| // 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 "main.h" |
| |
| template<typename MatrixType> void product_extra(const MatrixType& m) |
| { |
| typedef typename MatrixType::Index Index; |
| typedef typename MatrixType::Scalar Scalar; |
| typedef typename NumTraits<Scalar>::NonInteger NonInteger; |
| typedef Matrix<Scalar, 1, Dynamic> RowVectorType; |
| typedef Matrix<Scalar, Dynamic, 1> ColVectorType; |
| typedef Matrix<Scalar, Dynamic, Dynamic, |
| MatrixType::Flags&RowMajorBit> OtherMajorMatrixType; |
| |
| Index rows = m.rows(); |
| Index cols = m.cols(); |
| |
| MatrixType m1 = MatrixType::Random(rows, cols), |
| m2 = MatrixType::Random(rows, cols), |
| m3(rows, cols), |
| mzero = MatrixType::Zero(rows, cols), |
| identity = MatrixType::Identity(rows, rows), |
| square = MatrixType::Random(rows, rows), |
| res = MatrixType::Random(rows, rows), |
| square2 = MatrixType::Random(cols, cols), |
| res2 = MatrixType::Random(cols, cols); |
| RowVectorType v1 = RowVectorType::Random(rows), vrres(rows); |
| ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); |
| OtherMajorMatrixType tm1 = m1; |
| |
| Scalar s1 = internal::random<Scalar>(), |
| s2 = internal::random<Scalar>(), |
| s3 = internal::random<Scalar>(); |
| |
| VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval()); |
| VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval()); |
| VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2); |
| VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2); |
| VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (internal::conj(s1) * m1.adjoint()).eval() * m2); |
| VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval()); |
| VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2); |
| VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval()); |
| |
| // a very tricky case where a scale factor has to be automatically conjugated: |
| VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval()); |
| |
| |
| // test all possible conjugate combinations for the four matrix-vector product cases: |
| |
| VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2), |
| (-m1.conjugate()*s2).eval() * (s1 * vc2).eval()); |
| VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()), |
| (-m1*s2).eval() * (s1 * vc2.conjugate()).eval()); |
| VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()), |
| (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval()); |
| |
| VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2), |
| (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval()); |
| VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2), |
| (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval()); |
| VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2), |
| (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval()); |
| |
| VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()), |
| (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval()); |
| VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()), |
| (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval()); |
| VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), |
| (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); |
| |
| VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2), |
| (s1 * v1).eval() * (-m1.conjugate()*s2).eval()); |
| VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2), |
| (s1 * v1.conjugate()).eval() * (-m1*s2).eval()); |
| VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2), |
| (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval()); |
| |
| VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()), |
| (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval()); |
| |
| // test the vector-matrix product with non aligned starts |
| Index i = internal::random<Index>(0,m1.rows()-2); |
| Index j = internal::random<Index>(0,m1.cols()-2); |
| Index r = internal::random<Index>(1,m1.rows()-i); |
| Index c = internal::random<Index>(1,m1.cols()-j); |
| Index i2 = internal::random<Index>(0,m1.rows()-1); |
| Index j2 = internal::random<Index>(0,m1.cols()-1); |
| |
| 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()); |
| 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()); |
| |
| // regression test |
| MatrixType tmp = m1 * m1.adjoint() * s1; |
| VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1); |
| } |
| |
| // Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947 |
| void mat_mat_scalar_scalar_product() |
| { |
| Eigen::Matrix2Xd dNdxy(2, 3); |
| dNdxy << -0.5, 0.5, 0, |
| -0.3, 0, 0.3; |
| double det = 6.0, wt = 0.5; |
| VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy); |
| } |
| |
| void zero_sized_objects() |
| { |
| // Bug 127 |
| // |
| // a product of the form lhs*rhs with |
| // |
| // lhs: |
| // rows = 1, cols = 4 |
| // RowsAtCompileTime = 1, ColsAtCompileTime = -1 |
| // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5 |
| // |
| // rhs: |
| // rows = 4, cols = 0 |
| // RowsAtCompileTime = -1, ColsAtCompileTime = -1 |
| // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1 |
| // |
| // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the |
| // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1. |
| |
| Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4); |
| Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0); |
| a*b; |
| } |
| |
| void test_product_extra() |
| { |
| for(int i = 0; i < g_repeat; i++) { |
| CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); |
| CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); |
| CALL_SUBTEST_2( mat_mat_scalar_scalar_product() ); |
| CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); |
| CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) ); |
| CALL_SUBTEST_5( zero_sized_objects() ); |
| } |
| } |
