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80 lines
3.1 KiB
C++
80 lines
3.1 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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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 "product.h"
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void test_product_large()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,320), internal::random<int>(1,320))) );
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CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,320), internal::random<int>(1,320))) );
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CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,320), internal::random<int>(1,320))) );
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CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,150), internal::random<int>(1,150))) );
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CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,320), internal::random<int>(1,320))) );
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}
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#if defined EIGEN_TEST_PART_6
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{
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// test a specific issue in DiagonalProduct
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int N = 1000000;
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VectorXf v = VectorXf::Ones(N);
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MatrixXf m = MatrixXf::Ones(N,3);
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m = (v+v).asDiagonal() * m;
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VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2));
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}
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{
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// test deferred resizing in Matrix::operator=
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MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a;
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VERIFY_IS_APPROX((a = a * b), (c * b).eval());
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}
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{
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// check the functions to setup blocking sizes compile and do not segfault
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// FIXME check they do what they are supposed to do !!
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std::ptrdiff_t l1 = internal::random<int>(10000,20000);
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std::ptrdiff_t l2 = internal::random<int>(1000000,2000000);
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setCpuCacheSizes(l1,l2);
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VERIFY(l1==l1CacheSize());
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VERIFY(l2==l2CacheSize());
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std::ptrdiff_t k1 = internal::random<int>(10,100)*16;
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std::ptrdiff_t m1 = internal::random<int>(10,100)*16;
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std::ptrdiff_t n1 = internal::random<int>(10,100)*16;
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// only makes sure it compiles fine
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internal::computeProductBlockingSizes<float,float>(k1,m1,n1);
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}
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{
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// test regression in row-vector by matrix (bad Map type)
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MatrixXf mat1(10,32); mat1.setRandom();
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MatrixXf mat2(32,32); mat2.setRandom();
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MatrixXf r1 = mat1.row(2)*mat2.transpose();
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VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval());
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MatrixXf r2 = mat1.row(2)*mat2;
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VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval());
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}
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#endif
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}
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