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2120fed849
* fix all numerical instabilies in the unit tests, now all tests can be run 2000 times with almost zero failures.
100 lines
3.9 KiB
C++
100 lines
3.9 KiB
C++
// 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@math.jussieu.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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template<typename MatrixType> void linearStructure(const MatrixType& m)
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{
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/* this test covers the following files:
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Sum.h Difference.h Opposite.h ScalarMultiple.h
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*/
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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int rows = m.rows();
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int cols = m.cols();
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// this test relies a lot on Random.h, and there's not much more that we can do
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// to test it, hence I consider that we will have tested Random.h
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MatrixType m1 = test_random_matrix<MatrixType>(rows, cols),
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m2 = test_random_matrix<MatrixType>(rows, cols),
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m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols);
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Scalar s1 = test_random<Scalar>();
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while (ei_abs(s1)<1e-3) s1 = test_random<Scalar>();
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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), m1);
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VERIFY_IS_APPROX(m1+m1, 2*m1);
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VERIFY_IS_APPROX(m1+m2-m1, m2);
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VERIFY_IS_APPROX(-m2+m1+m2, m1);
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VERIFY_IS_APPROX(m1*s1, s1*m1);
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VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
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VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2);
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m3 = m2; m3 += m1;
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VERIFY_IS_APPROX(m3, m1+m2);
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m3 = m2; m3 -= m1;
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VERIFY_IS_APPROX(m3, m2-m1);
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m3 = m2; m3 *= s1;
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VERIFY_IS_APPROX(m3, s1*m2);
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if(NumTraits<Scalar>::HasFloatingPoint)
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{
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m3 = m2; m3 /= s1;
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VERIFY_IS_APPROX(m3, m2/s1);
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}
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// again, test operator() to check const-qualification
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VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c)));
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VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c)));
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VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
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VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c)));
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VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1);
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if(NumTraits<Scalar>::HasFloatingPoint)
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VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1);
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// use .block to disable vectorization and compare to the vectorized version
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VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1);
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VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1);
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VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1);
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VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1);
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}
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void test_linearstructure()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( linearStructure(Matrix<float, 1, 1>()) );
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CALL_SUBTEST( linearStructure(Matrix2f()) );
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CALL_SUBTEST( linearStructure(Vector3d()) );
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CALL_SUBTEST( linearStructure(Matrix4d()) );
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CALL_SUBTEST( linearStructure(MatrixXcf(3, 3)) );
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CALL_SUBTEST( linearStructure(MatrixXf(8, 12)) );
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CALL_SUBTEST( linearStructure(MatrixXi(8, 12)) );
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CALL_SUBTEST( linearStructure(MatrixXcd(20, 20)) );
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}
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}
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