GitHub-Proxy/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2025-08-14 20:56:00 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add support for row/col-wise lpNorm()

Browse Source
This commit is contained in:
Gael Guennebaud 2015-09-28 11:36:00 +02:00
parent 75861f6650
commit 8c1ee3629f
2 changed files with 32 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
Eigen/src/Core/VectorwiseOp.h
View File

@ -124,6 +124,16 @@ EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost); EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost); EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost);
template <int p, typename ResultType>
struct member_lpnorm {
typedef ResultType result_type;
template<typename Scalar, int Size> struct Cost
{ enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
EIGEN_DEVICE_FUNC explicit member_lpnorm() {}
template<typename XprType>
EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
{ return mat.template lpNorm<p>(); }
};
template <typename BinaryOp, typename Scalar> template <typename BinaryOp, typename Scalar>
struct member_redux { struct member_redux {
@ -290,6 +300,10 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename ReturnType<internal::member_prod>::Type ProdReturnType; typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType; typedef Reverse<ExpressionType, Direction> ReverseReturnType;
template<int p> struct LpNormReturnType {
typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar>,Direction> Type;
};
/** \returns a row (or column) vector expression of the smallest coefficient /** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression. * of each column (or row) of the referenced expression.
* *
@ -340,6 +354,19 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
const NormReturnType norm() const const NormReturnType norm() const
{ return NormReturnType(_expression()); } { return NormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* Example: \include PartialRedux_norm.cpp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa DenseBase::norm() */
EIGEN_DEVICE_FUNC
template<int p>
const typename LpNormReturnType<p>::Type lpNorm() const
{ return typename LpNormReturnType<p>::Type(_expression()); }
/** \returns a row (or column) vector expression of the norm /** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, using * of each column (or row) of the referenced expression, using

5
test/vectorwiseop.cpp
View File

@ -191,6 +191,11 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
rcres = m1.rowwise().norm(); rcres = m1.rowwise().norm();
VERIFY_IS_APPROX(rcres(r), m1.row(r).norm()); VERIFY_IS_APPROX(rcres(r), m1.row(r).norm());
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
// test normalized // test normalized
m2 = m1.colwise().normalized(); m2 = m1.colwise().normalized();
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized()); VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());