1// Ceres Solver - A fast non-linear least squares minimizer 2// Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3// http://code.google.com/p/ceres-solver/ 4// 5// Redistribution and use in source and binary forms, with or without 6// modification, are permitted provided that the following conditions are met: 7// 8// * Redistributions of source code must retain the above copyright notice, 9// this list of conditions and the following disclaimer. 10// * Redistributions in binary form must reproduce the above copyright notice, 11// this list of conditions and the following disclaimer in the documentation 12// and/or other materials provided with the distribution. 13// * Neither the name of Google Inc. nor the names of its contributors may be 14// used to endorse or promote products derived from this software without 15// specific prior written permission. 16// 17// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27// POSSIBILITY OF SUCH DAMAGE. 28// 29// Author: sameeragarwal@google.com (Sameer Agarwal) 30 31#include "ceres/normal_prior.h" 32 33#include <cstddef> 34 35#include "gtest/gtest.h" 36#include "ceres/internal/eigen.h" 37#include "ceres/random.h" 38 39namespace ceres { 40namespace internal { 41 42void RandomVector(Vector* v) { 43 for (int r = 0; r < v->rows(); ++r) 44 (*v)[r] = 2 * RandDouble() - 1; 45} 46 47void RandomMatrix(Matrix* m) { 48 for (int r = 0; r < m->rows(); ++r) { 49 for (int c = 0; c < m->cols(); ++c) { 50 (*m)(r, c) = 2 * RandDouble() - 1; 51 } 52 } 53} 54 55TEST(NormalPriorTest, ResidualAtRandomPosition) { 56 srand(5); 57 58 for (int num_rows = 1; num_rows < 5; ++num_rows) { 59 for (int num_cols = 1; num_cols < 5; ++num_cols) { 60 Vector b(num_cols); 61 RandomVector(&b); 62 63 Matrix A(num_rows, num_cols); 64 RandomMatrix(&A); 65 66 double * x = new double[num_cols]; 67 for (int i = 0; i < num_cols; ++i) 68 x[i] = 2 * RandDouble() - 1; 69 70 double * jacobian = new double[num_rows * num_cols]; 71 Vector residuals(num_rows); 72 73 NormalPrior prior(A, b); 74 prior.Evaluate(&x, residuals.data(), &jacobian); 75 76 // Compare the norm of the residual 77 double residual_diff_norm = 78 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm(); 79 EXPECT_NEAR(residual_diff_norm, 0, 1e-10); 80 81 // Compare the jacobians 82 MatrixRef J(jacobian, num_rows, num_cols); 83 double jacobian_diff_norm = (J - A).norm(); 84 EXPECT_NEAR(jacobian_diff_norm, 0.0, 1e-10); 85 86 delete []x; 87 delete []jacobian; 88 } 89 } 90} 91 92TEST(NormalPriorTest, ResidualAtRandomPositionNullJacobians) { 93 srand(5); 94 95 for (int num_rows = 1; num_rows < 5; ++num_rows) { 96 for (int num_cols = 1; num_cols < 5; ++num_cols) { 97 Vector b(num_cols); 98 RandomVector(&b); 99 100 Matrix A(num_rows, num_cols); 101 RandomMatrix(&A); 102 103 double * x = new double[num_cols]; 104 for (int i = 0; i < num_cols; ++i) 105 x[i] = 2 * RandDouble() - 1; 106 107 double* jacobians[1]; 108 jacobians[0] = NULL; 109 110 Vector residuals(num_rows); 111 112 NormalPrior prior(A, b); 113 prior.Evaluate(&x, residuals.data(), jacobians); 114 115 // Compare the norm of the residual 116 double residual_diff_norm = 117 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm(); 118 EXPECT_NEAR(residual_diff_norm, 0, 1e-10); 119 120 prior.Evaluate(&x, residuals.data(), NULL); 121 // Compare the norm of the residual 122 residual_diff_norm = 123 (residuals - A * (VectorRef(x, num_cols) - b)).squaredNorm(); 124 EXPECT_NEAR(residual_diff_norm, 0, 1e-10); 125 126 127 delete []x; 128 } 129 } 130} 131 132} // namespace internal 133} // namespace ceres 134