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682 lines
26 KiB
C++
682 lines
26 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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template <typename MatrixType>
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void product_extra(const MatrixType& m) {
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
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typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
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typedef Matrix<Scalar, Dynamic, Dynamic, MatrixType::Flags & RowMajorBit> OtherMajorMatrixType;
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Index rows = m.rows();
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Index cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols), identity = MatrixType::Identity(rows, rows),
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square = MatrixType::Random(rows, rows), res = MatrixType::Random(rows, rows),
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square2 = MatrixType::Random(cols, cols), res2 = MatrixType::Random(cols, cols);
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RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
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ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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OtherMajorMatrixType tm1 = m1;
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Scalar s1 = internal::random<Scalar>(), s2 = internal::random<Scalar>(), s3 = internal::random<Scalar>();
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VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval());
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VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
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VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2);
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VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
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VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2);
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VERIFY_IS_APPROX(m3.noalias() = (-m1.adjoint() * s1) * (s3 * m2), (-m1.adjoint() * s1).eval() * (s3 * m2).eval());
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VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
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VERIFY_IS_APPROX(m3.noalias() = (-m1 * s2) * s1 * m2.adjoint(), (-m1 * s2).eval() * (s1 * m2.adjoint()).eval());
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// a very tricky case where a scale factor has to be automatically conjugated:
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VERIFY_IS_APPROX(m1.adjoint() * (s1 * m2).conjugate(), (m1.adjoint()).eval() * ((s1 * m2).conjugate()).eval());
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// test all possible conjugate combinations for the four matrix-vector product cases:
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VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2), (-m1.conjugate() * s2).eval() * (s1 * vc2).eval());
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VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()), (-m1 * s2).eval() * (s1 * vc2.conjugate()).eval());
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VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
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(-m1.conjugate() * s2).eval() * (s1 * vc2.conjugate()).eval());
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VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
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(s1 * vc2.transpose()).eval() * (-m1.adjoint() * s2).eval());
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VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
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(s1 * vc2.adjoint()).eval() * (-m1.transpose() * s2).eval());
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VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
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(s1 * vc2.adjoint()).eval() * (-m1.adjoint() * s2).eval());
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
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(-m1.adjoint() * s2).eval() * (s1 * v1.transpose()).eval());
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VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
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(-m1.transpose() * s2).eval() * (s1 * v1.adjoint()).eval());
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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(-m1.adjoint() * s2).eval() * (s1 * v1.adjoint()).eval());
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VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2), (s1 * v1).eval() * (-m1.conjugate() * s2).eval());
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VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2), (s1 * v1.conjugate()).eval() * (-m1 * s2).eval());
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VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
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(s1 * v1.conjugate()).eval() * (-m1.conjugate() * s2).eval());
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VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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(-m1.adjoint() * s2).eval() * (s1 * v1.adjoint()).eval());
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// test the vector-matrix product with non aligned starts
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Index i = internal::random<Index>(0, m1.rows() - 2);
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Index j = internal::random<Index>(0, m1.cols() - 2);
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Index r = internal::random<Index>(1, m1.rows() - i);
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Index c = internal::random<Index>(1, m1.cols() - j);
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Index i2 = internal::random<Index>(0, m1.rows() - 1);
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Index j2 = internal::random<Index>(0, m1.cols() - 1);
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VERIFY_IS_APPROX(m1.col(j2).adjoint() * m1.block(0, j, m1.rows(), c),
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m1.col(j2).adjoint().eval() * m1.block(0, j, m1.rows(), c).eval());
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VERIFY_IS_APPROX(m1.block(i, 0, r, m1.cols()) * m1.row(i2).adjoint(),
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m1.block(i, 0, r, m1.cols()).eval() * m1.row(i2).adjoint().eval());
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// test negative strides
