// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2013 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// 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;
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,
};
// A test cost function, similar to the one in curve_fitting.c.
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