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 "main.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void product_extra(const MatrixType& m) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, 1, Dynamic> RowVectorType; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, Dynamic, 1> ColVectorType; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, Dynamic, Dynamic, 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType::Flags&RowMajorBit> OtherMajorMatrixType; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = m.rows(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = m.cols(); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols), 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath mzero = MatrixType::Zero(rows, cols), 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath identity = MatrixType::Identity(rows, rows), 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath square = MatrixType::Random(rows, rows), 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath res = MatrixType::Random(rows, rows), 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath square2 = MatrixType::Random(cols, cols), 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath res2 = MatrixType::Random(cols, cols); 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowVectorType v1 = RowVectorType::Random(rows), vrres(rows); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath OtherMajorMatrixType tm1 = m1; 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = internal::random<Scalar>(), 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s2 = internal::random<Scalar>(), 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath s3 = internal::random<Scalar>(); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval()); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval()); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2); 457faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval()); 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval()); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // a very tricky case where a scale factor has to be automatically conjugated: 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval()); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test all possible conjugate combinations for the four matrix-vector product cases: 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2), 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.conjugate()*s2).eval() * (s1 * vc2).eval()); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()), 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1*s2).eval() * (s1 * vc2.conjugate()).eval()); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()), 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval()); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2), 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval()); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2), 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval()); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2), 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval()); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()), 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval()); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()), 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval()); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2), 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * v1).eval() * (-m1.conjugate()*s2).eval()); 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2), 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * v1.conjugate()).eval() * (-m1*s2).eval()); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2), 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval()); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test the vector-matrix product with non aligned starts 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index i = internal::random<Index>(0,m1.rows()-2); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index j = internal::random<Index>(0,m1.cols()-2); 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index r = internal::random<Index>(1,m1.rows()-i); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index c = internal::random<Index>(1,m1.cols()-j); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index i2 = internal::random<Index>(0,m1.rows()-1); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index j2 = internal::random<Index>(0,m1.cols()-1); 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.col(j2).adjoint() * m1.block(0,j,m1.rows(),c), m1.col(j2).adjoint().eval() * m1.block(0,j,m1.rows(),c).eval()); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.block(i,0,r,m1.cols()) * m1.row(i2).adjoint(), m1.block(i,0,r,m1.cols()).eval() * m1.row(i2).adjoint().eval()); 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // regression test 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType tmp = m1 * m1.adjoint() * s1; 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1); 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid mat_mat_scalar_scalar_product() 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::Matrix2Xd dNdxy(2, 3); 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dNdxy << -0.5, 0.5, 0, 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath -0.3, 0, 0.3; 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath double det = 6.0, wt = 0.5; 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy); 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid zero_sized_objects() 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Bug 127 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // a product of the form lhs*rhs with 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // lhs: 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // rows = 1, cols = 4 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // RowsAtCompileTime = 1, ColsAtCompileTime = -1 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // rhs: 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // rows = 4, cols = 0 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // RowsAtCompileTime = -1, ColsAtCompileTime = -1 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1. 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4); 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0); 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath a*b; 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 1377faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandezvoid unaligned_objects() 1387faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez{ 1397faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // Regression test for the bug reported here: 1407faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // http://forum.kde.org/viewtopic.php?f=74&t=107541 1417faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then. 1427faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases, 1437faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault. 1447faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez for(int m=450;m<460;++m) 1457faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez { 1467faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez for(int n=8;n<12;++n) 1477faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez { 1487faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez MatrixXf M(m, n); 1497faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VectorXf v1(n), r1(500); 1507faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez RowVectorXf v2(m), r2(16); 1517faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 1527faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez M.setRandom(); 1537faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez v1.setRandom(); 1547faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez v2.setRandom(); 1557faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez for(int o=0; o<4; ++o) 1567faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez { 1577faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez r1.segment(o,m).noalias() = M * v1; 1587faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1)); 1597faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez r2.segment(o,n).noalias() = v2 * M; 1607faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M); 1617faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 1627faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 1637faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 1647faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez} 1657faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_product_extra() 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( mat_mat_scalar_scalar_product() ); 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 1757faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez CALL_SUBTEST_5( zero_sized_objects() ); 1767faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez CALL_SUBTEST_6( unaligned_objects() ); 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 178