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.
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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
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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
39 namespace ceres {
40 namespace internal {
41
RandomVector(Vector * v)42 void RandomVector(Vector* v) {
43 for (int r = 0; r < v->rows(); ++r)
44 (*v)[r] = 2 * RandDouble() - 1;
45 }
46
RandomMatrix(Matrix * m)47 void 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
TEST(NormalPriorTest,ResidualAtRandomPosition)55 TEST(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
TEST(NormalPriorTest,ResidualAtRandomPositionNullJacobians)92 TEST(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