64fcfd314f
This slightly complexifies the type of the expressions and implies that we now have to distinguish between scalar*expr and expr*scalar to catch scalar-multiple expression (e.g., see BlasUtil.h), but this brings several advantages: - it makes it clear on each side the scalar is applied, - it clearly reflects that we are dealing with a binary-expression, - the complexity of the type is hidden through macros defined at the end of Macros.h, - distinguishing between "scalar op expr" and "expr op scalar" is important to support non commutative fields (like quaternions) - "scalar op expr" is now fully equivalent to "ConstantExpr(scalar) op expr" - scalar_multiple_op, scalar_quotient1_op and scalar_quotient2_op are not used anymore in officially supported modules (still used in Tensor)
672 lines
26 KiB
C++
672 lines
26 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_XPRHELPER_H
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#define EIGEN_XPRHELPER_H
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// just a workaround because GCC seems to not really like empty structs
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// FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled
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// so currently we simply disable this optimization for gcc 4.3
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#if EIGEN_COMP_GNUC && !EIGEN_GNUC_AT(4,3)
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#define EIGEN_EMPTY_STRUCT_CTOR(X) \
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X() {} \
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X(const X& ) {}
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#else
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#define EIGEN_EMPTY_STRUCT_CTOR(X)
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#endif
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namespace Eigen {
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typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
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/**
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* \brief The Index type as used for the API.
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* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
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* \sa \blank \ref TopicPreprocessorDirectives, StorageIndex.
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*/
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typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
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namespace internal {
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template<typename IndexDest, typename IndexSrc>
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EIGEN_DEVICE_FUNC
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inline IndexDest convert_index(const IndexSrc& idx) {
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// for sizeof(IndexDest)>=sizeof(IndexSrc) compilers should be able to optimize this away:
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eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value to big for target type");
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return IndexDest(idx);
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}
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//classes inheriting no_assignment_operator don't generate a default operator=.
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class no_assignment_operator
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{
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private:
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no_assignment_operator& operator=(const no_assignment_operator&);
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};
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/** \internal return the index type with the largest number of bits */
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template<typename I1, typename I2>
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struct promote_index_type
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{
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typedef typename conditional<(sizeof(I1)<sizeof(I2)), I2, I1>::type type;
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};
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/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
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* can be accessed using value() and setValue().
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* Otherwise, this class is an empty structure and value() just returns the template parameter Value.
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*/
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template<typename T, int Value> class variable_if_dynamic
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{
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public:
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EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamic)
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
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EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T value() { return T(Value); }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T) {}
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};
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template<typename T> class variable_if_dynamic<T, Dynamic>
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{
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T m_value;
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EIGEN_DEVICE_FUNC variable_if_dynamic() { eigen_assert(false); }
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public:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T value) : m_value(value) {}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T value() const { return m_value; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
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};
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/** \internal like variable_if_dynamic but for DynamicIndex
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*/
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template<typename T, int Value> class variable_if_dynamicindex
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{
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public:
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EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamicindex)
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
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EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T value() { return T(Value); }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T) {}
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};
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template<typename T> class variable_if_dynamicindex<T, DynamicIndex>
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{
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T m_value;
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EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
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public:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T value) : m_value(value) {}
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EIGEN_DEVICE_FUNC T EIGEN_STRONG_INLINE value() const { return m_value; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
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};
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template<typename T> struct functor_traits
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{
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enum
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{
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Cost = 10,
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PacketAccess = false,
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IsRepeatable = false
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};
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};
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template<typename T> struct packet_traits;
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template<typename T> struct unpacket_traits
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{
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typedef T type;
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typedef T half;
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enum
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{
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size = 1,
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alignment = 1
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};
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};
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template<int Size, typename PacketType,
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bool Stop = Size==Dynamic || (Size%unpacket_traits<PacketType>::size)==0 || is_same<PacketType,typename unpacket_traits<PacketType>::half>::value>
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struct find_best_packet_helper;
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template< int Size, typename PacketType>
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struct find_best_packet_helper<Size,PacketType,true>
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{
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typedef PacketType type;
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};
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template<int Size, typename PacketType>
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struct find_best_packet_helper<Size,PacketType,false>
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{
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typedef typename find_best_packet_helper<Size,typename unpacket_traits<PacketType>::half>::type type;
