
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204 } |
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205 |
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206 operator R () { return m_max; } |
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207 |
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208 private: |
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209 R m_max; |
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210 }; |
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211 |
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212 // norm accumulator for the -inf pseudonorm (min abs value) |
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213 template <typename R> |
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214 class norm_accumulator_minf |
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215 { |
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216 public: |
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217 norm_accumulator_minf () : m_min (numeric_limits<R>::Inf ()) { } |
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218 template <typename U> |
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219 void accum (U val) |
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220 { |
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221 if (math::isnan (val)) |
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222 m_min = numeric_limits<R>::NaN (); |
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223 else |
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224 m_min = std::min (m_min, std::abs (val)); |
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225 } |
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226 |
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227 operator R () { return m_min; } |
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228 |
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229 private: |
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230 R m_min; |
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231 }; |
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232 |
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233 // norm accumulator for the 0-pseudonorm (hamming distance) |
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234 template <typename R> |
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235 class norm_accumulator_0 |
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236 { |
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237 public: |
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238 norm_accumulator_0 () : m_num (0) { } |
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239 template <typename U> |
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240 void accum (U val) |
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241 { |
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242 if (val != static_cast<U> (0)) ++m_num; |
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243 } |
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244 |
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245 operator R () { return m_num; } |
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246 |
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247 private: |
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248 unsigned int m_num; |
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249 }; |
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250 |
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251 // OK, we're armed :) Now let's go for the fun |
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252 |
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253 template <typename T, typename R, typename ACC> |
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254 inline void vector_norm (const Array<T>& v, R& res, ACC acc) |
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255 { |
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256 for (octave_idx_type i = 0; i < v.numel (); i++) |
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257 acc.accum (v(i)); |
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258 |
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259 res = acc; |
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260 } |
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261 |
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262 // dense versions |
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263 template <typename T, typename R, typename ACC> |
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264 void column_norms (const MArray<T>& m, MArray<R>& res, ACC acc) |
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265 { |
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266 res = MArray<R> (dim_vector (1, m.columns ())); |
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267 for (octave_idx_type j = 0; j < m.columns (); j++) |
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268 { |
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269 ACC accj = acc; |
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270 for (octave_idx_type i = 0; i < m.rows (); i++) |
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271 accj.accum (m(i, j)); |
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272 |
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273 res.xelem (j) = accj; |
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274 } |
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275 } |
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276 |
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277 template <typename T, typename R, typename ACC> |
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278 void row_norms (const MArray<T>& m, MArray<R>& res, ACC acc) |
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279 { |
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280 res = MArray<R> (dim_vector (m.rows (), 1)); |
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281 std::vector<ACC> acci (m.rows (), acc); |
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282 for (octave_idx_type j = 0; j < m.columns (); j++) |
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283 { |
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284 for (octave_idx_type i = 0; i < m.rows (); i++) |
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285 acci[i].accum (m(i, j)); |
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286 } |
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287 |
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288 for (octave_idx_type i = 0; i < m.rows (); i++) |
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289 res.xelem (i) = acci[i]; |
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290 } |
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291 |
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292 // sparse versions |
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293 template <typename T, typename R, typename ACC> |
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294 void column_norms (const MSparse<T>& m, MArray<R>& res, ACC acc) |
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295 { |
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296 res = MArray<R> (dim_vector (1, m.columns ())); |
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297 for (octave_idx_type j = 0; j < m.columns (); j++) |
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298 { |
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299 ACC accj = acc; |
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300 for (octave_idx_type k = m.cidx (j); k < m.cidx (j+1); k++) |
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301 accj.accum (m.data (k)); |
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302 |
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303 res.xelem (j) = accj; |
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304 } |
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305 } |
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306 |
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307 template <typename T, typename R, typename ACC> |
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308 void row_norms (const MSparse<T>& m, MArray<R>& res, ACC acc) |
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309 { |
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310 res = MArray<R> (dim_vector (m.rows (), 1)); |
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311 std::vector<ACC> acci (m.rows (), acc); |
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312 for (octave_idx_type j = 0; j < m.columns (); j++) |
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313 { |
