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merge pull request 198

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This commit is contained in:
Gael Guennebaud 2016-06-24 11:48:17 +02:00
commit 3852351793
109 changed files with 1692 additions and 1164 deletions
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1
Eigen/Eigenvalues
View File

@ -32,6 +32,7 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"

1
Eigen/SVD
View File

@ -31,6 +31,7 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/SVD/UpperBidiagonalization.h"
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"

4
Eigen/src/Core/Array.h
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@ -149,7 +149,7 @@ class Array
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other)
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
@ -157,7 +157,7 @@ class Array
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other)
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;

4
Eigen/src/Core/ArrayBase.h
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@ -176,7 +176,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -189,7 +189,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

16
Eigen/src/Core/AssignEvaluator.h
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@ -116,9 +116,9 @@ private:
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
&& int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(InnerSize) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit)
&& int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
};
public:
@ -687,7 +687,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(const DstX
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar>());
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
/***************************************************************************
@ -722,13 +722,13 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// Deal with "assume-aliasing"
@ -787,7 +787,7 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
@ -809,7 +809,7 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// forward declaration
@ -838,7 +838,7 @@ template< typename DstXprType, typename SrcXprType, typename Functor, typename S
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Scalar>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);

7
Eigen/src/Core/CommaInitializer.h
View File

@ -80,8 +80,11 @@ struct CommaInitializer
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if(other.cols()==0 || other.rows()==0)
if(other.rows()==0)
{
m_col += other.cols();
return *this;
}
if (m_col==m_xpr.cols())
{
m_row+=m_currentBlockRows;
@ -90,7 +93,7 @@ struct CommaInitializer
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
eigen_assert((m_col<m_xpr.cols() || (m_xpr.cols()==0 && m_col==0))
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
if (OtherDerived::SizeAtCompileTime != Dynamic)

4
Eigen/src/Core/CwiseBinaryOp.h
View File

@ -160,7 +160,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -173,7 +173,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

41
Eigen/src/Core/CwiseTernaryOp.h
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@ -34,22 +34,6 @@ struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
typedef typename result_of<TernaryOp(
const typename Arg1::Scalar&, const typename Arg2::Scalar&,
const typename Arg3::Scalar&)>::type Scalar;
EIGEN_STATIC_ASSERT(
(internal::is_same<typename internal::traits<Arg1>::StorageKind,
typename internal::traits<Arg2>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename internal::traits<Arg1>::StorageKind,
typename internal::traits<Arg3>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename internal::traits<Arg1>::StorageIndex,
typename internal::traits<Arg3>::StorageIndex>::value),
STORAGE_INDEX_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename internal::traits<Arg1>::StorageIndex,
typename internal::traits<Arg3>::StorageIndex>::value),
STORAGE_INDEX_MUST_MATCH)
typedef typename internal::traits<Arg1>::StorageKind StorageKind;
typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
@ -100,18 +84,8 @@ template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
class CwiseTernaryOp : public CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>,
internal::no_assignment_operator {
EIGEN_STATIC_ASSERT(
(internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
@ -137,6 +111,17 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
// The index types should match
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
a1.rows() == a3.rows() && a1.cols() == a3.cols());
}

15
Eigen/src/Core/DiagonalMatrix.h
View File

@ -71,18 +71,17 @@ class DiagonalBase : public EigenBase<Derived>
return InverseReturnType(diagonal().cwiseInverse());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > ScalarMultipleReturnType;
EIGEN_DEVICE_FUNC
inline const ScalarMultipleReturnType
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return ScalarMultipleReturnType(diagonal() * scalar);
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const ScalarMultipleReturnType
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return ScalarMultipleReturnType(other.diagonal() * scalar);
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
};
@ -320,16 +319,16 @@ template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2De
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense, Scalar>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};

12
Eigen/src/Core/Dot.h
View File

@ -28,22 +28,24 @@ template<typename T, typename U,
>
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.template binaryExpr<conj_prod>(b).sum();
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
};
@ -62,7 +64,7 @@ struct dot_nocheck<T, U, true>
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)

4
Eigen/src/Core/EigenBase.h
View File

@ -138,7 +138,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -146,7 +146,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

71
Eigen/src/Core/GlobalFunctions.h
View File

@ -89,14 +89,32 @@ namespace Eigen
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
*
* \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived,typename ScalarExponent>
inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
#else
template<typename Derived,typename ScalarExponent>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,ScalarExponent>::value)
&& ScalarBinaryOpTraits<typename Derived::Scalar,ScalarExponent,internal::scalar_pow_op<typename Derived::Scalar,ScalarExponent> >::Defined,
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,ScalarExponent,pow) >::type
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
return x.derived().pow(exponent);
}
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename Derived::Scalar,pow)
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
return x.derived().pow(exponent);
}
#endif
/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
*
@ -106,12 +124,14 @@ namespace Eigen
* Output: \verbinclude Cwise_array_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
@ -120,36 +140,39 @@ namespace Eigen
/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power between a scalar and an array of exponents.
* Beaware that the scalar type of the input scalar \a x and the exponents \a exponents must be the same.
*
* \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Scalar,typename Derived>
inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
#else
template<typename Scalar, typename Derived>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,Scalar>::value)
&& ScalarBinaryOpTraits<Scalar,typename Derived::Scalar,internal::scalar_pow_op<Scalar,typename Derived::Scalar> >::Defined,
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow) >::type
pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
template<typename Derived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
typename Derived::ConstantReturnType constant_x(exponents.rows(), exponents.cols(), x);
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>(
constant_x,
exponents.derived()
);
}
/**
* \brief Component-wise division of a scalar by array elements.
**/
template <typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>
operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>(s)
);
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,typename Derived::Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
#endif
/** \cpp11 \returns an expression of the coefficient-wise igamma(\a a, \a x) to the given arrays.
*

39
Eigen/src/Core/MathFunctions.h
View File

@ -462,7 +462,7 @@ struct arg_retval
template<typename Scalar, bool isComplex = NumTraits<Scalar>::IsComplex >
struct log1p_impl
{
static inline Scalar run(const Scalar& x)
static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
typedef typename NumTraits<Scalar>::Real RealScalar;
@ -472,7 +472,7 @@ struct log1p_impl
}
};
#if EIGEN_HAS_CXX11_MATH
#if EIGEN_HAS_CXX11_MATH && !defined(__CUDACC__)
template<typename Scalar>
struct log1p_impl<Scalar, false> {
static inline Scalar run(const Scalar& x)
@ -494,24 +494,26 @@ struct log1p_retval
* Implementation of pow *
****************************************************************************/
template<typename Scalar, bool IsInteger>
struct pow_default_impl
template<typename ScalarX,typename ScalarY, bool IsInteger = NumTraits<ScalarX>::IsInteger&&NumTraits<ScalarY>::IsInteger>
struct pow_impl
{
typedef Scalar retval;
static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y)
//typedef Scalar retval;
typedef typename ScalarBinaryOpTraits<ScalarX,ScalarY,internal::scalar_pow_op<ScalarX,ScalarY> >::ReturnType result_type;
static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y)
{
EIGEN_USING_STD_MATH(pow);
return pow(x, y);
}
};
template<typename Scalar>
struct pow_default_impl<Scalar, true>
template<typename ScalarX,typename ScalarY>
struct pow_impl<ScalarX,ScalarY, true>
{
static EIGEN_DEVICE_FUNC inline Scalar run(Scalar x, Scalar y)
typedef ScalarX result_type;
static EIGEN_DEVICE_FUNC inline ScalarX run(const ScalarX &x, const ScalarY &y)
{
Scalar res(1);
eigen_assert(!NumTraits<Scalar>::IsSigned || y >= 0);
ScalarX res(1);
eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
if(y & 1) res *= x;
y >>= 1;
while(y)
@ -524,15 +526,6 @@ struct pow_default_impl<Scalar, true>
}
};
template<typename Scalar>
struct pow_impl : pow_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
template<typename Scalar>
struct pow_retval
{
typedef Scalar type;
};
/****************************************************************************
* Implementation of random *
****************************************************************************/
@ -928,11 +921,11 @@ inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
}
template<typename Scalar>
template<typename ScalarX,typename ScalarY>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const ScalarX& x, const ScalarY& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
}
template<typename T> EIGEN_DEVICE_FUNC bool (isnan) (const T &x) { return internal::isnan_impl(x); }

8
Eigen/src/Core/MatrixBase.h
View File

@ -193,7 +193,7 @@ template<typename Derived> class MatrixBase
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
@ -381,7 +381,7 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
/// \internal helper struct to form the return type of the cross product
template<typename OtherDerived> struct cross_product_return_type {
typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
};
#endif // EIGEN_PARSED_BY_DOXYGEN
@ -403,7 +403,6 @@ template<typename Derived> class MatrixBase
inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
inline ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
: ColsAtCompileTime==1 ? Vertical : Horizontal };
@ -416,8 +415,7 @@ template<typename Derived> class MatrixBase
typedef Block<const Derived,
internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>,
const ConstStartMinusOne > HNormalizedReturnType;
typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
inline const HNormalizedReturnType hnormalized() const;

6
Eigen/src/Core/NoAlias.h
View File

@ -39,7 +39,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
@ -47,7 +47,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
@ -55,7 +55,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}

11
Eigen/src/Core/NumTraits.h
View File

@ -22,14 +22,16 @@ namespace Eigen {
* This class stores enums, typedefs and static methods giving information about a numeric type.
*
* The provided data consists of:
* \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
* then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
* \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
* then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
* is a typedef to \a U.
* \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
* \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
* such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
* \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
* take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
* only intended as a helper for code that needs to explicitly promote types.
* \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
* Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
* \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
* this means, just use \a T here.
* \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
@ -84,6 +86,7 @@ template<typename T> struct GenericNumTraits
T
>::type NonInteger;
typedef T Nested;
typedef T Literal;
EIGEN_DEVICE_FUNC
static inline Real epsilon()
@ -145,6 +148,7 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
{
typedef _Real Real;
typedef typename NumTraits<_Real>::Literal Literal;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
@ -168,6 +172,7 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
typedef ArrayType & Nested;
typedef typename NumTraits<Scalar>::Literal Literal;
enum {
IsComplex = NumTraits<Scalar>::IsComplex,

2
Eigen/src/Core/PlainObjectBase.h
View File

@ -718,7 +718,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//_resize_to_match(other);
// the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
// it wouldn't allow to copy a row-vector into a column-vector.
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar>());
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return this->derived();
}

35
Eigen/src/Core/Product.h
View File

@ -16,39 +16,6 @@ template<typename Lhs, typename Rhs, int Option, typename StorageKind> class Pro
namespace internal {
// Determine the scalar of Product<Lhs, Rhs>. This is normally the same as Lhs::Scalar times
// Rhs::Scalar, but product with permutation matrices inherit the scalar of the other factor.
template<typename Lhs, typename Rhs, typename LhsShape = typename evaluator_traits<Lhs>::Shape,
typename RhsShape = typename evaluator_traits<Rhs>::Shape >
struct product_result_scalar
{
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, PermutationShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, PermutationShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, TranspositionsShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, TranspositionsShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
@ -59,7 +26,7 @@ struct traits<Product<Lhs, Rhs, Option> >
typedef MatrixXpr XprKind;
typedef typename product_result_scalar<LhsCleaned,RhsCleaned>::Scalar Scalar;
typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
typename RhsTraits::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;

95
Eigen/src/Core/ProductEvaluators.h
View File

@ -35,22 +35,28 @@ struct evaluator<Product<Lhs, Rhs, Options> >
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
};
// Catch scalar * ( A * B ) and transform it to (A*scalar) * B
// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
// TODO we should apply that rule only if that's really helpful
template<typename Lhs, typename Rhs, typename Scalar>
struct evaluator_assume_aliasing<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > >
template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > >
{
static const bool value = true;
};
template<typename Lhs, typename Rhs, typename Scalar>
struct evaluator<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > >
: public evaluator<Product<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,const Lhs>, Rhs, DefaultProduct> >
template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > >
: public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
{
typedef CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > XprType;
typedef evaluator<Product<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,const Lhs>, Rhs, DefaultProduct> > Base;
typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > XprType;
typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: Base(xpr.functor().m_other * xpr.nestedExpression().lhs() * xpr.nestedExpression().rhs())
: Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
{}
};
@ -122,14 +128,17 @@ protected:
PlainObject m_result;
};
// The following three shortcuts are enabled only if the scalar types match excatly.
// TODO: we could enable them for different scalar types when the product is not vectorized.
// Dense = Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar>, Dense2Dense,
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
@ -138,12 +147,12 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scal
// Dense += Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar>, Dense2Dense,
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar> &)
void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
@ -152,12 +161,12 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<
// Dense -= Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar>, Dense2Dense,
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar> &)
void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
@ -168,16 +177,17 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<
// Dense ?= scalar * Product
// TODO we should apply that rule if that's really helpful
// for instance, this is not good for inner products
template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis>
struct Assignment<DstXprType, CwiseUnaryOp<internal::scalar_multiple_op<ScalarBis>,
template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense, Scalar>
{
typedef CwiseUnaryOp<internal::scalar_multiple_op<ScalarBis>,
const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
{
call_assignment_no_alias(dst, (src.functor().m_other * src.nestedExpression().lhs())*src.nestedExpression().rhs(), func);
call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
}
};
@ -187,37 +197,38 @@ struct Assignment<DstXprType, CwiseUnaryOp<internal::scalar_multiple_op<ScalarBi
// TODO enable it for "Dense ?= xpr - Product<>" as well.
template<typename OtherXpr, typename Lhs, typename Rhs>
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar>, const OtherXpr,
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
static const bool value = true;
};
template<typename DstXprType, typename OtherXpr, typename ProductType, typename Scalar, typename Func1, typename Func2>
template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
struct assignment_from_xpr_plus_product
{
typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr, const ProductType> SrcXprType;
typedef CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename ProductType::Scalar>, const OtherXpr, const ProductType> SrcXprType;
template<typename InitialFunc>
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const Func1& func)
void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
{
call_assignment_no_alias(dst, src.lhs(), func);
call_assignment_no_alias(dst, src.lhs(), Func1());
call_assignment_no_alias(dst, src.rhs(), Func2());
}
};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::assign_op<Scalar>, internal::add_assign_op<Scalar> >
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<OtherScalar,ProdScalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::assign_op<DstScalar,SrcScalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<DstScalar,OtherScalar>, internal::add_assign_op<DstScalar,ProdScalar> >
{};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::add_assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::add_assign_op<Scalar>, internal::add_assign_op<Scalar> >
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<OtherScalar,ProdScalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::add_assign_op<DstScalar,SrcScalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<DstScalar,OtherScalar>, internal::add_assign_op<DstScalar,ProdScalar> >
{};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::sub_assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::sub_assign_op<Scalar>, internal::sub_assign_op<Scalar> >
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<OtherScalar,ProdScalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::sub_assign_op<DstScalar,SrcScalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<DstScalar,OtherScalar>, internal::sub_assign_op<DstScalar,ProdScalar> >
{};
//----------------------------------------
@ -369,21 +380,21 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
{
// Same as: dst.noalias() = lhs.lazyProduct(rhs);
// but easier on the compiler side
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
}
template<typename Dst>
static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
// dst.noalias() += lhs.lazyProduct(rhs);
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
}
template<typename Dst>
static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
// dst.noalias() -= lhs.lazyProduct(rhs);
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
}
// template<typename Dst>
@ -735,7 +746,7 @@ template<typename MatrixType, typename DiagonalType, typename Derived, int Produ
struct diagonal_product_evaluator_base
: evaluator_base<Derived>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
public:
enum {
CoeffReadCost = NumTraits<Scalar>::MulCost + evaluator<MatrixType>::CoeffReadCost + evaluator<DiagonalType>::CoeffReadCost,

8
Eigen/src/Core/Redux.h
View File

@ -425,7 +425,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
{
return derived().redux(Eigen::internal::scalar_min_op<Scalar>());
return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@ -435,7 +435,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff() const
{
return derived().redux(Eigen::internal::scalar_max_op<Scalar>());
return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar>());
}
/** \returns the sum of all coefficients of \c *this
@ -450,7 +450,7 @@ DenseBase<Derived>::sum() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(0);
return derived().redux(Eigen::internal::scalar_sum_op<Scalar>());
return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
}
/** \returns the mean of all coefficients of *this
@ -465,7 +465,7 @@ DenseBase<Derived>::mean() const
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif

2
Eigen/src/Core/Ref.h
View File

@ -262,7 +262,7 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar>());
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
Base::construct(m_object);
}

2
Eigen/src/Core/SelfAdjointView.h
View File

@ -129,7 +129,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
}
friend EIGEN_DEVICE_FUNC
const SelfAdjointView<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,MatrixType>,UpLo>
const SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),UpLo>
operator*(const Scalar& s, const SelfAdjointView& mat)
{
return (s*mat.nestedExpression()).template selfadjointView<UpLo>();

