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//
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9//   this list of conditions and the following disclaimer.
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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
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21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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27// POSSIBILITY OF SUCH DAMAGE.
28//
29// Author: mierle@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.
39int num_observations = 67;
40double 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.
111int 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
134namespace ceres {
135namespace internal {
136
137TEST(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
165template<typename T>
166class 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
181TEST(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