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{
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Map<MatrixType, Unaligned, Stride<Dynamic, Dynamic> > map1(&m1(rows - 1, cols - 1), rows, cols,
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Stride<Dynamic, Dynamic>(-m1.outerStride(), -1));
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Map<MatrixType, Unaligned, Stride<Dynamic, Dynamic> > map2(&m2(rows - 1, cols - 1), rows, cols,
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Stride<Dynamic, Dynamic>(-m2.outerStride(), -1));
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Map<RowVectorType, Unaligned, InnerStride<-1> > mapv1(&v1(v1.size() - 1), v1.size(), InnerStride<-1>(-1));
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Map<ColVectorType, Unaligned, InnerStride<-1> > mapvc2(&vc2(vc2.size() - 1), vc2.size(), InnerStride<-1>(-1));
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VERIFY_IS_APPROX(MatrixType(map1), m1.reverse());
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VERIFY_IS_APPROX(MatrixType(map2), m2.reverse());
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VERIFY_IS_APPROX(m3.noalias() = MatrixType(map1) * MatrixType(map2).adjoint(),
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m1.reverse() * m2.reverse().adjoint());
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VERIFY_IS_APPROX(m3.noalias() = map1 * map2.adjoint(), m1.reverse() * m2.reverse().adjoint());
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VERIFY_IS_APPROX(map1 * vc2, m1.reverse() * vc2);
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VERIFY_IS_APPROX(m1 * mapvc2, m1 * mapvc2);
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VERIFY_IS_APPROX(map1.adjoint() * v1.transpose(), m1.adjoint().reverse() * v1.transpose());
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VERIFY_IS_APPROX(m1.adjoint() * mapv1.transpose(), m1.adjoint() * v1.reverse().transpose());
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}
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// regression test
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MatrixType tmp = m1 * m1.adjoint() * s1;
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VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1);
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// regression test for bug 1343, assignment to arrays
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Array<Scalar, Dynamic, 1> a1 = m1 * vc2;
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VERIFY_IS_APPROX(a1.matrix(), m1 * vc2);
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Array<Scalar, Dynamic, 1> a2 = s1 * (m1 * vc2);
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VERIFY_IS_APPROX(a2.matrix(), s1 * m1 * vc2);
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Array<Scalar, 1, Dynamic> a3 = v1 * m1;
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VERIFY_IS_APPROX(a3.matrix(), v1 * m1);
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Array<Scalar, Dynamic, Dynamic> a4 = m1 * m2.adjoint();
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VERIFY_IS_APPROX(a4.matrix(), m1 * m2.adjoint());
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}
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// Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947
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void mat_mat_scalar_scalar_product() {
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Eigen::Matrix2Xd dNdxy(2, 3);
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dNdxy << -0.5, 0.5, 0, -0.3, 0, 0.3;
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double det = 6.0, wt = 0.5;
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VERIFY_IS_APPROX(dNdxy.transpose() * dNdxy * det * wt, det * wt * dNdxy.transpose() * dNdxy);
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}
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template <typename MatrixType>
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void zero_sized_objects(const MatrixType& m) {
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typedef typename MatrixType::Scalar Scalar;
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const int PacketSize = internal::packet_traits<Scalar>::size;
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const int PacketSize1 = PacketSize > 1 ? PacketSize - 1 : 1;
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Index rows = m.rows();
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Index cols = m.cols();
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{
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MatrixType res, a(rows, 0), b(0, cols);
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VERIFY_IS_APPROX((res = a * b), MatrixType::Zero(rows, cols));
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VERIFY_IS_APPROX((res = a * a.transpose()), MatrixType::Zero(rows, rows));
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VERIFY_IS_APPROX((res = b.transpose() * b), MatrixType::Zero(cols, cols));
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VERIFY_IS_APPROX((res = b.transpose() * a.transpose()), MatrixType::Zero(cols, rows));
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}
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{
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MatrixType res, a(rows, cols), b(cols, 0);
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res = a * b;
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VERIFY(res.rows() == rows && res.cols() == 0);
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b.resize(0, rows);
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res = b * a;
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VERIFY(res.rows() == 0 && res.cols() == cols);
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}
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{
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Matrix<Scalar, PacketSize, 0> a;
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Matrix<Scalar, 0, 1> b;
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Matrix<Scalar, PacketSize, 1> res;
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VERIFY_IS_APPROX((res = a * b), MatrixType::Zero(PacketSize, 1));
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VERIFY_IS_APPROX((res = a.lazyProduct(b)), MatrixType::Zero(PacketSize, 1));
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}
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{
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Matrix<Scalar, PacketSize1, 0> a;
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Matrix<Scalar, 0, 1> b;
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Matrix<Scalar, PacketSize1, 1> res;