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};
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template<typename T, int Size>
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struct find_best_packet
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{
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typedef typename find_best_packet_helper<Size,typename packet_traits<T>::type>::type type;
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};
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#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
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template<int ArrayBytes, int AlignmentBytes,
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bool Match = bool((ArrayBytes%AlignmentBytes)==0),
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bool TryHalf = bool(AlignmentBytes>EIGEN_MIN_ALIGN_BYTES) >
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struct compute_default_alignment_helper
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{
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enum { value = 0 };
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};
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template<int ArrayBytes, int AlignmentBytes, bool TryHalf>
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struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, true, TryHalf> // Match
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{
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enum { value = AlignmentBytes };
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};
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template<int ArrayBytes, int AlignmentBytes>
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struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, false, true> // Try-half
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{
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// current packet too large, try with an half-packet
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enum { value = compute_default_alignment_helper<ArrayBytes, AlignmentBytes/2>::value };
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};
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#else
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// If static alignment is disabled, no need to bother.
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// This also avoids a division by zero in "bool Match = bool((ArrayBytes%AlignmentBytes)==0)"
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template<int ArrayBytes, int AlignmentBytes>
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struct compute_default_alignment_helper
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{
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enum { value = 0 };
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};
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#endif
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template<typename T, int Size> struct compute_default_alignment {
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enum { value = compute_default_alignment_helper<Size*sizeof(T),EIGEN_MAX_STATIC_ALIGN_BYTES>::value };
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};
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template<typename T> struct compute_default_alignment<T,Dynamic> {
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enum { value = EIGEN_MAX_ALIGN_BYTES };
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};
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template<typename _Scalar, int _Rows, int _Cols,
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int _Options = AutoAlign |
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( (_Rows==1 && _Cols!=1) ? RowMajor
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: (_Cols==1 && _Rows!=1) ? ColMajor
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: EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
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int _MaxRows = _Rows,
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int _MaxCols = _Cols
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> class make_proper_matrix_type
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{
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enum {
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IsColVector = _Cols==1 && _Rows!=1,
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IsRowVector = _Rows==1 && _Cols!=1,
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Options = IsColVector ? (_Options | ColMajor) & ~RowMajor
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: IsRowVector ? (_Options | RowMajor) & ~ColMajor
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: _Options
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};
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public:
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typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type;
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};
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template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
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class compute_matrix_flags
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{
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enum { row_major_bit = Options&RowMajor ? RowMajorBit : 0 };
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public:
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// FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
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// and then propagate this information to the evaluator's flags.
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// However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
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enum { ret = DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit };
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};
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template<int _Rows, int _Cols> struct size_at_compile_time
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{
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enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols };
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};
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template<typename XprType> struct size_of_xpr_at_compile_time
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{
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enum { ret = size_at_compile_time<traits<XprType>::RowsAtCompileTime,traits<XprType>::ColsAtCompileTime>::ret };
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};
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/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
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* whereas eval is a const reference in the case of a matrix
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*/
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template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type;
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template<typename T, typename BaseClassType, int Flags> struct plain_matrix_type_dense;
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template<typename T> struct plain_matrix_type<T,Dense>
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{
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typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, traits<T>::Flags>::type type;
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};
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template<typename T> struct plain_matrix_type<T,DiagonalShape>
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{
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typedef typename T::PlainObject type;
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};
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template<typename T, int Flags> struct plain_matrix_type_dense<T,MatrixXpr,Flags>
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{
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typedef Matrix<typename traits<T>::Scalar,
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traits<T>::RowsAtCompileTime,
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traits<T>::ColsAtCompileTime,
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AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
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traits<T>::MaxRowsAtCompileTime,
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traits<T>::MaxColsAtCompileTime
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> type;
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};
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template<typename T, int Flags> struct plain_matrix_type_dense<T,ArrayXpr,Flags>
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{
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typedef Array<typename traits<T>::Scalar,
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traits<T>::RowsAtCompileTime,
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traits<T>::ColsAtCompileTime,
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AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
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traits<T>::MaxRowsAtCompileTime,
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traits<T>::MaxColsAtCompileTime
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> type;
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};
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/* eval : the return type of eval(). For matrices, this is just a const reference
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* in order to avoid a useless copy
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*/
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template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval;
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template<typename T> struct eval<T,Dense>
