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 (at) 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 "main.h" 11 12 template<typename MatrixType> void product_extra(const MatrixType& m) 13 { 14 typedef typename MatrixType::Index Index; 15 typedef typename MatrixType::Scalar Scalar; 16 typedef Matrix<Scalar, 1, Dynamic> RowVectorType; 17 typedef Matrix<Scalar, Dynamic, 1> ColVectorType; 18 typedef Matrix<Scalar, Dynamic, Dynamic, 19 MatrixType::Flags&RowMajorBit> OtherMajorMatrixType; 20 21 Index rows = m.rows(); 22 Index cols = m.cols(); 23 24 MatrixType m1 = MatrixType::Random(rows, cols), 25 m2 = MatrixType::Random(rows, cols), 26 m3(rows, cols), 27 mzero = MatrixType::Zero(rows, cols), 28 identity = MatrixType::Identity(rows, rows), 29 square = MatrixType::Random(rows, rows), 30 res = MatrixType::Random(rows, rows), 31 square2 = MatrixType::Random(cols, cols), 32 res2 = MatrixType::Random(cols, cols); 33 RowVectorType v1 = RowVectorType::Random(rows), vrres(rows); 34 ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); 35 OtherMajorMatrixType tm1 = m1; 36 37 Scalar s1 = internal::random<Scalar>(), 38 s2 = internal::random<Scalar>(), 39 s3 = internal::random<Scalar>(); 40 41 VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval()); 42 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval()); 43 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2); 44 VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2); 45 VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2); 46 VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval()); 47 VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2); 48 VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval()); 49 50 // a very tricky case where a scale factor has to be automatically conjugated: 51 VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval()); 52 53 54 // test all possible conjugate combinations for the four matrix-vector product cases: 55 56 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2), 57 (-m1.conjugate()*s2).eval() * (s1 * vc2).eval()); 58 VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()), 59 (-m1*s2).eval() * (s1 * vc2.conjugate()).eval()); 60 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()), 61 (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval()); 62 63 VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2), 64 (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval()); 65 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2), 66 (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval()); 67 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2), 68 (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval()); 69 70 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()), 71 (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval()); 72 VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()), 73 (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval()); 74 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), 75 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); 76 77 VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2), 78 (s1 * v1).eval() * (-m1.conjugate()*s2).eval()); 79 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2), 80 (s1 * v1.conjugate()).eval() * (-m1*s2).eval()); 81 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2), 82 (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval()); 83 84 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), 85 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); 86 87 // test the vector-matrix product with non aligned starts 88 Index i = internal::random<Index>(0,m1.rows()-2); 89 Index j = internal::random<Index>(0,m1.cols()-2); 90 Index r = internal::random<Index>(1,m1.rows()-i); 91 Index c = internal::random<Index>(1,m1.cols()-j); 92 Index i2 = internal::random<Index>(0,m1.rows()-1); 93 Index j2 = internal::random<Index>(0,m1.cols()-1); 94 95 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()); 96 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()); 97 98 // regression test 99 MatrixType tmp = m1 * m1.adjoint() * s1; 100 VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1); 101 } 102 103 // Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947 104 void mat_mat_scalar_scalar_product() 105 { 106 Eigen::Matrix2Xd dNdxy(2, 3); 107 dNdxy << -0.5, 0.5, 0, 108 -0.3, 0, 0.3; 109 double det = 6.0, wt = 0.5; 110 VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy); 111 } 112 113 void zero_sized_objects() 114 { 115 // Bug 127 116 // 117 // a product of the form lhs*rhs with 118 // 119 // lhs: 120 // rows = 1, cols = 4 121 // RowsAtCompileTime = 1, ColsAtCompileTime = -1 122 // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5 123 // 124 // rhs: 125 // rows = 4, cols = 0 126 // RowsAtCompileTime = -1, ColsAtCompileTime = -1 127 // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1 128 // 129 // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the 130 // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1. 131 132 Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4); 133 Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0); 134 a*b; 135 } 136 137 void unaligned_objects() 138 { 139 // Regression test for the bug reported here: 140 // http://forum.kde.org/viewtopic.php?f=74&t=107541 141 // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then. 142 // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases, 143 // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault. 144 for(int m=450;m<460;++m) 145 { 146 for(int n=8;n<12;++n) 147 { 148 MatrixXf M(m, n); 149 VectorXf v1(n), r1(500); 150 RowVectorXf v2(m), r2(16); 151 152 M.setRandom(); 153 v1.setRandom(); 154 v2.setRandom(); 155 for(int o=0; o<4; ++o) 156 { 157 r1.segment(o,m).noalias() = M * v1; 158 VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1)); 159 r2.segment(o,n).noalias() = v2 * M; 160 VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M); 161 } 162 } 163 } 164 } 165 166 void test_product_extra() 167 { 168 for(int i = 0; i < g_repeat; i++) { 169 CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 170 CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 171 CALL_SUBTEST_2( mat_mat_scalar_scalar_product() ); 172 CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 173 CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); 174 } 175 CALL_SUBTEST_5( zero_sized_objects() ); 176 CALL_SUBTEST_6( unaligned_objects() ); 177 } 178
