11 #ifndef EIGEN_ORTHOMETHODS_H    12 #define EIGEN_ORTHOMETHODS_H    27 template<
typename Derived>
    28 template<
typename OtherDerived>
    29 #ifndef EIGEN_PARSED_BY_DOXYGEN    30 EIGEN_DEVICE_FUNC 
inline typename MatrixBase<Derived>::template cross_product_return_type<OtherDerived>::type
    43   return typename cross_product_return_type<OtherDerived>::type(
    44     numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
    45     numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
    46     numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
    52 template< 
int Arch,
typename VectorLhs,
typename VectorRhs,
    53           typename Scalar = 
typename VectorLhs::Scalar,
    54           bool Vectorizable = bool((VectorLhs::Flags&VectorRhs::Flags)&
PacketAccessBit)>
    57   run(
const VectorLhs& lhs, 
const VectorRhs& rhs)
    60       numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
    61       numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
    62       numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
    79 template<
typename Derived>
    80 template<
typename OtherDerived>
    89   DerivedNested lhs(derived());
    90   OtherDerivedNested rhs(other.derived());
   106 template<
typename ExpressionType, 
int Direction>
   107 template<
typename OtherDerived>
   114     YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
   122     eigen_assert(CrossReturnType::RowsAtCompileTime==3 && 
"the matrix must have exactly 3 rows");
   123     res.row(0) = (mat.row(1) * vec.coeff(2) - mat.row(2) * vec.coeff(1)).
conjugate();
   124     res.row(1) = (mat.row(2) * vec.coeff(0) - mat.row(0) * vec.coeff(2)).
conjugate();
   125     res.row(2) = (mat.row(0) * vec.coeff(1) - mat.row(1) * vec.coeff(0)).
conjugate();
   129     eigen_assert(CrossReturnType::ColsAtCompileTime==3 && 
"the matrix must have exactly 3 columns");
   130     res.col(0) = (mat.col(1) * vec.coeff(2) - mat.col(2) * vec.coeff(1)).
conjugate();
   131     res.col(1) = (mat.col(2) * vec.coeff(0) - mat.col(0) * vec.coeff(2)).
conjugate();
   132     res.col(2) = (mat.col(0) * vec.coeff(1) - mat.col(1) * vec.coeff(0)).
conjugate();
   139 template<
typename Derived, 
int Size = Derived::SizeAtCompileTime>
   147   static inline VectorType 
run(
const Derived& src)
   149     VectorType perp = VectorType::Zero(src.size());
   152     src.cwiseAbs().maxCoeff(&maxi);
   155     RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm();
   156     perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
   157     perp.coeffRef(sndi) =  numext::conj(src.coeff(maxi)) * invnm;
   163 template<
typename Derived>
   170   static inline VectorType 
run(
const Derived& src)
   180     if((!isMuchSmallerThan(src.x(), src.z()))
   181     || (!isMuchSmallerThan(src.y(), src.z())))
   183       RealScalar invnm = RealScalar(1)/src.template head<2>().norm();
   184       perp.coeffRef(0) = -numext::conj(src.y())*invnm;
   185       perp.coeffRef(1) = numext::conj(src.x())*invnm;
   186       perp.coeffRef(2) = 0;
   194       RealScalar invnm = RealScalar(1)/src.template tail<2>().norm();
   195       perp.coeffRef(0) = 0;
   196       perp.coeffRef(1) = -numext::conj(src.z())*invnm;
   197       perp.coeffRef(2) = numext::conj(src.y())*invnm;
   204 template<
typename Derived>
   209   static inline VectorType 
run(
const Derived& src)
   210   { 
return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); }
   224 template<
typename Derived>
   234 #endif // EIGEN_ORTHOMETHODS_H 
NumTraits< Scalar >::Real RealScalar
internal::traits< Derived >::Scalar Scalar
EIGEN_DEVICE_FUNC PlainObject unitOrthogonal(void) const
EIGEN_DEVICE_FUNC PlainObject cross3(const MatrixBase< OtherDerived > &other) const
traits< Derived >::Scalar Scalar
plain_matrix_type< Derived >::type VectorType
traits< Derived >::Scalar Scalar
Holds information about the various numeric (i.e. scalar) types allowed by Eigen. ...
#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
static EIGEN_DEVICE_FUNC internal::plain_matrix_type< VectorLhs >::type run(const VectorLhs &lhs, const VectorRhs &rhs)
const unsigned int PacketAccessBit
EIGEN_DEVICE_FUNC cross_product_return_type< OtherDerived >::type cross(const MatrixBase< OtherDerived > &other) const
Matrix< Scalar, 2, 1 > Vector2
static EIGEN_DEVICE_FUNC VectorType run(const Derived &src)
static EIGEN_DEVICE_FUNC VectorType run(const Derived &src)
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API. 
EIGEN_DEVICE_FUNC const CrossReturnType cross(const MatrixBase< OtherDerived > &other) const
Base::PlainObject PlainObject
EIGEN_DEVICE_FUNC ConjugateReturnType conjugate() const
plain_matrix_type< Derived >::type VectorType
static EIGEN_DEVICE_FUNC VectorType run(const Derived &src)
plain_matrix_type< Derived >::type VectorType
The matrix class, also used for vectors and row-vectors. 
#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE)
ExpressionType::PlainObject CrossReturnType
Base class for all dense matrices, vectors, and expressions. 
NumTraits< Scalar >::Real RealScalar
#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE)