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Diffstat (limited to 'eigen/test/product_extra.cpp')
| -rw-r--r-- | eigen/test/product_extra.cpp | 237 |
1 files changed, 237 insertions, 0 deletions
diff --git a/eigen/test/product_extra.cpp b/eigen/test/product_extra.cpp new file mode 100644 index 0000000..ea24869 --- /dev/null +++ b/eigen/test/product_extra.cpp @@ -0,0 +1,237 @@ +// 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 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, (numext::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); +} + +template <typename MatrixType> +void zero_sized_objects(const MatrixType& m) +{ + typedef typename MatrixType::Scalar Scalar; + const int PacketSize = internal::packet_traits<Scalar>::size; + const int PacketSize1 = PacketSize>1 ? PacketSize-1 : 1; + DenseIndex rows = m.rows(); + DenseIndex cols = m.cols(); + + { + MatrixType res, a(rows,0), b(0,cols); + VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(rows,cols) ); + VERIFY_IS_APPROX( (res=a*a.transpose()), MatrixType::Zero(rows,rows) ); + VERIFY_IS_APPROX( (res=b.transpose()*b), MatrixType::Zero(cols,cols) ); + VERIFY_IS_APPROX( (res=b.transpose()*a.transpose()), MatrixType::Zero(cols,rows) ); + } + + { + MatrixType res, a(rows,cols), b(cols,0); + res = a*b; + VERIFY(res.rows()==rows && res.cols()==0); + b.resize(0,rows); + res = b*a; + VERIFY(res.rows()==0 && res.cols()==cols); + } + + { + Matrix<Scalar,PacketSize,0> a; + Matrix<Scalar,0,1> b; + Matrix<Scalar,PacketSize,1> res; + VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) ); + VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) ); + } + + { + Matrix<Scalar,PacketSize1,0> a; + Matrix<Scalar,0,1> b; + Matrix<Scalar,PacketSize1,1> res; + VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) ); + VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) ); + } + + { + Matrix<Scalar,PacketSize,Dynamic> a(PacketSize,0); + Matrix<Scalar,Dynamic,1> b(0,1); + Matrix<Scalar,PacketSize,1> res; + VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) ); + VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) ); + } + + { + Matrix<Scalar,PacketSize1,Dynamic> a(PacketSize1,0); + Matrix<Scalar,Dynamic,1> b(0,1); + Matrix<Scalar,PacketSize1,1> res; + VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) ); + VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) ); + } +} + +void bug_127() +{ + // 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 unaligned_objects() +{ + // Regression test for the bug reported here: + // http://forum.kde.org/viewtopic.php?f=74&t=107541 + // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then. + // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases, + // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault. + for(int m=450;m<460;++m) + { + for(int n=8;n<12;++n) + { + MatrixXf M(m, n); + VectorXf v1(n), r1(500); + RowVectorXf v2(m), r2(16); + + M.setRandom(); + v1.setRandom(); + v2.setRandom(); + for(int o=0; o<4; ++o) + { + r1.segment(o,m).noalias() = M * v1; + VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1)); + r2.segment(o,n).noalias() = v2 * M; + VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M); + } + } + } +} + +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_1( zero_sized_objects(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); + } + CALL_SUBTEST_5( bug_127() ); + CALL_SUBTEST_6( unaligned_objects() ); +} |