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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PARTIAL_REDUX_H
#define EIGEN_PARTIAL_REDUX_H
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class PartialReduxExpr
* \ingroup Core_Module
*
* \brief Generic expression of a partially reduxed matrix
*
* \tparam MatrixType the type of the matrix we are applying the redux operation
* \tparam MemberOp type of the member functor
* \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
*
* This class represents an expression of a partial redux operator of a matrix.
* It is the return type of some VectorwiseOp functions,
* and most of the time this is the only way it is used.
*
* \sa class VectorwiseOp
*/
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr;
namespace internal {
template<typename MatrixType, typename MemberOp, int Direction>
struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
: traits<MatrixType>
{
typedef typename MemberOp::result_type Scalar;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename MatrixType::Scalar InputScalar;
enum {
RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime,
ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime
};
};
}
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
EIGEN_DEVICE_FUNC
explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
Index rows() const EIGEN_NOEXCEPT { return (Direction==Vertical ? 1 : m_matrix.rows()); }
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
Index cols() const EIGEN_NOEXCEPT { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
EIGEN_DEVICE_FUNC
typename MatrixType::Nested nestedExpression() const { return m_matrix; }
EIGEN_DEVICE_FUNC
const MemberOp& functor() const { return m_functor; }
protected:
typename MatrixType::Nested m_matrix;
const MemberOp m_functor;
};
template<typename A,typename B> struct partial_redux_dummy_func;
#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP) \
template <typename ResultType,typename Scalar> \
struct member_##MEMBER { \
typedef ResultType result_type; \
typedef BINARYOP<Scalar,Scalar> BinaryOp; \
template<int Size> struct Cost { enum { value = COST }; }; \
enum { Vectorizable = VECTORIZABLE }; \
template<typename XprType> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
ResultType operator()(const XprType& mat) const \
{ return mat.MEMBER(); } \
BinaryOp binaryFunc() const { return BinaryOp(); } \
}
#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,0,partial_redux_dummy_func)
namespace internal {
EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost );
EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op);
EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op);
EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op);
EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op);
template <int p, typename ResultType,typename Scalar>
struct member_lpnorm {
typedef ResultType result_type;
enum { Vectorizable = 0 };
template<int Size> struct Cost
{ enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
EIGEN_DEVICE_FUNC member_lpnorm() {}
template<typename XprType>
EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
{ return mat.template lpNorm<p>(); }
};
template <typename BinaryOpT, typename Scalar>
struct member_redux {
typedef BinaryOpT BinaryOp;
typedef typename result_of<
BinaryOp(const Scalar&,const Scalar&)
>::type result_type;
enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess };
template<int Size> struct Cost { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
template<typename Derived>
EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
{ return mat.redux(m_functor); }
const BinaryOp& binaryFunc() const { return m_functor; }
const BinaryOp m_functor;
};
}
/** \class VectorwiseOp
* \ingroup Core_Module
*
* \brief Pseudo expression providing broadcasting and partial reduction operations
*
* \tparam ExpressionType the type of the object on which to do partial reductions
* \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal)
*
* This class represents a pseudo expression with broadcasting and partial reduction features.
* It is the return type of DenseBase::colwise() and DenseBase::rowwise()
* and most of the time this is the only way it is explicitly used.
*
* To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()`
* where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each
* column of `A` and then re-assemble the outputs in a matrix expression:
* \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* The begin() and end() methods are obviously exceptions to the previous rule as they
* return STL-compatible begin/end iterators to the rows or columns of the nested expression.
* Typical use cases include for-range-loop and calls to STL algorithms:
*
* Example: \include MatrixBase_colwise_iterator_cxx11.cpp
* Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out
*
* For a partial reduction on an empty input, some rules apply.
* For the sake of clarity, let's consider a vertical reduction:
* - If the number of columns is zero, then a 1x0 row-major vector expression is returned.
