diff options
Diffstat (limited to 'Eigen/src/Core/functors')
| -rw-r--r-- | Eigen/src/Core/functors/AssignmentFunctors.h | 177 | ||||
| -rw-r--r-- | Eigen/src/Core/functors/BinaryFunctors.h | 541 | ||||
| -rw-r--r-- | Eigen/src/Core/functors/NullaryFunctors.h | 189 | ||||
| -rw-r--r-- | Eigen/src/Core/functors/StlFunctors.h | 166 | ||||
| -rw-r--r-- | Eigen/src/Core/functors/TernaryFunctors.h | 25 | ||||
| -rw-r--r-- | Eigen/src/Core/functors/UnaryFunctors.h | 1131 |
6 files changed, 2229 insertions, 0 deletions
diff --git a/Eigen/src/Core/functors/AssignmentFunctors.h b/Eigen/src/Core/functors/AssignmentFunctors.h new file mode 100644 index 0000000..bf64ef4 --- /dev/null +++ b/Eigen/src/Core/functors/AssignmentFunctors.h @@ -0,0 +1,177 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 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_ASSIGNMENT_FUNCTORS_H +#define EIGEN_ASSIGNMENT_FUNCTORS_H + +namespace Eigen { + +namespace internal { + +/** \internal + * \brief Template functor for scalar/packet assignment + * + */ +template<typename DstScalar,typename SrcScalar> struct assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(assign_op) + 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(DstScalar* a, const Packet& b) const + { internal::pstoret<DstScalar,Packet,Alignment>(a,b); } +}; + +// 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<DstScalar>::ReadCost, + PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable + }; +}; + +/** \internal + * \brief Template functor for scalar/packet assignment with addition + * + */ +template<typename DstScalar,typename SrcScalar> struct add_assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(add_assign_op) + 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(DstScalar* a, const Packet& b) const + { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); } +}; +template<typename DstScalar,typename SrcScalar> +struct functor_traits<add_assign_op<DstScalar,SrcScalar> > { + enum { + Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost, + PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd + }; +}; + +/** \internal + * \brief Template functor for scalar/packet assignment with subtraction + * + */ +template<typename DstScalar,typename SrcScalar> struct sub_assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op) + 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(DstScalar* a, const Packet& b) const + { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); } +}; +template<typename DstScalar,typename SrcScalar> +struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > { + enum { + Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost, + PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub + }; +}; + +/** \internal + * \brief Template functor for scalar/packet assignment with multiplication + * + */ +template<typename DstScalar, typename SrcScalar=DstScalar> +struct mul_assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(mul_assign_op) + 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(DstScalar* a, const Packet& b) const + { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); } +}; +template<typename DstScalar, typename SrcScalar> +struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > { + enum { + Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost, + PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul + }; +}; + +/** \internal + * \brief Template functor for scalar/packet assignment with diviving + * + */ +template<typename DstScalar, typename SrcScalar=DstScalar> struct div_assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(div_assign_op) + 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(DstScalar* a, const Packet& b) const + { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); } +}; +template<typename DstScalar, typename SrcScalar> +struct functor_traits<div_assign_op<DstScalar,SrcScalar> > { + enum { + Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost, + PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv + }; +}; + +/** \internal + * \brief Template functor for scalar/packet assignment with swapping + * + * It works as follow. For a non-vectorized evaluation loop, we have: + * for(i) func(A.coeffRef(i), B.coeff(i)); + * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef. + * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable + * B.coeff already returns a const reference to the underlying scalar value. + * + * The case of a vectorized loop is more tricky: + * for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j)); + * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*, + * the actual alignment and Packet type. + * + */ +template<typename Scalar> struct swap_assign_op { + + EIGEN_EMPTY_STRUCT_CTOR(swap_assign_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const + { +#ifdef EIGEN_GPUCC + // FIXME is there some kind of cuda::swap? + Scalar t=b; const_cast<Scalar&>(b)=a; a=t; +#else + using std::swap; + swap(a,const_cast<Scalar&>(b)); +#endif + } +}; +template<typename Scalar> +struct functor_traits<swap_assign_op<Scalar> > { + enum { + Cost = 3 * NumTraits<Scalar>::ReadCost, + PacketAccess = + #if