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