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85 lines
3.4 KiB
C++
85 lines
3.4 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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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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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,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
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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))) );
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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>(100000,200000);
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std::ptrdiff_t l3 = internal::random<int>(1000000,2000000);
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setCpuCacheSizes(l1,l2,l3);
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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,n1,1);
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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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{
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Eigen::MatrixXd A(10,10), B, C;
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A.setRandom();
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C = A;
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for(int k=0; k<79; ++k)
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C = C * A;
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B.noalias() = (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)))
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* (((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)) * ((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A))*((A*A)*(A*A)));
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VERIFY_IS_APPROX(B,C);
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}
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#endif
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// Regression test for bug 714:
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#if defined EIGEN_HAS_OPENMP
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omp_set_dynamic(1);
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_6( product(Matrix<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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}
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#endif
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}
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