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314 for (octave_idx_type k = m.cidx (j); k < m.cidx (j+1); k++) |
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315 acci[m.ridx (k)].accum (m.data (k)); |
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316 } |
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317 |
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318 for (octave_idx_type i = 0; i < m.rows (); i++) |
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319 res.xelem (i) = acci[i]; |
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320 } |
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321 |
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322 // now the dispatchers |
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323 #define DEFINE_DISPATCHER(FCN_NAME, ARG_TYPE, RES_TYPE) \ |
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324 template <typename T, typename R> \ |
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325 RES_TYPE FCN_NAME (const ARG_TYPE& v, R p) \ |
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326 { \ |
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327 RES_TYPE res; \ |
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328 if (p == 2) \ |
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329 FCN_NAME (v, res, norm_accumulator_2<R> ()); \ |
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330 else if (p == 1) \ |
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331 FCN_NAME (v, res, norm_accumulator_1<R> ()); \ |
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332 else if (lo_ieee_isinf (p)) \ |
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333 { \ |
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334 if (p > 0) \ |
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335 FCN_NAME (v, res, norm_accumulator_inf<R> ()); \ |
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336 else \ |
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337 FCN_NAME (v, res, norm_accumulator_minf<R> ()); \ |
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338 } \ |
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339 else if (p == 0) \ |
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340 FCN_NAME (v, res, norm_accumulator_0<R> ()); \ |
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341 else if (p > 0) \ |
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342 FCN_NAME (v, res, norm_accumulator_p<R> (p)); \ |
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343 else \ |
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344 FCN_NAME (v, res, norm_accumulator_mp<R> (p)); \ |
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345 return res; \ |
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346 } |
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347 |
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348 DEFINE_DISPATCHER (vector_norm, MArray<T>, R) |
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349 DEFINE_DISPATCHER (column_norms, MArray<T>, MArray<R>) |
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350 DEFINE_DISPATCHER (row_norms, MArray<T>, MArray<R>) |
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351 DEFINE_DISPATCHER (column_norms, MSparse<T>, MArray<R>) |
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352 DEFINE_DISPATCHER (row_norms, MSparse<T>, MArray<R>) |
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353 |
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354 // The approximate subproblem in Higham's method. Find lambda and mu such |
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355 // that norm ([lambda, mu], p) == 1 and norm (y*lambda + col*mu, p) is |
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356 // maximized. |
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357 // Real version. As in Higham's paper. |
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358 template <typename ColVectorT, typename R> |
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359 static void |
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360 higham_subp (const ColVectorT& y, const ColVectorT& col, |
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361 octave_idx_type nsamp, R p, R& lambda, R& mu) |
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362 { |
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363 R nrm = 0; |
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364 for (octave_idx_type i = 0; i < nsamp; i++) |
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365 { |
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366 octave_quit (); |
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367 R fi = i * static_cast<R> (M_PI) / nsamp; |
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368 R lambda1 = cos (fi); |
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369 R mu1 = sin (fi); |
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370 R lmnr = std::pow (std::pow (std::abs (lambda1), p) + |
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371 std::pow (std::abs (mu1), p), 1/p); |
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372 lambda1 /= lmnr; mu1 /= lmnr; |
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373 R nrm1 = vector_norm (lambda1 * y + mu1 * col, p); |
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374 if (nrm1 > nrm) |
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375 { |
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376 lambda = lambda1; |
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377 mu = mu1; |
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378 nrm = nrm1; |
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379 } |
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380 } |
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381 } |
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382 |
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383 // Complex version. Higham's paper does not deal with complex case, so we |
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384 // use a simple extension. First, guess the magnitudes as in real version, |
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385 // then try to rotate lambda to improve further. |
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386 template <typename ColVectorT, typename R> |
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387 static void |
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388 higham_subp (const ColVectorT& y, const ColVectorT& col, |
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389 octave_idx_type nsamp, R p, |
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390 std::complex<R>& lambda, std::complex<R>& mu) |
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391 { |
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392 typedef std::complex<R> CR; |
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393 R nrm = 0; |
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394 lambda = 1.0; |
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395 CR lamcu = lambda / std::abs (lambda); |
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396 // Probe magnitudes |
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397 for (octave_idx_type i = 0; i < nsamp; i++) |
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398 { |
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399 octave_quit (); |
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400 R fi = i * static_cast<R> (M_PI) / nsamp; |
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401 R lambda1 = cos (fi); |
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402 R mu1 = sin (fi); |
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403 R lmnr = std::pow (std::pow (std::abs (lambda1), p) + |
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404 std::pow (std::abs (mu1), p), 1/p); |
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405 lambda1 /= lmnr; mu1 /= lmnr; |
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406 R nrm1 = vector_norm (lambda1 * lamcu * y + mu1 * col, p); |
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407 if (nrm1 > nrm) |
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408 { |
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409 lambda = lambda1 * lamcu; |
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410 mu = mu1; |
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411 nrm = nrm1; |
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412 } |
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413 } |
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414 R lama = std::abs (lambda); |
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415 // Probe orientation |
