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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer.
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// this list of conditions and the following disclaimer in the documentation
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/tiny_solver_cost_function_adapter.h"
#include <algorithm>
#include <cmath>
#include <memory>
#include "ceres/cost_function.h"
#include "ceres/sized_cost_function.h"
#include "gtest/gtest.h"
namespace ceres {
class CostFunction2x3 : public SizedCostFunction<2, 3> {
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
double x = parameters[0][0];
double y = parameters[0][1];
double z = parameters[0][2];
residuals[0] = x + 2 * y + 4 * z;
residuals[1] = y * z;
if (jacobians && jacobians[0]) {
jacobians[0][0] = 1;
jacobians[0][1] = 2;
jacobians[0][2] = 4;
jacobians[0][3 + 0] = 0;
jacobians[0][3 + 1] = z;
jacobians[0][3 + 2] = y;
}
return true;
}
};
template <int kNumResiduals, int kNumParameters>
void TestHelper() {
std::unique_ptr<CostFunction> cost_function(new CostFunction2x3);
using CostFunctionAdapter =
TinySolverCostFunctionAdapter<kNumResiduals, kNumParameters>;
CostFunctionAdapter cfa(*cost_function);
EXPECT_EQ(CostFunctionAdapter::NUM_RESIDUALS, kNumResiduals);
EXPECT_EQ(CostFunctionAdapter::NUM_PARAMETERS, kNumParameters);
EXPECT_EQ(cfa.NumResiduals(), 2);
EXPECT_EQ(cfa.NumParameters(), 3);
Eigen::Matrix<double, 2, 1> actual_residuals, expected_residuals;
Eigen::Matrix<double, 2, 3, Eigen::ColMajor> actual_jacobian;
Eigen::Matrix<double, 2, 3, Eigen::RowMajor> expected_jacobian;
double xyz[3] = {1.0, -1.0, 2.0};
double* parameters[1] = {xyz};
// Check that residual only evaluation works.
cost_function->Evaluate(parameters, expected_residuals.data(), nullptr);
cfa(xyz, actual_residuals.data(), nullptr);
EXPECT_NEAR(
(expected_residuals - actual_residuals).norm() / actual_residuals.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nExpected residuals: " << expected_residuals.transpose()
<< "\nActual residuals: " << actual_residuals.transpose();
// Check that residual and jacobian evaluation works.
double* jacobians[1] = {expected_jacobian.data()};
cost_function->Evaluate(parameters, expected_residuals.data(), jacobians);
cfa(xyz, actual_residuals.data(), actual_jacobian.data());
EXPECT_NEAR(
(expected_residuals - actual_residuals).norm() / actual_residuals.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nExpected residuals: " << expected_residuals.transpose()
<< "\nActual residuals: " << actual_residuals.transpose();
EXPECT_NEAR(
(expected_jacobian - actual_jacobian).norm() / actual_jacobian.norm(),
0.0,
std::numeric_limits<double>::epsilon())
<< "\nExpected jacobian: " << expected_jacobian.transpose()
<< "\nActual jacobian: " << actual_jacobian.transpose();
}
TEST(TinySolverCostFunctionAdapter, StaticResidualsStaticParameterBlock) {
TestHelper<2, 3>();
}
TEST(TinySolverCostFunctionAdapter, DynamicResidualsStaticParameterBlock) {
TestHelper<Eigen::Dynamic, 3>();
}
TEST(TinySolverCostFunctionAdapter, StaticResidualsDynamicParameterBlock) {
TestHelper<2, Eigen::Dynamic>();
}
TEST(TinySolverCostFunctionAdapter, DynamicResidualsDynamicParameterBlock) {
TestHelper<Eigen::Dynamic, Eigen::Dynamic>();
}
} // namespace ceres