10
Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@ -12,11 +12,13 @@
namespace Eigen {
// TODO generalize the scalar type of 'other'
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
return derived();
}
@ -24,7 +26,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
return derived();
}
@ -32,7 +34,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
return derived();
}
@ -40,7 +42,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
return derived();
}

13
Eigen/src/Core/Solve.h
View File

@ -134,10 +134,10 @@ protected:
// Specialization for "dst = dec.solve(rhs)"
// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
// FIXME shall we resize dst here?
src.dec()._solve_impl(src.rhs(), dst);
@ -146,10 +146,10 @@ struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar
// Specialization for "dst = dec.transpose().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
}
@ -157,10 +157,11 @@ struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal:
// Specialization for "dst = dec.adjoint().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
}

4
Eigen/src/Core/SpecialFunctions.h
View File

@ -1050,8 +1050,8 @@ struct betainc_impl {
template <typename Scalar>
struct betainc_impl {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x) {
/* betaincf.c
static EIGEN_STRONG_INLINE Scalar run(Scalar, Scalar, Scalar) {
/* betaincf.c
*
* Incomplete beta integral
*

22
Eigen/src/Core/TriangularMatrix.h
View File

@ -367,14 +367,14 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
template<typename Other>
EIGEN_DEVICE_FUNC
TriangularViewType& operator+=(const DenseBase<Other>& other) {
internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename Other::Scalar>());
return derived();
}
/** \sa MatrixBase::operator-=() */
template<typename Other>
EIGEN_DEVICE_FUNC
TriangularViewType& operator-=(const DenseBase<Other>& other) {
internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename Other::Scalar>());
return derived();
}
@ -552,7 +552,7 @@ template<typename OtherDerived>
inline TriangularView<MatrixType, Mode>&
TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const MatrixBase<OtherDerived>& other)
{
internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -794,7 +794,7 @@ void call_triangular_assignment_loop(const DstXprType& dst, const SrcXprType& sr
enum {
unroll = DstXprType::SizeAtCompileTime != Dynamic
&& SrcEvaluatorType::CoeffReadCost < HugeCost
&& DstXprType::SizeAtCompileTime * SrcEvaluatorType::CoeffReadCost / 2 <= EIGEN_UNROLLING_LIMIT
&& DstXprType::SizeAtCompileTime * (DstEvaluatorType::CoeffReadCost+SrcEvaluatorType::CoeffReadCost) / 2 <= EIGEN_UNROLLING_LIMIT
};
triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(kernel);
@ -804,7 +804,7 @@ template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_triangular_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar>());
call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
template<> struct AssignmentKind<TriangularShape,TriangularShape> { typedef Triangular2Triangular Kind; };
@ -933,10 +933,10 @@ namespace internal {
// Triangular = Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular, Scalar>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst.setZero();
dst._assignProduct(src, 1);
@ -945,10 +945,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_
// Triangular += Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular, Scalar>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst._assignProduct(src, 1);
}
@ -956,10 +956,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_ass
// Triangular -= Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular, Scalar>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst._assignProduct(src, -1);
}

4
Eigen/src/Core/VectorwiseOp.h
View File

@ -540,7 +540,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator+(const DenseBase<OtherDerived>& other) const
@ -553,7 +553,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_difference_op<Scalar>,
CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator-(const DenseBase<OtherDerived>& other) const

2
Eigen/src/Core/arch/CUDA/PacketMathHalf.h
View File

@ -28,6 +28,8 @@ template<> struct packet_traits<Eigen::half> : default_packet_traits
AlignedOnScalar = 1,
size=2,
HasHalfPacket = 0,
HasAdd = 1,
HasMul = 1,
HasDiv = 1,
HasSqrt = 1,
HasRsqrt = 1,

8
Eigen/src/Core/arch/NEON/Complex.h
View File

@ -14,8 +14,9 @@ namespace Eigen {
namespace internal {
static uint32x4_t p4ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET4(0x00000000, 0x80000000, 0x00000000, 0x80000000);
static uint32x2_t p2ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x00000000, 0x80000000);
const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
static uint32x4_t p4ui_CONJ_XOR = vld1q_u32( conj_XOR_DATA );
static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );
//---------- float ----------
struct Packet2cf
@ -274,7 +275,8 @@ ptranspose(PacketBlock<Packet2cf,2>& kernel) {
//---------- double ----------
#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
static uint64x2_t p2ul_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x0, 0x8000000000000000);
const uint64_t p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
struct Packet1cd
{

20
Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -49,17 +49,6 @@ typedef uint32x4_t Packet4ui;
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
const Packet4i p4i_##NAME = pset1<Packet4i>(X)
#if EIGEN_COMP_LLVM && !EIGEN_COMP_CLANG
//Special treatment for Apple's llvm-gcc, its NEON packet types are unions
#define EIGEN_INIT_NEON_PACKET2(X, Y) {{X, Y}}
#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {{X, Y, Z, W}}
#else
//Default initializer for packets
#define EIGEN_INIT_NEON_PACKET2(X, Y) {X, Y}
#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {X, Y, Z, W}
#endif
// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
// which available on LLVM and GCC (at least)
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
@ -122,12 +111,14 @@ template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
{
Packet4f countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
const float32_t f[] = {0, 1, 2, 3};
Packet4f countdown = vld1q_f32(f);
return vaddq_f32(pset1<Packet4f>(a), countdown);
}
template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a)
{
Packet4i countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
const int32_t i[] = {0, 1, 2, 3};
Packet4i countdown = vld1q_s32(i);
return vaddq_s32(pset1<Packet4i>(a), countdown);
}
@ -585,7 +576,8 @@ template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { r
template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a)
{
Packet2d countdown = EIGEN_INIT_NEON_PACKET2(0, 1);
const double countdown_raw[] = {0.0,1.0};
const Packet2d countdown = vld1q_f64(countdown_raw);
return vaddq_f64(pset1<Packet2d>(a), countdown);
}
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }

54
Eigen/src/Core/functors/AssignmentFunctors.h
View File

@ -18,20 +18,24 @@ namespace internal {
* \brief Template functor for scalar/packet assignment
*
*/
template<typename Scalar> struct assign_op {
template<typename DstScalar,typename SrcScalar> struct assign_op {
EIGEN_EMPTY_STRUCT_CTOR(assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a = b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,b); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,b); }
};
template<typename Scalar>
struct functor_traits<assign_op<Scalar> > {
// Empty overload for void type (used by PermutationMatrix
template<typename DstScalar> struct assign_op<DstScalar,void> {};
template<typename DstScalar,typename SrcScalar>
struct functor_traits<assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost,
PacketAccess = packet_traits<Scalar>::Vectorizable
Cost = NumTraits<DstScalar>::ReadCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable
};
};
@ -39,20 +43,20 @@ struct functor_traits<assign_op<Scalar> > {
* \brief Template functor for scalar/packet assignment with addition
*
*/
template<typename Scalar> struct add_assign_op {
template<typename DstScalar,typename SrcScalar> struct add_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(add_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a += b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a += b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<add_assign_op<Scalar> > {
template<typename DstScalar,typename SrcScalar>
struct functor_traits<add_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd
};
};
@ -60,20 +64,20 @@ struct functor_traits<add_assign_op<Scalar> > {
* \brief Template functor for scalar/packet assignment with subtraction
*
*/
template<typename Scalar> struct sub_assign_op {
template<typename DstScalar,typename SrcScalar> struct sub_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a -= b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a -= b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<sub_assign_op<Scalar> > {
template<typename DstScalar,typename SrcScalar>
struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasSub
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub
};
};
@ -98,7 +102,6 @@ struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
};
};
template<typename DstScalar,typename SrcScalar> struct functor_is_product_like<mul_assign_op<DstScalar,SrcScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor for scalar/packet assignment with diviving
@ -120,7 +123,6 @@ struct functor_traits<div_assign_op<DstScalar,SrcScalar> > {
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv
};
};
template<typename DstScalar,typename SrcScalar> struct functor_is_product_like<div_assign_op<DstScalar,SrcScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor for scalar/packet assignment with swapping

424
Eigen/src/Core/functors/BinaryFunctors.h
View File

@ -16,27 +16,43 @@ namespace internal {
//---------- associative binary functors ----------
template<typename Arg1, typename Arg2>
struct binary_op_base
{
typedef Arg1 first_argument_type;
typedef Arg2 second_argument_type;
};
/** \internal
* \brief Template functor to compute the sum of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
*/
template<typename Scalar> struct scalar_sum_op {
// typedef Scalar result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
#else
scalar_sum_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::padd(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sum_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, // rough estimate!
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
// TODO vectorize mixed sum
};
};
@ -45,7 +61,7 @@ struct functor_traits<scalar_sum_op<Scalar> > {
* This is required to solve Bug 426.
* \sa DenseBase::count(), DenseBase::any(), ArrayBase::cast(), MatrixBase::cast()
*/
template<> struct scalar_sum_op<bool> : scalar_sum_op<int> {
template<> struct scalar_sum_op<bool,bool> : scalar_sum_op<int,int> {
EIGEN_DEPRECATED
scalar_sum_op() {}
};
@ -56,13 +72,17 @@ template<> struct scalar_sum_op<bool> : scalar_sum_op<int> {
*
* \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_product_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_product_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
#else
scalar_product_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
@ -75,7 +95,8 @@ template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate!
PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
// TODO vectorize mixed product
};
};
@ -84,13 +105,15 @@ struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
*
* This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_conj_product_op : binary_op_base<LhsScalar,RhsScalar>
{
enum {
Conj = NumTraits<LhsScalar>::IsComplex
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
@ -113,21 +136,24 @@ struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
*/
template<typename Scalar> struct scalar_min_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::mini(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::mini(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_min(a); }
};
template<typename Scalar>
struct functor_traits<scalar_min_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_min_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMin
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
};
};
@ -136,21 +162,24 @@ struct functor_traits<scalar_min_op<Scalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
*/
template<typename Scalar> struct scalar_max_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::maxi(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::maxi(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_max(a); }
};
template<typename Scalar>
struct functor_traits<scalar_max_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_max_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMax
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
};
};
@ -158,56 +187,70 @@ struct functor_traits<scalar_max_op<Scalar> > {
* \brief Template functors for comparison of two scalars
* \todo Implement packet-comparisons
*/
template<typename Scalar, ComparisonName cmp> struct scalar_cmp_op;
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op;
template<typename Scalar, ComparisonName cmp>
struct functor_traits<scalar_cmp_op<Scalar, cmp> > {
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp>
struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = false
};
};
template<ComparisonName Cmp, typename Scalar>
struct result_of<scalar_cmp_op<Scalar, Cmp>(Scalar,Scalar)> {
template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar>
struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> {
typedef bool type;
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_EQ> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_EQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a==b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a==b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LT> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LE> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<=b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GT> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GE> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>=b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_UNORD> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_UNORD> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return !(a<=b || b<=a);}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_NEQ> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a!=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a!=b;}
};
@ -216,7 +259,9 @@ template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_NEQ> {
*
* \sa MatrixBase::stableNorm(), class Redux
*/
template<typename Scalar> struct scalar_hypot_op {
template<typename Scalar>
struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
// typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
@ -237,7 +282,7 @@ template<typename Scalar> struct scalar_hypot_op {
}
};
template<typename Scalar>
struct functor_traits<scalar_hypot_op<Scalar> > {
struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
enum
{
Cost = 3 * NumTraits<Scalar>::AddCost +
@ -250,13 +295,24 @@ struct functor_traits<scalar_hypot_op<Scalar> > {
/** \internal
* \brief Template functor to compute the pow of two scalars
*/
template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op)
template<typename Scalar, typename Exponent>
struct scalar_pow_op : binary_op_base<Scalar,Exponent>
{
typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_pow_op)
#else
scalar_pow_op() {
typedef Scalar LhsScalar;
typedef Exponent RhsScalar;
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); }
inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
};
template<typename Scalar, typename OtherScalar>
struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
template<typename Scalar, typename Exponent>
struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
};
@ -269,18 +325,27 @@ struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::operator-
*/
template<typename Scalar> struct scalar_difference_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
#else
scalar_difference_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::psub(a,b); }
};
template<typename Scalar>
struct functor_traits<scalar_difference_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasSub
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
};
};
@ -289,13 +354,17 @@ struct functor_traits<scalar_difference_op<Scalar> > {
*
* \sa class CwiseBinaryOp, Cwise::operator/()
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_quotient_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_quotient_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
#else
scalar_quotient_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
@ -305,7 +374,7 @@ template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
enum {
PacketAccess = scalar_quotient_op<LhsScalar,RhsScalar>::Vectorizable,
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
Cost = NumTraits<result_type>::template Div<PacketAccess>::Cost
};
};
@ -365,7 +434,8 @@ template<> struct functor_traits<scalar_boolean_xor_op> {
*
* \sa class CwiseBinaryOp, Cwise::igamma
*/
template<typename Scalar> struct scalar_igamma_op {
template<typename Scalar> struct scalar_igamma_op : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
using numext::igamma; return igamma(a, x);
@ -390,7 +460,8 @@ struct functor_traits<scalar_igamma_op<Scalar> > {
*
* \sa class CwiseBinaryOp, Cwise::igammac
*/
template<typename Scalar> struct scalar_igammac_op {
template<typename Scalar> struct scalar_igammac_op : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_igammac_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
using numext::igammac; return igammac(a, x);
@ -413,183 +484,46 @@ struct functor_traits<scalar_igammac_op<Scalar> > {
//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
/** \internal
* \brief Template functor to multiply a scalar by a fixed other one
*
* \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/
*/
/* NOTE why doing the pset1() in packetOp *is* an optimization ?
* indeed it seems better to declare m_other as a Packet and do the pset1() once
* in the constructor. However, in practice:
* - GCC does not like m_other as a Packet and generate a load every time it needs it
* - on the other hand GCC is able to moves the pset1() outside the loop :)
* - simpler code ;)
* (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y)
*/
template<typename Scalar>
struct scalar_multiple_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_multiple_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
// They are analogues to std::binder1st/binder2nd but with the following differences:
// - they are compatible with packetOp
// - they are portable across C++ versions (the std::binder* are deprecated in C++11)
template<typename BinaryOp> struct bind1st_op : BinaryOp {
template<typename Scalar1, typename Scalar2>
struct scalar_multiple2_op {
typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
};
template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> >
{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
/** \internal
* \brief Template functor to divide a scalar by a fixed other one
*
* This functor is used to implement the quotient of a matrix by
* a scalar where the scalar type is not necessarily a floating point type.
*
* \sa class CwiseUnaryOp, MatrixBase::operator/
*/
template<typename Scalar>
struct scalar_quotient1_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const scalar_quotient1_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : m_other(other) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pdiv(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_op<Scalar> >
{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; };
bind1st_op(const first_argument_type &val) : m_value(val) {}
template<typename Scalar1, typename Scalar2>
struct scalar_quotient2_op {
typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient2_op(const scalar_quotient2_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient2_op(const Scalar2& other) : m_other(other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a / m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
};
template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_quotient2_op<Scalar1,Scalar2> >
{ enum { Cost = 2 * NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
// In Eigen, any binary op (Product, CwiseBinaryOp) require the Lhs and Rhs to have the same scalar type, except for multiplication
// where the mixing of different types is handled by scalar_product_traits
// In particular, real * complex<real> is allowed.
// FIXME move this to functor_traits adding a functor_default
template<typename Functor> struct functor_is_product_like { enum { ret = 0 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_quotient_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor to add a scalar to a fixed other one
* \sa class CwiseUnaryOp, Array::operator+
*/
/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */
template<typename Scalar>
struct scalar_add_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_add_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a + m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::padd(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_add_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to subtract a fixed scalar to another one
* \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_rsub_op
*/
template<typename Scalar>
struct scalar_sub_op {
EIGEN_DEVICE_FUNC inline scalar_sub_op(const scalar_sub_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_sub_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a - m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::psub(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_sub_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to subtract a scalar to fixed another one
* \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_sub_op
*/
template<typename Scalar>
struct scalar_rsub_op {
EIGEN_DEVICE_FUNC inline scalar_rsub_op(const scalar_rsub_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_rsub_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other - a; }
template <typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::psub(pset1<Packet>(m_other), a); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_rsub_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to raise a scalar to a power
* \sa class CwiseUnaryOp, Cwise::pow
*/
template<typename Scalar>
struct scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
EIGEN_DEVICE_FUNC inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); }
const Scalar m_exponent;
};
template<typename Scalar>
struct functor_traits<scalar_pow_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to compute the quotient between a scalar and array entries.
* \sa class CwiseUnaryOp, Cwise::inverse()
*/
template<typename Scalar>
struct scalar_inverse_mult_op {
EIGEN_DEVICE_FUNC scalar_inverse_mult_op(const Scalar& other) : m_other(other) {}
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other / a; }
template<typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::pdiv(pset1<Packet>(m_other),a); }
Scalar m_other;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
{ return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
first_argument_type m_value;
};
template<typename Scalar>
struct functor_traits<scalar_inverse_mult_op<Scalar> >
{ enum { PacketAccess = packet_traits<Scalar>::HasDiv, Cost = NumTraits<Scalar>::template Div<PacketAccess>::Cost }; };
template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
template<typename BinaryOp> struct bind2nd_op : BinaryOp {
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
bind2nd_op(const second_argument_type &val) : m_value(val) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
second_argument_type m_value;
};
template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
} // end namespace internal

