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eigen/Eigen/src/Core/util/XprHelper.h

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 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_XPRHELPER_H
#define EIGEN_XPRHELPER_H
// just a workaround because GCC seems to not really like empty structs
// FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled
// so currently we simply disable this optimization for gcc 4.3
#if EIGEN_COMP_GNUC && !EIGEN_GNUC_AT(4,3)
#define EIGEN_EMPTY_STRUCT_CTOR(X) \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X() {} \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X(const X& ) {}
#else
#define EIGEN_EMPTY_STRUCT_CTOR(X)
#endif
namespace Eigen {
typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
/**
* \brief The Index type as used for the API.
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \sa \ref TopicPreprocessorDirectives, StorageIndex.
*/
typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
namespace internal {
template<typename IndexDest, typename IndexSrc>
EIGEN_DEVICE_FUNC
inline IndexDest convert_index(const IndexSrc& idx) {
// for sizeof(IndexDest)>=sizeof(IndexSrc) compilers should be able to optimize this away:
eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value to big for target type");
return IndexDest(idx);
}
//classes inheriting no_assignment_operator don't generate a default operator=.
class no_assignment_operator
{
private:
no_assignment_operator& operator=(const no_assignment_operator&);
};
/** \internal return the index type with the largest number of bits */
template<typename I1, typename I2>
struct promote_index_type
{
typedef typename conditional<(sizeof(I1)<sizeof(I2)), I2, I1>::type type;
};
/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
* can be accessed using value() and setValue().
* Otherwise, this class is an empty structure and value() just returns the template parameter Value.
*/
template<typename T, int Value> class variable_if_dynamic
{
public:
EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamic)
EIGEN_DEVICE_FUNC explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
EIGEN_DEVICE_FUNC static T value() { return T(Value); }
EIGEN_DEVICE_FUNC void setValue(T) {}
};
template<typename T> class variable_if_dynamic<T, Dynamic>
{
T m_value;
EIGEN_DEVICE_FUNC variable_if_dynamic() { eigen_assert(false); }
public:
EIGEN_DEVICE_FUNC explicit variable_if_dynamic(T value) : m_value(value) {}
EIGEN_DEVICE_FUNC T value() const { return m_value; }
EIGEN_DEVICE_FUNC void setValue(T value) { m_value = value; }
};
/** \internal like variable_if_dynamic but for DynamicIndex
*/
template<typename T, int Value> class variable_if_dynamicindex
{
public:
EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamicindex)
EIGEN_DEVICE_FUNC explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
EIGEN_DEVICE_FUNC static T value() { return T(Value); }
EIGEN_DEVICE_FUNC void setValue(T) {}
};
template<typename T> class variable_if_dynamicindex<T, DynamicIndex>
{
T m_value;
EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
public:
EIGEN_DEVICE_FUNC explicit variable_if_dynamicindex(T value) : m_value(value) {}
EIGEN_DEVICE_FUNC T value() const { return m_value; }
EIGEN_DEVICE_FUNC void setValue(T value) { m_value = value; }
};
template<typename T> struct functor_traits
{
enum
{
Cost = 10,
PacketAccess = false,
IsRepeatable = false
};
};
template<typename T> struct packet_traits;
template<typename T> struct unpacket_traits
{
typedef T type;
typedef T half;
enum
{
size = 1,
alignment = 1
};
};
template<int Size, typename PacketType,
bool Stop = Size==Dynamic || (Size%unpacket_traits<PacketType>::size)==0 || is_same<PacketType,typename unpacket_traits<PacketType>::half>::value>
struct find_best_packet_helper;
template< int Size, typename PacketType>
struct find_best_packet_helper<Size,PacketType,true>
{
typedef PacketType type;
};
template<int Size, typename PacketType>
struct find_best_packet_helper<Size,PacketType,false>
{
typedef typename find_best_packet_helper<Size,typename unpacket_traits<PacketType>::half>::type type;
};
template<typename T, int Size>
struct find_best_packet
{
typedef typename find_best_packet_helper<Size,typename packet_traits<T>::type>::type type;
};
#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
template<int ArrayBytes, int AlignmentBytes,
bool Match = bool((ArrayBytes%AlignmentBytes)==0),
bool TryHalf = bool(AlignmentBytes>EIGEN_MIN_ALIGN_BYTES) >
struct compute_default_alignment_helper
{
enum { value = 0 };
};
template<int ArrayBytes, int AlignmentBytes, bool TryHalf>
struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, true, TryHalf> // Match
{
enum { value = AlignmentBytes };
};
template<int ArrayBytes, int AlignmentBytes>
struct compute_default_alignment_helper<ArrayBytes, AlignmentBytes, false, true> // Try-half
{
// current packet too large, try with an half-packet
enum { value = compute_default_alignment_helper<ArrayBytes, AlignmentBytes/2>::value };
};
#else
// If static alignment is disabled, no need to bother.