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{
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typedef typename plain_matrix_type<T>::type type;
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// typedef typename T::PlainObject type;
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// typedef T::Matrix<typename traits<T>::Scalar,
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// traits<T>::RowsAtCompileTime,
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// traits<T>::ColsAtCompileTime,
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// AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
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// traits<T>::MaxRowsAtCompileTime,
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// traits<T>::MaxColsAtCompileTime
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// > type;
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};
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template<typename T> struct eval<T,DiagonalShape>
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{
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typedef typename plain_matrix_type<T>::type type;
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};
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// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
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template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
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struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
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{
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typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
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};
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template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
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struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
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{
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typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
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};
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/* similar to plain_matrix_type, but using the evaluator's Flags */
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template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_object_eval;
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template<typename T>
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struct plain_object_eval<T,Dense>
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{
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typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, evaluator<T>::Flags>::type type;
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};
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/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
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*/
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template<typename T> struct plain_matrix_type_column_major
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{
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enum { Rows = traits<T>::RowsAtCompileTime,
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Cols = traits<T>::ColsAtCompileTime,
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MaxRows = traits<T>::MaxRowsAtCompileTime,
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MaxCols = traits<T>::MaxColsAtCompileTime
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};
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typedef Matrix<typename traits<T>::Scalar,
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Rows,
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Cols,
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(MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor,
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MaxRows,
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MaxCols
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> type;
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};
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/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
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*/
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template<typename T> struct plain_matrix_type_row_major
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{
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enum { Rows = traits<T>::RowsAtCompileTime,
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Cols = traits<T>::ColsAtCompileTime,
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MaxRows = traits<T>::MaxRowsAtCompileTime,
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MaxCols = traits<T>::MaxColsAtCompileTime
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};
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typedef Matrix<typename traits<T>::Scalar,
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Rows,
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Cols,
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(MaxCols==1&&MaxRows!=1) ? RowMajor : ColMajor,
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MaxRows,
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MaxCols
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> type;
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};
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/** \internal The reference selector for template expressions. The idea is that we don't
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* need to use references for expressions since they are light weight proxy
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* objects which should generate no copying overhead. */
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template <typename T>
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struct ref_selector
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{
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typedef typename conditional<
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bool(traits<T>::Flags & NestByRefBit),
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T const&,
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const T
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>::type type;
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typedef typename conditional<
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bool(traits<T>::Flags & NestByRefBit),
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T &,
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T
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>::type non_const_type;
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};
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/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
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template<typename T1, typename T2>
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struct transfer_constness
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{
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typedef typename conditional<
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bool(internal::is_const<T1>::value),
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typename internal::add_const_on_value_type<T2>::type,
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T2
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>::type type;
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};
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// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
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// has to be evaluated into a temporary.
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// That's the purpose of this new nested_eval helper:
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/** \internal Determines how a given expression should be nested when evaluated multiple times.
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* For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
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* evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
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* evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
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* a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
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* many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
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*
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* \tparam T the type of the expression being nested.
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* \tparam n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
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* \tparam PlainObject the type of the temporary if needed.
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*/
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template<typename T, int n, typename PlainObject = typename plain_object_eval<T>::type> struct nested_eval
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{
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enum {
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ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
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CoeffReadCost = evaluator<T>::CoeffReadCost, // NOTE What if an evaluator evaluate itself into a tempory?
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// Then CoeffReadCost will be small (e.g., 1) but we still have to evaluate, especially if n>1.
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// This situation is already taken care by the EvalBeforeNestingBit flag, which is turned ON
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// for all evaluator creating a temporary. This flag is then propagated by the parent evaluators.
