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This commit is contained in:
Valentin Leon Stöcker
2026-01-21 15:27:17 +01:00
parent 650e5cc6b8
commit 16334e4834
7 changed files with 195 additions and 143 deletions
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tests/ellipse.cpp
+24 -30
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@@ -3,6 +3,7 @@
#include <autoopt/ellipse.hpp>
#include <autoopt/optimization_problem.hpp>
#include <autoopt/util.hpp>
#include <autoopt/btls.hpp>
#include <iomanip>
#include <iostream>
@@ -21,11 +22,12 @@ TEST(Ellipse, ParamGradient) {
std::vector<std::pair<double, double>> data_points = {
{-10.0, -0.001}, {0.0, 0.0}, {10, 0.0009}};
std::array<double, 4> params = {100, 1000, deg2rad(1.0), 0.0};
Eigen::VectorX<double> params(4);
params << 100, 1000, deg2rad(1.0), 0.0;
auto loss_func = [&data_points]<typename T>(const std::array<T, 4>& p) {
ellipse<T> e{T{p[0]}, T{p[1]}, T{p[2]}};
quadric<T> q = e.to_quadric().rotated_by(T{p[3]});
auto loss_func = [&data_points]<typename T>(const Eigen::VectorX<T>& p) {
ellipse<T> e{T{p(0)}, T{p(1)}, T{p(2)}};
quadric<T> q = e.to_quadric().rotated_by(T{p(3)});
T loss = T{0};
for (const auto& [x, y_true] : data_points) {
T y_pred = q.slope_at(T{x});
@@ -35,44 +37,36 @@ TEST(Ellipse, ParamGradient) {
return loss / T(data_points.size());
};
auto_diff_optimization_problem<double, 4, decltype(loss_func)> problem(loss_func, params);
auto_diff_optimization_problem<double, decltype(loss_func)> problem(loss_func, params);
auto grad = problem.gradient(params);
EXPECT_NEAR(grad[0], -2.0789313126683308e-10, 1e-15); // d/d(left_arm)
EXPECT_NEAR(grad[1], -1.7464984353858657e-12, 1e-15); // d/d(right_arm)
EXPECT_NEAR(grad[2], 1.2013025455499119e-06, 1e-15); // d/d(entrance_angle)
EXPECT_NEAR(grad[3], -2.0332702665822054e-05, 1e-15); // d/d(rotation_angle)
std::cout << "Gradient:\n";
for (size_t i = 0; i < 4; ++i) {
std::cout << grad[i] << "\n";
}
std::cout << grad << std::endl;
EXPECT_NEAR(grad(0), -2.0789313126683308e-10, 1e-15); // d/d(left_arm)
EXPECT_NEAR(grad(1), -1.7464984353858657e-12, 1e-15); // d/d(right_arm)
EXPECT_NEAR(grad(2), 1.2013025455499119e-06, 1e-15); // d/d(entrance_angle)
EXPECT_NEAR(grad(3), -2.0332702665822054e-05, 1e-15); // d/d(rotation_angle)
auto hess = problem.hessian(params);
// set formatting for easier reading
std::cout << std::scientific;
// set field width for alignment
std::cout << "Hessian matrix:\n";
std::cout << hess << std::endl;
for (size_t i = 0; i < 4; ++i) {
;
for (size_t j = 0; j < 4; ++j) {
std::cout << std::setprecision(5) << std::setw(15) << hess[i][j];
}
std::cout << "\n";
}
Eigen::VectorX<double> params_delta(4);
params_delta << 1.0, 1.0, deg2rad(0.1), deg2rad(0.1);
params(0) += 0.9; // left_arm
// log barrier
log_barrier_optimization_problem<double, 4> log_barrier_problem(
log_barrier_optimization_problem<double> log_barrier_problem(
problem,
{1.0, 1.0, deg2rad(0.1), deg2rad(0.1)},
params_delta,
1e-3);
auto log_barrier_grad = log_barrier_problem.gradient(params);
std::cout << "Log Barrier Gradient:\n";
for (size_t i = 0; i < 4; ++i) {
std::cout << log_barrier_grad[i] << "\n";
}
std::cout << "Log barrier gradient:" << std::endl;
std::cout << log_barrier_grad << std::endl;
btls(problem);
}