1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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#define EIGEN_NO_STATIC_ASSERT
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h"
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void adjoint(const MatrixType& m)
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  /* this test covers the following files:
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath     Transpose.h Conjugate.h Dot.h
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Index Index;
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Scalar Scalar;
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename NumTraits<Scalar>::Real RealScalar;
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Index rows = m.rows();
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Index cols = m.cols();
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m1 = MatrixType::Random(rows, cols),
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             m2 = MatrixType::Random(rows, cols),
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             m3(rows, cols),
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             square = SquareMatrixType::Random(rows, rows);
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VectorType v1 = VectorType::Random(rows),
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             v2 = VectorType::Random(rows),
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             v3 = VectorType::Random(rows),
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             vzero = VectorType::Zero(rows);
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Scalar s1 = internal::random<Scalar>(),
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath         s2 = internal::random<Scalar>();
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check basic compatibility of adjoint, transpose, conjugate
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(),    m1);
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(),    m1);
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check multiplicative behavior
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(),           m2.adjoint() * m1);
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX((s1 * m1).adjoint(),                     internal::conj(s1) * m1.adjoint());
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check basic properties of dot, norm, norm2
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename NumTraits<Scalar>::Real RealScalar;
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  RealScalar ref = NumTraits<Scalar>::IsInteger ? RealScalar(0) : (std::max)((s1 * v1 + s2 * v2).norm(),v3.norm());
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3),     internal::conj(s1) * v1.dot(v3) + internal::conj(s2) * v2.dot(v3), ref));
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2),       s1*v3.dot(v1)+s2*v3.dot(v2), ref));
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(internal::conj(v1.dot(v2)),               v2.dot(v1));
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(internal::real(v1.dot(v1)),                v1.squaredNorm());
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if(!NumTraits<Scalar>::IsInteger) {
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v1.squaredNorm(),                v1.norm() * v1.norm());
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // check normalized() and normalize()
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v1, v1.norm() * v1.normalized());
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    v3 = v1;
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    v3.normalize();
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v1, v1.norm() * v3);
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v3, v1.normalized());
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(v3.norm(), RealScalar(1));
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(vzero.dot(v1)),  static_cast<RealScalar>(1));
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check compatibility of dot and adjoint
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm()));
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), ref));
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // like in testBasicStuff, test operator() to check const-qualification
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Index r = internal::random<Index>(0, rows-1),
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      c = internal::random<Index>(0, cols-1);
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.conjugate()(r,c), internal::conj(m1(r,c)));
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.adjoint()(c,r), internal::conj(m1(r,c)));
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if(!NumTraits<Scalar>::IsInteger)
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // check that Random().normalized() works: tricky as the random xpr must be evaluated by
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // normalized() in order to produce a consistent result.
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(VectorType::Random(rows).normalized().norm(), RealScalar(1));
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check inplace transpose
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3 = m1;
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3.transposeInPlace();
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1.transpose());
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3.transposeInPlace();
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1);
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check inplace adjoint
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3 = m1;
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3.adjointInPlace();
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1.adjoint());
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3.transposeInPlace();
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1.conjugate());
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check mixed dot product
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  RealVectorType rv1 = RealVectorType::Random(rows);
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1.dot(rv1.template cast<Scalar>()), v1.dot(rv1));
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(rv1.template cast<Scalar>().dot(v1), rv1.dot(v1));
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_adjoint()
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) );
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( adjoint(Matrix3d()) );
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( adjoint(Matrix4f()) );
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_5( adjoint(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_6( adjoint(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test a large static matrix only once
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) );
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifdef EIGEN_TEST_PART_4
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    MatrixXcf a(10,10), b(10,10);
123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = a.transpose());
124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = a.transpose() + b);
125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = b + a.transpose());
126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = a.conjugate().transpose());
127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = a.adjoint());
128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = a.adjoint() + b);
129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_RAISES_ASSERT(a = b + a.adjoint());
130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // no assertion should be triggered for these cases:
132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() = a.transpose();
133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() += a.transpose();
134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() += a.transpose() + b;
135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() = a.adjoint();
136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() += a.adjoint();
137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    a.transpose() += a.adjoint() + b;
138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif
140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
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