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VERIFY_IS_APPROX((res = a * b), MatrixType::Zero(PacketSize1, 1));
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VERIFY_IS_APPROX((res = a.lazyProduct(b)), MatrixType::Zero(PacketSize1, 1));
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}
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{
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Matrix<Scalar, PacketSize, Dynamic> a(PacketSize, 0);
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Matrix<Scalar, Dynamic, 1> b(0, 1);
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Matrix<Scalar, PacketSize, 1> res;
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VERIFY_IS_APPROX((res = a * b), MatrixType::Zero(PacketSize, 1));
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VERIFY_IS_APPROX((res = a.lazyProduct(b)), MatrixType::Zero(PacketSize, 1));
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}
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{
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Matrix<Scalar, PacketSize1, Dynamic> a(PacketSize1, 0);
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Matrix<Scalar, Dynamic, 1> b(0, 1);
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Matrix<Scalar, PacketSize1, 1> res;
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VERIFY_IS_APPROX((res = a * b), MatrixType::Zero(PacketSize1, 1));
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VERIFY_IS_APPROX((res = a.lazyProduct(b)), MatrixType::Zero(PacketSize1, 1));
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}
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}
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template <int>
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void bug_127() {
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// Bug 127
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//
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// a product of the form lhs*rhs with
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//
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// lhs:
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// rows = 1, cols = 4
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// RowsAtCompileTime = 1, ColsAtCompileTime = -1
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// MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
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//
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// rhs:
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// rows = 4, cols = 0
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// RowsAtCompileTime = -1, ColsAtCompileTime = -1
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// MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
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//
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// was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using
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// the max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
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Matrix<float, 1, Dynamic, RowMajor, 1, 5> a(1, 4);
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Matrix<float, Dynamic, Dynamic, ColMajor, 5, 1> b(4, 0);
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a* b;
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}
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template <int>
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void bug_817() {
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ArrayXXf B = ArrayXXf::Random(10, 10), C;
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VectorXf x = VectorXf::Random(10);
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C = (x.transpose() * B.matrix());
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B = (x.transpose() * B.matrix());
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VERIFY_IS_APPROX(B, C);
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}
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template <int>
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void unaligned_objects() {
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// Regression test for the bug reported here:
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// http://forum.kde.org/viewtopic.php?f=74&t=107541
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// Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then.
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// There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases,
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// memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault.
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for (int m = 450; m < 460; ++m) {
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for (int n = 8; n < 12; ++n) {
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MatrixXf M(m, n);
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VectorXf v1(n), r1(500);
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RowVectorXf v2(m), r2(16);
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M.setRandom();
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v1.setRandom();
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v2.setRandom();
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for (int o = 0; o < 4; ++o) {
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r1.segment(o, m).noalias() = M * v1;
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VERIFY_IS_APPROX(r1.segment(o, m), M * MatrixXf(v1));
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r2.segment(o, n).noalias() = v2 * M;
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VERIFY_IS_APPROX(r2.segment(o, n), MatrixXf(v2) * M);
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}
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}
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}
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}
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template <typename T>
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EIGEN_DONT_INLINE Index test_compute_block_size(Index m, Index n, Index k) {
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Index mc(m), nc(n), kc(k);
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internal::computeProductBlockingSizes<T, T>(kc, mc, nc);
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return kc + mc + nc;
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}
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template <typename T>
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Index compute_block_size() {
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Index ret = 0;
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// Zero-sized inputs: verify they compile and don't crash.