3
Eigen/src/Core/functors/NullaryFunctors.h
View File

@ -26,7 +26,8 @@ struct scalar_constant_op {
};
template<typename Scalar>
struct functor_traits<scalar_constant_op<Scalar> >
{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
{ enum { Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
template<typename Scalar> struct scalar_identity_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)

4
Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@ -363,7 +363,7 @@ class gebp_traits
public:
typedef _LhsScalar LhsScalar;
typedef _RhsScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
@ -478,7 +478,7 @@ class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false>
public:
typedef std::complex<RealScalar> LhsScalar;
typedef RealScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,

4
Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -25,7 +25,7 @@ struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLh
{
typedef gebp_traits<RhsScalar,LhsScalar> Traits;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(
Index rows, Index cols, Index depth,
const LhsScalar* lhs, Index lhsStride,
@ -55,7 +55,7 @@ struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLh
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static void run(Index rows, Index cols, Index depth,
const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsStride,

4
Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -40,7 +40,7 @@ template <typename Index, typename LhsScalar, int LhsStorageOrder, bool Conjugat
typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,UpLo,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride,
const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
@ -57,7 +57,7 @@ template <typename Index, typename LhsScalar, int LhsStorageOrder, bool Conjugat
typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsStride, ResScalar* _res, Index resStride,
const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)

4
Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -58,7 +58,7 @@ namespace internal {
template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable
@ -334,7 +334,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable

4
Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -20,7 +20,7 @@ struct triangular_matrix_vector_product;
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = ((Mode&Lower)==Lower),
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
@ -91,7 +91,7 @@ EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,Con
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = ((Mode&Lower)==Lower),
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,

21
Eigen/src/Core/util/BlasUtil.h
View File

@ -293,16 +293,27 @@ struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> >
};
// pop scalar multiple
template<typename Scalar, typename NestedXpr>
struct blas_traits<CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> >
template<typename Scalar, typename NestedXpr, typename Plain>
struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> >
: blas_traits<NestedXpr>
{
typedef blas_traits<NestedXpr> Base;
typedef CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> XprType;
typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> XprType;
typedef typename Base::ExtractType ExtractType;
static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
static inline ExtractType extract(const XprType& x) { return Base::extract(x.rhs()); }
static inline Scalar extractScalarFactor(const XprType& x)
{ return x.functor().m_other * Base::extractScalarFactor(x.nestedExpression()); }
{ return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs()); }
};
template<typename Scalar, typename NestedXpr, typename Plain>
struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > >
: blas_traits<NestedXpr>
{
typedef blas_traits<NestedXpr> Base;
typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > XprType;
typedef typename Base::ExtractType ExtractType;
static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
static inline Scalar extractScalarFactor(const XprType& x)
{ return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other; }
};
// pop opposite

18
Eigen/src/Core/util/ForwardDeclarations.h
View File

@ -175,9 +175,11 @@ namespace internal {
// with optional conjugation of the arguments.
template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper;
template<typename Scalar> struct scalar_sum_op;
template<typename Scalar> struct scalar_difference_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_sum_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_difference_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_conj_product_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_min_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_max_op;
template<typename Scalar> struct scalar_opposite_op;
template<typename Scalar> struct scalar_conjugate_op;
template<typename Scalar> struct scalar_real_op;
@ -193,17 +195,11 @@ template<typename Scalar> struct scalar_sin_op;
template<typename Scalar> struct scalar_acos_op;
template<typename Scalar> struct scalar_asin_op;
template<typename Scalar> struct scalar_tan_op;
template<typename Scalar> struct scalar_pow_op;
template<typename Scalar> struct scalar_inverse_op;
template<typename Scalar> struct scalar_square_op;
template<typename Scalar> struct scalar_cube_op;
template<typename Scalar, typename NewType> struct scalar_cast_op;
template<typename Scalar> struct scalar_multiple_op;
template<typename Scalar> struct scalar_quotient1_op;
template<typename Scalar> struct scalar_min_op;
template<typename Scalar> struct scalar_max_op;
template<typename Scalar> struct scalar_random_op;
template<typename Scalar> struct scalar_add_op;
template<typename Scalar> struct scalar_constant_op;
template<typename Scalar> struct scalar_identity_op;
template<typename Scalar,bool iscpx> struct scalar_sign_op;
@ -211,10 +207,10 @@ template<typename Scalar> struct scalar_igamma_op;
template<typename Scalar> struct scalar_igammac_op;
template<typename Scalar> struct scalar_betainc_op;
template<typename Scalar,typename ScalarExponent> struct scalar_pow_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_hypot_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_multiple2_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_quotient2_op;
} // end namespace internal

55
Eigen/src/Core/util/Macros.h
View File

@ -462,6 +462,8 @@
#define EIGEN_CAT2(a,b) a ## b
#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b)
#define EIGEN_COMMA ,
// convert a token to a string
#define EIGEN_MAKESTRING2(a) #a
#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
@ -876,18 +878,10 @@ namespace Eigen {
#define EIGEN_IMPLIES(a,b) (!(a) || (b))
#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> \
(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); \
}
// the expression type of a cwise product
#define EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS) \
// the expression type of a standard coefficient wise binary operation
#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME) \
CwiseBinaryOp< \
internal::scalar_product_op< \
EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)< \
typename internal::traits<LHS>::Scalar, \
typename internal::traits<RHS>::Scalar \
>, \
@ -895,6 +889,45 @@ namespace Eigen {
const RHS \
>
#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,OPNAME) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME) \
(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME)(derived(), other.derived()); \
}
#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR,SCALAR,OPNAME) \
CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<typename internal::traits<EXPR>::Scalar,SCALAR>, const EXPR, \
const typename internal::plain_constant_type<EXPR,SCALAR>::type>
#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR,EXPR,OPNAME) \
CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<SCALAR,typename internal::traits<EXPR>::Scalar>, \
const typename internal::plain_constant_type<EXPR,SCALAR>::type, const EXPR>
#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME) \
template <typename T> EIGEN_DEVICE_FUNC inline \
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA ScalarBinaryOpTraits<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<Scalar EIGEN_COMMA T> >::Defined>::type,OPNAME) \
(METHOD)(const T& scalar) const { \
typedef typename internal::promote_scalar_arg<Scalar,T,ScalarBinaryOpTraits<Scalar,T,EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<Scalar,T> >::Defined>::type PromotedT; \
return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedT,OPNAME)(derived(), \
typename internal::plain_constant_type<Derived,PromotedT>::type(derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar))); \
}
#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
template <typename T> EIGEN_DEVICE_FUNC inline friend \
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA ScalarBinaryOpTraits<T EIGEN_COMMA Scalar EIGEN_COMMA EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<T EIGEN_COMMA Scalar> >::Defined>::type,Derived,OPNAME) \
(METHOD)(const T& scalar, const StorageBaseType& matrix) { \
typedef typename internal::promote_scalar_arg<Scalar,T,ScalarBinaryOpTraits<T,Scalar,EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<T,Scalar> >::Defined>::type PromotedT; \
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT,Derived,OPNAME)( \
typename internal::plain_constant_type<Derived,PromotedT>::type(matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)), matrix.derived()); \
}
#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD,OPNAME) \
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME)
#ifdef EIGEN_EXCEPTIONS
# define EIGEN_THROW_X(X) throw X
# define EIGEN_THROW throw

112
Eigen/src/Core/util/Meta.h
View File

@ -328,6 +328,30 @@ struct result_of<Func(ArgType0,ArgType1)> {
enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
typedef typename binary_result_of_select<Func, ArgType0, ArgType1, FunctorType>::type type;
};
template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2, int SizeOf=sizeof(has_none)>
struct ternary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_std_result_type)>
{typedef typename Func::result_type type;};
template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
struct ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, sizeof(has_tr1_result)>
{typedef typename Func::template result<Func(ArgType0,ArgType1,ArgType2)>::type type;};
template<typename Func, typename ArgType0, typename ArgType1, typename ArgType2>
struct result_of<Func(ArgType0,ArgType1,ArgType2)> {
template<typename T>
static has_std_result_type testFunctor(T const *, typename T::result_type const * = 0);
template<typename T>
static has_tr1_result testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1,ArgType2)>::type const * = 0);
static has_none testFunctor(...);
// note that the following indirection is needed for gcc-3.3
enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))};
typedef typename ternary_result_of_select<Func, ArgType0, ArgType1, ArgType2, FunctorType>::type type;
};
#endif
/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
@ -375,33 +399,6 @@ template<typename T, typename U> struct scalar_product_traits
enum { Defined = 0 };
};
template<typename T> struct scalar_product_traits<T,T>
{
enum {
// Cost = NumTraits<T>::MulCost,
Defined = 1
};
typedef T ReturnType;
};
template<typename T> struct scalar_product_traits<T,std::complex<T> >
{
enum {
// Cost = 2*NumTraits<T>::MulCost,
Defined = 1
};
typedef std::complex<T> ReturnType;
};
template<typename T> struct scalar_product_traits<std::complex<T>, T>
{
enum {
// Cost = 2*NumTraits<T>::MulCost,
Defined = 1
};
typedef std::complex<T> ReturnType;
};
// FIXME quick workaround around current limitation of result_of
// template<typename Scalar, typename ArgType0, typename ArgType1>
// struct result_of<scalar_product_op<Scalar>(ArgType0,ArgType1)> {
@ -434,6 +431,67 @@ T div_ceil(const T &a, const T &b)
} // end namespace numext
/** \class ScalarBinaryOpTraits
* \ingroup Core_Module
*
* \brief Determines whether the given binary operation of two numeric types is allowed and what the scalar return type is.
*
* \sa CwiseBinaryOp
*/
template<typename ScalarA, typename ScalarB, typename BinaryOp=void>
struct ScalarBinaryOpTraits
#ifndef EIGEN_PARSED_BY_DOXYGEN
// for backward compatibility, use the hints given by the (deprecated) internal::scalar_product_traits class.
: internal::scalar_product_traits<ScalarA,ScalarB>
#endif // EIGEN_PARSED_BY_DOXYGEN
{};
template<typename T, typename BinaryOp>
struct ScalarBinaryOpTraits<T,T,BinaryOp>
{
enum { Defined = 1 };
typedef T ReturnType;
};
// For Matrix * Permutation
template<typename T, typename BinaryOp>
struct ScalarBinaryOpTraits<T,void,BinaryOp>
{
enum { Defined = 1 };
typedef T ReturnType;
};
// For Permutation * Matrix
template<typename T, typename BinaryOp>
struct ScalarBinaryOpTraits<void,T,BinaryOp>
{
enum { Defined = 1 };
typedef T ReturnType;
};
// for Permutation*Permutation
template<typename BinaryOp>
struct ScalarBinaryOpTraits<void,void,BinaryOp>
{
enum { Defined = 1 };
typedef void ReturnType;
};
template<typename T, typename BinaryOp>
struct ScalarBinaryOpTraits<T,std::complex<T>,BinaryOp>
{
enum { Defined = 1 };
typedef std::complex<T> ReturnType;
};
template<typename T, typename BinaryOp>
struct ScalarBinaryOpTraits<std::complex<T>, T,BinaryOp>
{
enum { Defined = 1 };
typedef std::complex<T> ReturnType;
};
} // end namespace Eigen
#endif // EIGEN_META_H

78
Eigen/src/Core/util/XprHelper.h
View File

@ -45,6 +45,56 @@ inline IndexDest convert_index(const IndexSrc& idx) {
}
// promote_scalar_arg is an helper used in operation between an expression and a scalar, like:
// expression * scalar
// Its role is to determine how the type T of the scalar operand should be promoted given the scalar type ExprScalar of the given expression.
// The IsSupported template parameter must be provided by the caller as: ScalarBinaryOpTraits<ExprScalar,T,op>::Defined using the proper order for ExprScalar and T.
// Then the logic is as follows:
// - if the operation is natively supported as defined by IsSupported, then the scalar type is not promoted, and T is returned.
// - otherwise, NumTraits<ExprScalar>::Literal is returned if T is implicitly convertible to NumTraits<ExprScalar>::Literal AND that this does not imply a float to integer conversion.
// - otherwise, ExprScalar is returned if T is implicitly convertible to ExprScalar AND that this does not imply a float to integer conversion.
// - In all other cases, the promoted type is not defined, and the respective operation is thus invalid and not available (SFINAE).
template<typename ExprScalar,typename T, bool IsSupported>
struct promote_scalar_arg;
template<typename S,typename T>
struct promote_scalar_arg<S,T,true>
{
typedef T type;
};
// Recursively check safe conversion to PromotedType, and then ExprScalar if they are different.
template<typename ExprScalar,typename T,typename PromotedType,
bool ConvertibleToLiteral = internal::is_convertible<T,PromotedType>::value,
bool IsSafe = NumTraits<T>::IsInteger || !NumTraits<PromotedType>::IsInteger>
struct promote_scalar_arg_unsupported;
// Start recursion with NumTraits<ExprScalar>::Literal
template<typename S,typename T>
struct promote_scalar_arg<S,T,false> : promote_scalar_arg_unsupported<S,T,typename NumTraits<S>::Literal> {};
// We found a match!
template<typename S,typename T, typename PromotedType>
struct promote_scalar_arg_unsupported<S,T,PromotedType,true,true>
{
typedef PromotedType type;
};
// No match, but no real-to-integer issues, and ExprScalar and current PromotedType are different,
// so let's try to promote to ExprScalar
template<typename ExprScalar,typename T, typename PromotedType>
struct promote_scalar_arg_unsupported<ExprScalar,T,PromotedType,false,true>
: promote_scalar_arg_unsupported<ExprScalar,T,ExprScalar>
{};
// Unsafe real-to-integer, let's stop.
template<typename S,typename T, typename PromotedType>
struct promote_scalar_arg_unsupported<S,T,PromotedType,false,false> {};
// T is not even convertible to ExprScalar, let's stop.
template<typename S,typename T>
struct promote_scalar_arg_unsupported<S,T,S,false,true> {};
//classes inheriting no_assignment_operator don't generate a default operator=.
class no_assignment_operator
{
@ -576,6 +626,20 @@ struct plain_diag_type
>::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
{
@ -610,11 +674,6 @@ bool is_same_dense(const T1 &, const T2 &, typename enable_if<!(has_direct_acces
return false;
}
template<typename T, typename U> struct is_same_or_void { enum { value = is_same<T,U>::value }; };
template<typename T> struct is_same_or_void<void,T> { enum { value = 1 }; };
template<typename T> struct is_same_or_void<T,void> { enum { value = 1 }; };
template<> struct is_same_or_void<void,void> { enum { value = 1 }; };
#ifdef EIGEN_DEBUG_ASSIGN
std::string demangle_traversal(int t)
{
@ -649,17 +708,12 @@ std::string demangle_flags(int f)
} // 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.
// 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((internal::functor_is_product_like<BINOP>::ret \
? int(internal::scalar_product_traits<LHS, RHS>::Defined) \
: int(internal::is_same_or_void<LHS, RHS>::value)), \
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

21
Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
View File

@ -327,13 +327,22 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
}
else
{
Scalar p = Scalar(0.5) * (m_matS.coeff(i, i) - m_matS.coeff(i+1, i+1));
Scalar z = sqrt(abs(p * p + m_matS.coeff(i+1, i) * m_matS.coeff(i, i+1)));
m_alphas.coeffRef(i) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, z);
m_alphas.coeffRef(i+1) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, -z);
// We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal 2x2 block T
// Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1 * S * U) * diag(T_11,T_00):
m_betas.coeffRef(i) = m_realQZ.matrixT().coeff(i,i);
m_betas.coeffRef(i+1) = m_realQZ.matrixT().coeff(i,i);
// T = [a 0]
// [0 b]
RealScalar a = m_realQZ.matrixT().coeff(i, i), b = m_realQZ.matrixT().coeff(i+1, i+1);
Matrix<RealScalar,2,2> S2 = m_matS.template block<2,2>(i,i) * Matrix<Scalar,2,1>(b,a).asDiagonal();
Scalar p = Scalar(0.5) * (S2.coeff(0,0) - S2.coeff(1,1));
Scalar z = sqrt(abs(p * p + S2.coeff(1,0) * S2.coeff(0,1)));
m_alphas.coeffRef(i) = ComplexScalar(S2.coeff(1,1) + p, z);
m_alphas.coeffRef(i+1) = ComplexScalar(S2.coeff(1,1) + p, -z);
m_betas.coeffRef(i) =
m_betas.coeffRef(i+1) = a*b;
i += 2;
}
}

