| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 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 <cstddef> |
| |
| #include "ceres/internal/eigen.h" |
| #include "ceres/random.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| namespace { |
| |
| 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; |
| } |
| } |
| } |
| |
| } // namespace |
| |
| 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 |