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// Another solution could be to count the number of temps?
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NAsInteger = n == Dynamic ? HugeCost : n,
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CostEval = (NAsInteger+1) * ScalarReadCost + CoeffReadCost,
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CostNoEval = NAsInteger * CoeffReadCost
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};
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typedef typename conditional<
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( (int(evaluator<T>::Flags) & EvalBeforeNestingBit) ||
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(int(CostEval) < int(CostNoEval)) ),
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PlainObject,
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typename ref_selector<T>::type
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>::type type;
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};
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template<typename T>
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EIGEN_DEVICE_FUNC
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inline T* const_cast_ptr(const T* ptr)
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{
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return const_cast<T*>(ptr);
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}
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template<typename Derived, typename XprKind = typename traits<Derived>::XprKind>
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struct dense_xpr_base
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{
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/* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */
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};
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template<typename Derived>
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struct dense_xpr_base<Derived, MatrixXpr>
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{
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typedef MatrixBase<Derived> type;
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};
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template<typename Derived>
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struct dense_xpr_base<Derived, ArrayXpr>
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{
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typedef ArrayBase<Derived> type;
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};
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template<typename Derived, typename XprKind = typename traits<Derived>::XprKind, typename StorageKind = typename traits<Derived>::StorageKind>
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struct generic_xpr_base;
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template<typename Derived, typename XprKind>
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struct generic_xpr_base<Derived, XprKind, Dense>
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{
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typedef typename dense_xpr_base<Derived,XprKind>::type type;
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};
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template<typename XprType, typename CastType> struct cast_return_type
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{
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typedef typename XprType::Scalar CurrentScalarType;
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typedef typename remove_all<CastType>::type _CastType;
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typedef typename _CastType::Scalar NewScalarType;
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typedef typename conditional<is_same<CurrentScalarType,NewScalarType>::value,
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const XprType&,CastType>::type type;
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};
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template <typename A, typename B> struct promote_storage_type;
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template <typename A> struct promote_storage_type<A,A>
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{
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typedef A ret;
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};
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template <typename A> struct promote_storage_type<A, const A>
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{
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|
typedef A ret;
|
|
};
|
|
template <typename A> struct promote_storage_type<const A, A>
|
|
{
|
|
typedef A ret;
|
|
};
|
|
|
|
/** \internal Specify the "storage kind" of applying a coefficient-wise
|
|
* binary operations between two expressions of kinds A and B respectively.
|
|
* The template parameter Functor permits to specialize the resulting storage kind wrt to
|
|
* the functor.
|
|
* The default rules are as follows:
|
|
* \code
|
|
* A op A -> A
|
|
* A op dense -> dense
|
|
* dense op B -> dense
|
|
* sparse op dense -> sparse
|
|
* dense op sparse -> sparse
|
|
* \endcode
|
|
*/
|
|
template <typename A, typename B, typename Functor> struct cwise_promote_storage_type;
|
|
|
|
template <typename A, typename Functor> struct cwise_promote_storage_type<A,A,Functor> { typedef A ret; };
|
|
template <typename Functor> struct cwise_promote_storage_type<Dense,Dense,Functor> { typedef Dense ret; };
|
|
template <typename A, typename Functor> struct cwise_promote_storage_type<A,Dense,Functor> { typedef Dense ret; };
|
|
template <typename B, typename Functor> struct cwise_promote_storage_type<Dense,B,Functor> { typedef Dense ret; };
|
|
template <typename Functor> struct cwise_promote_storage_type<Sparse,Dense,Functor> { typedef Sparse ret; };
|
|
template <typename Functor> struct cwise_promote_storage_type<Dense,Sparse,Functor> { typedef Sparse ret; };
|
|
|
|
/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
|
|
* The template parameter ProductTag permits to specialize the resulting storage kind wrt to
|
|
* some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.