32
Eigen/src/Eigenvalues/RealQZ.h
View File

@ -552,7 +552,6 @@ namespace Eigen {
m_T.coeffRef(l,l-1) = Scalar(0.0);
}
template<typename MatrixType>
RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ)
{
@ -616,6 +615,37 @@ namespace Eigen {
}
// check if we converged before reaching iterations limit
m_info = (local_iter<m_maxIters) ? Success : NoConvergence;
// For each non triangular 2x2 diagonal block of S,
// reduce the respective 2x2 diagonal block of T to positive diagonal form using 2x2 SVD.
// This step is not mandatory for QZ, but it does help further extraction of eigenvalues/eigenvectors,
// and is in par with Lapack/Matlab QZ.
if(m_info==Success)
{
for(Index i=0; i<dim-1; ++i)
{
if(m_S.coeff(i+1, i) != Scalar(0))
{
JacobiRotation<Scalar> j_left, j_right;
internal::real_2x2_jacobi_svd(m_T, i, i+1, &j_left, &j_right);
// Apply resulting Jacobi rotations
m_S.applyOnTheLeft(i,i+1,j_left);
m_S.applyOnTheRight(i,i+1,j_right);
m_T.applyOnTheLeft(i,i+1,j_left);
m_T.applyOnTheRight(i,i+1,j_right);
m_T(i+1,i) = m_T(i,i+1) = Scalar(0);
if(m_computeQZ) {
m_Q.applyOnTheRight(i,i+1,j_left.transpose());
m_Z.applyOnTheLeft(i,i+1,j_right.transpose());
}
i++;
}
}
}
return *this;
} // end compute

4
Eigen/src/Eigenvalues/Tridiagonalization.h
View File

@ -367,10 +367,10 @@ void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
* (conj(h) * matA.col(i).tail(remainingSize)));
hCoeffs.tail(n-i-1) += (conj(h)*Scalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
hCoeffs.tail(n-i-1) += (conj(h)*RealScalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>()
.rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), -1);
.rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), Scalar(-1));
matA.col(i).coeffRef(i+1) = beta;
hCoeffs.coeffRef(i) = h;

12
Eigen/src/Geometry/AlignedBox.h
View File

@ -36,8 +36,9 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
typedef NumTraits<Scalar> ScalarTraits;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename ScalarTraits::Real RealScalar;
typedef typename ScalarTraits::NonInteger NonInteger;
typedef typename ScalarTraits::NonInteger NonInteger;
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> VectorTypeSum;
/** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
enum CornerType
@ -111,16 +112,15 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
inline VectorType& (max)() { return m_max; }
/** \returns the center of the box */
inline const CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>,
const CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> >
inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(VectorTypeSum, RealScalar, quotient)
center() const
{ return (m_min+m_max)/2; }
{ return (m_min+m_max)/RealScalar(2); }
/** \returns the lengths of the sides of the bounding box.
* Note that this function does not get the same
* result for integral or floating scalar types: see
*/
inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar>, const VectorType, const VectorType> sizes() const
inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> sizes() const
{ return m_max - m_min; }
/** \returns the volume of the bounding box */
@ -131,7 +131,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
* if the length of the diagonal is needed: diagonal().norm()
* will provide it.
*/
inline CwiseBinaryOp< internal::scalar_difference_op<Scalar>, const VectorType, const VectorType> diagonal() const
inline CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> diagonal() const
{ return sizes(); }
/** \returns the vertex of the bounding box at the corner defined by

12
Eigen/src/Geometry/Homogeneous.h
View File

@ -329,10 +329,10 @@ protected:
// dense = homogeneous
template< typename DstXprType, typename ArgType, typename Scalar>
struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense, Scalar>
{
typedef Homogeneous<ArgType,Vertical> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
{
dst.template topRows<ArgType::RowsAtCompileTime>(src.nestedExpression().rows()) = src.nestedExpression();
dst.row(dst.rows()-1).setOnes();
@ -341,10 +341,10 @@ struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op
// dense = homogeneous
template< typename DstXprType, typename ArgType, typename Scalar>
struct Assignment<DstXprType, Homogeneous<ArgType,Horizontal>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Homogeneous<ArgType,Horizontal>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense, Scalar>
{
typedef Homogeneous<ArgType,Horizontal> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
{
dst.template leftCols<ArgType::ColsAtCompileTime>(src.nestedExpression().cols()) = src.nestedExpression();
dst.col(dst.cols()-1).setOnes();
@ -373,7 +373,7 @@ struct homogeneous_right_product_refactoring_helper
typedef typename Rhs::ConstRowXpr ConstantColumn;
typedef Replicate<const ConstantColumn,Rows,1> ConstantBlock;
typedef Product<Lhs,LinearBlock,LazyProduct> LinearProduct;
typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
};
template<typename Lhs, typename Rhs, int ProductTag>
@ -414,7 +414,7 @@ struct homogeneous_left_product_refactoring_helper
typedef typename Lhs::ConstColXpr ConstantColumn;
typedef Replicate<const ConstantColumn,1,Cols> ConstantBlock;
typedef Product<LinearBlock,Rhs,LazyProduct> LinearProduct;
typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
};
template<typename Lhs, typename Rhs, int ProductTag>

11
Eigen/src/Geometry/Scaling.h
View File

@ -107,12 +107,15 @@ public:
/** \addtogroup Geometry_Module */
//@{
/** Concatenates a linear transformation matrix and a uniform scaling */
/** Concatenates a linear transformation matrix and a uniform scaling
* \relates UniformScaling
*/
// NOTE this operator is defiend in MatrixBase and not as a friend function
// of UniformScaling to fix an internal crash of Intel's ICC
template<typename Derived> typename MatrixBase<Derived>::ScalarMultipleReturnType
MatrixBase<Derived>::operator*(const UniformScaling<Scalar>& s) const
{ return derived() * s.factor(); }
template<typename Derived,typename Scalar>
EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,Scalar,product)
operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s)
{ return matrix.derived() * s.factor(); }
/** Constructs a uniform scaling from scale factor \a s */
static inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }

2
Eigen/src/Geometry/Transform.h
View File

@ -1367,7 +1367,7 @@ struct transform_right_product_impl< TransformType, MatrixType, 2, 1> // rhs is
EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
Matrix<typename ResultType::Scalar, Dim+1, 1> rhs;
rhs << other,1;
rhs.template head<Dim>() = other; rhs[Dim] = typename ResultType::Scalar(1);
Matrix<typename ResultType::Scalar, WorkingRows, 1> res(T.matrix() * rhs);
return res.template head<Dim>();
}

2
Eigen/src/Householder/HouseholderSequence.h
View File

@ -108,7 +108,7 @@ struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight>
template<typename OtherScalarType, typename MatrixType> struct matrix_type_times_scalar_type
{
typedef typename scalar_product_traits<OtherScalarType, typename MatrixType::Scalar>::ReturnType
typedef typename ScalarBinaryOpTraits<OtherScalarType, typename MatrixType::Scalar>::ReturnType
ResultScalar;
typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
0, MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime> Type;

4
Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
View File

@ -91,10 +91,10 @@ protected:
// Specialization for "dst = dec.solveWithGuess(rhs)"
// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
template<typename DstXprType, typename DecType, typename RhsType, typename GuessType, typename Scalar>
struct Assignment<DstXprType, SolveWithGuess<DecType,RhsType,GuessType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, SolveWithGuess<DecType,RhsType,GuessType>, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef SolveWithGuess<DecType,RhsType,GuessType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
// FIXME shall we resize dst here?
dst = src.guess();

6
Eigen/src/LU/FullPivLU.h
View File

@ -839,12 +839,12 @@ namespace internal {
/***** Implementation of inverse() *****************************************************/
template<typename DstXprType, typename MatrixType, typename Scalar>
struct Assignment<DstXprType, Inverse<FullPivLU<MatrixType> >, internal::assign_op<Scalar>, Dense2Dense, Scalar>
template<typename DstXprType, typename MatrixType>
struct Assignment<DstXprType, Inverse<FullPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivLU<MatrixType>::Scalar>, Dense2Dense>
{
typedef FullPivLU<MatrixType> LuType;
typedef Inverse<LuType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename MatrixType::Scalar> &)
{
dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
}

6
Eigen/src/LU/InverseImpl.h
View File

@ -286,11 +286,11 @@ struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4>
namespace internal {
// Specialization for "dense = dense_xpr.inverse()"
template<typename DstXprType, typename XprType, typename Scalar>
struct Assignment<DstXprType, Inverse<XprType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
template<typename DstXprType, typename XprType>
struct Assignment<DstXprType, Inverse<XprType>, internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar>, Dense2Dense>
{
typedef Inverse<XprType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar> &)
{
// FIXME shall we resize dst here?
const int Size = EIGEN_PLAIN_ENUM_MIN(XprType::ColsAtCompileTime,DstXprType::ColsAtCompileTime);

6
Eigen/src/LU/PartialPivLU.h
View File

@ -525,12 +525,12 @@ MatrixType PartialPivLU<MatrixType>::reconstructedMatrix() const
namespace internal {
/***** Implementation of inverse() *****************************************************/
template<typename DstXprType, typename MatrixType, typename Scalar>
struct Assignment<DstXprType, Inverse<PartialPivLU<MatrixType> >, internal::assign_op<Scalar>, Dense2Dense, Scalar>
template<typename DstXprType, typename MatrixType>
struct Assignment<DstXprType, Inverse<PartialPivLU<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename PartialPivLU<MatrixType>::Scalar>, Dense2Dense>
{
typedef PartialPivLU<MatrixType> LuType;
typedef Inverse<LuType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename LuType::Scalar> &)
{
dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
}

35
Eigen/src/PardisoSupport/PardisoSupport.h
View File

@ -183,7 +183,7 @@ class PardisoImpl : public SparseSolverBase<Derived>
{
if(m_isInitialized) // Factorization ran at least once
{
internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, m_size,0, 0, 0, m_perm.data(), 0,
internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, internal::convert_index<StorageIndex>(m_size),0, 0, 0, m_perm.data(), 0,
m_iparm.data(), m_msglvl, NULL, NULL);
m_isInitialized = false;
}
@ -194,11 +194,11 @@ class PardisoImpl : public SparseSolverBase<Derived>
m_type = type;
bool symmetric = std::abs(m_type) < 10;
m_iparm[0] = 1; // No solver default
m_iparm[1] = 3; // use Metis for the ordering
m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS
m_iparm[1] = 2; // use Metis for the ordering
m_iparm[2] = 0; // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??)
m_iparm[3] = 0; // No iterative-direct algorithm
m_iparm[4] = 0; // No user fill-in reducing permutation
m_iparm[5] = 0; // Write solution into x
m_iparm[5] = 0; // Write solution into x, b is left unchanged
m_iparm[6] = 0; // Not in use
m_iparm[7] = 2; // Max numbers of iterative refinement steps
m_iparm[8] = 0; // Not in use
@ -219,7 +219,8 @@ class PardisoImpl : public SparseSolverBase<Derived>
m_iparm[26] = 0; // No matrix checker
m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
m_iparm[34] = 1; // C indexing
m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes
m_iparm[36] = 0; // CSR
m_iparm[59] = 0; // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
memset(m_pt, 0, sizeof(m_pt));
}
@ -246,7 +247,7 @@ class PardisoImpl : public SparseSolverBase<Derived>
mutable SparseMatrixType m_matrix;
mutable ComputationInfo m_info;
bool m_analysisIsOk, m_factorizationIsOk;
Index m_type, m_msglvl;
StorageIndex m_type, m_msglvl;
mutable void *m_pt[64];
mutable ParameterType m_iparm;
mutable IntColVectorType m_perm;
@ -265,10 +266,9 @@ Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
derived().getMatrix(a);
Index error;
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, m_size,
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size),
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
manageErrorCode(error);
m_analysisIsOk = true;
m_factorizationIsOk = true;
@ -287,7 +287,7 @@ Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
derived().getMatrix(a);
Index error;
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, m_size,
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size),
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
@ -306,8 +306,8 @@ Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
derived().getMatrix(a);
Index error;
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, m_size,
Index error;
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size),
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
@ -354,9 +354,9 @@ void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase
}
Index error;
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, m_size,
error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size),
m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
m_perm.data(), internal::convert_index<StorageIndex>(nrhs), m_iparm.data(), m_msglvl,
rhs_ptr, x.derived().data());
manageErrorCode(error);
@ -371,6 +371,9 @@ void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase
* using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
* The vectors or matrices X and B can be either dense or sparse.
*
* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
* \code solver.pardisoParameterArray()[59] = 1; \endcode
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
*
* \implsparsesolverconcept
@ -421,6 +424,9 @@ class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
* The vectors or matrices X and B can be either dense or sparse.
*
* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
* \code solver.pardisoParameterArray()[59] = 1; \endcode
*
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
* Upper|Lower can be used to tell both triangular parts can be used as input.
@ -480,6 +486,9 @@ class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
* For complex matrices, A can also be symmetric only, see the \a Options template parameter.
* The vectors or matrices X and B can be either dense or sparse.
*
* By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
* \code solver.pardisoParameterArray()[59] = 1; \endcode
*
* \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
* Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.

4
Eigen/src/QR/ColPivHouseholderQR.h
View File

@ -598,11 +598,11 @@ void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &
namespace internal {
template<typename DstXprType, typename MatrixType, typename Scalar>
struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType> >, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType> >, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef ColPivHouseholderQR<MatrixType> QrType;
typedef Inverse<QrType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
}

4
Eigen/src/QR/CompleteOrthogonalDecomposition.h
View File

@ -510,11 +510,11 @@ void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl(
namespace internal {
template<typename DstXprType, typename MatrixType, typename Scalar>
struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType> >, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType> >, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef CompleteOrthogonalDecomposition<MatrixType> CodType;
typedef Inverse<CodType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.rows()));
}

4
Eigen/src/QR/FullPivHouseholderQR.h
View File

@ -560,11 +560,11 @@ void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType
namespace internal {
template<typename DstXprType, typename MatrixType, typename Scalar>
struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType> >, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType> >, internal::assign_op<Scalar,Scalar>, Dense2Dense, Scalar>
{
typedef FullPivHouseholderQR<MatrixType> QrType;
typedef Inverse<QrType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
}

32
Eigen/src/SVD/JacobiSVD.h
View File

@ -419,38 +419,6 @@ struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
}
};
template<typename MatrixType, typename RealScalar, typename Index>
void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
JacobiRotation<RealScalar> *j_left,
JacobiRotation<RealScalar> *j_right)
{
using std::sqrt;
using std::abs;
Matrix<RealScalar,2,2> m;
m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
JacobiRotation<RealScalar> rot1;
RealScalar t = m.coeff(0,0) + m.coeff(1,1);
RealScalar d = m.coeff(1,0) - m.coeff(0,1);
if(d == RealScalar(0))
{
rot1.s() = RealScalar(0);
rot1.c() = RealScalar(1);
}
else
{
// If d!=0, then t/d cannot overflow because the magnitude of the
// entries forming d are not too small compared to the ones forming t.
RealScalar u = t / d;
RealScalar tmp = sqrt(RealScalar(1) + numext::abs2(u));
rot1.s() = RealScalar(1) / tmp;
rot1.c() = u / tmp;
}
m.applyOnTheLeft(0,1,rot1);
j_right->makeJacobi(m,0,1);
*j_left = rot1 * j_right->transpose();
}
template<typename _MatrixType, int QRPreconditioner>
struct traits<JacobiSVD<_MatrixType,QRPreconditioner> >
{

20
Eigen/src/SparseCore/SparseAssign.h
View File

@ -34,8 +34,8 @@ template<typename OtherDerived>
inline Derived& SparseMatrixBase<Derived>::operator=(const SparseMatrixBase<OtherDerived>& other)
{
// by default sparse evaluation do not alias, so we can safely bypass the generic call_assignment routine
internal::Assignment<Derived,OtherDerived,internal::assign_op<Scalar> >
::run(derived(), other.derived(), internal::assign_op<Scalar>());
internal::Assignment<Derived,OtherDerived,internal::assign_op<Scalar,typename OtherDerived::Scalar> >
::run(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -127,7 +127,7 @@ void assign_sparse_to_sparse(DstXprType &dst, const SrcXprType &src)
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Sparse, Scalar>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
assign_sparse_to_sparse(dst.derived(), src.derived());
}
@ -141,7 +141,7 @@ struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Scalar>
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
if(internal::is_same<Functor,internal::assign_op<Scalar> >::value)
if(internal::is_same<Functor,internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> >::value)
dst.setZero();
internal::evaluator<SrcXprType> srcEval(src);
@ -156,10 +156,10 @@ struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Scalar>
// Specialization for "dst = dec.solve(rhs)"
// NOTE we need to specialize it for Sparse2Sparse to avoid ambiguous specialization error
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar>, Sparse2Sparse, Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Sparse2Sparse, Scalar>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
src.dec()._solve_impl(src.rhs(), dst);
}
@ -176,7 +176,7 @@ struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse, Scalar>
typedef Array<StorageIndex,Dynamic,1> ArrayXI;
typedef Array<Scalar,Dynamic,1> ArrayXS;
template<int Options>
static void run(SparseMatrix<Scalar,Options,StorageIndex> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(SparseMatrix<Scalar,Options,StorageIndex> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index size = src.diagonal().size();
dst.makeCompressed();
@ -187,15 +187,15 @@ struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse, Scalar>
}
template<typename DstDerived>
static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // end namespace internal

