mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
5a30cae583
1. Add a version history 2. Update copyright years across the code base 3. Run format_all.sh 4. Update version strings from 2.1.0 to 2.2.0 in the docs and elsewhere. Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
119 lines
4.1 KiB
C++
119 lines
4.1 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2023 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: 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
|