GoogleGit

blob: 210411ee0605dc7ce9ce13c56a2540a40353977a [file] [log] [blame]
  1. // Ceres Solver - A fast non-linear least squares minimizer
  2. // Copyright 2015 Google Inc. All rights reserved.
  3. // http://ceres-solver.org/
  4. //
  5. // Redistribution and use in source and binary forms, with or without
  6. // modification, are permitted provided that the following conditions are met:
  7. //
  8. // * Redistributions of source code must retain the above copyright notice,
  9. // this list of conditions and the following disclaimer.
  10. // * Redistributions in binary form must reproduce the above copyright notice,
  11. // this list of conditions and the following disclaimer in the documentation
  12. // and/or other materials provided with the distribution.
  13. // * Neither the name of Google Inc. nor the names of its contributors may be
  14. // used to endorse or promote products derived from this software without
  15. // specific prior written permission.
  16. //
  17. // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  18. // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  19. // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  20. // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
  21. // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  22. // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  23. // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  24. // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  25. // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  26. // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  27. // POSSIBILITY OF SUCH DAMAGE.
  28. //
  29. // Author: sameeragarwal@google.com (Sameer Agarwal)
  30. #include "ceres/ceres.h"
  31. #include "glog/logging.h"
  32. using ceres::AutoDiffCostFunction;
  33. using ceres::CostFunction;
  34. using ceres::Problem;
  35. using ceres::Solver;
  36. using ceres::Solve;
  37. // Data generated using the following octave code.
  38. // randn('seed', 23497);
  39. // m = 0.3;
  40. // c = 0.1;
  41. // x=[0:0.075:5];
  42. // y = exp(m * x + c);
  43. // noise = randn(size(x)) * 0.2;
  44. // y_observed = y + noise;
  45. // data = [x', y_observed'];
  46. const int kNumObservations = 67;
  47. const double data[] = {
  48. 0.000000e+00, 1.133898e+00,
  49. 7.500000e-02, 1.334902e+00,
  50. 1.500000e-01, 1.213546e+00,
  51. 2.250000e-01, 1.252016e+00,
  52. 3.000000e-01, 1.392265e+00,
  53. 3.750000e-01, 1.314458e+00,
  54. 4.500000e-01, 1.472541e+00,
  55. 5.250000e-01, 1.536218e+00,
  56. 6.000000e-01, 1.355679e+00,
  57. 6.750000e-01, 1.463566e+00,
  58. 7.500000e-01, 1.490201e+00,
  59. 8.250000e-01, 1.658699e+00,
  60. 9.000000e-01, 1.067574e+00,
  61. 9.750000e-01, 1.464629e+00,
  62. 1.050000e+00, 1.402653e+00,
  63. 1.125000e+00, 1.713141e+00,
  64. 1.200000e+00, 1.527021e+00,
  65. 1.275000e+00, 1.702632e+00,
  66. 1.350000e+00, 1.423899e+00,
  67. 1.425000e+00, 1.543078e+00,
  68. 1.500000e+00, 1.664015e+00,
  69. 1.575000e+00, 1.732484e+00,
  70. 1.650000e+00, 1.543296e+00,
  71. 1.725000e+00, 1.959523e+00,
  72. 1.800000e+00, 1.685132e+00,
  73. 1.875000e+00, 1.951791e+00,
  74. 1.950000e+00, 2.095346e+00,
  75. 2.025000e+00, 2.361460e+00,
  76. 2.100000e+00, 2.169119e+00,
  77. 2.175000e+00, 2.061745e+00,
  78. 2.250000e+00, 2.178641e+00,
  79. 2.325000e+00, 2.104346e+00,
  80. 2.400000e+00, 2.584470e+00,
  81. 2.475000e+00, 1.914158e+00,
  82. 2.550000e+00, 2.368375e+00,
  83. 2.625000e+00, 2.686125e+00,
  84. 2.700000e+00, 2.712395e+00,
  85. 2.775000e+00, 2.499511e+00,
  86. 2.850000e+00, 2.558897e+00,
  87. 2.925000e+00, 2.309154e+00,
  88. 3.000000e+00, 2.869503e+00,
  89. 3.075000e+00, 3.116645e+00,
  90. 3.150000e+00, 3.094907e+00,
  91. 3.225000e+00, 2.471759e+00,
  92. 3.300000e+00, 3.017131e+00,
  93. 3.375000e+00, 3.232381e+00,
  94. 3.450000e+00, 2.944596e+00,
  95. 3.525000e+00, 3.385343e+00,
  96. 3.600000e+00, 3.199826e+00,
  97. 3.675000e+00, 3.423039e+00,
  98. 3.750000e+00, 3.621552e+00,
  99. 3.825000e+00, 3.559255e+00,
  100. 3.900000e+00, 3.530713e+00,
  101. 3.975000e+00, 3.561766e+00,
  102. 4.050000e+00, 3.544574e+00,
  103. 4.125000e+00, 3.867945e+00,
  104. 4.200000e+00, 4.049776e+00,
  105. 4.275000e+00, 3.885601e+00,
  106. 4.350000e+00, 4.110505e+00,
  107. 4.425000e+00, 4.345320e+00,
  108. 4.500000e+00, 4.161241e+00,
  109. 4.575000e+00, 4.363407e+00,
  110. 4.650000e+00, 4.161576e+00,
  111. 4.725000e+00, 4.619728e+00,
  112. 4.800000e+00, 4.737410e+00,
  113. 4.875000e+00, 4.727863e+00,
  114. 4.950000e+00, 4.669206e+00,
  115. };
  116. struct ExponentialResidual {
  117. ExponentialResidual(double x, double y)
  118. : x_(x), y_(y) {}
  119. template <typename T> bool operator()(const T* const m,
  120. const T* const c,
  121. T* residual) const {
  122. residual[0] = T(y_) - exp(m[0] * T(x_) + c[0]);
  123. return true;
  124. }
  125. private:
  126. const double x_;
  127. const double y_;
  128. };
  129. int main(int argc, char** argv) {
  130. google::InitGoogleLogging(argv[0]);
  131. double m = 0.0;
  132. double c = 0.0;
  133. Problem problem;
  134. for (int i = 0; i < kNumObservations; ++i) {
  135. problem.AddResidualBlock(
  136. new AutoDiffCostFunction<ExponentialResidual, 1, 1, 1>(
  137. new ExponentialResidual(data[2 * i], data[2 * i + 1])),
  138. NULL,
  139. &m, &c);
  140. }
  141. Solver::Options options;
  142. options.max_num_iterations = 25;
  143. options.linear_solver_type = ceres::DENSE_QR;
  144. options.minimizer_progress_to_stdout = true;
  145. Solver::Summary summary;
  146. Solve(options, &problem, &summary);
  147. std::cout << summary.BriefReport() << "\n";
  148. std::cout << "Initial m: " << 0.0 << " c: " << 0.0 << "\n";
  149. std::cout << "Final m: " << m << " c: " << c << "\n";
  150. return 0;
  151. }