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Diffstat (limited to 'test/eigen2/eigen2_adjoint.cpp')
| -rw-r--r-- | test/eigen2/eigen2_adjoint.cpp | 116 |
1 files changed, 116 insertions, 0 deletions
diff --git a/test/eigen2/eigen2_adjoint.cpp b/test/eigen2/eigen2_adjoint.cpp new file mode 100644 index 000000000..c090b9650 --- /dev/null +++ b/test/eigen2/eigen2_adjoint.cpp @@ -0,0 +1,116 @@ +// 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> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#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).dot(v3), s1 * v1.dot(v3) + s2 * v2.dot(v3), largerEps)); + VERIFY(ei_isApprox(v3.dot(s1 * v1 + s2 * v2), ei_conj(s1)*v3.dot(v1)+ei_conj(s2)*v3.dot(v2), largerEps)); + VERIFY_IS_APPROX(ei_conj(v1.dot(v2)), v2.dot(v1)); + VERIFY_IS_APPROX(ei_abs(v1.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.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.dot(square * v2), (square.adjoint() * v1).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>()) ); +} + |