internal/ceres/c_api_test.cc - ceres-solver - Git at Google

 // 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;
 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
	// 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;
	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