47
Eigen/src/SparseCore/SparseCwiseBinaryOp.h
View File

@ -28,6 +28,9 @@ namespace Eigen {
// generic sparse
// 4 - dense op dense product dense
// generic dense
//
// TODO to ease compiler job, we could specialize product/quotient with a scalar
// and fallback to cwise-unary evaluator using bind1st_op and bind2nd_op.
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
@ -323,12 +326,12 @@ protected:
};
// "sparse .* sparse"
template<typename T, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs>, IteratorBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs> >
template<typename T1, typename T2, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
{
protected:
typedef scalar_product_op<T> BinaryOp;
typedef scalar_product_op<T1,T2> BinaryOp;
typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
typedef typename evaluator<Rhs>::InnerIterator RhsIterator;
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
@ -407,12 +410,12 @@ protected:
};
// "dense .* sparse"
template<typename T, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs>, IndexBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs> >
template<typename T1, typename T2, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IndexBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
{
protected:
typedef scalar_product_op<T> BinaryOp;
typedef scalar_product_op<T1,T2> BinaryOp;
typedef evaluator<Lhs> LhsEvaluator;
typedef typename evaluator<Rhs>::InnerIterator RhsIterator;
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
@ -480,12 +483,12 @@ protected:
};
// "sparse .* dense"
template<typename T, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs>, IteratorBased, IndexBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs> >
template<typename T1, typename T2, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
: evaluator_base<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
{
protected:
typedef scalar_product_op<T> BinaryOp;
typedef scalar_product_op<T1,T2> BinaryOp;
typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
typedef evaluator<Rhs> RhsEvaluator;
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
@ -579,7 +582,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& SparseMatrixBase<Derived>::operator+=(const DiagonalBase<OtherDerived>& other)
{
call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -587,7 +590,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& SparseMatrixBase<Derived>::operator-=(const DiagonalBase<OtherDerived>& other)
{
call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -600,31 +603,31 @@ SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) c
}
template<typename DenseDerived, typename SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
operator+(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
}
template<typename SparseDerived, typename DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
operator+(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
return CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
}
template<typename DenseDerived, typename SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
operator-(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
}
template<typename SparseDerived, typename DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
operator-(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
return CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
}
} // end namespace Eigen

4
Eigen/src/SparseCore/SparseDenseProduct.h
View File

@ -74,7 +74,7 @@ struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, t
// FIXME: what is the purpose of the following specialization? Is it for the BlockedSparse format?
// -> let's disable it for now as it is conflicting with generic scalar*matrix and matrix*scalar operators
// template<typename T1, typename T2/*, int _Options, typename _StrideType*/>
// struct scalar_product_traits<T1, Ref<T2/*, _Options, _StrideType*/> >
// struct ScalarBinaryOpTraits<T1, Ref<T2/*, _Options, _StrideType*/> >
// {
// enum {
// Defined = 1
@ -97,7 +97,7 @@ struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, A
for(Index j=0; j<lhs.outerSize(); ++j)
{
// typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c);
typename internal::scalar_product_traits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c));
typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c));
for(LhsInnerIterator it(lhsEval,j); it ;++it)
res.coeffRef(it.index(),c) += it.value() * rhs_j;
}

4
Eigen/src/SparseCore/SparseMatrix.h
View File

@ -440,7 +440,7 @@ class SparseMatrix
template<typename InputIterators,typename DupFunctor>
void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
void sumupDuplicates() { collapseDuplicates(internal::scalar_sum_op<Scalar>()); }
void sumupDuplicates() { collapseDuplicates(internal::scalar_sum_op<Scalar,Scalar>()); }
template<typename DupFunctor>
void collapseDuplicates(DupFunctor dup_func = DupFunctor());
@ -979,7 +979,7 @@ template<typename Scalar, int _Options, typename _Index>
template<typename InputIterators>
void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
{
internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_Index> >(begin, end, *this, internal::scalar_sum_op<Scalar>());
internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,_Options,_Index> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
}
/** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:

2
Eigen/src/SparseCore/SparseMatrixBase.h
View File

@ -256,7 +256,7 @@ template<typename Derived> class SparseMatrixBase
Derived& operator/=(const Scalar& other);
template<typename OtherDerived> struct CwiseProductDenseReturnType {
typedef CwiseBinaryOp<internal::scalar_product_op<typename internal::scalar_product_traits<
typedef CwiseBinaryOp<internal::scalar_product_op<typename ScalarBinaryOpTraits<
typename internal::traits<Derived>::Scalar,
typename internal::traits<OtherDerived>::Scalar
>::ReturnType>,

12
Eigen/src/SparseCore/SparseProduct.h
View File

@ -99,10 +99,10 @@ struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, SparseShape, Produc
// dense = sparse-product (can be sparse*sparse, sparse*perm, etc.)
template< typename DstXprType, typename Lhs, typename Rhs>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assign_op<typename DstXprType::Scalar>, Sparse2Dense>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
{
typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
{
generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
}
@ -110,10 +110,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assig
// dense += sparse-product (can be sparse*sparse, sparse*perm, etc.)
template< typename DstXprType, typename Lhs, typename Rhs>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_assign_op<typename DstXprType::Scalar>, Sparse2Dense>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
{
typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
{
generic_product_impl<Lhs, Rhs>::addTo(dst,src.lhs(),src.rhs());
}
@ -121,10 +121,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_a
// dense -= sparse-product (can be sparse*sparse, sparse*perm, etc.)
template< typename DstXprType, typename Lhs, typename Rhs>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::sub_assign_op<typename DstXprType::Scalar>, Sparse2Dense>
struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::sub_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
{
typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
{
generic_product_impl<Lhs, Rhs>::subTo(dst,src.lhs(),src.rhs());
}

10
Eigen/src/SparseCore/SparseSelfAdjointView.h
View File

@ -223,13 +223,13 @@ struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse, Sca
{
typedef typename DstXprType::StorageIndex StorageIndex;
template<typename DestScalar,int StorageOrder>
static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), dst);
}
template<typename DestScalar>
static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DynamicSparseMatrix<DestScalar,ColMajor,StorageIndex>& dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
// TODO directly evaluate into dst;
SparseMatrix<DestScalar,ColMajor,StorageIndex> tmp(dst.rows(),dst.cols());
@ -586,12 +586,12 @@ class SparseSymmetricPermutationProduct
namespace internal {
template<typename DstXprType, typename MatrixType, int Mode, typename Scalar>
struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>, internal::assign_op<Scalar>, Sparse2Sparse>
struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>, internal::assign_op<Scalar,typename MatrixType::Scalar>, Sparse2Sparse>
{
typedef SparseSymmetricPermutationProduct<MatrixType,Mode> SrcXprType;
typedef typename DstXprType::StorageIndex DstIndex;
template<int Options>
static void run(SparseMatrix<Scalar,Options,DstIndex> &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(SparseMatrix<Scalar,Options,DstIndex> &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
{
// internal::permute_symm_to_fullsymm<Mode>(m_matrix,_dest,m_perm.indices().data());
SparseMatrix<Scalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
@ -600,7 +600,7 @@ struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>
}
template<typename DestType,unsigned int DestMode>
static void run(SparseSelfAdjointView<DestType,DestMode>& dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(SparseSelfAdjointView<DestType,DestMode>& dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
{
internal::permute_symm_to_symm<Mode,DestMode>(src.matrix(),dst.matrix(),src.perm().indices().data());
}

8
Eigen/src/SparseQR/SparseQR.h
View File

@ -705,12 +705,12 @@ struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> >
};
template< typename DstXprType, typename SparseQRType>
struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar>, Sparse2Sparse>
struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Sparse>
{
typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
typedef typename DstXprType::Scalar Scalar;
typedef typename DstXprType::StorageIndex StorageIndex;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
{
typename DstXprType::PlainObject idMat(src.m_qr.rows(), src.m_qr.rows());
idMat.setIdentity();
@ -721,12 +721,12 @@ struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal:
};
template< typename DstXprType, typename SparseQRType>
struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar>, Sparse2Dense>
struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Dense>
{
typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
typedef typename DstXprType::Scalar Scalar;
typedef typename DstXprType::StorageIndex StorageIndex;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
{
dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows());
}

54
Eigen/src/misc/RealSvd2x2.h Normal file
View File

@ -0,0 +1,54 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2013-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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_REALSVD2X2_H
#define EIGEN_REALSVD2X2_H
namespace Eigen {
namespace internal {
template<typename MatrixType, typename RealScalar, typename Index>
void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
JacobiRotation<RealScalar> *j_left,
JacobiRotation<RealScalar> *j_right)
{
using std::sqrt;
using std::abs;
Matrix<RealScalar,2,2> m;
m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
JacobiRotation<RealScalar> rot1;
RealScalar t = m.coeff(0,0) + m.coeff(1,1);
RealScalar d = m.coeff(1,0) - m.coeff(0,1);
if(d == RealScalar(0))
{
rot1.s() = RealScalar(0);
rot1.c() = RealScalar(1);
}
else
{
// If d!=0, then t/d cannot overflow because the magnitude of the
// entries forming d are not too small compared to the ones forming t.
RealScalar u = t / d;
RealScalar tmp = sqrt(RealScalar(1) + numext::abs2(u));
rot1.s() = RealScalar(1) / tmp;
rot1.c() = u / tmp;
}
m.applyOnTheLeft(0,1,rot1);
j_right->makeJacobi(m,0,1);
*j_left = rot1 * j_right->transpose();
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_REALSVD2X2_H

129
Eigen/src/plugins/ArrayCwiseBinaryOps.h
View File

@ -1,13 +1,14 @@
/** \returns an expression of the coefficient wise product of \c *this and \a other
*
* \sa MatrixBase::cwiseProduct
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
{
return EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)(derived(), other.derived());
return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
}
/** \returns an expression of the coefficient wise quotient of \c *this and \a other
@ -16,10 +17,10 @@ operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>
operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
{
return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
return CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
}
/** \returns an expression of the coefficient-wise min of \c *this and \a other
@ -29,14 +30,14 @@ operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
*
* \sa max()
*/
EIGEN_MAKE_CWISE_BINARY_OP(min,internal::scalar_min_op)
EIGEN_MAKE_CWISE_BINARY_OP(min,min)
/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
*
* \sa max()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived,
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
#ifdef EIGEN_PARSED_BY_DOXYGEN
min
@ -55,14 +56,14 @@ min
*
* \sa min()
*/
EIGEN_MAKE_CWISE_BINARY_OP(max,internal::scalar_max_op)
EIGEN_MAKE_CWISE_BINARY_OP(max,max)
/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
*
* \sa min()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived,
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
#ifdef EIGEN_PARSED_BY_DOXYGEN
max
@ -81,27 +82,38 @@ max
* Example: \include Cwise_array_power_array.cpp
* Output: \verbinclude Cwise_array_power_array.out
*/
template<typename ExponentDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const CwiseBinaryOp<internal::scalar_binary_pow_op<Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const ArrayBase<ExponentDerived>& exponents) const
{
return CwiseBinaryOp<internal::scalar_binary_pow_op<Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
this->derived(),
exponents.derived()
);
}
EIGEN_MAKE_CWISE_BINARY_OP(pow,pow)
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(pow,pow)
#else
/** \returns an expression of the coefficients of \c *this rasied to the constant power \a exponent
*
* \tparam T is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression.
*
* This function computes the coefficient-wise power. The function MatrixBase::pow() in the
* unsupported module MatrixFunctions computes the matrix power.
*
* Example: \include Cwise_pow.cpp
* Output: \verbinclude Cwise_pow.out
*
* \sa ArrayBase::pow(ArrayBase), square(), cube(), exp(), log()
*/
template<typename T>
const CwiseBinaryOp<internal::scalar_pow_op<Scalar,T>,Derived,Constant<T> > pow(const T& exponent) const;
#endif
// TODO code generating macros could be moved to Macros.h and could include generation of documentation
#define EIGEN_MAKE_CWISE_COMP_OP(OP, COMPARATOR) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived> \
OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived>(derived(), other.derived()); \
return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived>(derived(), other.derived()); \
}\
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > Cmp ## COMPARATOR ## ReturnType; \
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_ ## COMPARATOR>, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived > RCmp ## COMPARATOR ## ReturnType; \
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > Cmp ## COMPARATOR ## ReturnType; \
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived > RCmp ## COMPARATOR ## ReturnType; \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Cmp ## COMPARATOR ## ReturnType \
OP(const Scalar& s) const { \
return this->OP(Derived::PlainObject::Constant(rows(), cols(), s)); \
@ -113,10 +125,10 @@ OP(const Scalar& s, const Derived& d) { \
#define EIGEN_MAKE_CWISE_COMP_R_OP(OP, R_OP, RCOMPARATOR) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived> \
OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived>(other.derived(), derived()); \
return CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived>(other.derived(), derived()); \
} \
EIGEN_DEVICE_FUNC \
inline const RCmp ## RCOMPARATOR ## ReturnType \
@ -199,48 +211,63 @@ EIGEN_MAKE_CWISE_COMP_OP(operator!=, NEQ)
#undef EIGEN_MAKE_CWISE_COMP_R_OP
// scalar addition
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP(operator+,sum)
#else
/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*
* Example: \include Cwise_plus.cpp
* Output: \verbinclude Cwise_plus.out
*
* \sa operator+=(), operator-()
*/
EIGEN_DEVICE_FUNC
inline const CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>
operator+(const Scalar& scalar) const
{
return CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>(derived(), internal::scalar_add_op<Scalar>(scalar));
}
EIGEN_DEVICE_FUNC
friend inline const CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>
operator+(const Scalar& scalar,const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& other)
{
return other + scalar;
}
template<typename T>
const CwiseBinaryOp<internal::scalar_sum_op<Scalar,T>,Derived,Constant<T> > operator+(const T& scalar) const;
/** \returns an expression of \a expr with each coeff incremented by the constant \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*/
template<typename T> friend
const CwiseBinaryOp<internal::scalar_sum_op<T,Scalar>,Constant<T>,Derived> operator+(const T& scalar, const StorageBaseType& expr);
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP(operator-,difference)
#else
/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*
* Example: \include Cwise_minus.cpp
* Output: \verbinclude Cwise_minus.out
*
* \sa operator+(), operator-=()
* \sa operator+=(), operator-()
*/
EIGEN_DEVICE_FUNC
inline const CwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived>
operator-(const Scalar& scalar) const
{
return CwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived>(derived(), internal::scalar_sub_op<Scalar>(scalar));;
}
template<typename T>
const CwiseBinaryOp<internal::scalar_difference_op<Scalar,T>,Derived,Constant<T> > operator-(const T& scalar) const;
/** \returns an expression of the constant matrix of value \a scalar decremented by the coefficients of \a expr
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*/
template<typename T> friend
const CwiseBinaryOp<internal::scalar_difference_op<T,Scalar>,Constant<T>,Derived> operator-(const T& scalar, const StorageBaseType& expr);
#endif
EIGEN_DEVICE_FUNC
friend inline const CwiseUnaryOp<internal::scalar_rsub_op<Scalar>, const Derived>
operator-(const Scalar& scalar,const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& other)
{
return CwiseUnaryOp<internal::scalar_rsub_op<Scalar>, const Derived>(other.derived(), internal::scalar_rsub_op<Scalar>(scalar));;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(operator/,quotient)
#else
/**
* \brief Component-wise division of the scalar \a s by array elements of \a a.
*
* \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
*/
template<typename T> friend
inline const CwiseBinaryOp<internal::scalar_quotient_op<T,Scalar>,Constant<T>,Derived>
operator/(const T& s,const StorageBaseType& a);
#endif
/** \returns an expression of the coefficient-wise && operator of *this and \a other
*

