1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2013 Google Inc. All rights reserved. 3 // http://code.google.com/p/ceres-solver/ 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: mierle (at) gmail.com (Keir Mierle) 30 31 #include "ceres/c_api.h" 32 33 #include <cmath> 34 35 #include "glog/logging.h" 36 #include "gtest/gtest.h" 37 38 // Duplicated from curve_fitting.cc. 39 int num_observations = 67; 40 double data[] = { 41 0.000000e+00, 1.133898e+00, 42 7.500000e-02, 1.334902e+00, 43 1.500000e-01, 1.213546e+00, 44 2.250000e-01, 1.252016e+00, 45 3.000000e-01, 1.392265e+00, 46 3.750000e-01, 1.314458e+00, 47 4.500000e-01, 1.472541e+00, 48 5.250000e-01, 1.536218e+00, 49 6.000000e-01, 1.355679e+00, 50 6.750000e-01, 1.463566e+00, 51 7.500000e-01, 1.490201e+00, 52 8.250000e-01, 1.658699e+00, 53 9.000000e-01, 1.067574e+00, 54 9.750000e-01, 1.464629e+00, 55 1.050000e+00, 1.402653e+00, 56 1.125000e+00, 1.713141e+00, 57 1.200000e+00, 1.527021e+00, 58 1.275000e+00, 1.702632e+00, 59 1.350000e+00, 1.423899e+00, 60 1.425000e+00, 1.543078e+00, 61 1.500000e+00, 1.664015e+00, 62 1.575000e+00, 1.732484e+00, 63 1.650000e+00, 1.543296e+00, 64 1.725000e+00, 1.959523e+00, 65 1.800000e+00, 1.685132e+00, 66 1.875000e+00, 1.951791e+00, 67 1.950000e+00, 2.095346e+00, 68 2.025000e+00, 2.361460e+00, 69 2.100000e+00, 2.169119e+00, 70 2.175000e+00, 2.061745e+00, 71 2.250000e+00, 2.178641e+00, 72 2.325000e+00, 2.104346e+00, 73 2.400000e+00, 2.584470e+00, 74 2.475000e+00, 1.914158e+00, 75 2.550000e+00, 2.368375e+00, 76 2.625000e+00, 2.686125e+00, 77 2.700000e+00, 2.712395e+00, 78 2.775000e+00, 2.499511e+00, 79 2.850000e+00, 2.558897e+00, 80 2.925000e+00, 2.309154e+00, 81 3.000000e+00, 2.869503e+00, 82 3.075000e+00, 3.116645e+00, 83 3.150000e+00, 3.094907e+00, 84 3.225000e+00, 2.471759e+00, 85 3.300000e+00, 3.017131e+00, 86 3.375000e+00, 3.232381e+00, 87 3.450000e+00, 2.944596e+00, 88 3.525000e+00, 3.385343e+00, 89 3.600000e+00, 3.199826e+00, 90 3.675000e+00, 3.423039e+00, 91 3.750000e+00, 3.621552e+00, 92 3.825000e+00, 3.559255e+00, 93 3.900000e+00, 3.530713e+00, 94 3.975000e+00, 3.561766e+00, 95 4.050000e+00, 3.544574e+00, 96 4.125000e+00, 3.867945e+00, 97 4.200000e+00, 4.049776e+00, 98 4.275000e+00, 3.885601e+00, 99 4.350000e+00, 4.110505e+00, 100 4.425000e+00, 4.345320e+00, 101 4.500000e+00, 4.161241e+00, 102 4.575000e+00, 4.363407e+00, 103 4.650000e+00, 4.161576e+00, 104 4.725000e+00, 4.619728e+00, 105 4.800000e+00, 4.737410e+00, 106 4.875000e+00, 4.727863e+00, 107 4.950000e+00, 4.669206e+00, 108 }; 109 110 // A test cost function, similar to the one in curve_fitting.c. 111 int exponential_residual(void* user_data, 112 double** parameters, 113 double* residuals, 114 double** jacobians) { 115 double* measurement = (double*) user_data; 116 double x = measurement[0]; 117 double y = measurement[1]; 118 double m = parameters[0][0]; 119 double c = parameters[1][0]; 120 121 residuals[0] = y - exp(m * x + c); 122 if (jacobians == NULL) { 123 return 1; 124 } 125 if (jacobians[0] != NULL) { 126 jacobians[0][0] = - x * exp(m * x + c); // dr/dm 127 } 128 if (jacobians[1] != NULL) { 129 jacobians[1][0] = - exp(m * x + c); // dr/dc 130 } 131 return 1; 132 } 133 134 namespace ceres { 135 namespace internal { 136 137 TEST(C_API, SimpleEndToEndTest) { 138 double m = 0.0; 139 double c = 0.0; 140 double *parameter_pointers[] = { &m, &c }; 141 int parameter_sizes[] = { 1, 1 }; 142 143 ceres_problem_t* problem = ceres_create_problem(); 144 for (int i = 0; i < num_observations; ++i) { 145 ceres_problem_add_residual_block( 146 problem, 147 exponential_residual, // Cost function 148 &data[2 * i], // Points to the (x,y) measurement 149 NULL, // Loss function 150 NULL, // Loss function user data 151 1, // Number of residuals 152 2, // Number of parameter blocks 153 parameter_sizes, 154 parameter_pointers); 155 } 156 157 ceres_solve(problem); 158 159 EXPECT_NEAR(0.3, m, 0.02); 160 EXPECT_NEAR(0.1, c, 0.04); 161 162 ceres_free_problem(problem); 163 } 164 165 template<typename T> 166 class ScopedSetValue { 167 public: 168 ScopedSetValue(T* variable, T new_value) 169 : variable_(variable), old_value_(*variable) { 170 *variable = new_value; 171 } 172 ~ScopedSetValue() { 173 *variable_ = old_value_; 174 } 175 176 private: 177 T* variable_; 178 T old_value_; 179 }; 180 181 TEST(C_API, LossFunctions) { 182 double m = 0.2; 183 double c = 0.03; 184 double *parameter_pointers[] = { &m, &c }; 185 int parameter_sizes[] = { 1, 1 }; 186 187 // Create two outliers, but be careful to leave the data intact. 188 ScopedSetValue<double> outlier1x(&data[12], 2.5); 189 ScopedSetValue<double> outlier1y(&data[13], 1.0e3); 190 ScopedSetValue<double> outlier2x(&data[14], 3.2); 191 ScopedSetValue<double> outlier2y(&data[15], 30e3); 192 193 // Create a cauchy cost function, and reuse it many times. 194 void* cauchy_loss_data = 195 ceres_create_cauchy_loss_function_data(5.0); 196 197 ceres_problem_t* problem = ceres_create_problem(); 198 for (int i = 0; i < num_observations; ++i) { 199 ceres_problem_add_residual_block( 200 problem, 201 exponential_residual, // Cost function 202 &data[2 * i], // Points to the (x,y) measurement 203 ceres_stock_loss_function, 204 cauchy_loss_data, // Loss function user data 205 1, // Number of residuals 206 2, // Number of parameter blocks 207 parameter_sizes, 208 parameter_pointers); 209 } 210 211 ceres_solve(problem); 212 213 EXPECT_NEAR(0.3, m, 0.02); 214 EXPECT_NEAR(0.1, c, 0.04); 215 216 ceres_free_stock_loss_function_data(cauchy_loss_data); 217 ceres_free_problem(problem); 218 } 219 220 } // namespace internal 221 } // namespace ceres 222
