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