33
Eigen/src/plugins/ArrayCwiseUnaryOps.h
View File

@ -26,7 +26,6 @@ typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaRe
typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
typedef CwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived> PowReturnType;
typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
typedef CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> CubeReturnType;
typedef CwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> RoundReturnType;
@ -248,6 +247,7 @@ tan() const
*
* \sa tan(), asin(), acos()
*/
EIGEN_DEVICE_FUNC
inline const AtanReturnType
atan() const
{
@ -289,6 +289,7 @@ asin() const
*
* \sa tan(), sinh(), cosh()
*/
EIGEN_DEVICE_FUNC
inline const TanhReturnType
tanh() const
{
@ -302,6 +303,7 @@ tanh() const
*
* \sa sin(), tanh(), cosh()
*/
EIGEN_DEVICE_FUNC
inline const SinhReturnType
sinh() const
{
@ -315,6 +317,7 @@ sinh() const
*
* \sa tan(), sinh(), cosh()
*/
EIGEN_DEVICE_FUNC
inline const CoshReturnType
cosh() const
{
@ -332,6 +335,7 @@ cosh() const
*
* \sa digamma()
*/
EIGEN_DEVICE_FUNC
inline const LgammaReturnType
lgamma() const
{
@ -346,6 +350,7 @@ lgamma() const
*
* \sa Eigen::digamma(), Eigen::polygamma(), lgamma()
*/
EIGEN_DEVICE_FUNC
inline const DigammaReturnType
digamma() const
{
@ -364,6 +369,7 @@ digamma() const
*
* \sa erfc()
*/
EIGEN_DEVICE_FUNC
inline const ErfReturnType
erf() const
{
@ -382,30 +388,13 @@ erf() const
*
* \sa erf()
*/
EIGEN_DEVICE_FUNC
inline const ErfcReturnType
erfc() const
{
return ErfcReturnType(derived());
}
/** \returns an expression of the coefficient-wise power of *this to the given exponent.
*
* This function computes the coefficient-wise power. The function MatrixBase::pow() in the
* unsupported module MatrixFunctions computes the matrix power.
*
* Example: \include Cwise_pow.cpp
* Output: \verbinclude Cwise_pow.out
*
* \sa exp(), log()
*/
EIGEN_DEVICE_FUNC
inline const PowReturnType
pow(const Scalar& exponent) const
{
return PowReturnType(derived(), internal::scalar_pow_op<Scalar>(exponent));
}
/** \returns an expression of the coefficient-wise inverse of *this.
*
* Example: \include Cwise_inverse.cpp
@ -455,6 +444,7 @@ cube() const
*
* \sa ceil(), floor()
*/
EIGEN_DEVICE_FUNC
inline const RoundReturnType
round() const
{
@ -468,6 +458,7 @@ round() const
*
* \sa ceil(), round()
*/
EIGEN_DEVICE_FUNC
inline const FloorReturnType
floor() const
{
@ -481,6 +472,7 @@ floor() const
*
* \sa floor(), round()
*/
EIGEN_DEVICE_FUNC
inline const CeilReturnType
ceil() const
{
@ -494,6 +486,7 @@ ceil() const
*
* \sa isfinite(), isinf()
*/
EIGEN_DEVICE_FUNC
inline const IsNaNReturnType
isNaN() const
{
@ -507,6 +500,7 @@ isNaN() const
*
* \sa isnan(), isfinite()
*/
EIGEN_DEVICE_FUNC
inline const IsInfReturnType
isInf() const
{
@ -520,6 +514,7 @@ isInf() const
*
* \sa isnan(), isinf()
*/
EIGEN_DEVICE_FUNC
inline const IsFiniteReturnType
isFinite() const
{

36
Eigen/src/plugins/CommonCwiseBinaryOps.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2016 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
@ -16,7 +16,7 @@
*
* \sa class CwiseBinaryOp, operator-=()
*/
EIGEN_MAKE_CWISE_BINARY_OP(operator-,internal::scalar_difference_op)
EIGEN_MAKE_CWISE_BINARY_OP(operator-,difference)
/** \returns an expression of the sum of \c *this and \a other
*
@ -24,7 +24,7 @@ EIGEN_MAKE_CWISE_BINARY_OP(operator-,internal::scalar_difference_op)
*
* \sa class CwiseBinaryOp, operator+=()
*/
EIGEN_MAKE_CWISE_BINARY_OP(operator+,internal::scalar_sum_op)
EIGEN_MAKE_CWISE_BINARY_OP(operator+,sum)
/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
*
@ -45,3 +45,33 @@ binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other, const Cu
return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP(operator*,product)
#else
/** \returns an expression of \c *this scaled by the scalar factor \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*/
template<typename T>
const CwiseBinaryOp<internal::scalar_product_op<Scalar,T>,Derived,Constant<T> > operator*(const T& scalar) const;
/** \returns an expression of \a expr scaled by the scalar factor \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*/
template<typename T> friend
const CwiseBinaryOp<internal::scalar_product_op<T,Scalar>,Constant<T>,Derived> operator*(const T& scalar, const StorageBaseType& expr);
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(operator/,quotient)
#else
/** \returns an expression of \c *this divided by the scalar value \a scalar
*
* \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
*/
template<typename T>
const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,T>,Derived,Constant<T> > operator/(const T& scalar) const;
#endif

72
Eigen/src/plugins/CommonCwiseUnaryOps.h
View File

@ -12,12 +12,6 @@
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a scalar multiple of an expression */
typedef CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived> ScalarMultipleReturnType;
typedef CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,std::complex<Scalar> >, const Derived> ScalarComplexMultipleReturnType;
/** \internal Represents a quotient of an expression by a scalar*/
typedef CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived> ScalarQuotient1ReturnType;
/** \internal the return type of conjugate() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
@ -39,7 +33,6 @@ typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturn
typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType;
typedef CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> NegativeReturnType;
//typedef CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived>
#endif // not EIGEN_PARSED_BY_DOXYGEN
@ -50,71 +43,6 @@ inline const NegativeReturnType
operator-() const { return NegativeReturnType(derived()); }
/** \returns an expression of \c *this scaled by the scalar factor \a scalar */
EIGEN_DEVICE_FUNC
inline const ScalarMultipleReturnType
operator*(const Scalar& scalar) const
{
return ScalarMultipleReturnType(derived(), internal::scalar_multiple_op<Scalar>(scalar));
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
const ScalarMultipleReturnType operator*(const RealScalar& scalar) const;
#endif
/** \returns an expression of \c *this divided by the scalar value \a scalar */
EIGEN_DEVICE_FUNC
inline const ScalarQuotient1ReturnType
operator/(const Scalar& scalar) const
{
return ScalarQuotient1ReturnType(derived(), internal::scalar_quotient1_op<Scalar>(scalar));
}
/** Overloaded for efficiently multipling with compatible scalar types */
template <typename T>
EIGEN_DEVICE_FUNC inline
typename internal::enable_if<internal::scalar_product_traits<T,Scalar>::Defined,
const CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,T>, const Derived> >::type
operator*(const T& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,T>, const Derived>(
derived(), internal::scalar_multiple2_op<Scalar,T>(scalar) );
}
EIGEN_DEVICE_FUNC
inline friend const ScalarMultipleReturnType
operator*(const Scalar& scalar, const StorageBaseType& matrix)
{ return matrix*scalar; }
template <typename T>
EIGEN_DEVICE_FUNC inline friend
typename internal::enable_if<internal::scalar_product_traits<Scalar,T>::Defined,
const CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,T>, const Derived> >::type
operator*(const T& scalar, const StorageBaseType& matrix)
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,T>, const Derived>(
matrix.derived(), internal::scalar_multiple2_op<Scalar,T>(scalar) );
}
template <typename T>
EIGEN_DEVICE_FUNC inline
typename internal::enable_if<internal::scalar_product_traits<Scalar,T>::Defined,
const CwiseUnaryOp<internal::scalar_quotient2_op<Scalar,T>, const Derived> >::type
operator/(const T& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<internal::scalar_quotient2_op<Scalar,T>, const Derived>(
derived(), internal::scalar_quotient2_op<Scalar,T>(scalar) );
}
template<class NewType> struct CastXpr { typedef typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::scalar_cast_op<Scalar, NewType>, const Derived> >::type Type; };
/** \returns an expression of *this with the \a Scalar type casted to

20
Eigen/src/plugins/MatrixCwiseBinaryOps.h
View File

@ -19,10 +19,10 @@
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
{
return EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)(derived(), other.derived());
return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
}
/** \returns an expression of the coefficient-wise == operator of *this and \a other
@ -74,10 +74,10 @@ cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>
cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
{
return CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
}
/** \returns an expression of the coefficient-wise min of *this and scalar \a other
@ -85,7 +85,7 @@ cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
* \sa class CwiseBinaryOp, min()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const ConstantReturnType>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
cwiseMin(const Scalar &other) const
{
return cwiseMin(Derived::Constant(rows(), cols(), other));
@ -100,10 +100,10 @@ cwiseMin(const Scalar &other) const
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>
cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
{
return CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
}
/** \returns an expression of the coefficient-wise max of *this and scalar \a other
@ -111,7 +111,7 @@ cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
* \sa class CwiseBinaryOp, min()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const ConstantReturnType>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
cwiseMax(const Scalar &other) const
{
return cwiseMax(Derived::Constant(rows(), cols(), other));
@ -133,7 +133,7 @@ cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
}
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,internal::cmp_EQ>, const Derived, const ConstantReturnType> CwiseScalarEqualReturnType;
typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>, const Derived, const ConstantReturnType> CwiseScalarEqualReturnType;
/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
*
@ -148,5 +148,5 @@ EIGEN_DEVICE_FUNC
inline const CwiseScalarEqualReturnType
cwiseEqual(const Scalar& s) const
{
return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s), internal::scalar_cmp_op<Scalar,internal::cmp_EQ>());
return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s), internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>());
}

4
bench/perf_monitoring/gemm/changesets.txt
View File

@ -44,4 +44,8 @@ before-evaluators
7013:f875e75f07e5 # organize a little our default cache sizes, and use a saner default L1 outside of x86 (10% faster on Nexus 5)
7591:09a8e2186610 # 3.3-alpha1
7650:b0f3c8f43025 # help clang inlining
8744:74b789ada92a # Improved the matrix multiplication blocking in the case where mr is not a power of 2 (e.g on Haswell CPUs)
8789:efcb912e4356 # Made the index type a template parameter to evaluateProductBlockingSizes. Use numext::mini and numext::maxi instead of std::min/std::max to compute blocking sizes
8972:81d53c711775 # Don't optimize the processing of the last rows of a matrix matrix product in cases that violate the assumptions made by the optimized code path
8985:d935df21a082 # Remove the rotating kernel.

4
blas/PackedTriangularMatrixVector.h
View File

@ -18,7 +18,7 @@ struct packed_triangular_matrix_vector_product;
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs>
struct packed_triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = (Mode & Lower) ==Lower,
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
@ -47,7 +47,7 @@ struct packed_triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsS
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs>
struct packed_triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = (Mode & Lower) ==Lower,
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,

8
doc/CustomizingEigen.dox
View File

@ -56,13 +56,13 @@ void makeFloor(const MatrixBase<OtherDerived>& other) { derived() = derived().cw
template<typename OtherDerived>
void makeCeil(const MatrixBase<OtherDerived>& other) { derived() = derived().cwiseMax(other.derived()); }
const CwiseUnaryOp<internal::scalar_add_op<Scalar>, Derived>
const CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const ConstantReturnType>
operator+(const Scalar& scalar) const
{ return CwiseUnaryOp<internal::scalar_add_op<Scalar>, Derived>(derived(), internal::scalar_add_op<Scalar>(scalar)); }
{ return CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const ConstantReturnType>(derived(), Constant(rows(),cols(),scalar)); }
friend const CwiseUnaryOp<internal::scalar_add_op<Scalar>, Derived>
friend const CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ConstantReturnType, Derived>
operator+(const Scalar& scalar, const MatrixBase<Derived>& mat)
{ return CwiseUnaryOp<internal::scalar_add_op<Scalar>, Derived>(mat.derived(), internal::scalar_add_op<Scalar>(scalar)); }
{ return CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ConstantReturnType, Derived>(Constant(rows(),cols(),scalar), mat.derived()); }
\endcode
Then one can the following declaration in the config.h or whatever prerequisites header file of his project:

4
test/array.cpp
View File

@ -72,7 +72,7 @@ template<typename ArrayType> void array(const ArrayType& m)
VERIFY_IS_MUCH_SMALLER_THAN(abs(m1.rowwise().sum().sum() - m1.sum()), m1.abs().sum());
if (!internal::isMuchSmallerThan(abs(m1.sum() - (m1+m2).sum()), m1.abs().sum(), test_precision<Scalar>()))
VERIFY_IS_NOT_APPROX(((m1+m2).rowwise().sum()).sum(), m1.sum());
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>()));
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>()));
// vector-wise ops
m3 = m1;
@ -807,7 +807,7 @@ void test_array()
VERIFY((internal::is_same< internal::global_math_functions_filtering_base<int>::type, int >::value));
VERIFY((internal::is_same< internal::global_math_functions_filtering_base<float>::type, float >::value));
VERIFY((internal::is_same< internal::global_math_functions_filtering_base<Array2i>::type, ArrayBase<Array2i> >::value));
typedef CwiseUnaryOp<internal::scalar_multiple_op<double>, ArrayXd > Xpr;
typedef CwiseUnaryOp<internal::scalar_abs_op<double>, ArrayXd > Xpr;
VERIFY((internal::is_same< internal::global_math_functions_filtering_base<Xpr>::type,
ArrayBase<Xpr>
>::value));

2
test/array_for_matrix.cpp
View File

@ -45,7 +45,7 @@ template<typename MatrixType> void array_for_matrix(const MatrixType& m)
VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm());
VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>()));
VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>()));
// vector-wise ops
m3 = m1;

23
test/commainitializer.cpp
View File

@ -43,4 +43,27 @@ void test_commainitializer()
4, 5, 6,
vec[2].transpose();
VERIFY_IS_APPROX(m3, ref);
// Check with empty matrices (bug #1242)
{
int const M = 0;
int const N1 = 2;
int const N2 = 1;
{
Matrix<double, M, N1> A1;
Matrix<double, M, N2> A2;
Matrix<double, M, N1 + N2> B;
B << A1, A2;
}
{
Matrix<double, N1, M> A1;
Matrix<double, N2, M> A2;
Matrix<double, N1 + N2, M> B;
B << A1,
A2;
}
}
}

32
test/eigensolver_generalized_real.cpp
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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
@ -10,6 +10,7 @@
#include "main.h"
#include <limits>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
template<typename MatrixType> void generalized_eigensolver_real(const MatrixType& m)
{
@ -21,6 +22,7 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType
Index cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
typedef std::complex<Scalar> ComplexScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
MatrixType a = MatrixType::Random(rows,cols);
@ -31,14 +33,28 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType
MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1;
// lets compare to GeneralizedSelfAdjointEigenSolver
GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB);
GeneralizedEigenSolver<MatrixType> eig(spdA, spdB);
{
GeneralizedSelfAdjointEigenSolver<MatrixType> symmEig(spdA, spdB);
GeneralizedEigenSolver<MatrixType> eig(spdA, spdB);
VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0);
VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0);
VectorType realEigenvalues = eig.eigenvalues().real();
std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size());
VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues());
VectorType realEigenvalues = eig.eigenvalues().real();
std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size());
VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues());
}
// non symmetric case:
{
GeneralizedEigenSolver<MatrixType> eig(a,b);
for(Index k=0; k<cols; ++k)
{
Matrix<ComplexScalar,Dynamic,Dynamic> tmp = (eig.betas()(k)*a).template cast<ComplexScalar>() - eig.alphas()(k)*b;
if(tmp.norm()>(std::numeric_limits<Scalar>::min)())
tmp /= tmp.norm();
VERIFY_IS_MUCH_SMALLER_THAN( std::abs(tmp.determinant()), Scalar(1) );
}
}
// regression test for bug 1098
{
@ -57,7 +73,7 @@ void test_eigensolver_generalized_real()
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) );
// some trivial but implementation-wise tricky cases
// some trivial but implementation-wise special cases
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(1,1)) );
CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(2,2)) );
CALL_SUBTEST_3( generalized_eigensolver_real(Matrix<double,1,1>()) );

14
test/evaluators.cpp
View File

@ -21,7 +21,7 @@ namespace Eigen {
EIGEN_STRONG_INLINE
DstXprType& copy_using_evaluator(const EigenBase<DstXprType> &dst, const SrcXprType &src)
{
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.const_cast_derived();
}
@ -29,7 +29,7 @@ namespace Eigen {
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst, const SrcXprType &src)
{
call_assignment(dst, src.derived(), internal::assign_op<typename DstXprType::Scalar>());
call_assignment(dst, src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.expression();
}
@ -45,7 +45,7 @@ namespace Eigen {
dst.const_cast_derived().resizeLike(src.derived());
#endif
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
call_assignment(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
return dst.const_cast_derived();
}
@ -53,28 +53,28 @@ namespace Eigen {
void add_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::add_assign_op<Scalar>());
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::add_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::sub_assign_op<Scalar>());
call_assignment(const_cast<DstXprType&>(dst), src.derived(), internal::sub_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void multiply_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(dst.const_cast_derived(), src.derived(), internal::mul_assign_op<Scalar>());
call_assignment(dst.const_cast_derived(), src.derived(), internal::mul_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void divide_assign_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
typedef typename DstXprType::Scalar Scalar;
call_assignment(dst.const_cast_derived(), src.derived(), internal::div_assign_op<Scalar>());
call_assignment(dst.const_cast_derived(), src.derived(), internal::div_assign_op<Scalar,typename SrcXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType>

2
test/geo_alignedbox.cpp
View File

@ -48,6 +48,8 @@ template<typename BoxType> void alignedbox(const BoxType& _box)
b0.extend(p0);
b0.extend(p1);
VERIFY(b0.contains(p0*s1+(Scalar(1)-s1)*p1));
VERIFY(b0.contains(b0.center()));
VERIFY(b0.center()==(p0+p1)/Scalar(2));
(b2 = b0).extend(b1);
VERIFY(b2.contains(b0));

