// 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> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "product.h" template<typename T> void test_aliasing() { int rows = internal::random<int>(1,12); int cols = internal::random<int>(1,12); typedef Matrix<T,Dynamic,Dynamic> MatrixType; typedef Matrix<T,Dynamic,1> VectorType; VectorType x(cols); x.setRandom(); VectorType z(x); VectorType y(rows); y.setZero(); MatrixType A(rows,cols); A.setRandom(); // CwiseBinaryOp VERIFY_IS_APPROX(x = y + A*x, A*z); // OK because "y + A*x" is marked as "assume-aliasing" x = z; // CwiseUnaryOp VERIFY_IS_APPROX(x = T(1.)*(A*x), A*z); // OK because 1*(A*x) is replaced by (1*A*x) which is a Product<> expression x = z; // VERIFY_IS_APPROX(x = y-A*x, -A*z); // Not OK in 3.3 because x is resized before A*x gets evaluated x = z; } void test_product_large() { for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_1( product(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); CALL_SUBTEST_2( product(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); CALL_SUBTEST_3( product(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); CALL_SUBTEST_4( product(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 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))) ); CALL_SUBTEST_1( test_aliasing<float>() ); } #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 = internal::random<int>(10000,20000); std::ptrdiff_t l2 = internal::random<int>(100000,200000); std::ptrdiff_t l3 = internal::random<int>(1000000,2000000); setCpuCacheSizes(l1,l2,l3); VERIFY(l1==l1CacheSize()); VERIFY(l2==l2CacheSize()); std::ptrdiff_t k1 = internal::random<int>(10,100)*16; std::ptrdiff_t m1 = internal::random<int>(10,100)*16; std::ptrdiff_t n1 = internal::random<int>(10,100)*16; // only makes sure it compiles fine internal::computeProductBlockingSizes<float,float,std::ptrdiff_t>(k1,m1,n1,1); } { // 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()); } { Eigen::MatrixXd A(10,10), B, C; A.setRandom(); C = A; for(int k=0; k<79; ++k) C = C * A; 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))) * (((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))); VERIFY_IS_APPROX(B,C); } #endif // Regression test for bug 714: #if defined EIGEN_HAS_OPENMP omp_set_dynamic(1); for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_6( product(Matrix<float,Dynamic,Dynamic>(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); } #endif }