|
|
* The default rules are as follows:
|
|
* \code
|
|
* K * K -> K
|
|
* dense * K -> dense
|
|
* K * dense -> dense
|
|
* diag * K -> K
|
|
* K * diag -> K
|
|
* Perm * K -> K
|
|
* K * Perm -> K
|
|
* \endcode
|
|
*/
|
|
template <typename A, typename B, int ProductTag> struct product_promote_storage_type;
|
|
|
|
template <typename A, int ProductTag> struct product_promote_storage_type<A, A, ProductTag> { typedef A ret;};
|
|
template <int ProductTag> struct product_promote_storage_type<Dense, Dense, ProductTag> { typedef Dense ret;};
|
|
template <typename A, int ProductTag> struct product_promote_storage_type<A, Dense, ProductTag> { typedef Dense ret; };
|
|
template <typename B, int ProductTag> struct product_promote_storage_type<Dense, B, ProductTag> { typedef Dense ret; };
|
|
|
|
template <typename A, int ProductTag> struct product_promote_storage_type<A, DiagonalShape, ProductTag> { typedef A ret; };
|
|
template <typename B, int ProductTag> struct product_promote_storage_type<DiagonalShape, B, ProductTag> { typedef B ret; };
|
|
template <int ProductTag> struct product_promote_storage_type<Dense, DiagonalShape, ProductTag> { typedef Dense ret; };
|
|
template <int ProductTag> struct product_promote_storage_type<DiagonalShape, Dense, ProductTag> { typedef Dense ret; };
|
|
|
|
template <typename A, int ProductTag> struct product_promote_storage_type<A, PermutationStorage, ProductTag> { typedef A ret; };
|
|
template <typename B, int ProductTag> struct product_promote_storage_type<PermutationStorage, B, ProductTag> { typedef B ret; };
|
|
template <int ProductTag> struct product_promote_storage_type<Dense, PermutationStorage, ProductTag> { typedef Dense ret; };
|
|
template <int ProductTag> struct product_promote_storage_type<PermutationStorage, Dense, ProductTag> { typedef Dense ret; };
|
|
|
|
/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type.
|
|
* \tparam Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType.
|
|
*/
|
|
template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
|
|
struct plain_row_type
|
|
{
|
|
typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime,
|
|
ExpressionType::PlainObject::Options | RowMajor, 1, ExpressionType::MaxColsAtCompileTime> MatrixRowType;
|
|
typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime,
|
|
ExpressionType::PlainObject::Options | RowMajor, 1, ExpressionType::MaxColsAtCompileTime> ArrayRowType;
|
|
|
|
typedef typename conditional<
|
|
is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
|
|
MatrixRowType,
|
|
ArrayRowType
|
|
>::type type;
|
|
};
|
|
|
|
template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
|
|
struct plain_col_type
|
|
{
|
|
typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1,
|
|
ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> MatrixColType;
|
|
typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1,
|
|
ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> ArrayColType;
|
|
|
|
typedef typename conditional<
|
|
is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
|
|
MatrixColType,
|
|
ArrayColType
|
|
>::type type;
|
|
};
|
|
|
|
template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
|
|
struct plain_diag_type
|
|
{
|
|
enum { diag_size = EIGEN_SIZE_MIN_PREFER_DYNAMIC(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime),
|
|
max_diag_size = EIGEN_SIZE_MIN_PREFER_FIXED(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime)
|
|
};
|
|
typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> MatrixDiagType;
|
|
typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType;
|
|
|
|
typedef typename conditional<
|
|
is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
|