2
test/geo_homogeneous.cpp
View File

@ -58,6 +58,8 @@ template<typename Scalar,int Size> void homogeneous(void)
T2MatrixType t2 = T2MatrixType::Random();
VERIFY_IS_APPROX(t2 * (v0.homogeneous().eval()), t2 * v0.homogeneous());
VERIFY_IS_APPROX(t2 * (m0.colwise().homogeneous().eval()), t2 * m0.colwise().homogeneous());
VERIFY_IS_APPROX(t2 * (v0.homogeneous().asDiagonal()), t2 * hv0.asDiagonal());
VERIFY_IS_APPROX((v0.homogeneous().asDiagonal()) * t2, hv0.asDiagonal() * t2);
VERIFY_IS_APPROX((v0.transpose().rowwise().homogeneous().eval()) * t2,
v0.transpose().rowwise().homogeneous() * t2);

18
test/linearstructure.cpp
View File

@ -9,7 +9,7 @@
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
static bool g_called;
#define EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN { g_called = true; }
#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); }
#include "main.h"
@ -93,6 +93,22 @@ template<typename MatrixType> void real_complex(DenseIndex rows = MatrixType::Ro
g_called = false;
VERIFY_IS_APPROX(m1/s, m1/Scalar(s));
VERIFY(g_called && "matrix<complex> / real not properly optimized");
g_called = false;
VERIFY_IS_APPROX(s+m1.array(), Scalar(s)+m1.array());
VERIFY(g_called && "real + matrix<complex> not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1.array()+s, m1.array()+Scalar(s));
VERIFY(g_called && "matrix<complex> + real not properly optimized");
g_called = false;
VERIFY_IS_APPROX(s-m1.array(), Scalar(s)-m1.array());
VERIFY(g_called && "real - matrix<complex> not properly optimized");
g_called = false;
VERIFY_IS_APPROX(m1.array()-s, m1.array()-Scalar(s));
VERIFY(g_called && "matrix<complex> - real not properly optimized");
}
void test_linearstructure()

96
test/mixingtypes.cpp
View File

@ -23,10 +23,18 @@
#endif
static bool g_called;
#define EIGEN_SCALAR_BINARY_OP_PLUGIN { g_called |= (!internal::is_same<LhsScalar,RhsScalar>::value); }
#include "main.h"
using namespace std;
#define VERIFY_MIX_SCALAR(XPR,REF) \
g_called = false; \
VERIFY_IS_APPROX(XPR,REF); \
VERIFY( g_called && #XPR" not properly optimized");
template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
{
typedef std::complex<float> CF;
@ -42,6 +50,7 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
Mat_f mf = Mat_f::Random(size,size);
Mat_d md = mf.template cast<double>();
//Mat_d rd = md;
Mat_cf mcf = Mat_cf::Random(size,size);
Mat_cd mcd = mcf.template cast<complex<double> >();
Mat_cd rcd = mcd;
@ -56,23 +65,50 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
mf+mf;
VERIFY_RAISES_ASSERT(mf+md);
#if !EIGEN_HAS_STD_RESULT_OF
// this one does not even compile with C++11
VERIFY_RAISES_ASSERT(mf+mcf);
#endif
// VERIFY_RAISES_ASSERT(mf+md); // does not even compile
#ifdef EIGEN_DONT_VECTORIZE
VERIFY_RAISES_ASSERT(vf=vd);
VERIFY_RAISES_ASSERT(vf+=vd);
VERIFY_RAISES_ASSERT(mcd=md);
#endif
// check scalar products
VERIFY_IS_APPROX(vcf * sf , vcf * complex<float>(sf));
VERIFY_IS_APPROX(sd * vcd, complex<double>(sd) * vcd);
VERIFY_IS_APPROX(vf * scf , vf.template cast<complex<float> >() * scf);
VERIFY_IS_APPROX(scd * vd, scd * vd.template cast<complex<double> >());
VERIFY_MIX_SCALAR(vcf * sf , vcf * complex<float>(sf));
VERIFY_MIX_SCALAR(sd * vcd , complex<double>(sd) * vcd);
VERIFY_MIX_SCALAR(vf * scf , vf.template cast<complex<float> >() * scf);
VERIFY_MIX_SCALAR(scd * vd , scd * vd.template cast<complex<double> >());
VERIFY_MIX_SCALAR(vcf * 2 , vcf * complex<float>(2));
VERIFY_MIX_SCALAR(vcf * 2.1 , vcf * complex<float>(2.1));
VERIFY_MIX_SCALAR(2 * vcf, vcf * complex<float>(2));
VERIFY_MIX_SCALAR(2.1 * vcf , vcf * complex<float>(2.1));
// check scalar quotients
VERIFY_MIX_SCALAR(vcf / sf , vcf / complex<float>(sf));
VERIFY_MIX_SCALAR(vf / scf , vf.template cast<complex<float> >() / scf);
VERIFY_MIX_SCALAR(vf.array() / scf, vf.template cast<complex<float> >().array() / scf);
VERIFY_MIX_SCALAR(scd / vd.array() , scd / vd.template cast<complex<double> >().array());
// check scalar increment
VERIFY_MIX_SCALAR(vcf.array() + sf , vcf.array() + complex<float>(sf));
VERIFY_MIX_SCALAR(sd + vcd.array(), complex<double>(sd) + vcd.array());
VERIFY_MIX_SCALAR(vf.array() + scf, vf.template cast<complex<float> >().array() + scf);
VERIFY_MIX_SCALAR(scd + vd.array() , scd + vd.template cast<complex<double> >().array());
// check scalar subtractions
VERIFY_MIX_SCALAR(vcf.array() - sf , vcf.array() - complex<float>(sf));
VERIFY_MIX_SCALAR(sd - vcd.array(), complex<double>(sd) - vcd.array());
VERIFY_MIX_SCALAR(vf.array() - scf, vf.template cast<complex<float> >().array() - scf);
VERIFY_MIX_SCALAR(scd - vd.array() , scd - vd.template cast<complex<double> >().array());
// check scalar powers
VERIFY_MIX_SCALAR( pow(vcf.array(), sf), pow(vcf.array(), complex<float>(sf)) );
VERIFY_MIX_SCALAR( vcf.array().pow(sf) , pow(vcf.array(), complex<float>(sf)) );
VERIFY_MIX_SCALAR( pow(sd, vcd.array()), pow(complex<double>(sd), vcd.array()) );
VERIFY_MIX_SCALAR( pow(vf.array(), scf), pow(vf.template cast<complex<float> >().array(), scf) );
VERIFY_MIX_SCALAR( vf.array().pow(scf) , pow(vf.template cast<complex<float> >().array(), scf) );
VERIFY_MIX_SCALAR( pow(scd, vd.array()), pow(scd, vd.template cast<complex<double> >().array()) );
// check dot product
vf.dot(vf);
@ -186,16 +222,50 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
Mat_cd((scd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>()));
VERIFY_IS_APPROX( md.array() * mcd.array(), md.template cast<CD>().eval().array() * mcd.array() );
VERIFY_IS_APPROX( mcd.array() * md.array(), mcd.array() * md.template cast<CD>().eval().array() );
// VERIFY_IS_APPROX( md.array() / mcd.array(), md.template cast<CD>().eval().array() / mcd.array() );
VERIFY_IS_APPROX( md.array() * mcd.array(), md.template cast<CD>().eval().array() * mcd.array() );
VERIFY_IS_APPROX( mcd.array() * md.array(), mcd.array() * md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array() + mcd.array(), md.template cast<CD>().eval().array() + mcd.array() );
VERIFY_IS_APPROX( mcd.array() + md.array(), mcd.array() + md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array() - mcd.array(), md.template cast<CD>().eval().array() - mcd.array() );
VERIFY_IS_APPROX( mcd.array() - md.array(), mcd.array() - md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array() / mcd.array(), md.template cast<CD>().eval().array() / mcd.array() );
VERIFY_IS_APPROX( mcd.array() / md.array(), mcd.array() / md.template cast<CD>().eval().array() );
VERIFY_IS_APPROX( md.array().pow(mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) );
VERIFY_IS_APPROX( mcd.array().pow(md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) );
VERIFY_IS_APPROX( pow(md.array(),mcd.array()), md.template cast<CD>().eval().array().pow(mcd.array()) );
VERIFY_IS_APPROX( pow(mcd.array(),md.array()), mcd.array().pow(md.template cast<CD>().eval().array()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd = md, md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd += md, mcd + md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd -= md, mcd - md.template cast<CD>().eval() );
rcd = mcd;
VERIFY_IS_APPROX( rcd.array() *= md.array(), mcd.array() * md.template cast<CD>().eval().array() );
rcd = mcd;
VERIFY_IS_APPROX( rcd.array() /= md.array(), mcd.array() / md.template cast<CD>().eval().array() );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += md + mcd*md, mcd + (md.template cast<CD>().eval()) + mcd*(md.template cast<CD>().eval()));
VERIFY_IS_APPROX( rcd.noalias() = md*md, ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += md*md, mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() -= md*md, mcd - ((md*md).eval().template cast<CD>()) );
VERIFY_IS_APPROX( rcd.noalias() = mcd + md*md, mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() += mcd + md*md, mcd + mcd + ((md*md).eval().template cast<CD>()) );
rcd = mcd;
VERIFY_IS_APPROX( rcd.noalias() -= mcd + md*md, - ((md*md).eval().template cast<CD>()) );
}
void test_mixingtypes()

4
test/nesting_ops.cpp
View File

@ -75,8 +75,8 @@ template <typename MatrixType> void run_nesting_ops_2(const MatrixType& _m)
}
else
{
VERIFY( verify_eval_type<1>(2*m1, 2*m1) );
VERIFY( verify_eval_type<2>(2*m1, m1) );
VERIFY( verify_eval_type<2>(2*m1, 2*m1) );
VERIFY( verify_eval_type<3>(2*m1, m1) );
}
VERIFY( verify_eval_type<2>(m1+m1, m1+m1) );
VERIFY( verify_eval_type<3>(m1+m1, m1) );

9
test/real_qz.cpp
View File

@ -49,11 +49,20 @@ template<typename MatrixType> void real_qz(const MatrixType& m)
for (Index i=0; i<A.cols(); i++)
for (Index j=0; j<i; j++) {
if (abs(qz.matrixT()(i,j))!=Scalar(0.0))
{
std::cerr << "Error: T(" << i << "," << j << ") = " << qz.matrixT()(i,j) << std::endl;
all_zeros = false;
}
if (j<i-1 && abs(qz.matrixS()(i,j))!=Scalar(0.0))
{
std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << std::endl;
all_zeros = false;
}
if (j==i-1 && j>0 && abs(qz.matrixS()(i,j))!=Scalar(0.0) && abs(qz.matrixS()(i-1,j-1))!=Scalar(0.0))
{
std::cerr << "Error: S(" << i << "," << j << ") = " << qz.matrixS()(i,j) << " && S(" << i-1 << "," << j-1 << ") = " << qz.matrixS()(i-1,j-1) << std::endl;
all_zeros = false;
}
}
VERIFY_IS_EQUAL(all_zeros, true);
VERIFY_IS_APPROX(qz.matrixQ()*qz.matrixS()*qz.matrixZ(), A);

7
test/vectorization_logic.cpp
View File

@ -29,7 +29,7 @@ using internal::demangle_unrolling;
template<typename Dst, typename Src>
bool test_assign(const Dst&, const Src&, int traversal, int unrolling)
{
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar> > traits;
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits;
bool res = traits::Traversal==traversal;
if(unrolling==InnerUnrolling+CompleteUnrolling)
res = res && (int(traits::Unrolling)==InnerUnrolling || int(traits::Unrolling)==CompleteUnrolling);
@ -53,7 +53,7 @@ bool test_assign(const Dst&, const Src&, int traversal, int unrolling)
template<typename Dst, typename Src>
bool test_assign(int traversal, int unrolling)
{
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar> > traits;
typedef internal::copy_using_evaluator_traits<internal::evaluator<Dst>,internal::evaluator<Src>, internal::assign_op<typename Dst::Scalar,typename Src::Scalar> > traits;
bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
if(!res)
{
@ -73,7 +73,8 @@ bool test_assign(int traversal, int unrolling)
template<typename Xpr>
bool test_redux(const Xpr&, int traversal, int unrolling)
{
typedef internal::redux_traits<internal::scalar_sum_op<typename Xpr::Scalar>,internal::redux_evaluator<Xpr> > traits;
typedef typename Xpr::Scalar Scalar;
typedef internal::redux_traits<internal::scalar_sum_op<Scalar,Scalar>,internal::redux_evaluator<Xpr> > traits;
bool res = traits::Traversal==traversal && traits::Unrolling==unrolling;
if(!res)

86
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
View File

@ -204,64 +204,62 @@ class TensorBase<Derived, ReadOnlyAccessors>
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >, const Derived>
pow(Scalar exponent) const {
return unaryExpr(internal::scalar_pow_op<Scalar>(exponent));
return unaryExpr(internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >(exponent));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >, const Derived>
operator+ (Scalar rhs) const {
return unaryExpr(internal::scalar_add_op<Scalar>(rhs));
return unaryExpr(internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE friend
const TensorCwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>
const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_sum_op<Scalar> >, const Derived>
operator+ (Scalar lhs, const Derived& rhs) {
return rhs + lhs;
return rhs.unaryExpr(internal::bind1st_op<internal::scalar_sum_op<Scalar> >(lhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >, const Derived>
operator- (Scalar rhs) const {
EIGEN_STATIC_ASSERT((NumTraits<Scalar>::IsSigned || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
return unaryExpr(internal::scalar_sub_op<Scalar>(rhs));
return unaryExpr(internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE friend
const TensorCwiseUnaryOp<internal::scalar_add_op<Scalar>,
const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> >
const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_difference_op<Scalar> >, const Derived>
operator- (Scalar lhs, const Derived& rhs) {
return -rhs + lhs;
return rhs.unaryExpr(internal::bind1st_op<internal::scalar_difference_op<Scalar> >(lhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >, const Derived>
operator* (Scalar rhs) const {
return unaryExpr(internal::scalar_multiple_op<Scalar>(rhs));
return unaryExpr(internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE friend
const TensorCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived>
const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_product_op<Scalar> >, const Derived>
operator* (Scalar lhs, const Derived& rhs) {
return rhs * lhs;
return rhs.unaryExpr(internal::bind1st_op<internal::scalar_product_op<Scalar> >(lhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived>
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >, const Derived>
operator/ (Scalar rhs) const {
return unaryExpr(internal::scalar_quotient1_op<Scalar>(rhs));
return unaryExpr(internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE friend
const TensorCwiseUnaryOp<internal::scalar_multiple_op<Scalar>,
const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> >
const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_quotient_op<Scalar> >, const Derived>
operator/ (Scalar lhs, const Derived& rhs) {
return rhs.inverse() * lhs;
return rhs.unaryExpr(internal::bind1st_op<internal::scalar_quotient_op<Scalar> >(lhs));
}
EIGEN_DEVICE_FUNC
@ -371,66 +369,66 @@ class TensorBase<Derived, ReadOnlyAccessors>
// Comparisons and tests.
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_LT>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>
operator<(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_LT>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_LE>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>
operator<=(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_LE>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_GT>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>
operator>(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_GT>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_GE>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>
operator>=(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_GE>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>
operator==(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_EQ>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>
const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>
operator!=(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, internal::cmp_NEQ>());
return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>());
}
// comparisons and tests for Scalars
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator<(Scalar threshold) const {
return operator<(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator<=(Scalar threshold) const {
return operator<=(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator>(Scalar threshold) const {
return operator>(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator>=(Scalar threshold) const {
return operator>=(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator==(Scalar threshold) const {
return operator==(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
operator!=(Scalar threshold) const {
return operator!=(constant(threshold));
}
@ -486,22 +484,22 @@ class TensorBase<Derived, ReadOnlyAccessors>
typedef TensorScanOp<internal::SumReducer<CoeffReturnType>, const Derived> TensorScanSumOp;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorScanSumOp
cumsum(const Index& axis) const {
return TensorScanSumOp(derived(), axis);
cumsum(const Index& axis, bool exclusive = false) const {
return TensorScanSumOp(derived(), axis, exclusive);
}
typedef TensorScanOp<internal::ProdReducer<CoeffReturnType>, const Derived> TensorScanProdOp;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorScanProdOp
cumprod(const Index& axis) const {
return TensorScanProdOp(derived(), axis);
cumprod(const Index& axis, bool exclusive = false) const {
return TensorScanProdOp(derived(), axis, exclusive);
}
template <typename Reducer>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorScanOp<Reducer, const Derived>
scan(const Index& axis, const Reducer& reducer) const {
return TensorScanOp<Reducer, const Derived>(derived(), axis, reducer);
scan(const Index& axis, const Reducer& reducer, bool exclusive = false) const {
return TensorScanOp<Reducer, const Derived>(derived(), axis, exclusive, reducer);
}
// Reductions.

