1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifndef EIGEN_REVERSE_H
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_REVERSE_H
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen {
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class Reverse
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \ingroup Core_Module
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \brief Expression of the reverse of a vector or matrix
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \param MatrixType the type of the object of which we are taking the reverse
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * This class represents an expression of the reverse of a vector.
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * and most of the time this is the only way it is used.
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \sa MatrixBase::reverse(), VectorwiseOp::reverse()
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal {
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, int Direction>
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<Reverse<MatrixType, Direction> >
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : traits<MatrixType>
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Scalar Scalar;
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename traits<MatrixType>::StorageKind StorageKind;
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename traits<MatrixType>::XprKind XprKind;
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename nested<MatrixType>::type MatrixTypeNested;
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  enum {
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // let's enable LinearAccess only with vectorization because of the product overhead
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    LinearAccess = ( (Direction==BothDirections) && (int(_MatrixTypeNested::Flags)&PacketAccessBit) )
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                 ? LinearAccessBit : 0,
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    Flags = int(_MatrixTypeNested::Flags) & (HereditaryBits | LvalueBit | PacketAccessBit | LinearAccess),
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CoeffReadCost = _MatrixTypeNested::CoeffReadCost
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  };
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline PacketScalar run(const PacketScalar& x) { return preverse(x); }
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename PacketScalar> struct reverse_packet_cond<PacketScalar,false>
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  static inline PacketScalar run(const PacketScalar& x) { return x; }
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, int Direction> class Reverse
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  : public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  public:
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef typename internal::dense_xpr_base<Reverse>::type Base;
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    using Base::IsRowMajor;
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // next line is necessary because otherwise const version of operator()
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // is hidden by non-const version defined in this file
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    using Base::operator();
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  protected:
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    enum {
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      PacketSize = internal::packet_traits<Scalar>::size,
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      IsColMajor = !IsRowMajor,
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1,
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      ReversePacket = (Direction == BothDirections)
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                    || ((Direction == Vertical)   && IsColMajor)
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                    || ((Direction == Horizontal) && IsRowMajor)
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    };
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  public:
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Index rows() const { return m_matrix.rows(); }
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Index cols() const { return m_matrix.cols(); }
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Index innerStride() const
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return -m_matrix.innerStride();
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Scalar& operator()(Index row, Index col)
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return coeffRef(row, col);
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Scalar& coeffRef(Index row, Index col)
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return m_matrix.const_cast_derived().coeffRef(ReverseRow ? m_matrix.rows() - row - 1 : row,
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                                    ReverseCol ? m_matrix.cols() - col - 1 : col);
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline CoeffReturnType coeff(Index row, Index col) const
123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row,
125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                            ReverseCol ? m_matrix.cols() - col - 1 : col);
126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline CoeffReturnType coeff(Index index) const
129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return m_matrix.coeff(m_matrix.size() - index - 1);
131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Scalar& coeffRef(Index index)
134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1);
136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline Scalar& operator()(Index index)
139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      eigen_assert(index >= 0 && index < m_matrix.size());
141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return coeffRef(index);
142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    template<int LoadMode>
145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline const PacketScalar packet(Index row, Index col) const
146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return reverse_packet::run(m_matrix.template packet<LoadMode>(
148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                    ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                    ReverseCol ? m_matrix.cols() - col - OffsetCol : col));
150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    template<int LoadMode>
153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline void writePacket(Index row, Index col, const PacketScalar& x)
154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      m_matrix.const_cast_derived().template writePacket<LoadMode>(
156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                      ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                      ReverseCol ? m_matrix.cols() - col - OffsetCol : col,
158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                                      reverse_packet::run(x));
159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    template<int LoadMode>
162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline const PacketScalar packet(Index index) const
163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return internal::preverse(m_matrix.template packet<LoadMode>( m_matrix.size() - index - PacketSize ));
165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    template<int LoadMode>
168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    inline void writePacket(Index index, const PacketScalar& x)
169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      m_matrix.const_cast_derived().template writePacket<LoadMode>(m_matrix.size() - index - PacketSize, internal::preverse(x));
171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    const typename internal::remove_all<typename MatrixType::Nested>::type&
174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    nestedExpression() const
175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      return m_matrix;
177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  protected:
180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    typename MatrixType::Nested m_matrix;
181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns an expression of the reverse of *this.
184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Example: \include MatrixBase_reverse.cpp
186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * Output: \verbinclude MatrixBase_reverse.out
187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename DenseBase<Derived>::ReverseReturnType
191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::reverse()
192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return derived();
194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** This is the const version of reverse(). */
197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const typename DenseBase<Derived>::ConstReverseReturnType
199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::reverse() const
200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return derived();
202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** This is the "in place" version of reverse: it reverses \c *this.
205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * In most cases it is probably better to simply use the reversed expression
207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * of a matrix. However, when reversing the matrix data itself is really needed,
208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * then this "in-place" version is probably the right choice because it provides
209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * the following additional features:
210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *  - less error prone: doing the same operation with .reverse() requires special care:
211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *    \code m = m.reverse().eval(); \endcode
212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *  - this API allows to avoid creating a temporary (the current implementation creates a temporary, but that could be avoided using swap)
213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *  - it allows future optimizations (cache friendliness, etc.)
214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  *
215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  * \sa reverse() */
216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived>
217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline void DenseBase<Derived>::reverseInPlace()
218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  derived() = derived().reverse().eval();
220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen
223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_REVERSE_H
225