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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>
//
// 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 linearStructure(const MatrixType& m)
{
/* this test covers the following files:
Sum.h Difference.h Opposite.h ScalarMultiple.h
*/
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
int rows = m.rows();
int cols = m.cols();
// this test relies a lot on Random.h, and there's not much more that we can do
// to test it, hence I consider that we will have tested Random.h
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3(rows, cols),
mzero = MatrixType::Zero(rows, cols);
Scalar s1 = ei_random<Scalar>();
while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX(-(-m1), m1);
VERIFY_IS_APPROX(m1+m1, 2*m1);
VERIFY_IS_APPROX(m1+m2-m1, m2);
VERIFY_IS_APPROX(-m2+m1+m2, m1);
VERIFY_IS_APPROX(m1*s1, s1*m1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2);
m3 = m2; m3 += m1;
VERIFY_IS_APPROX(m3, m1+m2);
m3 = m2; m3 -= m1;
VERIFY_IS_APPROX(m3, m2-m1);
m3 = m2; m3 *= s1;
VERIFY_IS_APPROX(m3, s1*m2);
if(NumTraits<Scalar>::HasFloatingPoint)
{
m3 = m2; m3 /= s1;
VERIFY_IS_APPROX(m3, m2/s1);
}
// again, test operator() to check const-qualification
VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c)));
VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c)));
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c)));
VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1);
if(NumTraits<Scalar>::HasFloatingPoint)
VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1);
// use .block to disable vectorization and compare to the vectorized version
VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1);
VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1);
VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1);
VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1);
}
void test_linearstructure()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( linearStructure(Matrix<float, 1, 1>()) );
CALL_SUBTEST( linearStructure(Matrix2f()) );
CALL_SUBTEST( linearStructure(Vector3d()) );
CALL_SUBTEST( linearStructure(Matrix4d()) );
CALL_SUBTEST( linearStructure(MatrixXcf(3, 3)) );
CALL_SUBTEST( linearStructure(MatrixXf(8, 12)) );
CALL_SUBTEST( linearStructure(MatrixXi(8, 12)) );
CALL_SUBTEST( linearStructure(MatrixXcd(20, 20)) );
}
}
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