237 lines
9.3 KiB
C++
237 lines
9.3 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 <bool IsInteger>
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struct adjoint_specific;
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template <>
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struct adjoint_specific<true> {
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template <typename Vec, typename Mat, typename Scalar>
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static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) {
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VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3),
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numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), 0));
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VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1 * v3.dot(v1) + s2 * v3.dot(v2), 0));
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// check compatibility of dot and adjoint
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VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), 0));
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}
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};
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template <>
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struct adjoint_specific<false> {
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template <typename Vec, typename Mat, typename Scalar>
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static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) {
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typedef typename NumTraits<Scalar>::Real RealScalar;
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using std::abs;
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RealScalar ref = NumTraits<Scalar>::IsInteger ? RealScalar(0) : (std::max)((s1 * v1 + s2 * v2).norm(), v3.norm());
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VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3),
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numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), ref));
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VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1 * v3.dot(v1) + s2 * v3.dot(v2), ref));
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VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm());
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// check normalized() and normalize()
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VERIFY_IS_APPROX(v1, v1.norm() * v1.normalized());
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v3 = v1;
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v3.normalize();
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VERIFY_IS_APPROX(v1, v1.norm() * v3);
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VERIFY_IS_APPROX(v3, v1.normalized());
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VERIFY_IS_APPROX(v3.norm(), RealScalar(1));
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// check null inputs
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VERIFY_IS_APPROX((v1 * 0).normalized(), (v1 * 0));
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#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE)
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RealScalar very_small = (std::numeric_limits<RealScalar>::min)();
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VERIFY(numext::is_exactly_zero((v1 * very_small).norm()));
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VERIFY_IS_APPROX((v1 * very_small).normalized(), (v1 * very_small));
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v3 = v1 * very_small;
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v3.normalize();
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VERIFY_IS_APPROX(v3, (v1 * very_small));
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#endif
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// check compatibility of dot and adjoint
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ref = NumTraits<Scalar>::IsInteger ? 0
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: (std::max)((std::max)(v1.norm(), v2.norm()),
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(std::max)((square * v2).norm(), (square.adjoint() * v1).norm()));
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VERIFY(internal::isMuchSmallerThan(abs(v1.dot(square * v2) - (square.adjoint() * v1).dot(v2)), ref,
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test_precision<Scalar>()));
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// check that Random().normalized() works: tricky as the random xpr must be evaluated by
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// normalized() in order to produce a consistent result.
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VERIFY_IS_APPROX(Vec::Random(v1.size()).normalized().norm(), RealScalar(1));
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}
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};
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template <typename MatrixType, typename Scalar = typename MatrixType::Scalar>
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MatrixType RandomMatrix(Index rows, Index cols, Scalar min, Scalar max) {
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MatrixType M = MatrixType(rows, cols);
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for (Index i = 0; i < rows; ++i) {
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for (Index j = 0; j < cols; ++j) {
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M(i, j) = Eigen::internal::random<Scalar>(min, max);
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}
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}
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return M;
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}
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template <typename MatrixType>
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void adjoint(const MatrixType& m) {
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/* this test covers the following files:
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Transpose.h Conjugate.h Dot.h
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*/
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using std::abs;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
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const Index PacketSize = internal::packet_traits<Scalar>::size;
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Index rows = m.rows();
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Index cols = m.cols();
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// Avoid integer overflow by limiting input values.
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RealScalar rmin = static_cast<RealScalar>(NumTraits<Scalar>::IsInteger ? NumTraits<Scalar>::IsSigned ? -100 : 0 : -1);
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RealScalar rmax = static_cast<RealScalar>(NumTraits<Scalar>::IsInteger ? 100 : 1);
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MatrixType m1 = RandomMatrix<MatrixType>(rows, cols, rmin, rmax),
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m2 = RandomMatrix<MatrixType>(rows, cols, rmin, rmax), m3(rows, cols),
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square = RandomMatrix<SquareMatrixType>(rows, rows, rmin, rmax);
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VectorType v1 = RandomMatrix<VectorType>(rows, 1, rmin, rmax), v2 = RandomMatrix<VectorType>(rows, 1, rmin, rmax),
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v3 = RandomMatrix<VectorType>(rows, 1, rmin, rmax), vzero = VectorType::Zero(rows);
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Scalar s1 = internal::random<Scalar>(rmin, rmax), s2 = internal::random<Scalar>(rmin, rmax);
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// check basic compatibility of adjoint, transpose, conjugate
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VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1);
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VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1);
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// check multiplicative behavior
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VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1);
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VERIFY_IS_APPROX((s1 * m1).adjoint(), numext::conj(s1) * m1.adjoint());
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// check basic properties of dot, squaredNorm
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VERIFY_IS_APPROX(numext::conj(v1.dot(v2)), v2.dot(v1));
