// This file is part of Eigen, a lightweight C++ template library // for linear algebra. Eigen itself is part of the KDE project. // // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" template<typename MatrixType> void adjoint(const MatrixType& m) { /* this test covers the following files: Transpose.h Conjugate.h Dot.h */ typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits<Scalar>::Real RealScalar; typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType; int rows = m.rows(); int cols = m.cols(); RealScalar largerEps = test_precision<RealScalar>(); if (ei_is_same_type<RealScalar,float>::ret) largerEps = RealScalar(1e-3f); MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols), mzero = MatrixType::Zero(rows, cols), identity = SquareMatrixType::Identity(rows, rows), square = SquareMatrixType::Random(rows, rows); VectorType v1 = VectorType::Random(rows), v2 = VectorType::Random(rows), v3 = VectorType::Random(rows), vzero = VectorType::Zero(rows); Scalar s1 = ei_random<Scalar>(), s2 = ei_random<Scalar>(); // check basic compatibility of adjoint, transpose, conjugate VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1); VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1); // check multiplicative behavior VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1); VERIFY_IS_APPROX((s1 * m1).adjoint(), ei_conj(s1) * m1.adjoint()); // check basic properties of dot, norm, norm2 typedef typename NumTraits<Scalar>::Real RealScalar; VERIFY(ei_isApprox((s1 * v1 + s2 * v2).eigen2_dot(v3), s1 * v1.eigen2_dot(v3) + s2 * v2.eigen2_dot(v3), largerEps)); 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)); VERIFY_IS_APPROX(ei_conj(v1.eigen2_dot(v2)), v2.eigen2_dot(v1)); VERIFY_IS_APPROX(ei_real(v1.eigen2_dot(v1)), v1.squaredNorm()); if(NumTraits<Scalar>::HasFloatingPoint) VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm()); VERIFY_IS_MUCH_SMALLER_THAN(ei_abs(vzero.eigen2_dot(v1)), static_cast<RealScalar>(1)); if(NumTraits<Scalar>::HasFloatingPoint) VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1)); // check compatibility of dot and adjoint VERIFY(ei_isApprox(v1.eigen2_dot(square * v2), (square.adjoint() * v1).eigen2_dot(v2), largerEps)); // like in testBasicStuff, test operator() to check const-qualification int r = ei_random<int>(0, rows-1), c = ei_random<int>(0, cols-1); VERIFY_IS_APPROX(m1.conjugate()(r,c), ei_conj(m1(r,c))); VERIFY_IS_APPROX(m1.adjoint()(c,r), ei_conj(m1(r,c))); if(NumTraits<Scalar>::HasFloatingPoint) { // check that Random().normalized() works: tricky as the random xpr must be evaluated by // normalized() in order to produce a consistent result. VERIFY_IS_APPROX(VectorType::Random(rows).normalized().norm(), RealScalar(1)); } // check inplace transpose m3 = m1; m3.transposeInPlace(); VERIFY_IS_APPROX(m3,m1.transpose()); m3.transposeInPlace(); VERIFY_IS_APPROX(m3,m1); } void test_eigen2_adjoint() { for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_1( adjoint(Matrix<float, 1, 1>()) ); CALL_SUBTEST_2( adjoint(Matrix3d()) ); CALL_SUBTEST_3( adjoint(Matrix4f()) ); CALL_SUBTEST_4( adjoint(MatrixXcf(4, 4)) ); CALL_SUBTEST_5( adjoint(MatrixXi(8, 12)) ); CALL_SUBTEST_6( adjoint(MatrixXf(21, 21)) ); } // test a large matrix only once CALL_SUBTEST_7( adjoint(Matrix<float, 100, 100>()) ); }