1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "product.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_product_large() 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( product(Matrix<float,Dynamic,Dynamic,RowMajor>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#if defined EIGEN_TEST_PART_6 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test a specific issue in DiagonalProduct 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int N = 1000000; 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorXf v = VectorXf::Ones(N); 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf m = MatrixXf::Ones(N,3); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m = (v+v).asDiagonal() * m; 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m, MatrixXf::Constant(N,3,2)); 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test deferred resizing in Matrix::operator= 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf a = MatrixXf::Random(10,4), b = MatrixXf::Random(4,10), c = a; 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((a = a * b), (c * b).eval()); 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check the functions to setup blocking sizes compile and do not segfault 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // FIXME check they do what they are supposed to do !! 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::ptrdiff_t l1 = internal::random<int>(10000,20000); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::ptrdiff_t l2 = internal::random<int>(1000000,2000000); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath setCpuCacheSizes(l1,l2); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(l1==l1CacheSize()); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(l2==l2CacheSize()); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::ptrdiff_t k1 = internal::random<int>(10,100)*16; 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::ptrdiff_t m1 = internal::random<int>(10,100)*16; 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::ptrdiff_t n1 = internal::random<int>(10,100)*16; 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // only makes sure it compiles fine 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::computeProductBlockingSizes<float,float>(k1,m1,n1); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test regression in row-vector by matrix (bad Map type) 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf mat1(10,32); mat1.setRandom(); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf mat2(32,32); mat2.setRandom(); 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf r1 = mat1.row(2)*mat2.transpose(); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(r1, (mat1.row(2)*mat2.transpose()).eval()); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixXf r2 = mat1.row(2)*mat2; 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(r2, (mat1.row(2)*mat2).eval()); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 65