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-rw-r--r--test/eigen2/eigen2_adjoint.cpp99
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diff --git a/test/eigen2/eigen2_adjoint.cpp b/test/eigen2/eigen2_adjoint.cpp
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index c0f811459..000000000
--- a/test/eigen2/eigen2_adjoint.cpp
+++ /dev/null
@@ -1,99 +0,0 @@
-// 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>()) );
-}
-