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// 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),
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>()) );
}
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