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416 for (octave_idx_type i = 0; i < nsamp; i++) |
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417 { |
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418 octave_quit (); |
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419 R fi = i * static_cast<R> (M_PI) / nsamp; |
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420 lamcu = CR (cos (fi), sin (fi)); |
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421 R nrm1 = vector_norm (lama * lamcu * y + mu * col, p); |
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422 if (nrm1 > nrm) |
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423 { |
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424 lambda = lama * lamcu; |
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425 nrm = nrm1; |
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426 } |
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427 } |
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428 } |
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429 |
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430 // the p-dual element (should work for both real and complex) |
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431 template <typename T, typename R> |
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432 inline T elem_dual_p (T x, R p) |
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433 { |
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434 return math::signum (x) * std::pow (std::abs (x), p-1); |
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435 } |
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436 |
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437 // the VectorT is used for vectors, but actually it has to be |
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438 // a Matrix type to allow all the operations. For instance SparseMatrix |
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439 // does not support multiplication with column/row vectors. |
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440 // the dual vector |
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441 template <typename VectorT, typename R> |
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442 VectorT dual_p (const VectorT& x, R p, R q) |
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443 { |
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444 VectorT res (x.dims ()); |
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445 for (octave_idx_type i = 0; i < x.numel (); i++) |
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446 res.xelem (i) = elem_dual_p (x(i), p); |
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447 return res / vector_norm (res, q); |
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448 } |
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449 |
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450 // Higham's hybrid method |
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451 template <typename MatrixT, typename VectorT, typename R> |
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452 R higham (const MatrixT& m, R p, R tol, int maxiter, |
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453 VectorT& x) |
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454 { |
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455 x.resize (m.columns (), 1); |
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456 // the OSE part |
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457 VectorT y(m.rows (), 1, 0), z(m.rows (), 1); |
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458 typedef typename VectorT::element_type RR; |
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459 RR lambda = 0; |
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460 RR mu = 1; |
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461 for (octave_idx_type k = 0; k < m.columns (); k++) |
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462 { |
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463 octave_quit (); |
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464 VectorT col (m.column (k)); |
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465 if (k > 0) |
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466 higham_subp (y, col, 4*k, p, lambda, mu); |
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467 for (octave_idx_type i = 0; i < k; i++) |
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468 x(i) *= lambda; |
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469 x(k) = mu; |
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470 y = lambda * y + mu * col; |
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471 } |
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472 |
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473 // the PM part |
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474 x = x / vector_norm (x, p); |
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475 R q = p/(p-1); |
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476 |
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477 R gamma = 0, gamma1; |
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478 int iter = 0; |
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479 while (iter < maxiter) |
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480 { |
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481 octave_quit (); |
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482 y = m*x; |
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483 gamma1 = gamma; |
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484 gamma = vector_norm (y, p); |
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485 z = dual_p (y, p, q); |
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486 z = z.hermitian (); |
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487 z = z * m; |
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488 |
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489 if (iter > 0 && (vector_norm (z, q) <= gamma |
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490 || (gamma - gamma1) <= tol*gamma)) |
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491 break; |
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492 |
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493 z = z.hermitian (); |
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494 x = dual_p (z, q, p); |
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495 iter++; |
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496 } |
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497 |
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498 return gamma; |
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499 } |
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500 |
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501 // derive column vector and SVD types |
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502 |
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503 static const char *p_less1_gripe = "xnorm: p must be >= 1"; |
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504 |
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505 // Static constant to control the maximum number of iterations. 100 seems to |
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506 // be a good value. Eventually, we can provide a means to change this |
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507 // constant from Octave. |
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508 static int max_norm_iter = 100; |
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509 |
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510 // version with SVD for dense matrices |
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511 template <typename MatrixT, typename VectorT, typename R> |
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512 R svd_matrix_norm (const MatrixT& m, R p, VectorT) |
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513 { |
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514 R res = 0; |
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515 if (p == 2) |
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516 { |
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517 math::svd<MatrixT> fact (m, math::svd<MatrixT>::Type::sigma_only); |
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518 res = fact.singular_values () (0, 0); |
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519 } |
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520 else if (p == 1) |
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521 res = xcolnorms (m, static_cast<R> (1)).max (); |
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522 else if (lo_ieee_isinf (p) && p > 1) |
