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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// 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.
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// used to endorse or promote products derived from this software without
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/normal_prior.h"
#include <algorithm>
#include <cstddef>
#include <random>
#include "ceres/internal/eigen.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
TEST(NormalPriorTest, ResidualAtRandomPosition) {
std::mt19937 prng;
std::uniform_real_distribution<double> distribution(-1.0, 1.0);
auto randu = [&distribution, &prng] { return distribution(prng); };
for (int num_rows = 1; num_rows < 5; ++num_rows) {
for (int num_cols = 1; num_cols < 5; ++num_cols) {
Vector b(num_cols);
b.setRandom();
Matrix A(num_rows, num_cols);
A.setRandom();
auto* x = new double[num_cols];
std::generate_n(x, num_cols, randu);
auto* jacobian = new double[num_rows * num_cols];
Vector residuals(num_rows);
NormalPrior prior(A, b);
prior.Evaluate(&x, residuals.data(), &jacobian);
// Compare the norm of the residual
double residual_diff_norm =
(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
// Compare the jacobians
MatrixRef J(jacobian, num_rows, num_cols);
double jacobian_diff_norm = (J - A).norm();
EXPECT_NEAR(jacobian_diff_norm, 0.0, 1e-10);
delete[] x;
delete[] jacobian;
}
}
}
TEST(NormalPriorTest, ResidualAtRandomPositionNullJacobians) {
std::mt19937 prng;
std::uniform_real_distribution<double> distribution(-1.0, 1.0);
auto randu = [&distribution, &prng] { return distribution(prng); };
for (int num_rows = 1; num_rows < 5; ++num_rows) {
for (int num_cols = 1; num_cols < 5; ++num_cols) {
Vector b(num_cols);
b.setRandom();
Matrix A(num_rows, num_cols);
A.setRandom();
auto* x = new double[num_cols];
std::generate_n(x, num_cols, randu);
double* jacobians[1];
jacobians[0] = nullptr;
Vector residuals(num_rows);
NormalPrior prior(A, b);
prior.Evaluate(&x, residuals.data(), jacobians);
// Compare the norm of the residual
double residual_diff_norm =
(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
prior.Evaluate(&x, residuals.data(), nullptr);
// Compare the norm of the residual
residual_diff_norm =
(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
delete[] x;
}
}
}
} // namespace internal
} // namespace ceres