|
MatrixDiagType,
|
|
ArrayDiagType
|
|
>::type type;
|
|
};
|
|
|
|
template<typename Expr,typename Scalar = typename Expr::Scalar>
|
|
struct plain_constant_type
|
|
{
|
|
enum { Options = (traits<Expr>::Flags&RowMajorBit)?RowMajor:0 };
|
|
|
|
typedef Array<Scalar, traits<Expr>::RowsAtCompileTime, traits<Expr>::ColsAtCompileTime,
|
|
Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> array_type;
|
|
|
|
typedef Matrix<Scalar, traits<Expr>::RowsAtCompileTime, traits<Expr>::ColsAtCompileTime,
|
|
Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> matrix_type;
|
|
|
|
typedef CwiseNullaryOp<scalar_constant_op<Scalar>, const typename conditional<is_same< typename traits<Expr>::XprKind, MatrixXpr >::value, matrix_type, array_type>::type > type;
|
|
};
|
|
|
|
template<typename ExpressionType>
|
|
struct is_lvalue
|
|
{
|
|
enum { value = !bool(is_const<ExpressionType>::value) &&
|
|
bool(traits<ExpressionType>::Flags & LvalueBit) };
|
|
};
|
|
|
|
template<typename T> struct is_diagonal
|
|
{ enum { ret = false }; };
|
|
|
|
template<typename T> struct is_diagonal<DiagonalBase<T> >
|
|
{ enum { ret = true }; };
|
|
|
|
template<typename T> struct is_diagonal<DiagonalWrapper<T> >
|
|
{ enum { ret = true }; };
|
|
|
|
template<typename T, int S> struct is_diagonal<DiagonalMatrix<T,S> >
|
|
{ enum { ret = true }; };
|
|
|
|
template<typename S1, typename S2> struct glue_shapes;
|
|
template<> struct glue_shapes<DenseShape,TriangularShape> { typedef TriangularShape type; };
|
|
|
|
template<typename T1, typename T2>
|
|
bool is_same_dense(const T1 &mat1, const T2 &mat2, typename enable_if<has_direct_access<T1>::ret&&has_direct_access<T2>::ret, T1>::type * = 0)
|
|
{
|
|
return (mat1.data()==mat2.data()) && (mat1.innerStride()==mat2.innerStride()) && (mat1.outerStride()==mat2.outerStride());
|
|
}
|
|
|
|
template<typename T1, typename T2>
|
|
bool is_same_dense(const T1 &, const T2 &, typename enable_if<!(has_direct_access<T1>::ret&&has_direct_access<T2>::ret), T1>::type * = 0)
|
|
{
|
|
return false;
|
|
}
|
|
|
|
#ifdef EIGEN_DEBUG_ASSIGN
|
|
std::string demangle_traversal(int t)
|
|
{
|
|
if(t==DefaultTraversal) return "DefaultTraversal";
|
|
if(t==LinearTraversal) return "LinearTraversal";
|
|
if(t==InnerVectorizedTraversal) return "InnerVectorizedTraversal";
|
|
if(t==LinearVectorizedTraversal) return "LinearVectorizedTraversal";
|
|
if(t==SliceVectorizedTraversal) return "SliceVectorizedTraversal";
|
|
return "?";
|
|
}
|
|
std::string demangle_unrolling(int t)
|
|
{
|
|
if(t==NoUnrolling) return "NoUnrolling";
|
|
if(t==InnerUnrolling) return "InnerUnrolling";
|
|
if(t==CompleteUnrolling) return "CompleteUnrolling";
|
|
return "?";
|
|
}
|
|
std::string demangle_flags(int f)
|
|
{
|
|
std::string res;
|
|
if(f&RowMajorBit) res += " | RowMajor";
|
|
if(f&PacketAccessBit) res += " | Packet";
|
|
if(f&LinearAccessBit) res += " | Linear";
|
|
if(f&LvalueBit) res += " | Lvalue";
|
|
if(f&DirectAccessBit) res += " | Direct";
|
|
if(f&NestByRefBit) res += " | NestByRef";
|
|
if(f&NoPreferredStorageOrderBit) res += " | NoPreferredStorageOrderBit";
|
|
|
|
return res;
|
|
}
|
|
#endif
|
|
|
|
} // end namespace internal
|
|
|
|
// We require Lhs and Rhs to have "compatible" scalar types.
|
|
// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
|
|
// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
|
|
// add together a float matrix and a double matrix.
|
|
#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
|
|
EIGEN_STATIC_ASSERT(int(ScalarBinaryOpTraits<LHS, RHS,BINOP>::Defined), \
|
|
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
|
|
|
|
} // end namespace Eigen
|
|
|
|
#endif // EIGEN_XPRHELPER_H
|