defined(EIGEN_VECTORIZE_AVX) && EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<800 || defined(__apple_build_version__)) + // This is a partial workaround for a bug in clang generating bad code + // when mixing 256/512 bits loads and 128 bits moves. + // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1684 + // https://bugs.llvm.org/show_bug.cgi?id=40815 + 0 + #else + packet_traits<Scalar>::Vectorizable + #endif + }; +}; + +} // namespace internal + +} // namespace Eigen + +#endif // EIGEN_ASSIGNMENT_FUNCTORS_H diff --git a/Eigen/src/Core/functors/BinaryFunctors.h b/Eigen/src/Core/functors/BinaryFunctors.h new file mode 100644 index 0000000..63f09ab --- /dev/null +++ b/Eigen/src/Core/functors/BinaryFunctors.h @@ -0,0 +1,541 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 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_BINARY_FUNCTORS_H +#define EIGEN_BINARY_FUNCTORS_H + +namespace Eigen { + +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 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) +#else + scalar_sum_op() { + EIGEN_SCALAR_BINARY_OP_PLUGIN + } +#endif + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const + { return internal::padd(a,b); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const + { return internal::predux(a); } +}; +template<typename LhsScalar,typename RhsScalar> +struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > { + enum { + Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2, // rough estimate! + PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd + // TODO vectorize mixed sum + }; +}; + + +template<> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_sum_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a || b; } + + +/** \internal + * \brief Template functor to compute the product of two scalars + * + * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux() + */ +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 result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const + { return internal::pmul(a,b); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const + { return internal::predux_mul(a); } +}; +template<typename LhsScalar,typename RhsScalar> +struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > { + enum { + Cost = (int(NumTraits<LhsScalar>::MulCost) + int(NumTraits<RhsScalar>::MulCost))/2, // rough estimate! + PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul + // TODO vectorize mixed product + }; +}; + +template<> +EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_product_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a && b; } + + +/** \internal + * \brief Template functor to compute the conjugate product of two scalars + * + * 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 : binary_op_base<LhsScalar,RhsScalar> +{ + + enum { + Conj = NumTraits<LhsScalar>::IsComplex + }; + + 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 result_type operator() (const LhsScalar& a, const RhsScalar& b) const + { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); } + + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const + { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); } +}; +template<typename LhsScalar,typename RhsScalar> +struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > { + enum { + Cost = NumTraits<LhsScalar>::MulCost, + PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul + }; +}; + +/** \internal + * \brief Template functor to compute the min of two scalars + * + * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff() + */ +template<typename LhsScalar,typename RhsScalar, int NaNPropagation> +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 result_type operator() (const LhsScalar& a, const RhsScalar& b) const { + return internal::pmin<NaNPropagation>(a, b); + } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const + { + return internal::pmin<NaNPropagation>(a,b); + } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const + { + return internal::predux_min<NaNPropagation>(a); + } +}; + +template<typename LhsScalar,typename RhsScalar, int NaNPropagation> +struct functor_traits<scalar_min_op<LhsScalar,RhsScalar, NaNPropagation> > { + enum { + Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, + PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin + }; +}; + +/** \internal + * \brief Template functor to compute the max of two scalars + * + * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff() + */ +template<typename LhsScalar,typename RhsScalar, int NaNPropagation> +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 result_type operator() (const LhsScalar& a, const RhsScalar& b) const { + return internal::pmax<NaNPropagation>(a,b); + } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const + { + return internal::pmax<NaNPropagation>(a,b); + } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const + { + return internal::predux_max<NaNPropagation>(a); + } +}; + +template<typename LhsScalar,typename RhsScalar, int NaNPropagation> +struct functor_traits<scalar_max_op<LhsScalar,RhsScalar, NaNPropagation> > { + enum { + Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, + PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax + }; +}; + +/** \internal + * \brief Template functors for comparison of two scalars + * \todo Implement packet-comparisons + */ +template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op; + +template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> +struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > { + enum { + Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, + PacketAccess = false + }; +}; + +template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar> +struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> { + typedef bool type; +}; + + +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 LhsScalar& a, const RhsScalar& b) const {return a==b;} +}; +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 LhsScalar& a, const RhsScalar& b) const {return a<b;} +}; +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 LhsScalar& a, const RhsScalar& b) const {return a<=b;} +}; +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 LhsScalar& a, const RhsScalar& b) const {return a>b;} +}; +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 LhsScalar& a, const RhsScalar& b) const {return a>=b;} +}; +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 LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);} +}; +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 LhsScalar& a, const RhsScalar& b) const {return a!=b;} +}; + +/** \internal + * \brief Template functor to compute the hypot of two \b positive \b and \b real scalars + * + * \sa MatrixBase::stableNorm(), class Redux + */ +template<typename Scalar> +struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar> +{ + EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const + { + // This functor is used by hypotNorm only for which it is faster to first apply abs + // on all coefficients prior to reduction through hypot. + // This way we avoid calling abs on positive and real entries, and this also permits + // to seamlessly handle complexes. Otherwise we would have to handle both real and complexes + // through the same functor... + return internal::positive_real_hypot(x,y); + } +}; +template<typename Scalar> +struct functor_traits<scalar_hypot_op<Scalar,Scalar> > { + enum + { + Cost = 3 * NumTraits<Scalar>::AddCost + + 2 * NumTraits<Scalar>::MulCost + + 2 * scalar_div_cost<Scalar,false>::value, + PacketAccess = false + }; +}; + +/** \internal + * \brief Template functor to compute the pow of two scalars + * See the specification of pow in https://en.cppreference.com/w/cpp/numeric/math/pow + */ +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 result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); } + + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const + { + return generic_pow(a,b); + } +}; + +template<typename Scalar, typename Exponent> +struct functor_traits<scalar_pow_op<Scalar,Exponent> > { + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = (!NumTraits<Scalar>::IsComplex && !NumTraits<Scalar>::IsInteger && + packet_traits<Scalar>::HasExp && packet_traits<Scalar>::HasLog && + packet_traits<Scalar>::HasRound && packet_traits<Scalar>::HasCmp && + // Temporarly disable packet access for half/bfloat16 until + // accuracy is improved. + !is_same<Scalar, half>::value && !is_same<Scalar, bfloat16>::value + ) + }; +}; + +//---------- non associative binary functors ---------- + +/** \internal + * \brief Template functor to compute the difference of two scalars + * + * \sa class CwiseBinaryOp, MatrixBase::operator- + */ +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) +#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 LhsScalar,typename RhsScalar> +struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > { + enum { + Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2, + PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub + }; +}; + +/** \internal + * \brief Template functor to compute the quotient of two scalars + * + * \sa class CwiseBinaryOp, Cwise::operator/() + */ +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 + { return internal::pdiv(a,b); } +}; +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 = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv, + Cost = scalar_div_cost<result_type,PacketAccess>::value + }; +}; + + + +/** \internal + * \brief Template functor to compute the and of two booleans + * + * \sa class CwiseBinaryOp, ArrayBase::operator&& + */ +struct scalar_boolean_and_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& 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::pand(a,b); } +}; +template<> struct functor_traits<scalar_boolean_and_op> { + enum { + Cost = NumTraits<bool>::AddCost, + PacketAccess = true + }; +}; + +/** \internal + * \brief Template functor to compute the or of two booleans + * + * \sa class CwiseBinaryOp, ArrayBase::operator|| + */ +struct scalar_boolean_or_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& 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::por(a,b); } +}; +template<> struct functor_traits<scalar_boolean_or_op> { + enum { + Cost = NumTraits<bool>::AddCost, + PacketAccess = true + }; +}; + +/** \internal + * \brief Template functor to compute the xor