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ret += test_compute_block_size<T>(0, 1, 1);
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ret += test_compute_block_size<T>(1, 0, 1);
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ret += test_compute_block_size<T>(1, 1, 0);
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ret += test_compute_block_size<T>(0, 0, 1);
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ret += test_compute_block_size<T>(0, 1, 0);
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ret += test_compute_block_size<T>(1, 0, 0);
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ret += test_compute_block_size<T>(0, 0, 0);
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// Sanity checks: blocking sizes must be positive and not exceed the original.
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{
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Index m = 200, n = 200, k = 200;
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Index mc = m, nc = n, kc = k;
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internal::computeProductBlockingSizes<T, T>(kc, mc, nc);
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VERIFY(kc > 0 && kc <= k);
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VERIFY(mc > 0 && mc <= m);
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VERIFY(nc > 0 && nc <= n);
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}
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// With EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS (l1=9KB, l2=32KB, l3=512KB),
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// large sizes must be actually blocked (not returned as-is).
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{
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Index m = 500, n = 500, k = 500;
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Index mc = m, nc = n, kc = k;
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internal::computeProductBlockingSizes<T, T>(kc, mc, nc);
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VERIFY(kc < k);
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}
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return ret;
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}
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// Verify correctness of GEMM at sizes that require multiple blocking passes
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// under EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS (l1=9KB, l2=32KB, l3=512KB).
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// The blocking early-return threshold is max(k,m,n) < 48, so sizes >= 48
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// trigger actual multi-pass blocking with these tiny cache sizes.
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// Verifies GEMM against column-by-column GEMV (a different code path).
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template <int>
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void test_small_block_correctness() {
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const int sizes[] = {48, 64, 96, 128, 200};
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for (int si = 0; si < 5; ++si) {
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int n = sizes[si];
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MatrixXd A = MatrixXd::Random(n, n);
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MatrixXd B = MatrixXd::Random(n, n);
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MatrixXd C(n, n);
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C.noalias() = A * B;
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MatrixXd Cref(n, n);
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for (int j = 0; j < n; ++j) Cref.col(j) = A * B.col(j);
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VERIFY_IS_APPROX(C, Cref);
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}
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// Non-square: exercise different blocking in m, n, k dimensions.
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{
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MatrixXd A = MatrixXd::Random(200, 64);
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MatrixXd B = MatrixXd::Random(64, 300);
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MatrixXd C(200, 300);
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C.noalias() = A * B;
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MatrixXd Cref(200, 300);
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for (int j = 0; j < 300; ++j) Cref.col(j) = A * B.col(j);
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VERIFY_IS_APPROX(C, Cref);
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}
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}
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template <typename>
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void aliasing_with_resize() {
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Index m = internal::random<Index>(10, 50);
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Index n = internal::random<Index>(10, 50);
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MatrixXd A, B, C(m, n), D(m, m);
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VectorXd a, b, c(n);