// This also avoids a division by zero in "bool Match = bool((ArrayBytes%AlignmentBytes)==0)"
template<int ArrayBytes, int AlignmentBytes>
struct compute_default_alignment_helper
{
enum { value = 0 };
};
#endif
template<typename T, int Size> struct compute_default_alignment {
enum { value = compute_default_alignment_helper<Size*sizeof(T),EIGEN_MAX_STATIC_ALIGN_BYTES>::value };
};
template<typename T> struct compute_default_alignment<T,Dynamic> {
enum { value = EIGEN_MAX_ALIGN_BYTES };
};
template<typename _Scalar, int _Rows, int _Cols,
int _Options = AutoAlign |
( (_Rows==1 && _Cols!=1) ? RowMajor
: (_Cols==1 && _Rows!=1) ? ColMajor
: EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
int _MaxRows = _Rows,
int _MaxCols = _Cols
> class make_proper_matrix_type
{
enum {
IsColVector = _Cols==1 && _Rows!=1,
IsRowVector = _Rows==1 && _Cols!=1,
Options = IsColVector ? (_Options | ColMajor) & ~RowMajor
: IsRowVector ? (_Options | RowMajor) & ~ColMajor
: _Options
};
public:
typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type;
};
template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
class compute_matrix_flags
{
enum { row_major_bit = Options&RowMajor ? RowMajorBit : 0 };
public:
// FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
// and then propagate this information to the evaluator's flags.
// However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
enum { ret = DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit };
};
template<int _Rows, int _Cols> struct size_at_compile_time
{
enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols };
};
template<typename XprType> struct size_of_xpr_at_compile_time
{
enum { ret = size_at_compile_time<traits<XprType>::RowsAtCompileTime,traits<XprType>::ColsAtCompileTime>::ret };
};
/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
* whereas eval is a const reference in the case of a matrix
*/
template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type;
template<typename T, typename BaseClassType> struct plain_matrix_type_dense;
template<typename T> struct plain_matrix_type<T,Dense>
{
typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind>::type type;
};
template<typename T> struct plain_matrix_type<T,DiagonalShape>
{
typedef typename T::PlainObject type;
};
template<typename T> struct plain_matrix_type_dense<T,MatrixXpr>
{
typedef Matrix<typename traits<T>::Scalar,
traits<T>::RowsAtCompileTime,
traits<T>::ColsAtCompileTime,
AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
traits<T>::MaxRowsAtCompileTime,
traits<T>::MaxColsAtCompileTime
> type;
};
template<typename T> struct plain_matrix_type_dense<T,ArrayXpr>
{
typedef Array<typename traits<T>::Scalar,
traits<T>::RowsAtCompileTime,
traits<T>::ColsAtCompileTime,
AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
traits<T>::MaxRowsAtCompileTime,
traits<T>::MaxColsAtCompileTime
> type;
};
/* eval : the return type of eval(). For matrices, this is just a const reference
* in order to avoid a useless copy
*/
template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval;
template<typename T> struct eval<T,Dense>
{
typedef typename plain_matrix_type<T>::type type;
// typedef typename T::PlainObject type;
// typedef T::Matrix<typename traits<T>::Scalar,
// traits<T>::RowsAtCompileTime,
// traits<T>::ColsAtCompileTime,
// AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
// traits<T>::MaxRowsAtCompileTime,
// traits<T>::MaxColsAtCompileTime
// > type;
};
template<typename T> struct eval<T,DiagonalShape>
{