of two booleans + * + * \sa class CwiseBinaryOp, ArrayBase::operator^ + */ +struct scalar_boolean_xor_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& 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::pxor(a,b); } +}; +template<> struct functor_traits<scalar_boolean_xor_op> { + enum { + Cost = NumTraits<bool>::AddCost, + PacketAccess = true + }; +}; + +/** \internal + * \brief Template functor to compute the absolute difference of two scalars + * + * \sa class CwiseBinaryOp, MatrixBase::absolute_difference + */ +template<typename LhsScalar,typename RhsScalar> +struct scalar_absolute_difference_op : binary_op_base<LhsScalar,RhsScalar> +{ + typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_absolute_difference_op>::ReturnType result_type; +#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN + EIGEN_EMPTY_STRUCT_CTOR(scalar_absolute_difference_op) +#else + scalar_absolute_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 numext::absdiff(a,b); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const + { return internal::pabsdiff(a,b); } +}; +template<typename LhsScalar,typename RhsScalar> +struct functor_traits<scalar_absolute_difference_op<LhsScalar,RhsScalar> > { + enum { + Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, + PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff + }; +}; + + + +//---------- binary functors bound to a constant, thus appearing as a unary functor ---------- + +// 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 { + + 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; + + EIGEN_DEVICE_FUNC explicit bind1st_op(const first_argument_type &val) : m_value(val) {} + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); } + + template<typename Packet> + 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 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; + + EIGEN_DEVICE_FUNC explicit 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 + +} // end namespace Eigen + +#endif // EIGEN_BINARY_FUNCTORS_H diff --git a/Eigen/src/Core/functors/NullaryFunctors.h b/Eigen/src/Core/functors/NullaryFunctors.h new file mode 100644 index 0000000..192f225 --- /dev/null +++ b/Eigen/src/Core/functors/NullaryFunctors.h @@ -0,0 +1,189 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2016 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_NULLARY_FUNCTORS_H +#define EIGEN_NULLARY_FUNCTORS_H + +namespace Eigen { + +namespace internal { + +template<typename Scalar> +struct scalar_constant_op { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() () const { return m_other; } + template<typename PacketType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp() const { return internal::pset1<PacketType>(m_other); } + const Scalar m_other; +}; +template<typename Scalar> +struct functor_traits<scalar_constant_op<Scalar> > +{ 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) + template<typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType row, IndexType col) const { return row==col ? Scalar(1) : Scalar(0); } +}; +template<typename Scalar> +struct functor_traits<scalar_identity_op<Scalar> > +{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; }; + +template <typename Scalar, bool IsInteger> struct linspaced_op_impl; + +template <typename Scalar> +struct linspaced_op_impl<Scalar,/*IsInteger*/false> +{ + typedef typename NumTraits<Scalar>::Real RealScalar; + + EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) : + m_low(low), m_high(high), m_size1(num_steps==1 ? 1 : num_steps-1), m_step(num_steps==1 ? Scalar() : Scalar((high-low)/RealScalar(num_steps-1))), + m_flip(numext::abs(high)<numext::abs(low)) + {} + + template<typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { + if(m_flip) + return (i==0)? m_low : Scalar(m_high - RealScalar(m_size1-i)*m_step); + else + return (i==m_size1)? m_high : Scalar(m_low + RealScalar(i)*m_step); + } + + template<typename Packet, typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const + { + // Principle: + // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) ) + if(m_flip) + { + Packet pi = plset<Packet>(Scalar(i-m_size1)); + Packet res = padd(pset1<Packet>(m_high), pmul(pset1<Packet>(m_step), pi)); + if (EIGEN_PREDICT_TRUE(i != 0)) return res; + Packet mask = pcmp_lt(pset1<Packet>(0), plset<Packet>(0)); + return pselect<Packet>(mask, res, pset1<Packet>(m_low)); + } + else + { + Packet pi = plset<Packet>(Scalar(i)); + Packet res = padd(pset1<Packet>(m_low), pmul(pset1<Packet>(m_step), pi)); + if(EIGEN_PREDICT_TRUE(i != m_size1-unpacket_traits<Packet>::size+1)) return res; + Packet mask = pcmp_lt(plset<Packet>(0), pset1<Packet>(unpacket_traits<Packet>::size-1)); + return pselect<Packet>(mask, res, pset1<Packet>(m_high)); + } + } + + const Scalar m_low; + const Scalar m_high; + const Index m_size1; + const Scalar m_step; + const bool m_flip; +}; + +template <typename Scalar> +struct linspaced_op_impl<Scalar,/*IsInteger*/true> +{ + EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) : + m_low(low), + m_multiplier((high-low)/convert_index<Scalar>(num_steps<=1 ? 