mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-15 07:10:37 +08:00
a6d387a359
still fail at runtime in ei_aligned_free() (even without vectorization).
105 lines
4.4 KiB
C++
105 lines
4.4 KiB
C++
// 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@math.jussieu.fr>
|
|
//
|
|
// 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 Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
|
int rows = m.rows();
|
|
int cols = m.cols();
|
|
|
|
MatrixType m1 = MatrixType::Random(rows, cols),
|
|
m2 = MatrixType::Random(rows, cols),
|
|
m3(rows, cols),
|
|
mzero = MatrixType::Zero(rows, cols),
|
|
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
|
::Identity(rows, rows),
|
|
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
|
::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_IS_APPROX((s1 * v1 + s2 * v2).dot(v3), s1 * v1.dot(v3) + s2 * v2.dot(v3));
|
|
VERIFY_IS_APPROX(v3.dot(s1 * v1 + s2 * v2), ei_conj(s1)*v3.dot(v1)+ei_conj(s2)*v3.dot(v2));
|
|
VERIFY_IS_APPROX(ei_conj(v1.dot(v2)), v2.dot(v1));
|
|
VERIFY_IS_APPROX(ei_abs(v1.dot(v1)), v1.norm2());
|
|
if(NumTraits<Scalar>::HasFloatingPoint)
|
|
VERIFY_IS_APPROX(v1.norm2(), 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
|
|
// FIXME this line failed with MSVC and complex<double> in the ei_aligned_free()
|
|
VERIFY_IS_APPROX(v1.dot(square * v2), (square.adjoint() * v1).dot(v2));
|
|
|
|
// 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));
|
|
}
|
|
}
|
|
|
|
void test_adjoint()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST( adjoint(Matrix<float, 1, 1>()) );
|
|
CALL_SUBTEST( adjoint(Matrix4d()) );
|
|
CALL_SUBTEST( adjoint(MatrixXcf(3, 3)) );
|
|
CALL_SUBTEST( adjoint(MatrixXi(8, 12)) );
|
|
CALL_SUBTEST( adjoint(MatrixXcd(20, 20)) );
|
|
}
|
|
// test a large matrix only once
|
|
CALL_SUBTEST( adjoint(Matrix<float, 100, 100>()) );
|
|
}
|
|
|