typedef typename plain_matrix_type<T>::type type;
};
// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
{
typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense>
{
typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type;
};
/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
*/
template<typename T> struct plain_matrix_type_column_major
{
enum { Rows = traits<T>::RowsAtCompileTime,
Cols = traits<T>::ColsAtCompileTime,
MaxRows = traits<T>::MaxRowsAtCompileTime,
MaxCols = traits<T>::MaxColsAtCompileTime
};
typedef Matrix<typename traits<T>::Scalar,
Rows,
Cols,
(MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor,
MaxRows,
MaxCols
> type;
};
/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
*/
template<typename T> struct plain_matrix_type_row_major
{
enum { Rows = traits<T>::RowsAtCompileTime,
Cols = traits<T>::ColsAtCompileTime,
MaxRows = traits<T>::MaxRowsAtCompileTime,
MaxCols = traits<T>::MaxColsAtCompileTime
};
typedef Matrix<typename traits<T>::Scalar,
Rows,
Cols,
(MaxCols==1&&MaxRows!=1) ? RowMajor : ColMajor,
MaxRows,
MaxCols
> type;
};
/** \internal The reference selector for template expressions. The idea is that we don't
* need to use references for expressions since they are light weight proxy
* objects which should generate no copying overhead. */
template <typename T>
struct ref_selector
{
typedef typename conditional<
bool(traits<T>::Flags & NestByRefBit),
T const&,
const T
>::type type;
typedef typename conditional<
bool(traits<T>::Flags & NestByRefBit),
T &,
T
>::type non_const_type;
};
/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
template<typename T1, typename T2>
struct transfer_constness
{
typedef typename conditional<
bool(internal::is_const<T1>::value),
typename internal::add_const_on_value_type<T2>::type,
T2
>::type type;
};
// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
// has to be evaluated into a temporary.
// That's the purpose of this new nested_eval helper:
/** \internal Determines how a given expression should be nested when evaluated multiple times.
* For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
* evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
* evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
* a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
* many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
*
* \param T the type of the expression being nested.
* \param n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
* \param PlainObject the type of the temporary if needed.
*/
template<typename T, int n, typename PlainObject = typename eval<T>::type> struct nested_eval
{
enum {
// For the purpose of this test, to keep it reasonably simple, we arbitrarily choose a value of Dynamic values.
// the choice of 10000 makes it larger than any practical fixed value and even most dynamic values.
// in extreme cases where these assumptions would be wrong, we would still at worst suffer performance issues
// (poor choice of temporaries).
// It's important that this value can still be squared without integer overflowing.
DynamicAsInteger = 10000,
ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
ScalarReadCostAsInteger = ScalarReadCost == Dynamic ? int(DynamicAsInteger) : int(ScalarReadCost),
CoeffReadCost = evaluator<T>::CoeffReadCost, // TODO What if an evaluator evaluate itself into a tempory?
// Then CoeffReadCost will be small but we still have to evaluate if n>1...
// The solution might be to ask the evaluator if it creates a temp. Perhaps we could even ask the number of temps?