* - Otherwise, if the number of rows is zero, then
* - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
* - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>)
* - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
*
* \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
*/
template<typename ExpressionType, int Direction> class VectorwiseOp
{
public:
typedef typename ExpressionType::Scalar Scalar;
typedef typename ExpressionType::RealScalar RealScalar;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
typedef internal::remove_all_t<ExpressionTypeNested> ExpressionTypeNestedCleaned;
template<template<typename OutScalar,typename InputScalar> class Functor,
typename ReturnScalar=Scalar> struct ReturnType
{
typedef PartialReduxExpr<ExpressionType,
Functor<ReturnScalar,Scalar>,
Direction
> Type;
};
template<typename BinaryOp> struct ReduxReturnType
{
typedef PartialReduxExpr<ExpressionType,
internal::member_redux<BinaryOp,Scalar>,
Direction
> Type;
};
enum {
isVertical = (Direction==Vertical) ? 1 : 0,
isHorizontal = (Direction==Horizontal) ? 1 : 0
};
protected:
template<typename OtherDerived> struct ExtendedType {
typedef Replicate<OtherDerived,
isVertical ? 1 : ExpressionType::RowsAtCompileTime,
isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename ExtendedType<OtherDerived>::Type
extendedTo(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename ExtendedType<OtherDerived>::Type
(other.derived(),
isVertical ? 1 : m_matrix.rows(),
isHorizontal ? 1 : m_matrix.cols());
}
template<typename OtherDerived> struct OppositeExtendedType {
typedef Replicate<OtherDerived,
isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
isVertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector in the opposite direction to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename OppositeExtendedType<OtherDerived>::Type
extendedToOpposite(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename OppositeExtendedType<OtherDerived>::Type
(other.derived(),
isHorizontal ? 1 : m_matrix.rows(),
isVertical ? 1 : m_matrix.cols());
}
public:
EIGEN_DEVICE_FUNC
explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
EIGEN_DEVICE_FUNC
inline const ExpressionType& _expression() const { return m_matrix; }
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
* iterator type over the columns or rows as returned by the begin() and end() methods.
*/
random_access_iterator_type iterator;
/** This is the const version of iterator (aka read-only) */
random_access_iterator_type const_iterator;
#else
typedef internal::subvector_stl_iterator<ExpressionType, DirectionType(Direction)> iterator;
typedef internal::subvector_stl_iterator<const ExpressionType, DirectionType(Direction)> const_iterator;
typedef internal::subvector_stl_reverse_iterator<ExpressionType, DirectionType(Direction)> reverse_iterator;
typedef internal::subvector_stl_reverse_iterator<const ExpressionType, DirectionType(Direction)> const_reverse_iterator;
#endif
/** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression.
* \sa end(), cbegin()
*/
iterator begin() { return iterator (m_matrix, 0); }
/** const version of begin() */
const_iterator begin() const { return const_iterator(m_matrix, 0); }
/** const version of begin() */
const_iterator cbegin() const { return const_iterator(m_matrix, 0); }
/** returns a reverse iterator to the last row (rowwise) or column (colwise) of the nested expression.
* \sa rend(), crbegin()
*/
reverse_iterator rbegin() { return reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
/** const version of rbegin() */
const_reverse_iterator rbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
/** const version of rbegin() */
const_reverse_iterator crbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
/** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression
* \sa begin(), cend()
*/
iterator end() { return iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
/** const version of end() */
const_iterator end() const { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
/** const version of end() */
const_iterator cend() const { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
/** returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression
* \sa begin(), cend()
*/
reverse_iterator rend() { return reverse_iterator (m_matrix, -1); }
/** const version of rend() */
const_reverse_iterator rend() const { return const_reverse_iterator (m_matrix, -1); }
/** const version of rend() */
const_reverse_iterator crend() const { return const_reverse_iterator (m_matrix, -1); }
/** \returns a row or column vector expression of \c *this reduxed by \a func
*
* The template parameter \a BinaryOp is the type of the functor
* of the custom redux operator. Note that func must be an associative operator.
*
* \warning the size along the reduction direction must be strictly positive,
* otherwise an assertion is triggered.
*
* \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
*/
template<typename BinaryOp>
EIGEN_DEVICE_FUNC
const typename ReduxReturnType<BinaryOp>::Type
redux(const BinaryOp& func = BinaryOp()) const
{
eigen_assert(redux_length()>0 && "you are using an empty matrix");
return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func));
}
typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,internal::member_sum<RealScalar,RealScalar>,Direction> SquaredNormReturnType;
typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType;
typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType,Scalar,quotient) MeanReturnType;
typedef typename ReturnType<internal::member_all, bool>::Type AllReturnType;
typedef typename ReturnType<internal::member_any, bool>::Type AnyReturnType;
typedef PartialReduxExpr<ExpressionType, internal::member_count<Index,Scalar>, Direction> CountReturnType;
typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType;
template<int p> struct LpNormReturnType {
typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar,Scalar>,Direction> Type;
};
/** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression.
