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Add support for row/col-wise lpNorm()
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@ -124,6 +124,16 @@ EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
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EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
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EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost);
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template <int p, typename ResultType>
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struct member_lpnorm {
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typedef ResultType result_type;
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template<typename Scalar, int Size> struct Cost
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{ enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
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EIGEN_DEVICE_FUNC explicit member_lpnorm() {}
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template<typename XprType>
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EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
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{ return mat.template lpNorm<p>(); }
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};
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template <typename BinaryOp, typename Scalar>
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struct member_redux {
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@ -290,6 +300,10 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
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typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
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typedef Reverse<ExpressionType, Direction> ReverseReturnType;
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template<int p> struct LpNormReturnType {
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typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar>,Direction> Type;
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};
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/** \returns a row (or column) vector expression of the smallest coefficient
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* of each column (or row) of the referenced expression.
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*
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@ -340,6 +354,19 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
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const NormReturnType norm() const
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{ return NormReturnType(_expression()); }
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/** \returns a row (or column) vector expression of the norm
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* of each column (or row) of the referenced expression.
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* This is a vector with real entries, even if the original matrix has complex entries.
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*
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* Example: \include PartialRedux_norm.cpp
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* Output: \verbinclude PartialRedux_norm.out
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*
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* \sa DenseBase::norm() */
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EIGEN_DEVICE_FUNC
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template<int p>
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const typename LpNormReturnType<p>::Type lpNorm() const
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{ return typename LpNormReturnType<p>::Type(_expression()); }
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/** \returns a row (or column) vector expression of the norm
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* of each column (or row) of the referenced expression, using
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@ -191,6 +191,11 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
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rcres = m1.rowwise().norm();
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VERIFY_IS_APPROX(rcres(r), m1.row(r).norm());
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VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
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VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
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VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
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VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
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// test normalized
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m2 = m1.colwise().normalized();
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VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
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