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 142