*
* \warning the size along the reduction direction must be strictly positive,
* otherwise an assertion is triggered.
*
* \warning the result is undefined if \c *this contains NaN.
*
* Example: \include PartialRedux_minCoeff.cpp
* Output: \verbinclude PartialRedux_minCoeff.out
*
* \sa DenseBase::minCoeff() */
EIGEN_DEVICE_FUNC
const MinCoeffReturnType minCoeff() const
{
eigen_assert(redux_length()>0 && "you are using an empty matrix");
return MinCoeffReturnType(_expression());
}
/** \returns a row (or column) vector expression of the largest coefficient
* of each column (or row) of the referenced expression.
*
* \warning the size along the reduction direction must be strictly positive,
* otherwise an assertion is triggered.
*
* \warning the result is undefined if \c *this contains NaN.
*
* Example: \include PartialRedux_maxCoeff.cpp
* Output: \verbinclude PartialRedux_maxCoeff.out
*
* \sa DenseBase::maxCoeff() */
EIGEN_DEVICE_FUNC
const MaxCoeffReturnType maxCoeff() const
{
eigen_assert(redux_length()>0 && "you are using an empty matrix");
return MaxCoeffReturnType(_expression());
}
/** \returns a row (or column) vector expression of the squared 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_squaredNorm.cpp
* Output: \verbinclude PartialRedux_squaredNorm.out
*
* \sa DenseBase::squaredNorm() */
EIGEN_DEVICE_FUNC
const SquaredNormReturnType squaredNorm() const
{ return SquaredNormReturnType(m_matrix.cwiseAbs2()); }
/** \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
const NormReturnType norm() const
{ return NormReturnType(squaredNorm()); }
/** \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() */
template<int p>
EIGEN_DEVICE_FUNC
const typename LpNormReturnType<p>::Type lpNorm() const
{ return typename LpNormReturnType<p>::Type(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, using
* Blue's algorithm.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::blueNorm() */
EIGEN_DEVICE_FUNC
const BlueNormReturnType blueNorm() const
{ return BlueNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, avoiding
* underflow and overflow.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::stableNorm() */
EIGEN_DEVICE_FUNC
const StableNormReturnType stableNorm() const
{ return StableNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, avoiding
* underflow and overflow using a concatenation of hypot() calls.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::hypotNorm() */
EIGEN_DEVICE_FUNC
const HypotNormReturnType hypotNorm() const
{ return HypotNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the sum
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_sum.cpp
* Output: \verbinclude PartialRedux_sum.out
*
* \sa DenseBase::sum() */
EIGEN_DEVICE_FUNC
const SumReturnType sum() const
{ return SumReturnType(_expression()); }
/** \returns a row (or column) vector expression of the mean
* of each column (or row) of the referenced expression.
*
* \sa DenseBase::mean() */
EIGEN_DEVICE_FUNC
const MeanReturnType mean() const
{ return sum() / Scalar(Direction==Vertical?m_matrix.rows():m_matrix.cols()); }
/** \returns a row (or column) vector expression representing
* whether \b all coefficients of each respective column (or row) are \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::all() */
EIGEN_DEVICE_FUNC
const AllReturnType all() const
{ return AllReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* whether \b at \b least one coefficient of each respective column (or row) is \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::any() */
EIGEN_DEVICE_FUNC
const AnyReturnType any() const
{ return AnyReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* the number of \c true coefficients of each respective column (or row).
* This expression can be assigned to a vector whose entries have the same type as is used to
* index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
*
* Example: \include PartialRedux_count.cpp
* Output: \verbinclude PartialRedux_count.out
*
* \sa DenseBase::count() */
EIGEN_DEVICE_FUNC
const CountReturnType count() const
{ return CountReturnType(_expression()); }
/** \returns a row (or column) vector expression of the product
* of each column (or row) of the referenced expression.
*
* Example: \include PartialRedux_prod.cpp
* Output: \verbinclude PartialRedux_prod.out
*
* \sa DenseBase::prod() */
EIGEN_DEVICE_FUNC
const ProdReturnType prod() const
{ return ProdReturnType(_expression()); }
/** \returns a matrix expression
* where each column (or row) are reversed.
