1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h"
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/Geometry>
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/LU>
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SVD>
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/* this test covers the following files:
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath   Geometry/OrthoMethods.h
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath*/
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> void orthomethods_3()
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename NumTraits<Scalar>::Real RealScalar;
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,3,3> Matrix3;
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,3,1> Vector3;
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,4,1> Vector4;
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Vector3 v0 = Vector3::Random(),
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          v1 = Vector3::Random(),
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          v2 = Vector3::Random();
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // cross product
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v1), Scalar(1));
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(v1.dot(v1.cross(v2)), Scalar(1));
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(v1.cross(v2).dot(v2), Scalar(1));
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(v2.dot(v1.cross(v2)), Scalar(1));
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Matrix3 mat3;
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mat3 << v0.normalized(),
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath         (v0.cross(v1)).normalized(),
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath         (v0.cross(v1).cross(v0)).normalized();
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY(mat3.isUnitary());
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // colwise/rowwise cross product
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mat3.setRandom();
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Vector3 vec3 = Vector3::Random();
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Matrix3 mcross;
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int i = internal::random<int>(0,2);
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mcross = mat3.colwise().cross(vec3);
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(mcross.col(i), mat3.col(i).cross(vec3));
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mcross = mat3.rowwise().cross(vec3);
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(mcross.row(i), mat3.row(i).cross(vec3));
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // cross3
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Vector4 v40 = Vector4::Random(),
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          v41 = Vector4::Random(),
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          v42 = Vector4::Random();
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  v40.w() = v41.w() = v42.w() = 0;
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  v42.template head<3>() = v40.template head<3>().cross(v41.template head<3>());
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v40.cross3(v41), v42);
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check mixed product
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<RealScalar, 3, 1> RealVector3;
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  RealVector3 rv1 = RealVector3::Random();
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1.cross(rv1.template cast<Scalar>()), v1.cross(rv1));
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(rv1.template cast<Scalar>().cross(v1), rv1.cross(v1));
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar, int Size> void orthomethods(int size=Size)
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename NumTraits<Scalar>::Real RealScalar;
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Size,1> VectorType;
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,3,Size> Matrix3N;
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Size,3> MatrixN3;
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,3,1> Vector3;
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VectorType v0 = VectorType::Random(size);
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // unitOrthogonal
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1));
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1));
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if (size>=3)
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    v0.template head<2>().setZero();
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    v0.tail(size-2).setRandom();
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_MUCH_SMALLER_THAN(v0.unitOrthogonal().dot(v0), Scalar(1));
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v0.unitOrthogonal().norm(), RealScalar(1));
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // colwise/rowwise cross product
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Vector3 vec3 = Vector3::Random();
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int i = internal::random<int>(0,size-1);
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Matrix3N mat3N(3,size), mcross3N(3,size);
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mat3N.setRandom();
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mcross3N = mat3N.colwise().cross(vec3);
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(mcross3N.col(i), mat3N.col(i).cross(vec3));
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixN3 matN3(size,3), mcrossN3(size,3);
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  matN3.setRandom();
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  mcrossN3 = matN3.rowwise().cross(vec3);
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(mcrossN3.row(i), matN3.row(i).cross(vec3));
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_geo_orthomethods()
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( orthomethods_3<float>() );
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( orthomethods_3<double>() );
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_4( orthomethods_3<std::complex<double> >() );
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( (orthomethods<float,2>()) );
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( (orthomethods<double,2>()) );
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( (orthomethods<float,3>()) );
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( (orthomethods<double,3>()) );
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( (orthomethods<float,7>()) );
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_4( (orthomethods<std::complex<double>,8>()) );
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_5( (orthomethods<float,Dynamic>(36)) );
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_6( (orthomethods<double,Dynamic>(35)) );
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
122