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annotate OrthancStone/Sources/Toolbox/LinearAlgebra.h @ 2114:c23eef785569

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update year to 2024
author Sebastien Jodogne <s.jodogne@gmail.com>
date Wed, 24 Jan 2024 16:44:04 +0100
parents 07964689cb0b
children 16c01cc201e7
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1 /**
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2 * Stone of Orthanc
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3 * Copyright (C) 2012-2016 Sebastien Jodogne, Medical Physics
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4 * Department, University Hospital of Liege, Belgium
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5 * Copyright (C) 2017-2024 Osimis S.A., Belgium
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6 * Copyright (C) 2021-2024 Sebastien Jodogne, ICTEAM UCLouvain, Belgium
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7 *
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8 * This program is free software: you can redistribute it and/or
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9 * modify it under the terms of the GNU Lesser General Public License
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10 * as published by the Free Software Foundation, either version 3 of
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11 * the License, or (at your option) any later version.
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12 *
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13 * This program is distributed in the hope that it will be useful, but
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14 * WITHOUT ANY WARRANTY; without even the implied warranty of
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15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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16 * Lesser General Public License for more details.
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17 *
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18 * You should have received a copy of the GNU Lesser General Public
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19 * License along with this program. If not, see
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20 * <http://www.gnu.org/licenses/>.
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21 **/
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22
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23
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24 #pragma once
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25
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26 // Patch for ublas in Boost 1.64.0
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27 // https://github.com/dealii/dealii/issues/4302
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28 #include <boost/version.hpp>
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29 #if BOOST_VERSION >= 106300 // or 64, need to check
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30 # include <boost/serialization/array_wrapper.hpp>
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31 #endif
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32
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33 #include <DicomFormat/DicomMap.h>
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34
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35 #include <boost/numeric/ublas/matrix.hpp>
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36 #include <boost/numeric/ublas/vector.hpp>
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37
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38 namespace OrthancStone
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39 {
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40 typedef boost::numeric::ublas::matrix<double> Matrix;
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41 typedef boost::numeric::ublas::vector<double> Vector;
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42
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43 // logs, debugging...
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44 std::ostream& operator<<(std::ostream& s, const Vector& vec);
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45 std::ostream& operator<<(std::ostream& s, const Matrix& m);
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46
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47 namespace LinearAlgebra
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48 {
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49 class OnlineVarianceEstimator
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50 {
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51 private:
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52 unsigned int count_;
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53 double sum_;
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54 double sumOfSquares_;
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55
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56 public:
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57 OnlineVarianceEstimator()
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58 {
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59 Clear();
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60 }
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61
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62 unsigned int GetCount() const
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63 {
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64 return count_;
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65 }
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66
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67 void AddSample(double value);
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68
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69 void Clear();
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70
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71 double GetMean() const; // Same as "mean()" in Matlab/Octave
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72
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73 double GetVariance() const; // Same as "var()" in Matlab/Octave
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74
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75 double GetStandardDeviation() const; // Same as "std()" in Matlab/Octave
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76 };
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77
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78 void Print(const Vector& v);
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79
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80 void Print(const Matrix& m);
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81
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82 bool ParseVector(Vector& target,
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83 const std::string& s);
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84
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85 bool ParseVector(Vector& target,
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86 const Orthanc::DicomMap& dataset,
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87 const Orthanc::DicomTag& tag);
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88
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89 inline void AssignVector(Vector& v,
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90 double v1,
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91 double v2)
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92 {
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93 v.resize(2);
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94 v[0] = v1;
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95 v[1] = v2;
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96 }
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97
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98
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99 inline void AssignVector(Vector& v,
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100 double v1)
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101 {
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102 v.resize(1);
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103 v[0] = v1;
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104 }
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105
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107 inline void AssignVector(Vector& v,
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108 double v1,
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109 double v2,
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110 double v3)
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111 {
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112 v.resize(3);
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113 v[0] = v1;
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114 v[1] = v2;
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115 v[2] = v3;
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116 }
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117
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118
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119 inline void AssignVector(Vector& v,
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120 double v1,
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121 double v2,
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122 double v3,
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123 double v4)
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124 {
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125 v.resize(4);
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126 v[0] = v1;
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127 v[1] = v2;
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128 v[2] = v3;
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129 v[3] = v4;
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130 }
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132
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133 inline Vector CreateVector(double v1)
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134 {
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135 Vector v;
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136 AssignVector(v, v1);
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137 return v;
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138 }
165
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139
181
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140
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141 inline Vector CreateVector(double v1,
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142 double v2)
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143 {
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144 Vector v;
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145 AssignVector(v, v1, v2);
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146 return v;
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147 }
165
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148
181
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149
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150 inline Vector CreateVector(double v1,
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151 double v2,
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152 double v3)
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153 {
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154 Vector v;
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155 AssignVector(v, v1, v2, v3);
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156 return v;
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157 }
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158
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159
181
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160 inline Vector CreateVector(double v1,
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161 double v2,
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162 double v3,
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163 double v4)
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164 {
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165 Vector v;
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166 AssignVector(v, v1, v2, v3, v4);