*
* Example: \include Vectorwise_reverse.cpp
* Output: \verbinclude Vectorwise_reverse.out
*
* \sa DenseBase::reverse() */
EIGEN_DEVICE_FUNC
const ConstReverseReturnType reverse() const
{ return ConstReverseReturnType( _expression() ); }
/** \returns a writable matrix expression
* where each column (or row) are reversed.
*
* \sa reverse() const */
EIGEN_DEVICE_FUNC
ReverseReturnType reverse()
{ return ReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
EIGEN_DEVICE_FUNC
const ReplicateReturnType replicate(Index factor) const;
/**
* \return an expression of the replication of each column (or row) of \c *this
*
* Example: \include DirectionWise_replicate.cpp
* Output: \verbinclude DirectionWise_replicate.out
*
* \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
*/
// NOTE implemented here because of sunstudio's compilation errors
// isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
EIGEN_DEVICE_FUNC
replicate(Index factor = Factor) const
{
return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
(_expression(),isVertical?factor:1,isHorizontal?factor:1);
}
/////////// Artithmetic operators ///////////
/** Copies the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
//eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME
return m_matrix = extendedTo(other.derived());
}
/** Adds the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix += extendedTo(other.derived());
}
/** Subtracts the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix -= extendedTo(other.derived());
}
/** Multiplies each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
m_matrix *= extendedTo(other.derived());
return m_matrix;
}
/** Divides each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
m_matrix /= extendedTo(other.derived());
return m_matrix;
}
/** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator+(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix + extendedTo(other.derived());
}
/** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator-(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix - extendedTo(other.derived());
}
/** Returns the expression where each subvector is the product of the vector \a other
* by the corresponding subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_product_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
EIGEN_DEVICE_FUNC
operator*(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix * extendedTo(other.derived());
}
/** Returns the expression where each subvector is the quotient of the corresponding
* subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator/(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix / extendedTo(other.derived());
}
/** \returns an expression where each column (or row) of the referenced matrix are normalized.
* The referenced matrix is \b not modified.
* \sa MatrixBase::normalized(), normalize()
*/
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename OppositeExtendedType<NormReturnType>::Type>
normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); }
/** Normalize in-place each row or columns of the referenced matrix.
* \sa MatrixBase::normalize(), normalized()
*/
EIGEN_DEVICE_FUNC void normalize() {
m_matrix = this->normalized();
}
EIGEN_DEVICE_FUNC inline void reverseInPlace();
/////////// Geometry module ///////////
typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
EIGEN_DEVICE_FUNC
HomogeneousReturnType homogeneous() const;
typedef typename ExpressionType::PlainObject CrossReturnType;
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
enum {
HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime
: internal::traits<ExpressionType>::ColsAtCompileTime,
HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1
};
typedef Block<const ExpressionType,
Direction==Vertical ? int(HNormalized_SizeMinusOne)
: int(internal::traits<ExpressionType>::RowsAtCompileTime),
Direction==Horizontal ? int(HNormalized_SizeMinusOne)
: int(internal::traits<ExpressionType>::ColsAtCompileTime)>
HNormalized_Block;
typedef Block<const ExpressionType,
Direction==Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime),
Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
HNormalized_Factors;
typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>,
const HNormalized_Block,
const Replicate<HNormalized_Factors,
Direction==Vertical ? HNormalized_SizeMinusOne : 1,
Direction==Horizontal ? HNormalized_SizeMinusOne : 1> >
HNormalizedReturnType;
EIGEN_DEVICE_FUNC
const HNormalizedReturnType hnormalized() const;
# ifdef EIGEN_VECTORWISEOP_PLUGIN
# include EIGEN_VECTORWISEOP_PLUGIN
# endif
protected:
EIGEN_DEVICE_FUNC Index redux_length() const
{
return Direction==Vertical ? m_matrix.rows() : m_matrix.cols();
}
ExpressionTypeNested m_matrix;
};
//const colwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
template<typename Derived>
EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType
DenseBase<Derived>::colwise()
{
return ColwiseReturnType(derived());
}
//const rowwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
template<typename Derived>
EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType
DenseBase<Derived>::rowwise()
{
return RowwiseReturnType(derived());
}
} // end namespace Eigen
#endif // EIGEN_PARTIAL_REDUX_H