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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@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/>.
#define throw(X)
#define EIGEN_DONT_VECTORIZE 1
#include "main.h"
void* operator new[] (size_t n)
{
ei_assert(false && "operator new should never be called with fixed size path");
// the following is in case assertion are disabled
std::cerr << "operator new should never be called with fixed size path" << std::endl;
exit(2);
void* p = malloc(n);
return p;
}
void operator delete[](void* p) throw()
{
free(p);
}
template<typename MatrixType> void nomalloc(const MatrixType& m)
{
/* this test check no dynamic memory allocation are issued with fixed-size matrices
*/
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),
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),
vzero = VectorType::zero(rows);
Scalar s1 = ei_random<Scalar>();
int r = ei_random<int>(0, rows-1),
c = ei_random<int>(0, cols-1);
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), m1.cwiseProduct(m1));
VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
}
void test_nomalloc()
{
// check that our operator new is indeed called:
VERIFY_RAISES_ASSERT(MatrixXd dummy = MatrixXd::random(3,3));
CALL_SUBTEST( nomalloc(Matrix<float, 1, 1>()) );
CALL_SUBTEST( nomalloc(Matrix4d()) );
}
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