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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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 <cstddef>
#include "gtest/gtest.h"
#include "ceres/internal/eigen.h"
#include "ceres/random.h"
namespace ceres {
namespace internal {
void RandomVector(Vector* v) {
for (int r = 0; r < v->rows(); ++r)
(*v)[r] = 2 * RandDouble() - 1;
}
void RandomMatrix(Matrix* m) {
for (int r = 0; r < m->rows(); ++r) {
for (int c = 0; c < m->cols(); ++c) {
(*m)(r, c) = 2 * RandDouble() - 1;
}
}
}
TEST(NormalPriorTest, ResidualAtRandomPosition) {
srand(5);
for (int num_rows = 1; num_rows < 5; ++num_rows) {
for (int num_cols = 1; num_cols < 5; ++num_cols) {
Vector b(num_cols);
RandomVector(&b);
Matrix A(num_rows, num_cols);
RandomMatrix(&A);
double * x = new double[num_cols];
for (int i = 0; i < num_cols; ++i)
x[i] = 2 * RandDouble() - 1;
double * 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) {
srand(5);
for (int num_rows = 1; num_rows < 5; ++num_rows) {
for (int num_cols = 1; num_cols < 5; ++num_cols) {
Vector b(num_cols);
RandomVector(&b);
Matrix A(num_rows, num_cols);
RandomMatrix(&A);
double * x = new double[num_cols];
for (int i = 0; i < num_cols; ++i)
x[i] = 2 * RandDouble() - 1;
double* jacobians[1];
jacobians[0] = NULL;
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(), NULL);
// 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
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