mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
80 lines
3.0 KiB
C++
80 lines
3.0 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// 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 "product.h"
|
|
|
|
void test_product_large()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_1( product(MatrixXf(ei_random<int>(1,320), ei_random<int>(1,320))) );
|
|
CALL_SUBTEST_2( product(MatrixXd(ei_random<int>(1,320), ei_random<int>(1,320))) );
|
|
CALL_SUBTEST_3( product(MatrixXi(ei_random<int>(1,320), ei_random<int>(1,320))) );
|
|
CALL_SUBTEST_4( product(MatrixXcf(ei_random<int>(1,150), ei_random<int>(1,150))) );
|
|
CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(ei_random<int>(1,320), ei_random<int>(1,320))) );
|
|
}
|
|
|
|
#if defined EIGEN_TEST_PART_6
|
|
{
|
|
// test a specific issue in DiagonalProduct
|
|
int N = 1000000;
|
|
VectorXf v = VectorXf::Ones(N);
|
|
MatrixXf m = MatrixXf::Ones(N,3);
|
|
m = (v+v).asDiagonal() * m;
|
|
VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
|
|
}
|
|
|
|
{
|
|
// test deferred resizing in Matrix::operator=
|
|
MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
|
|
VERIFY_IS_APPROX((a = a * b), (c * b).eval());
|
|
}
|
|
|
|
{
|
|
// check the functions to setup blocking sizes compile and do not segfault
|
|
// FIXME check they do what they are supposed to do !!
|
|
std::ptrdiff_t l1 = ei_random<int>(10000,20000);
|
|
std::ptrdiff_t l2 = ei_random<int>(1000000,2000000);
|
|
setCpuCacheSizes(l1,l2);
|
|
VERIFY(l1==l1CacheSize());
|
|
VERIFY(l2==l2CacheSize());
|
|
std::ptrdiff_t k1 = ei_random<int>(10,100)*16;
|
|
std::ptrdiff_t m1 = ei_random<int>(10,100)*16;
|
|
std::ptrdiff_t n1 = ei_random<int>(10,100)*16;
|
|
// only makes sure it compiles fine
|
|
computeProductBlockingSizes<float,float>(k1,m1,n1);
|
|
}
|
|
|
|
{
|
|
// test regression in row-vector by matrix (bad Map type)
|
|
MatrixXf mat1(10,32); mat1.setRandom();
|
|
MatrixXf mat2(32,32); mat2.setRandom();
|
|
MatrixXf r1 = mat1.row(2)*mat2.transpose();
|
|
VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());
|
|
|
|
MatrixXf r2 = mat1.row(2)*mat2;
|
|
VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
|
|
}
|
|
#endif
|
|
}
|