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C.setRandom();
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D.setRandom();
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c.setRandom();
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double s = internal::random<double>(1, 10);
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A = C;
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B = A * A.transpose();
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A = A * A.transpose();
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VERIFY_IS_APPROX(A, B);
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A = C;
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B = (A * A.transpose()) / s;
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A = (A * A.transpose()) / s;
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VERIFY_IS_APPROX(A, B);
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A = C;
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B = (A * A.transpose()) + D;
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A = (A * A.transpose()) + D;
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VERIFY_IS_APPROX(A, B);
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A = C;
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B = D + (A * A.transpose());
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A = D + (A * A.transpose());
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VERIFY_IS_APPROX(A, B);
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A = C;
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B = s * (A * A.transpose());
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A = s * (A * A.transpose());
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VERIFY_IS_APPROX(A, B);
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A = C;
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a = c;
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b = (A * a) / s;
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a = (A * a) / s;
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VERIFY_IS_APPROX(a, b);
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}
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template <int>
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void bug_1308() {
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int n = 10;
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MatrixXd r(n, n);
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VectorXd v = VectorXd::Random(n);
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r = v * RowVectorXd::Ones(n);
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VERIFY_IS_APPROX(r, v.rowwise().replicate(n));
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r = VectorXd::Ones(n) * v.transpose();
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VERIFY_IS_APPROX(r, v.rowwise().replicate(n).transpose());
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Matrix4d ones44 = Matrix4d::Ones();
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Matrix4d m44 = Matrix4d::Ones() * Matrix4d::Ones();
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VERIFY_IS_APPROX(m44, Matrix4d::Constant(4));
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VERIFY_IS_APPROX(m44.noalias() = ones44 * Matrix4d::Ones(), Matrix4d::Constant(4));
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VERIFY_IS_APPROX(m44.noalias() = ones44.transpose() * Matrix4d::Ones(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(m44.noalias() = Matrix4d::Ones() * ones44, Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(m44.noalias() = Matrix4d::Ones() * ones44.transpose(), Matrix4d::Constant(4));
|
||
|
||
typedef Matrix<double, 4, 4, RowMajor> RMatrix4d;
|
||
RMatrix4d r44 = Matrix4d::Ones() * Matrix4d::Ones();
|
||
VERIFY_IS_APPROX(r44, Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = ones44 * Matrix4d::Ones(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = ones44.transpose() * Matrix4d::Ones(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = Matrix4d::Ones() * ones44, Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = Matrix4d::Ones() * ones44.transpose(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = ones44 * RMatrix4d::Ones(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = ones44.transpose() * RMatrix4d::Ones(), Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = RMatrix4d::Ones() * ones44, Matrix4d::Constant(4));
|
||
VERIFY_IS_APPROX(r44.noalias() = RMatrix4d::Ones() * ones44.transpose(), Matrix4d::Constant(4));
|
||
|
||
// RowVector4d r4;
|
||
m44.setOnes();
|
||
r44.setZero();
|
||
VERIFY_IS_APPROX(r44.noalias() += m44.row(0).transpose() * RowVector4d::Ones(), ones44);
|
||
r44.setZero();
|
||
VERIFY_IS_APPROX(r44.noalias() += m44.col(0) * RowVector4d::Ones(), ones44);
|
||
r44.setZero();
|
||
VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.row(0), ones44);
|
||
r44.setZero();
|
||
VERIFY_IS_APPROX(r44.noalias() += Vector4d::Ones() * m44.col(0).transpose(), ones44);
|
||
}
|
||
|
||
// Regression test for issue #3059: GEBP asm register constraints fail
|
||
// for custom (non-vectorizable) scalar types. Type T has a non-trivial
|
||
// destructor (making sizeof(T) > sizeof(double)), while type U is a
|
||
// simple wrapper. Both must compile and produce correct products.