CoeffReadCostAsInteger = CoeffReadCost == Dynamic ? int(DynamicAsInteger) : int(CoeffReadCost),
NAsInteger = n == Dynamic ? int(DynamicAsInteger) : n,
CostEvalAsInteger = (NAsInteger+1) * ScalarReadCostAsInteger + CoeffReadCostAsInteger,
CostNoEvalAsInteger = NAsInteger * CoeffReadCostAsInteger
};
typedef typename conditional<
( (int(evaluator<T>::Flags) & EvalBeforeNestingBit) ||
(int(CostEvalAsInteger) < int(CostNoEvalAsInteger)) ),
PlainObject,
typename ref_selector<T>::type
>::type type;
};
template<typename T>
EIGEN_DEVICE_FUNC
inline T* const_cast_ptr(const T* ptr)
{
return const_cast<T*>(ptr);
}
template<typename Derived, typename XprKind = typename traits<Derived>::XprKind>
struct dense_xpr_base
{
/* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */
};
template<typename Derived>
struct dense_xpr_base<Derived, MatrixXpr>
{
typedef MatrixBase<Derived> type;
};
template<typename Derived>
struct dense_xpr_base<Derived, ArrayXpr>
{
typedef ArrayBase<Derived> type;
};
template<typename Derived, typename XprKind = typename traits<Derived>::XprKind, typename StorageKind = typename traits<Derived>::StorageKind>
struct generic_xpr_base;
template<typename Derived, typename XprKind>
struct generic_xpr_base<Derived, XprKind, Dense>
{
typedef typename dense_xpr_base<Derived,XprKind>::type type;
};
/** \internal Helper base class to add a scalar multiple operator
* overloads for complex types */
template<typename Derived,typename Scalar,typename OtherScalar,
bool EnableIt = !is_same<Scalar,OtherScalar>::value >
struct special_scalar_op_base : public DenseCoeffsBase<Derived>
{
// dummy operator* so that the
// "using special_scalar_op_base::operator*" compiles
struct dummy {};
void operator*(dummy) const;
void operator/(dummy) const;
};
template<typename Derived,typename Scalar,typename OtherScalar>
struct special_scalar_op_base<Derived,Scalar,OtherScalar,true> : public DenseCoeffsBase<Derived>
{
const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
operator*(const OtherScalar& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
(*static_cast<const Derived*>(this), scalar_multiple2_op<Scalar,OtherScalar>(scalar));
}
inline friend const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
operator*(const OtherScalar& scalar, const Derived& matrix)
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return static_cast<const special_scalar_op_base&>(matrix).operator*(scalar);
}
const CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, Derived>
operator/(const OtherScalar& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, Derived>
(*static_cast<const Derived*>(this), scalar_quotient2_op<Scalar,OtherScalar>(scalar));
}
};
template<typename XprType, typename CastType> struct cast_return_type
{
typedef typename XprType::Scalar CurrentScalarType;
typedef typename remove_all<CastType>::type _CastType;
typedef typename _CastType::Scalar NewScalarType;
typedef typename conditional<is_same<CurrentScalarType,NewScalarType>::value,
const XprType&,CastType>::type type;
};
template <typename A, typename B> struct promote_storage_type;
template <typename A> struct promote_storage_type<A,A>
{
typedef A ret;
};
template <typename A> struct promote_storage_type<A, const A>
{
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
* A * dense -> A
* dense * B -> B
* \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 ScalarA, typename ScalarB> struct cwise_promote_storage_type<Dense,Dense,scalar_product_op<ScalarA,ScalarB> > { 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 A, typename ScalarA, typename ScalarB> struct cwise_promote_storage_type<A,Dense,scalar_product_op<ScalarA,ScalarB> > { typedef A ret; };
template <typename B, typename ScalarA, typename ScalarB> struct cwise_promote_storage_type<Dense,B,scalar_product_op<ScalarA,ScalarB> > { typedef B 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.
* \param 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 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;
}
} // end namespace internal
// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
// that would take two operands of different types. If there were such an example, then this check should be
// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
// currently they take only one typename Scalar template parameter.
// 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((internal::functor_is_product_like<BINOP>::ret \
? int(internal::scalar_product_traits<LHS, RHS>::Defined) \
: int(internal::is_same<LHS, RHS>::value)), \
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
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