1 : num_steps-1)), + m_divisor(convert_index<Scalar>((high>=low?num_steps:-num_steps)+(high-low))/((numext::abs(high-low)+1)==0?1:(numext::abs(high-low)+1))), + m_use_divisor(num_steps>1 && (numext::abs(high-low)+1)<num_steps) + {} + + template<typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + const Scalar operator() (IndexType i) const + { + if(m_use_divisor) return m_low + convert_index<Scalar>(i)/m_divisor; + else return m_low + convert_index<Scalar>(i)*m_multiplier; + } + + const Scalar m_low; + const Scalar m_multiplier; + const Scalar m_divisor; + const bool m_use_divisor; +}; + +// ----- Linspace functor ---------------------------------------------------------------- + +// Forward declaration (we default to random access which does not really give +// us a speed gain when using packet access but it allows to use the functor in +// nested expressions). +template <typename Scalar> struct linspaced_op; +template <typename Scalar> struct functor_traits< linspaced_op<Scalar> > +{ + enum + { + Cost = 1, + PacketAccess = (!NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasBlend, + /*&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),*/ // <- vectorization for integer is currently disabled + IsRepeatable = true + }; +}; +template <typename Scalar> struct linspaced_op +{ + EIGEN_DEVICE_FUNC linspaced_op(const Scalar& low, const Scalar& high, Index num_steps) + : impl((num_steps==1 ? high : low),high,num_steps) + {} + + template<typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return impl(i); } + + template<typename Packet,typename IndexType> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const { return impl.template packetOp<Packet>(i); } + + // This proxy object handles the actual required temporaries and the different + // implementations (integer vs. floating point). + const linspaced_op_impl<Scalar,NumTraits<Scalar>::IsInteger> impl; +}; + +// Linear access is automatically determined from the operator() prototypes available for the given functor. +// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently +// and linear access is not possible. In all other cases, linear access is enabled. +// Users should not have to deal with this structure. +template<typename Functor> struct functor_has_linear_access { enum { ret = !has_binary_operator<Functor>::value }; }; + +// For unreliable compilers, let's specialize the has_*ary_operator +// helpers so that at least built-in nullary functors work fine. +#if !( (EIGEN_COMP_MSVC>1600) || (EIGEN_GNUC_AT_LEAST(4,8)) || (EIGEN_COMP_ICC>=1600)) +template<typename Scalar,typename IndexType> +struct has_nullary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 1}; }; +template<typename Scalar,typename IndexType> +struct has_unary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; }; +template<typename Scalar,typename IndexType> +struct has_binary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; }; + +template<typename Scalar,typename IndexType> +struct has_nullary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; }; +template<typename Scalar,typename IndexType> +struct has_unary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; }; +template<typename Scalar,typename IndexType> +struct has_binary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 1}; }; + +template<typename Scalar,typename IndexType> +struct has_nullary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; }; +template<typename Scalar,typename IndexType> +struct has_unary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 1}; }; +template<typename Scalar,typename IndexType> +struct has_binary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; }; + +template<typename Scalar,typename IndexType> +struct has_nullary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 1}; }; +template<typename Scalar,typename IndexType> +struct has_unary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; }; +template<typename Scalar,typename IndexType> +struct has_binary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; }; +#endif + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_NULLARY_FUNCTORS_H diff --git a/Eigen/src/Core/functors/StlFunctors.h b/Eigen/src/Core/functors/StlFunctors.h new file mode 100644 index 0000000..4570c9b --- /dev/null +++ b/Eigen/src/Core/functors/StlFunctors.h @@ -0,0 +1,166 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 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_STL_FUNCTORS_H +#define EIGEN_STL_FUNCTORS_H + +namespace Eigen { + +// Portable replacements for certain functors. +namespace numext { + +template<typename T = void> +struct equal_to { + typedef bool result_type; + EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const { + return lhs == rhs; + } +}; + +template<typename T = void> +struct not_equal_to { + typedef bool result_type; + EIGEN_DEVICE_FUNC bool operator()(const T& lhs, const T& rhs) const { + return lhs != rhs; + } +}; + +} + + +namespace internal { + +// default functor traits for STL functors: + +template<typename T> +struct