134 lines
4.2 KiB
C++
134 lines
4.2 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/normal_prior.h"
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#include <cstddef>
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#include "gtest/gtest.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/random.h"
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namespace ceres {
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namespace internal {
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void RandomVector(Vector* v) {
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for (int r = 0; r < v->rows(); ++r)
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(*v)[r] = 2 * RandDouble() - 1;
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}
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void RandomMatrix(Matrix* m) {
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for (int r = 0; r < m->rows(); ++r) {
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for (int c = 0; c < m->cols(); ++c) {
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(*m)(r, c) = 2 * RandDouble() - 1;
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}
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}
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}
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TEST(NormalPriorTest, ResidualAtRandomPosition) {
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srand(5);
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for (int num_rows = 1; num_rows < 5; ++num_rows) {
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for (int num_cols = 1; num_cols < 5; ++num_cols) {
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Vector b(num_cols);
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RandomVector(&b);
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Matrix A(num_rows, num_cols);
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RandomMatrix(&A);
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double * x = new double[num_cols];
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for (int i = 0; i < num_cols; ++i)
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x[i] = 2 * RandDouble() - 1;
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double * jacobian = new double[num_rows * num_cols];
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Vector residuals(num_rows);
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NormalPrior prior(A, b);
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prior.Evaluate(&x, residuals.data(), &jacobian);
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// Compare the norm of the residual
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double residual_diff_norm =
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(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
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EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
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// Compare the jacobians
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MatrixRef J(jacobian, num_rows, num_cols);
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double jacobian_diff_norm = (J - A).norm();
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EXPECT_NEAR(jacobian_diff_norm, 0.0, 1e-10);
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delete []x;
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delete []jacobian;
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}
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}
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}
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TEST(NormalPriorTest, ResidualAtRandomPositionNullJacobians) {
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srand(5);
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for (int num_rows = 1; num_rows < 5; ++num_rows) {
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for (int num_cols = 1; num_cols < 5; ++num_cols) {
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Vector b(num_cols);
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RandomVector(&b);
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Matrix A(num_rows, num_cols);
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RandomMatrix(&A);
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double * x = new double[num_cols];
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for (int i = 0; i < num_cols; ++i)
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x[i] = 2 * RandDouble() - 1;
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double* jacobians[1];
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jacobians[0] = NULL;
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Vector residuals(num_rows);
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NormalPrior prior(A, b);
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prior.Evaluate(&x, residuals.data(), jacobians);
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// Compare the norm of the residual
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double residual_diff_norm =
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(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
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EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
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prior.Evaluate(&x, residuals.data(), NULL);
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// Compare the norm of the residual
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residual_diff_norm =
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(residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm();
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EXPECT_NEAR(residual_diff_norm, 0, 1e-10);
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delete []x;
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}
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}
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}
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} // namespace internal
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} // namespace ceres
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