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VERIFY_IS_APPROX(numext::real(v1.dot(v1)), v1.squaredNorm());
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adjoint_specific<NumTraits<Scalar>::IsInteger>::run(v1, v2, v3, square, s1, s2);
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VERIFY_IS_MUCH_SMALLER_THAN(abs(vzero.dot(v1)), static_cast<RealScalar>(1));
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// like in testBasicStuff, test operator() to check const-qualification
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Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
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VERIFY_IS_APPROX(m1.conjugate()(r, c), numext::conj(m1(r, c)));
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VERIFY_IS_APPROX(m1.adjoint()(c, r), numext::conj(m1(r, c)));
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// check inplace transpose
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m3 = m1;
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m3.transposeInPlace();
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VERIFY_IS_APPROX(m3, m1.transpose());
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m3.transposeInPlace();
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VERIFY_IS_APPROX(m3, m1);
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if (PacketSize < m3.rows() && PacketSize < m3.cols()) {
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m3 = m1;
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Index i = internal::random<Index>(0, m3.rows() - PacketSize);
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Index j = internal::random<Index>(0, m3.cols() - PacketSize);
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m3.template block<PacketSize, PacketSize>(i, j).transposeInPlace();
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VERIFY_IS_APPROX((m3.template block<PacketSize, PacketSize>(i, j)),
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(m1.template block<PacketSize, PacketSize>(i, j).transpose()));
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m3.template block<PacketSize, PacketSize>(i, j).transposeInPlace();
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VERIFY_IS_APPROX(m3, m1);
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}
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// check inplace adjoint
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m3 = m1;
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m3.adjointInPlace();
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VERIFY_IS_APPROX(m3, m1.adjoint());
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m3.transposeInPlace();
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VERIFY_IS_APPROX(m3, m1.conjugate());
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// check mixed dot product
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typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealVectorType;
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RealVectorType rv1 = RandomMatrix<RealVectorType>(rows, 1, rmin, rmax);
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VERIFY_IS_APPROX(v1.dot(rv1.template cast<Scalar>()), v1.dot(rv1));
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VERIFY_IS_APPROX(rv1.template cast<Scalar>().dot(v1), rv1.dot(v1));
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VERIFY(is_same_type(m1, m1.template conjugateIf<false>()));
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VERIFY(is_same_type(m1.conjugate(), m1.template conjugateIf<true>()));
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}
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template <int>
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void adjoint_extra() {
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MatrixXcf a(10, 10), b(10, 10);
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VERIFY_RAISES_ASSERT(a = a.transpose());
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VERIFY_RAISES_ASSERT(a = a.transpose() + b);
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VERIFY_RAISES_ASSERT(a = b + a.transpose());
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VERIFY_RAISES_ASSERT(a = a.conjugate().transpose());
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VERIFY_RAISES_ASSERT(a = a.adjoint());
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VERIFY_RAISES_ASSERT(a = a.adjoint() + b);
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VERIFY_RAISES_ASSERT(a = b + a.adjoint());
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// no assertion should be triggered for these cases:
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a.transpose() = a.transpose();
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a.transpose() += a.transpose();
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a.transpose() += a.transpose() + b;
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a.transpose() = a.adjoint();
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a.transpose() += a.adjoint();
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a.transpose() += a.adjoint() + b;
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// regression tests for check_for_aliasing
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MatrixXd c(10, 10);
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c = 1.0 * MatrixXd::Ones(10, 10) + c;
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c = MatrixXd::Ones(10, 10) * 1.0 + c;
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c = c + MatrixXd::Ones(10, 10).cwiseProduct(MatrixXd::Zero(10, 10));
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c = MatrixXd::Ones(10, 10) * MatrixXd::Zero(10, 10);
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// regression for bug 1646
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for (int j = 0; j < 10; ++j) {
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c.col(j).head(j) = c.row(j).head(j);
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}
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for (int j = 0; j < 10; ++j) {
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c.col(j) = c.row(j);
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}
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a.conservativeResize(1, 1);
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a = a.transpose();
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a.conservativeResize(0, 0);
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a = a.transpose();
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}
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EIGEN_DECLARE_TEST(adjoint) {
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for (int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1(adjoint(Matrix<float, 1, 1>()));
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CALL_SUBTEST_2(adjoint(Matrix3d()));
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CALL_SUBTEST_3(adjoint(Matrix4f()));
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CALL_SUBTEST_4(adjoint(MatrixXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2),
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internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2))));
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CALL_SUBTEST_5(adjoint(
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MatrixXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
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CALL_SUBTEST_6(adjoint(
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MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
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// Complement for 128 bits vectorization:
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CALL_SUBTEST_8(adjoint(Matrix2d()));
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CALL_SUBTEST_9(adjoint(Matrix<int, 4, 4>()));
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// 256 bits vectorization:
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CALL_SUBTEST_10(adjoint(Matrix<float, 8, 8>()));
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CALL_SUBTEST_11(adjoint(Matrix<double, 4, 4>()));
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CALL_SUBTEST_12(adjoint(Matrix<int, 8, 8>()));
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}
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// test a large static matrix only once
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CALL_SUBTEST_7(adjoint(Matrix<float, 100, 100>()));
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CALL_SUBTEST_13(adjoint_extra<0>());
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}
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