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523 res = xrownorms (m, static_cast<R> (1)).max (); |
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524 else if (p > 1) |
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525 { |
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526 VectorT x; |
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527 const R sqrteps = std::sqrt (std::numeric_limits<R>::epsilon ()); |
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528 res = higham (m, p, sqrteps, max_norm_iter, x); |
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529 } |
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530 else |
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531 (*current_liboctave_error_handler) ("%s", p_less1_gripe); |
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532 |
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533 return res; |
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534 } |
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535 |
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536 // SVD-free version for sparse matrices |
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537 template <typename MatrixT, typename VectorT, typename R> |
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538 R matrix_norm (const MatrixT& m, R p, VectorT) |
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539 { |
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540 R res = 0; |
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541 if (p == 1) |
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542 res = xcolnorms (m, static_cast<R> (1)).max (); |
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543 else if (lo_ieee_isinf (p) && p > 1) |
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544 res = xrownorms (m, static_cast<R> (1)).max (); |
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545 else if (p > 1) |
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546 { |
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547 VectorT x; |
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548 const R sqrteps = std::sqrt (std::numeric_limits<R>::epsilon ()); |
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549 res = higham (m, p, sqrteps, max_norm_iter, x); |
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550 } |
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551 else |
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552 (*current_liboctave_error_handler) ("%s", p_less1_gripe); |
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553 |
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554 return res; |
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555 } |
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556 |
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557 // and finally, here's what we've promised in the header file |
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558 |
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559 #define DEFINE_XNORM_FCNS(PREFIX, RTYPE) \ |
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560 RTYPE xnorm (const PREFIX##ColumnVector& x, RTYPE p) \ |
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561 { \ |
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562 return vector_norm (x, p); \ |
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563 } \ |
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564 RTYPE xnorm (const PREFIX##RowVector& x, RTYPE p) \ |
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565 { \ |
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566 return vector_norm (x, p); \ |
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567 } \ |
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568 RTYPE xnorm (const PREFIX##Matrix& x, RTYPE p) \ |
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569 { \ |
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570 return svd_matrix_norm (x, p, PREFIX##Matrix ()); \ |
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571 } \ |
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572 RTYPE xfrobnorm (const PREFIX##Matrix& x) \ |
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573 { \ |
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574 return vector_norm (x, static_cast<RTYPE> (2)); \ |
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575 } |
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576 |
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577 DEFINE_XNORM_FCNS(, double) |
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578 DEFINE_XNORM_FCNS(Complex, double) |
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579 DEFINE_XNORM_FCNS(Float, float) |
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580 DEFINE_XNORM_FCNS(FloatComplex, float) |
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581 |
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582 // this is needed to avoid copying the sparse matrix for xfrobnorm |
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583 template <typename T, typename R> |
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584 inline void array_norm_2 (const T *v, octave_idx_type n, R& res) |
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585 { |
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586 norm_accumulator_2<R> acc; |
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587 for (octave_idx_type i = 0; i < n; i++) |
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588 acc.accum (v[i]); |
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589 |
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590 res = acc; |
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591 } |
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592 |
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593 #define DEFINE_XNORM_SPARSE_FCNS(PREFIX, RTYPE) \ |
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594 RTYPE xnorm (const Sparse##PREFIX##Matrix& x, RTYPE p) \ |
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595 { \ |
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596 return matrix_norm (x, p, PREFIX##Matrix ()); \ |
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597 } \ |
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598 RTYPE xfrobnorm (const Sparse##PREFIX##Matrix& x) \ |
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599 { \ |
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600 RTYPE res; \ |
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601 array_norm_2 (x.data (), x.nnz (), res); \ |
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602 return res; \ |
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603 } |
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604 |
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605 DEFINE_XNORM_SPARSE_FCNS(, double) |
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606 DEFINE_XNORM_SPARSE_FCNS(Complex, double) |
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607 |
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608 #define DEFINE_COLROW_NORM_FCNS(PREFIX, RPREFIX, RTYPE) \ |
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609 RPREFIX##RowVector \ |
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610 xcolnorms (const PREFIX##Matrix& m, RTYPE p) \ |
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611 { \ |
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612 return column_norms (m, p); \ |
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613 } \ |
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614 RPREFIX##ColumnVector \ |
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615 xrownorms (const PREFIX##Matrix& m, RTYPE p) \ |
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616 { \ |
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617 return row_norms (m, p); \ |
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618 } \ |
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619 |
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620 DEFINE_COLROW_NORM_FCNS(, , double) |
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621 DEFINE_COLROW_NORM_FCNS(Complex, , double) |
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622 DEFINE_COLROW_NORM_FCNS(Float, Float, float) |
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623 DEFINE_COLROW_NORM_FCNS(FloatComplex, Float, float) |
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624 |
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625 DEFINE_COLROW_NORM_FCNS(Sparse, , double) |
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626 DEFINE_COLROW_NORM_FCNS(SparseComplex, , double) |
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627 |
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628 OCTAVE_END_NAMESPACE(octave) |