12
unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
View File

@ -202,7 +202,7 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
// across k dimension.
const TensorOpCost cost =
contractionCost(m, n, bm, bn, bk, shard_by_col, false);
Index num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
static_cast<double>(n) * m, cost, this->m_device.numThreads());
// TODO(dvyukov): this is a stop-gap to prevent regressions while the cost
@ -301,7 +301,7 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
class Context {
public:
Context(const Device& device, int num_threads, LhsMapper& lhs,
RhsMapper& rhs, Scalar* buffer, Index m, Index n, Index k, Index bm,
RhsMapper& rhs, Scalar* buffer, Index tm, Index tn, Index tk, Index bm,
Index bn, Index bk, Index nm, Index nn, Index nk, Index gm,
Index gn, Index nm0, Index nn0, bool shard_by_col,
bool parallel_pack)
@ -309,13 +309,13 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
lhs_(lhs),
rhs_(rhs),
buffer_(buffer),
output_(buffer, m),
output_(buffer, tm),
num_threads_(num_threads),
shard_by_col_(shard_by_col),
parallel_pack_(parallel_pack),
m_(m),
n_(n),
k_(k),
m_(tm),
n_(tn),
k_(tk),
bm_(bm),
bn_(bn),
bk_(bk),

16
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
View File

@ -106,7 +106,7 @@ static EIGEN_STRONG_INLINE void wait_until_ready(SyncType* n) {
// Build a thread pool device on top the an existing pool of threads.
struct ThreadPoolDevice {
// The ownership of the thread pool remains with the caller.
ThreadPoolDevice(ThreadPoolInterface* pool, size_t num_cores) : pool_(pool), num_threads_(num_cores) { }
ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores) : pool_(pool), num_threads_(num_cores) { }
EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
return internal::aligned_malloc(num_bytes);
@ -130,7 +130,7 @@ struct ThreadPoolDevice {
::memset(buffer, c, n);
}
EIGEN_STRONG_INLINE size_t numThreads() const {
EIGEN_STRONG_INLINE int numThreads() const {
return num_threads_;
}
@ -172,6 +172,12 @@ struct ThreadPoolDevice {
pool_->Schedule(func);
}
// Returns a logical thread index between 0 and pool_->NumThreads() - 1 if
// called from one of the threads in pool_. Returns -1 otherwise.
EIGEN_STRONG_INLINE int currentThreadId() const {
return pool_->CurrentThreadId();
}
// parallelFor executes f with [0, n) arguments in parallel and waits for
// completion. F accepts a half-open interval [first, last).
// Block size is choosen based on the iteration cost and resulting parallel
@ -182,7 +188,7 @@ struct ThreadPoolDevice {
std::function<void(Index, Index)> f) const {
typedef TensorCostModel<ThreadPoolDevice> CostModel;
if (n <= 1 || numThreads() == 1 ||
CostModel::numThreads(n, cost, numThreads()) == 1) {
CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
f(0, n);
return;
}
@ -242,7 +248,7 @@ struct ThreadPoolDevice {
// Recursively divide size into halves until we reach block_size.
// Division code rounds mid to block_size, so we are guaranteed to get
// block_count leaves that do actual computations.
Barrier barrier(block_count);
Barrier barrier(static_cast<unsigned int>(block_count));
std::function<void(Index, Index)> handleRange;
handleRange = [=, &handleRange, &barrier, &f](Index first, Index last) {
if (last - first <= block_size) {
@ -268,7 +274,7 @@ struct ThreadPoolDevice {
private:
ThreadPoolInterface* pool_;
size_t num_threads_;
int num_threads_;
};

14
unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
View File

@ -426,6 +426,20 @@ struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type,
m_arg3Impl(op.arg3Expression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
typename internal::traits<Arg2Type>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
typename internal::traits<Arg3Type>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
}

16
unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
View File

@ -227,22 +227,6 @@ struct traits<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprT
TernaryOp(typename Arg1XprType::Scalar,
typename Arg2XprType::Scalar,
typename Arg3XprType::Scalar)>::type Scalar;
EIGEN_STATIC_ASSERT(
(internal::is_same<typename traits<Arg1XprType>::StorageKind,
typename traits<Arg2XprType>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename traits<Arg1XprType>::StorageKind,
typename traits<Arg3XprType>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename traits<Arg1XprType>::Index,
typename traits<Arg2XprType>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
EIGEN_STATIC_ASSERT(
(internal::is_same<typename traits<Arg1XprType>::Index,
typename traits<Arg3XprType>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
typedef traits<Arg1XprType> XprTraits;
typedef typename traits<Arg1XprType>::StorageKind StorageKind;
typedef typename traits<Arg1XprType>::Index Index;

86
unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
View File

@ -84,6 +84,14 @@ struct functor_traits<scalar_sigmoid_op<T> > {
};
template<typename Reducer, typename Device>
struct reducer_traits {
enum {
Cost = 1,
PacketAccess = false
};
};
// Standard reduction functors
template <typename T> struct SumReducer
{
@ -119,6 +127,15 @@ template <typename T> struct SumReducer
}
};
template <typename T, typename Device>
struct reducer_traits<SumReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasAdd
};
};
template <typename T> struct MeanReducer
{
static const bool PacketAccess = packet_traits<T>::HasAdd && !NumTraits<T>::IsInteger;
@ -162,6 +179,15 @@ template <typename T> struct MeanReducer
DenseIndex packetCount_;
};
template <typename T, typename Device>
struct reducer_traits<MeanReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasAdd
};
};
template <typename T> struct MaxReducer
{
static const bool PacketAccess = packet_traits<T>::HasMax;
@ -195,6 +221,15 @@ template <typename T> struct MaxReducer
}
};
template <typename T, typename Device>
struct reducer_traits<MaxReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasMax
};
};
template <typename T> struct MinReducer
{
static const bool PacketAccess = packet_traits<T>::HasMin;
@ -228,6 +263,14 @@ template <typename T> struct MinReducer
}
};
template <typename T, typename Device>
struct reducer_traits<MinReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = PacketType<T, Device>::HasMin
};
};
template <typename T> struct ProdReducer
{
@ -263,6 +306,14 @@ template <typename T> struct ProdReducer
}
};
template <typename T, typename Device>
struct reducer_traits<ProdReducer<T>, Device> {
enum {
Cost = NumTraits<T>::MulCost,
PacketAccess = PacketType<T, Device>::HasMul
};
};
struct AndReducer
{
@ -280,6 +331,15 @@ struct AndReducer
}
};
template <typename Device>
struct reducer_traits<AndReducer, Device> {
enum {
Cost = 1,
PacketAccess = false
};
};
struct OrReducer {
static const bool PacketAccess = false;
static const bool IsStateful = false;
@ -295,6 +355,15 @@ struct OrReducer {
}
};
template <typename Device>
struct reducer_traits<OrReducer, Device> {
enum {
Cost = 1,
PacketAccess = false
};
};
// Argmin/Argmax reducers
template <typename T> struct ArgMaxTupleReducer
{
@ -312,6 +381,15 @@ template <typename T> struct ArgMaxTupleReducer
}
};
template <typename T, typename Device>
struct reducer_traits<ArgMaxTupleReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = false
};
};
template <typename T> struct ArgMinTupleReducer
{
static const bool PacketAccess = false;
@ -328,6 +406,14 @@ template <typename T> struct ArgMinTupleReducer
}
};
template <typename T, typename Device>
struct reducer_traits<ArgMinTupleReducer<T>, Device> {
enum {
Cost = NumTraits<T>::AddCost,
PacketAccess = false
};
};
// Random number generation
namespace {

66
unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
View File

@ -17,34 +17,62 @@ template<>
struct significant_decimals_impl<std::string>
: significant_decimals_default_impl<std::string, true>
{};
}
// Print the tensor as a 2d matrix
template <typename Tensor, int Rank>
struct TensorPrinter {
static void run (std::ostream& os, const Tensor& tensor) {
typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
typedef typename Tensor::Index Index;
const Index total_size = internal::array_prod(tensor.dimensions());
if (total_size > 0) {
const Index first_dim = Eigen::internal::array_get<0>(tensor.dimensions());
static const int layout = Tensor::Layout;
Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim);
os << matrix;
}
}
};
// Print the tensor as a vector
template <typename Tensor>
struct TensorPrinter<Tensor, 1> {
static void run (std::ostream& os, const Tensor& tensor) {
typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
typedef typename Tensor::Index Index;
const Index total_size = internal::array_prod(tensor.dimensions());
if (total_size > 0) {
Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size);
os << array;
}
}
};
// Print the tensor as a scalar
template <typename Tensor>
struct TensorPrinter<Tensor, 0> {
static void run (std::ostream& os, const Tensor& tensor) {
os << tensor.coeff(0);
}
};
}
template <typename T>
std::ostream& operator << (std::ostream& os, const TensorBase<T, ReadOnlyAccessors>& expr) {
typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator;
typedef typename Evaluator::Dimensions Dimensions;
// Evaluate the expression if needed
TensorForcedEvalOp<const T> eval = expr.eval();
TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> tensor(eval, DefaultDevice());
Evaluator tensor(eval, DefaultDevice());
tensor.evalSubExprsIfNeeded(NULL);
typedef typename internal::remove_const<typename T::Scalar>::type Scalar;
typedef typename T::Index Index;
typedef typename TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice>::Dimensions Dimensions;
const Index total_size = internal::array_prod(tensor.dimensions());
// Print the tensor as a 1d vector or a 2d matrix.
// Print the result
static const int rank = internal::array_size<Dimensions>::value;
if (rank == 0) {
os << tensor.coeff(0);
} else if (rank == 1) {
Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size);
os << array;
} else {
const Index first_dim = Eigen::internal::array_get<0>(tensor.dimensions());
static const int layout = TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice>::Layout;
Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim);
os << matrix;
}
internal::TensorPrinter<Evaluator, rank>::run(os, tensor);
// Cleanup.
tensor.cleanup();

37
unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
View File

@ -47,22 +47,39 @@ template <> struct max_n_1<0> {
// Default packet types
template <typename Scalar, typename Device>
struct PacketType {
struct PacketType : internal::packet_traits<Scalar> {
typedef typename internal::packet_traits<Scalar>::type type;
enum { size = internal::unpacket_traits<type>::size };
};
// For CUDA packet types when using a GpuDevice
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) && defined(EIGEN_HAS_CUDA_FP16)
template <>
struct PacketType<float, GpuDevice> {
typedef float4 type;
static const int size = 4;
};
template <>
struct PacketType<double, GpuDevice> {
typedef double2 type;
struct PacketType<half, GpuDevice> {
typedef half2 type;
static const int size = 2;
enum {
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasNegate = 1,
HasAbs = 1,
HasArg = 0,
HasAbs2 = 0,
HasMin = 1,
HasMax = 1,
HasConj = 0,
HasSetLinear = 0,
HasBlend = 0,
HasDiv = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasExp = 1,
HasLog = 1,
HasLog1p = 0,
HasLog10 = 0,
HasPow = 1,
};
};
#endif

2
unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
View File

@ -148,7 +148,7 @@ struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
EIGEN_DEVICE_FUNC Scalar* data() const { return const_cast<Scalar*>(m_impl.data()); }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
protected:
TensorEvaluator<ArgType, Device> m_impl;

28
unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
View File

@ -130,15 +130,17 @@ __global__ void FullReductionKernel(Reducer reducer, const Self input, Index num
if (block == 0) {
// We're the first block to run, initialize the output value
atomicExch(output, reducer.initialize());
unsigned int old = atomicExch(semaphore, 2u);
assert(old == 1u);
__threadfence();
atomicExch(semaphore, 2u);
}
else {
// Wait for the first block to initialize the output value.
// Use atomicCAS here to ensure that the reads aren't cached
unsigned int val = atomicCAS(semaphore, 2u, 2u);
while (val < 2u) {
unsigned int val;
do {
val = atomicCAS(semaphore, 2u, 2u);
}
while (val < 2u);
}
}
}
@ -166,12 +168,8 @@ __global__ void FullReductionKernel(Reducer reducer, const Self input, Index num
}
if (gridDim.x > 1 && threadIdx.x == 0) {
unsigned int ticket = atomicInc(semaphore, UINT_MAX);
assert(ticket >= 2u);
if (ticket == gridDim.x + 1) {
// We're the last block, reset the semaphore
*semaphore = 0;
}
// Let the last block reset the semaphore
atomicInc(semaphore, gridDim.x + 1);
}
}
@ -330,10 +328,10 @@ struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
// Unfortunately nvidia doesn't support well exotic types such as complex,
// so reduce the scope of the optimized version of the code to the simple case
// of floats and half floats.
#ifdef EIGEN_HAS_CUDA_FP16
#ifdef EIGEN_HAS_CUDA_FP16
static const bool HasOptimizedImplementation = !Op::IsStateful &&
(internal::is_same<typename Self::CoeffReturnType, float>::value ||
(internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && Op::PacketAccess));
(internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
#else
static const bool HasOptimizedImplementation = !Op::IsStateful &&
internal::is_same<typename Self::CoeffReturnType, float>::value;
@ -348,7 +346,7 @@ struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
return;
}
FullReductionLauncher<Self, Op, OutputType, Op::PacketAccess>::run(self, reducer, device, output, num_coeffs);
FullReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs);
}
};
@ -610,7 +608,7 @@ struct InnerReducer<Self, Op, GpuDevice> {
#ifdef EIGEN_HAS_CUDA_FP16
static const bool HasOptimizedImplementation = !Op::IsStateful &&
(internal::is_same<typename Self::CoeffReturnType, float>::value ||
(internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && Op::PacketAccess));
(internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
#else
static const bool HasOptimizedImplementation = !Op::IsStateful &&
internal::is_same<typename Self::CoeffReturnType, float>::value;
@ -629,7 +627,7 @@ struct InnerReducer<Self, Op, GpuDevice> {
return true;
}
return InnerReductionLauncher<Self, Op, OutputType, Op::PacketAccess>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals);
return InnerReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals);
}
};

34
unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
View File

@ -57,8 +57,8 @@ public:
typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(
const XprType& expr, const Index& axis, const Op& op = Op())
: m_expr(expr), m_axis(axis), m_accumulator(op) {}
const XprType& expr, const Index& axis, bool exclusive = false, const Op& op = Op())
: m_expr(expr), m_axis(axis), m_accumulator(op), m_exclusive(exclusive) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Index axis() const { return m_axis; }
@ -66,11 +66,14 @@ public:
const XprType& expression() const { return m_expr; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Op accumulator() const { return m_accumulator; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
bool exclusive() const { return m_exclusive; }
protected:
typename XprType::Nested m_expr;
const Index m_axis;
const Op m_accumulator;
const bool m_exclusive;
};
// Eval as rvalue
@ -81,7 +84,7 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
@ -99,6 +102,7 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
: m_impl(op.expression(), device),
m_device(device),
m_axis(op.axis()),
m_exclusive(op.exclusive()),
m_accumulator(op.accumulator()),
m_dimensions(m_impl.dimensions()),
m_size(m_dimensions[m_axis]),
@ -106,7 +110,7 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
m_output(NULL) {
// Accumulating a scalar isn't supported.
EIGEN_STATIC_ASSERT(NumDims > 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(m_axis >= 0 && m_axis < NumDims);
// Compute stride of scan axis
@ -122,7 +126,7 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
return m_dimensions;
return m_dimensions;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
@ -136,7 +140,7 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
return true;
}
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
@ -152,6 +156,10 @@ struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
return m_output[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
if (m_output != NULL) {
m_device.deallocate(m_output);
@ -164,6 +172,7 @@ protected:
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
const Index m_axis;
const bool m_exclusive;
Op m_accumulator;
const Dimensions& m_dimensions;
const Index& m_size;
@ -172,7 +181,7 @@ protected:
// TODO(ibab) Parallelize this single-threaded implementation if desired
EIGEN_DEVICE_FUNC void accumulateTo(Scalar* data) {
// We fix the index along the scan axis to 0 and perform an
// We fix the index along the scan axis to 0 and perform a
// scan per remaining entry. The iteration is split into two nested
// loops to avoid an integer division by keeping track of each idx1 and idx2.
for (Index idx1 = 0; idx1 < dimensions().TotalSize() / m_size; idx1 += m_stride) {
@ -180,12 +189,17 @@ protected:
// Calculate the starting offset for the scan
Index offset = idx1 * m_size + idx2;
// Compute the prefix sum along the axis, starting at the calculated offset
// Compute the scan along the axis, starting at the calculated offset
CoeffReturnType accum = m_accumulator.initialize();
for (Index idx3 = 0; idx3 < m_size; idx3++) {
Index curr = offset + idx3 * m_stride;
m_accumulator.reduce(m_impl.coeff(curr), &accum);
data[curr] = m_accumulator.finalize(accum);
if (m_exclusive) {
data[curr] = m_accumulator.finalize(accum);
m_accumulator.reduce(m_impl.coeff(curr), &accum);
} else {
m_accumulator.reduce(m_impl.coeff(curr), &accum);
data[curr] = m_accumulator.finalize(accum);
}
}
}
}

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