| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: mierle@gmail.com (Keir Mierle) |
| |
| #include "ceres/c_api.h" |
| |
| #include <cmath> |
| |
| #include "glog/logging.h" |
| #include "gtest/gtest.h" |
| |
| // Duplicated from curve_fitting.cc. |
| int num_observations = 67; |
| // clang-format off |
| double data[] = { |
| 0.000000e+00, 1.133898e+00, |
| 7.500000e-02, 1.334902e+00, |
| 1.500000e-01, 1.213546e+00, |
| 2.250000e-01, 1.252016e+00, |
| 3.000000e-01, 1.392265e+00, |
| 3.750000e-01, 1.314458e+00, |
| 4.500000e-01, 1.472541e+00, |
| 5.250000e-01, 1.536218e+00, |
| 6.000000e-01, 1.355679e+00, |
| 6.750000e-01, 1.463566e+00, |
| 7.500000e-01, 1.490201e+00, |
| 8.250000e-01, 1.658699e+00, |
| 9.000000e-01, 1.067574e+00, |
| 9.750000e-01, 1.464629e+00, |
| 1.050000e+00, 1.402653e+00, |
| 1.125000e+00, 1.713141e+00, |
| 1.200000e+00, 1.527021e+00, |
| 1.275000e+00, 1.702632e+00, |
| 1.350000e+00, 1.423899e+00, |
| 1.425000e+00, 1.543078e+00, |
| 1.500000e+00, 1.664015e+00, |
| 1.575000e+00, 1.732484e+00, |
| 1.650000e+00, 1.543296e+00, |
| 1.725000e+00, 1.959523e+00, |
| 1.800000e+00, 1.685132e+00, |
| 1.875000e+00, 1.951791e+00, |
| 1.950000e+00, 2.095346e+00, |
| 2.025000e+00, 2.361460e+00, |
| 2.100000e+00, 2.169119e+00, |
| 2.175000e+00, 2.061745e+00, |
| 2.250000e+00, 2.178641e+00, |
| 2.325000e+00, 2.104346e+00, |
| 2.400000e+00, 2.584470e+00, |
| 2.475000e+00, 1.914158e+00, |
| 2.550000e+00, 2.368375e+00, |
| 2.625000e+00, 2.686125e+00, |
| 2.700000e+00, 2.712395e+00, |
| 2.775000e+00, 2.499511e+00, |
| 2.850000e+00, 2.558897e+00, |
| 2.925000e+00, 2.309154e+00, |
| 3.000000e+00, 2.869503e+00, |
| 3.075000e+00, 3.116645e+00, |
| 3.150000e+00, 3.094907e+00, |
| 3.225000e+00, 2.471759e+00, |
| 3.300000e+00, 3.017131e+00, |
| 3.375000e+00, 3.232381e+00, |
| 3.450000e+00, 2.944596e+00, |
| 3.525000e+00, 3.385343e+00, |
| 3.600000e+00, 3.199826e+00, |
| 3.675000e+00, 3.423039e+00, |
| 3.750000e+00, 3.621552e+00, |
| 3.825000e+00, 3.559255e+00, |
| 3.900000e+00, 3.530713e+00, |
| 3.975000e+00, 3.561766e+00, |
| 4.050000e+00, 3.544574e+00, |
| 4.125000e+00, 3.867945e+00, |
| 4.200000e+00, 4.049776e+00, |
| 4.275000e+00, 3.885601e+00, |
| 4.350000e+00, 4.110505e+00, |
| 4.425000e+00, 4.345320e+00, |
| 4.500000e+00, 4.161241e+00, |
| 4.575000e+00, 4.363407e+00, |
| 4.650000e+00, 4.161576e+00, |
| 4.725000e+00, 4.619728e+00, |
| 4.800000e+00, 4.737410e+00, |
| 4.875000e+00, 4.727863e+00, |
| 4.950000e+00, 4.669206e+00, |
| }; |
| // clang-format on |
| |
| // A test cost function, similar to the one in curve_fitting.c. |
| static int exponential_residual(void* user_data, |
| double** parameters, |
| double* residuals, |
| double** jacobians) { |
| double* measurement = (double*)user_data; |
| double x = measurement[0]; |
| double y = measurement[1]; |
| double m = parameters[0][0]; |
| double c = parameters[1][0]; |
| |
| residuals[0] = y - exp(m * x + c); |
| if (jacobians == NULL) { |
| return 1; |
| } |
| if (jacobians[0] != NULL) { |
| jacobians[0][0] = -x * exp(m * x + c); // dr/dm |
| } |
| if (jacobians[1] != NULL) { |
| jacobians[1][0] = -exp(m * x + c); // dr/dc |
| } |
| return 1; |
| } |
| |
| namespace ceres { |
| namespace internal { |
| |
| TEST(C_API, SimpleEndToEndTest) { |
| double m = 0.0; |
| double c = 0.0; |
| double* parameter_pointers[] = {&m, &c}; |
| int parameter_sizes[] = {1, 1}; |
| |
| ceres_problem_t* problem = ceres_create_problem(); |
| for (int i = 0; i < num_observations; ++i) { |
| ceres_problem_add_residual_block( |
| problem, |
| exponential_residual, // Cost function |
| &data[2 * i], // Points to the (x,y) measurement |
| NULL, // Loss function |
| NULL, // Loss function user data |
| 1, // Number of residuals |
| 2, // Number of parameter blocks |
| parameter_sizes, |
| parameter_pointers); |
| } |
| |
| ceres_solve(problem); |
| |
| EXPECT_NEAR(0.3, m, 0.02); |
| EXPECT_NEAR(0.1, c, 0.04); |
| |
| ceres_free_problem(problem); |
| } |
| |
| template <typename T> |
| class ScopedSetValue { |
| public: |
| ScopedSetValue(T* variable, T new_value) |
| : variable_(variable), old_value_(*variable) { |
| *variable = new_value; |
| } |
| ~ScopedSetValue() { *variable_ = old_value_; } |
| |
| private: |
| T* variable_; |
| T old_value_; |
| }; |
| |
| TEST(C_API, LossFunctions) { |
| double m = 0.2; |
| double c = 0.03; |
| double* parameter_pointers[] = {&m, &c}; |
| int parameter_sizes[] = {1, 1}; |
| |
| // Create two outliers, but be careful to leave the data intact. |
| ScopedSetValue<double> outlier1x(&data[12], 2.5); |
| ScopedSetValue<double> outlier1y(&data[13], 1.0e3); |
| ScopedSetValue<double> outlier2x(&data[14], 3.2); |
| ScopedSetValue<double> outlier2y(&data[15], 30e3); |
| |
| // Create a cauchy cost function, and reuse it many times. |
| void* cauchy_loss_data = ceres_create_cauchy_loss_function_data(5.0); |
| |
| ceres_problem_t* problem = ceres_create_problem(); |
| for (int i = 0; i < num_observations; ++i) { |
| ceres_problem_add_residual_block( |
| problem, |
| exponential_residual, // Cost function |
| &data[2 * i], // Points to the (x,y) measurement |
| ceres_stock_loss_function, // |
| cauchy_loss_data, // Loss function user data |
| 1, // Number of residuals |
| 2, // Number of parameter blocks |
| parameter_sizes, |
| parameter_pointers); |
| } |
| |
| ceres_solve(problem); |
| |
| EXPECT_NEAR(0.3, m, 0.02); |
| EXPECT_NEAR(0.1, c, 0.04); |
| |
| ceres_free_stock_loss_function_data(cauchy_loss_data); |
| ceres_free_problem(problem); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |