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//
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6// modification, are permitted provided that the following conditions are met:
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16//
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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
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