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