|
||
namespace issue_3059 {
|
||
|
||
class Ptr {
|
||
public:
|
||
~Ptr() {}
|
||
double* m_ptr = nullptr;
|
||
};
|
||
|
||
class T {
|
||
public:
|
||
T() = default;
|
||
T(double v) : m_value(v) {}
|
||
|
||
friend T operator*(const T& a, const T& b) { return T(a.m_value * b.m_value); }
|
||
T& operator*=(const T& o) {
|
||
m_value *= o.m_value;
|
||
return *this;
|
||
}
|
||
friend T operator/(const T& a, const T& b) { return T(a.m_value / b.m_value); }
|
||
T& operator/=(const T& o) {
|
||
m_value /= o.m_value;
|
||
return *this;
|
||
}
|
||
friend T operator+(const T& a, const T& b) { return T(a.m_value + b.m_value); }
|
||
T& operator+=(const T& o) {
|
||
m_value += o.m_value;
|
||
return *this;
|
||
}
|
||
friend T operator-(const T& a, const T& b) { return T(a.m_value - b.m_value); }
|
||
T& operator-=(const T& o) {
|
||
m_value -= o.m_value;
|
||
return *this;
|
||
}
|
||
friend T operator-(const T& a) { return T(-a.m_value); }
|
||
|
||
bool operator==(const T& o) const { return m_value == o.m_value; }
|
||
bool operator<(const T& o) const { return m_value < o.m_value; }
|
||
bool operator<=(const T& o) const { return m_value <= o.m_value; }
|
||
bool operator>(const T& o) const { return m_value > o.m_value; }
|
||
bool operator>=(const T& o) const { return m_value >= o.m_value; }
|
||
bool operator!=(const T& o) const { return m_value != o.m_value; }
|
||
|
||
double value() const { return m_value; }
|
||
|
||
private:
|
||
double m_value = 0.0;
|
||
Ptr m_ptr; // Makes sizeof(T) > sizeof(double)
|
||
};
|
||
|
||
T sqrt(const T& x) { return T(std::sqrt(x.value())); }
|
||
T abs(const T& x) { return T(std::abs(x.value())); }
|
||
T abs2(const T& x) { return T(x.value() * x.value()); }
|
||
|
||
class U {
|
||
public:
|
||
U() = default;
|
||
U(double v) : m_value(v) {}
|
||
|
||
friend U operator*(const U& a, const U& b) { return U(a.m_value * b.m_value); }
|
||
U& operator*=(const U& o) {
|
||
m_value *= o.m_value;
|
||
return *this;
|
||
}
|
||
friend U operator/(const U& a, const U& b) { return U(a.m_value / b.m_value); }
|
||
U& operator/=(const U& o) {
|
||
m_value /= o.m_value;
|
||
return *this;
|
||
}
|
||
friend U operator+(const U& a, const U& b) { return U(a.m_value + b.m_value); }
|
||
U& operator+=(const U& o) {
|
||
m_value += o.m_value;
|
||
return *this;
|
||
}
|
||
friend U operator-(const U& a, const U& b) { return U(a.m_value - b.m_value); }
|
||
U& operator-=(const U& o) {
|
||
m_value -= o.m_value;
|
||
return *this;
|
||
}
|
||
friend U operator-(const U& a) { return U(-a.m_value); }
|
||
|
||
bool operator==(const U& o) const { return m_value == o.m_value; }
|
||
bool operator<(const U& o) const { return m_value < o.m_value; }
|
||
bool operator<=(const U& o) const { return m_value <= o.m_value; }
|
||
bool operator>(const U& o) const { return m_value > o.m_value; }
|
||
bool operator>=(const U& o) const { return m_value >= o.m_value; }
|
||
bool operator!=(const U& o) const { return m_value != o.m_value; }
|
||
|
||
double value() const { return m_value; }
|
||
|
||
private:
|
||
double m_value = 0.0;
|
||
};
|
||
|
||
U sqrt(const U& x) { return U(std::sqrt(x.value())); }
|
||
U abs(const U& x) { return U(std::abs(x.value())); }
|
||
U abs2(const U& x) { return U(x.value() * x.value()); }