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167 return v;
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168 }
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169
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170
158
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171 inline bool IsNear(double x,
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172 double y,
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173 double threshold)
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174 {
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175 return fabs(x - y) <= threshold;
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176 }
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177
181
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178 inline bool IsNear(double x,
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179 double y)
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180 {
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181 // As most input is read as single-precision numbers, we take the
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182 // epsilon machine for float32 into consideration to compare numbers
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183 return IsNear(x, y, 10.0 * std::numeric_limits<float>::epsilon());
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184 }
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185
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186 inline bool IsCloseToZero(double x)
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187 {
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188 return IsNear(x, 0.0);
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189 }
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190
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191 void NormalizeVector(Vector& u);
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192
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193 void CrossProduct(Vector& result,
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194 const Vector& u,
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195 const Vector& v);
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196
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197 double DotProduct(const Vector& u, const Vector& v);
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198
158
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199 void FillMatrix(Matrix& target,
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200 size_t rows,
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201 size_t columns,
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202 const double values[]);
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203
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204 void FillVector(Vector& target,
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205 size_t size,
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206 const double values[]);
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207
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208 void Convert(Matrix& target,
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209 const Vector& source);
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210
165
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211 inline Matrix Transpose(const Matrix& a)
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212 {
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213 return boost::numeric::ublas::trans(a);
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214 }
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215
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216
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217 inline Matrix IdentityMatrix(size_t size)
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218 {
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219 return boost::numeric::ublas::identity_matrix<double>(size);
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220 }
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221
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222
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223 inline Matrix ZeroMatrix(size_t size1,
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224 size_t size2)
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225 {
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226 return boost::numeric::ublas::zero_matrix<double>(size1, size2);
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227 }
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228
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229
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230 inline Matrix Product(const Matrix& a,
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231 const Matrix& b)
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232 {
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233 return boost::numeric::ublas::prod(a, b);
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234 }
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235
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236
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237 inline Vector Product(const Matrix& a,
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238 const Vector& b)
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239 {
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240 return boost::numeric::ublas::prod(a, b);
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241 }
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242
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243
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244 inline Matrix Product(const Matrix& a,
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245 const Matrix& b,
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246 const Matrix& c)
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247 {
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248 return Product(a, Product(b, c));
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249 }
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250
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251
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252 inline Matrix Product(const Matrix& a,
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253 const Matrix& b,
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254 const Matrix& c,
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255 const Matrix& d)
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256 {
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257 return Product(a, Product(b, c, d));
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258 }
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259
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260
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261 inline Matrix Product(const Matrix& a,
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262 const Matrix& b,
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263 const Matrix& c,
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264 const Matrix& d,
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265 const Matrix& e)
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266 {
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267 return Product(a, Product(b, c, d, e));
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268 }
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269
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270
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271 inline Vector Product(const Matrix& a,
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272 const Matrix& b,
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273 const Vector& c)
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274 {
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275 return Product(Product(a, b), c);
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276 }
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277
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278
166
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279 inline Vector Product(const Matrix& a,
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280 const Matrix& b,
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281 const Matrix& c,
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282 const Vector& d)
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283 {
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284 return Product(Product(a, b, c), d);
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285 }
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286
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287
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288 double ComputeDeterminant(const Matrix& a);
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289
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290 bool IsOrthogonalMatrix(const Matrix& q,
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291 double threshold);
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292
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293 bool IsOrthogonalMatrix(const Matrix& q);
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294
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295 bool IsRotationMatrix(const Matrix& r,
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296 double threshold);
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297
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298 bool IsRotationMatrix(const Matrix& r);
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299
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300 void InvertUpperTriangularMatrix(Matrix& output,
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301 const Matrix& k);
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302
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303 /**
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304 * This function computes the RQ decomposition of a 3x3 matrix,
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305 * using Givens rotations. Reference: Algorithm A4.1 (page 579) of
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306 * "Multiple View Geometry in Computer Vision" (2nd edition). The
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307 * output matrix "Q" is a rotation matrix, and "R" is upper
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308 * triangular.
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309 **/
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310 void RQDecomposition3x3(Matrix& r,
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311 Matrix& q,
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312 const Matrix& a);
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313
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314 void InvertMatrix(Matrix& target,
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315 const Matrix& source);
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316
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317 // This is the same as "InvertMatrix()", but without exception
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318 bool InvertMatrixUnsafe(Matrix& target,
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319 const Matrix& source);
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320
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321 void CreateSkewSymmetric(Matrix& s,
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322 const Vector& v);
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323
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324 Matrix InvertScalingTranslationMatrix(const Matrix& t);
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325
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326 bool IsShearMatrix(const Matrix& shear);
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327
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328 Matrix InvertShearMatrix(const Matrix& shear);
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329
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330 double ComputeMedian(std::vector<double>& v);
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331
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332 float ComputeMedian(std::vector<float>& v);
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333 }
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334 }