1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. Eigen itself is part of the KDE project. 3 // 4 // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1 (at) gmail.com> 5 // 6 // This Source Code Form is subject to the terms of the Mozilla 7 // Public License v. 2.0. If a copy of the MPL was not distributed 8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 10 #include "main.h" 11 12 template<typename MatrixType> void adjoint(const MatrixType& m) 13 { 14 /* this test covers the following files: 15 Transpose.h Conjugate.h Dot.h 16 */ 17 18 typedef typename MatrixType::Scalar Scalar; 19 typedef typename NumTraits<Scalar>::Real RealScalar; 20 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 21 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; 22 int rows = m.rows(); 23 int cols = m.cols(); 24 25 RealScalar largerEps = test_precision<RealScalar>(); 26 if (ei_is_same_type<RealScalar,float>::ret) 27 largerEps = RealScalar(1e-3f); 28 29 MatrixType m1 = MatrixType::Random(rows, cols), 30 m2 = MatrixType::Random(rows, cols), 31 m3(rows, cols), 32 mzero = MatrixType::Zero(rows, cols), 33 identity = SquareMatrixType::Identity(rows, rows), 34 square = SquareMatrixType::Random(rows, rows); 35 VectorType v1 = VectorType::Random(rows), 36 v2 = VectorType::Random(rows), 37 v3 = VectorType::Random(rows), 38 vzero = VectorType::Zero(rows); 39 40 Scalar s1 = ei_random<Scalar>(), 41 s2 = ei_random<Scalar>(); 42 43 // check basic compatibility of adjoint, transpose, conjugate 44 VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1); 45 VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1); 46 47 // check multiplicative behavior 48 VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1); 49 VERIFY_IS_APPROX((s1 * m1).adjoint(), ei_conj(s1) * m1.adjoint()); 50 51 // check basic properties of dot, norm, norm2 52 typedef typename NumTraits<Scalar>::Real RealScalar; 53 VERIFY(ei_isApprox((s1 * v1 + s2 * v2).eigen2_dot(v3), s1 * v1.eigen2_dot(v3) + s2 * v2.eigen2_dot(v3), largerEps)); 54 VERIFY(ei_isApprox(v3.eigen2_dot(s1 * v1 + s2 * v2), ei_conj(s1)*v3.eigen2_dot(v1)+ei_conj(s2)*v3.eigen2_dot(v2), largerEps)); 55 VERIFY_IS_APPROX(ei_conj(v1.eigen2_dot(v2)), v2.eigen2_dot(v1)); 56 VERIFY_IS_APPROX(ei_real(v1.eigen2_dot(v1)), v1.squaredNorm()); 57 if(NumTraits<Scalar>::HasFloatingPoint) 58 VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm()); 59 VERIFY_IS_MUCH_SMALLER_THAN(ei_abs(vzero.eigen2_dot(v1)), static_cast<RealScalar>(1)); 60 if(NumTraits<Scalar>::HasFloatingPoint) 61 VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1)); 62 63 // check compatibility of dot and adjoint 64 VERIFY(ei_isApprox(v1.eigen2_dot(square * v2), (square.adjoint() * v1).eigen2_dot(v2), largerEps)); 65 66 // like in testBasicStuff, test operator() to check const-qualification 67 int r = ei_random<int>(0, rows-1), 68 c = ei_random<int>(0, cols-1); 69 VERIFY_IS_APPROX(m1.conjugate()(r,c), ei_conj(m1(r,c))); 70 VERIFY_IS_APPROX(m1.adjoint()(c,r), ei_conj(m1(r,c))); 71 72 if(NumTraits<Scalar>::HasFloatingPoint) 73 { 74 // check that Random().normalized() works: tricky as the random xpr must be evaluated by 75 // normalized() in order to produce a consistent result. 76 VERIFY_IS_APPROX(VectorType::Random(rows).normalized().norm(), RealScalar(1)); 77 } 78 79 // check inplace transpose 80 m3 = m1; 81 m3.transposeInPlace(); 82 VERIFY_IS_APPROX(m3,m1.transpose()); 83 m3.transposeInPlace(); 84 VERIFY_IS_APPROX(m3,m1); 85 86 } 87 88 void test_eigen2_adjoint() 89 { 90 for(int i = 0; i < g_repeat; i++) { 91 CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) ); 92 CALL_SUBTEST_2( adjoint(Matrix3d()) ); 93 CALL_SUBTEST_3( adjoint(Matrix4f()) ); 94 CALL_SUBTEST_4( adjoint(MatrixXcf(4, 4)) ); 95 CALL_SUBTEST_5( adjoint(MatrixXi(8, 12)) ); 96 CALL_SUBTEST_6( adjoint(MatrixXf(21, 21)) ); 97 } 98 // test a large matrix only once 99 CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) ); 100 } 101 102