|
||
|
||
} // namespace issue_3059
|
||
|
||
namespace Eigen {
|
||
|
||
template <>
|
||
struct NumTraits<issue_3059::T> : NumTraits<double> {
|
||
using Real = issue_3059::T;
|
||
using NonInteger = issue_3059::T;
|
||
using Nested = issue_3059::T;
|
||
enum { IsComplex = 0, RequireInitialization = 1 };
|
||
};
|
||
|
||
template <>
|
||
struct NumTraits<issue_3059::U> : NumTraits<double> {
|
||
using Real = issue_3059::U;
|
||
using NonInteger = issue_3059::U;
|
||
using Nested = issue_3059::U;
|
||
enum { IsComplex = 0, RequireInitialization = 0 };
|
||
};
|
||
|
||
} // namespace Eigen
|
||
|
||
template <int>
|
||
void product_custom_scalar_types() {
|
||
using namespace issue_3059;
|
||
// Type T: has non-trivial destructor, sizeof(T) > sizeof(double)
|
||
{
|
||
Matrix<T, Dynamic, Dynamic> A(4, 4), B(4, 4), C(4, 4);
|
||
for (int i = 0; i < 4; ++i)
|
||
for (int j = 0; j < 4; ++j) {
|
||
A(i, j) = T(static_cast<double>(i + 1));
|
||
B(i, j) = T(static_cast<double>(j + 1));
|
||
}
|
||
C.noalias() = A * B;
|
||
// A*B: C(i,j) = sum_k (i+1)*(k+1) * ... no, A(i,k)=(i+1), B(k,j)=(j+1)
|
||
// so C(i,j) = sum_k (i+1)*(j+1) = 4*(i+1)*(j+1)
|
||
for (int i = 0; i < 4; ++i)
|
||
for (int j = 0; j < 4; ++j) VERIFY(C(i, j) == T(4.0 * (i + 1) * (j + 1)));
|
||
}
|
||
// Type U: simple wrapper, sizeof(U) == sizeof(double)
|
||
{
|
||
Matrix<U, Dynamic, Dynamic> A(4, 4), B(4, 4), C(4, 4);
|
||
for (int i = 0; i < 4; ++i)
|
||
for (int j = 0; j < 4; ++j) {
|
||
A(i, j) = U(static_cast<double>(i + 1));
|
||
B(i, j) = U(static_cast<double>(j + 1));
|
||
}
|
||
C.noalias() = A * B;
|
||
for (int i = 0; i < 4; ++i)
|
||
for (int j = 0; j < 4; ++j) VERIFY(C(i, j) == U(4.0 * (i + 1) * (j + 1)));
|
||
}
|
||
// Larger matrices to exercise GEBP blocking.
|
||
{
|
||
const int n = 33;
|
||
Matrix<U, Dynamic, Dynamic> A(n, n), B(n, n), C(n, n);
|
||
for (int i = 0; i < n; ++i)
|
||
for (int j = 0; j < n; ++j) {
|
||
A(i, j) = U(static_cast<double>((i * 7 + j * 3) % 13));
|
||
B(i, j) = U(static_cast<double>((i * 5 + j * 11) % 17));
|
||
}
|
||
C.noalias() = A * B;
|
||
// Verify against explicit triple loop.
|
||
for (int i = 0; i < n; ++i)
|
||
for (int j = 0; j < n; ++j) {
|
||
double sum = 0;
|
||
for (int k = 0; k < n; ++k) sum += A(i, k).value() * B(k, j).value();
|
||
VERIFY(C(i, j) == U(sum));
|
||
}
|
||
}
|
||
}
|
||
|
||
// Test complex GEMV with all conjugation combinations at sizes that
|
||
// exercise full, half, and quarter packet code paths.
|
||
// The GEMV kernels in GeneralMatrixVector.h use conj_helper at three
|
||
// packet levels. The existing product_extra tests cover conjugation
|
||
// but only at random sizes, never systematically at packet boundaries.
|
||
template <int>
|
||
void gemv_complex_conjugate() {
|
||
typedef std::complex<float> Scf;
|
||
typedef std::complex<double> Scd;
|
||
const Index PS_f = internal::packet_traits<Scf>::size;
|
||
const Index PS_d = internal::packet_traits<Scd>::size;
|
||
|
||
// Sizes chosen to exercise packet boundaries for both float and double.
|
||
const Index sizes[] = {1, 2, 3, 4, 5, 7, 8, 9, 15, 16, 17, 31, 32, 33};
|
||
|
||
for (int si = 0; si < 14; ++si) {
|
||
Index m = sizes[si];
|
||
// Test complex<float> GEMV with all conjugation combos.
