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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) 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 matrixSum(const MatrixType& m)
{
typedef typename MatrixType::Scalar Scalar;
int rows = m.rows();
int cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols);
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
Scalar x = Scalar(0);
for(int i = 0; i < rows; i++) for(int j = 0; j < cols; j++) x += m1(i,j);
VERIFY_IS_APPROX(m1.sum(), x);
}
template<typename VectorType> void vectorSum(const VectorType& w)
{
typedef typename VectorType::Scalar Scalar;
int size = w.size();
VectorType v = VectorType::Random(size);
for(int i = 1; i < size; i++)
{
Scalar s = Scalar(0);
for(int j = 0; j < i; j++) s += v[j];
VERIFY_IS_APPROX(s, v.start(i).sum());
}
for(int i = 0; i < size-1; i++)
{
Scalar s = Scalar(0);
for(int j = i; j < size; j++) s += v[j];
VERIFY_IS_APPROX(s, v.end(size-i).sum());
}
for(int i = 0; i < size/2; i++)
{
Scalar s = Scalar(0);
for(int j = i; j < size-i; j++) s += v[j];
VERIFY_IS_APPROX(s, v.segment(i, size-2*i).sum());
}
}
void test_eigen2_sum()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixSum(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( matrixSum(Matrix2f()) );
CALL_SUBTEST_3( matrixSum(Matrix4d()) );
CALL_SUBTEST_4( matrixSum(MatrixXcf(3, 3)) );
CALL_SUBTEST_5( matrixSum(MatrixXf(8, 12)) );
CALL_SUBTEST_6( matrixSum(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_5( vectorSum(VectorXf(5)) );
CALL_SUBTEST_7( vectorSum(VectorXd(10)) );
CALL_SUBTEST_5( vectorSum(VectorXf(33)) );
}
}
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