|
||
{
|
||
typedef Matrix<Scf, Dynamic, Dynamic> Mat;
|
||
typedef Matrix<Scf, Dynamic, 1> Vec;
|
||
Mat A = Mat::Random(m, m);
|
||
Vec v = Vec::Random(m);
|
||
Vec res(m);
|
||
|
||
// A * v (no conjugation)
|
||
res.noalias() = A * v;
|
||
VERIFY_IS_APPROX(res, (A.eval() * v.eval()).eval());
|
||
|
||
// A.conjugate() * v
|
||
res.noalias() = A.conjugate() * v;
|
||
VERIFY_IS_APPROX(res, (A.conjugate().eval() * v.eval()).eval());
|
||
|
||
// A * v.conjugate()
|
||
res.noalias() = A * v.conjugate();
|
||
VERIFY_IS_APPROX(res, (A.eval() * v.conjugate().eval()).eval());
|
||
|
||
// A.conjugate() * v.conjugate()
|
||
res.noalias() = A.conjugate() * v.conjugate();
|
||
VERIFY_IS_APPROX(res, (A.conjugate().eval() * v.conjugate().eval()).eval());
|
||
|
||
// A.adjoint() * v (transpose + conjugate of lhs)
|
||
Vec res2(m);
|
||
res2.noalias() = A.adjoint() * v;
|
||
VERIFY_IS_APPROX(res2, (A.adjoint().eval() * v.eval()).eval());
|
||
|
||
// Row-major complex GEMV
|
||
typedef Matrix<Scf, Dynamic, Dynamic, RowMajor> RMat;
|
||
RMat B = A;
|
||
res.noalias() = B * v;
|
||
VERIFY_IS_APPROX(res, (A.eval() * v.eval()).eval());
|
||
|
||
res.noalias() = B.conjugate() * v;
|
||
VERIFY_IS_APPROX(res, (A.conjugate().eval() * v.eval()).eval());
|
||
}
|
||
|
||
// Test complex<double> GEMV with conjugation.
|
||
{
|
||
typedef Matrix<Scd, Dynamic, Dynamic> Mat;
|
||
typedef Matrix<Scd, Dynamic, 1> Vec;
|
||
Mat A = Mat::Random(m, m);
|
||
Vec v = Vec::Random(m);
|
||
Vec res(m);
|
||
|
||
res.noalias() = A.conjugate() * v;
|
||
VERIFY_IS_APPROX(res, (A.conjugate().eval() * v.eval()).eval());
|
||
|
||
res.noalias() = A * v.conjugate();
|
||
VERIFY_IS_APPROX(res, (A.eval() * v.conjugate().eval()).eval());
|
||
|
||
// Non-square: wide matrix × vector (exercises different cols path).
|
||
Mat C = Mat::Random(m, m + 3);
|
||
Vec w = Vec::Random(m + 3);
|
||
Vec res3(m);
|
||
res3.noalias() = C.conjugate() * w;
|
||
VERIFY_IS_APPROX(res3, (C.conjugate().eval() * w.eval()).eval());
|
||
}
|
||
}
|
||
(void)PS_f;
|
||
(void)PS_d;
|
||
}
|
||
|
||
EIGEN_DECLARE_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<0>());
|
||
CALL_SUBTEST_5(bug_817<0>());
|
||
CALL_SUBTEST_5(bug_1308<0>());
|
||
CALL_SUBTEST_6(unaligned_objects<0>());
|
||
CALL_SUBTEST_7(compute_block_size<float>());
|
||
CALL_SUBTEST_7(compute_block_size<double>());
|
||
CALL_SUBTEST_7(compute_block_size<std::complex<double> >());
|
||
CALL_SUBTEST_8(aliasing_with_resize<void>());
|
||
CALL_SUBTEST_9(product_custom_scalar_types<0>());
|
||
CALL_SUBTEST_10(test_small_block_correctness<0>());
|
||
|
||
// Complex GEMV conjugation at varied sizes (deterministic, outside g_repeat).
|
||
CALL_SUBTEST_11(gemv_complex_conjugate<0>());
|
||
}
|