functor_traits<std::multiplies<T> > +{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::divides<T> > +{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::plus<T> > +{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::minus<T> > +{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::negate<T> > +{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::logical_or<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::logical_and<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::logical_not<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::greater<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::less<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::greater_equal<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::less_equal<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::equal_to<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<numext::equal_to<T> > + : functor_traits<std::equal_to<T> > {}; + +template<typename T> +struct functor_traits<std::not_equal_to<T> > +{ enum { Cost = 1, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<numext::not_equal_to<T> > + : functor_traits<std::not_equal_to<T> > {}; + +#if (EIGEN_COMP_CXXVER < 11) +// std::binder* are deprecated since c++11 and will be removed in c++17 +template<typename T> +struct functor_traits<std::binder2nd<T> > +{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; + +template<typename T> +struct functor_traits<std::binder1st<T> > +{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; +#endif + +#if (EIGEN_COMP_CXXVER < 17) +// std::unary_negate is deprecated since c++17 and will be removed in c++20 +template<typename T> +struct functor_traits<std::unary_negate<T> > +{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; + +// std::binary_negate is deprecated since c++17 and will be removed in c++20 +template<typename T> +struct functor_traits<std::binary_negate<T> > +{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; +#endif + +#ifdef EIGEN_STDEXT_SUPPORT + +template<typename T0,typename T1> +struct functor_traits<std::project1st<T0,T1> > +{ enum { Cost = 0, PacketAccess = false }; }; + +template<typename T0,typename T1> +struct functor_traits<std::project2nd<T0,T1> > +{ enum { Cost = 0, PacketAccess = false }; }; + +template<typename T0,typename T1> +struct functor_traits<std::select2nd<std::pair<T0,T1> > > +{ enum { Cost = 0, PacketAccess = false }; }; + +template<typename T0,typename T1> +struct functor_traits<std::select1st<std::pair<T0,T1> > > +{ enum { Cost = 0, PacketAccess = false }; }; + +template<typename T0,typename T1> +struct functor_traits<std::unary_compose<T0,T1> > +{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; }; + +template<typename T0,typename T1,typename T2> +struct functor_traits<std::binary_compose<T0,T1,T2> > +{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; }; + +#endif // EIGEN_STDEXT_SUPPORT + +// allow to add new functors and specializations of functor_traits from outside Eigen. +// this macro is really needed because functor_traits must be specialized after it is declared but before it is used... +#ifdef EIGEN_FUNCTORS_PLUGIN +#include EIGEN_FUNCTORS_PLUGIN +#endif + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_STL_FUNCTORS_H diff --git a/Eigen/src/Core/functors/TernaryFunctors.h b/Eigen/src/Core/functors/TernaryFunctors.h new file mode 100644 index 0000000..b254e96 --- /dev/null +++ b/Eigen/src/Core/functors/TernaryFunctors.h @@ -0,0 +1,25 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_TERNARY_FUNCTORS_H +#define EIGEN_TERNARY_FUNCTORS_H + +namespace Eigen { + +namespace internal { + +//---------- associative ternary functors ---------- + + + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_TERNARY_FUNCTORS_H diff --git a/Eigen/src/Core/functors/UnaryFunctors.h b/Eigen/src/Core/functors/UnaryFunctors.h new file mode 100644 index 0000000..16136d1 --- /dev/null +++ b/Eigen/src/Core/functors/UnaryFunctors.h @@ -0,0 +1,1131 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2016 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_UNARY_FUNCTORS_H +#define EIGEN_UNARY_FUNCTORS_H + +namespace Eigen { + +namespace internal { + +/** \internal + * \brief Template functor to compute the opposite of a scalar + * + * \sa class CwiseUnaryOp, MatrixBase::operator- + */ +template<typename Scalar> struct scalar_opposite_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::pnegate(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_opposite_op<Scalar> > +{ enum { + Cost = NumTraits<Scalar>::AddCost, + PacketAccess = packet_traits<Scalar>::HasNegate }; +}; + +/** \internal + * \brief Template functor to compute the absolute value of a scalar + * + * \sa class CwiseUnaryOp, Cwise::abs + */ +template<typename Scalar> struct scalar_abs_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs(a); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::pabs(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_abs_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::AddCost, + PacketAccess = packet_traits<Scalar>::HasAbs + }; +}; + +/** \internal + * \brief Template functor to compute the score of a scalar, to chose a pivot + * + * \sa class CwiseUnaryOp + */ +template<typename Scalar> struct scalar_score_coeff_op : scalar_abs_op<Scalar> +{ + typedef void Score_is_abs; +}; +template<typename Scalar> +struct functor_traits<scalar_score_coeff_op<Scalar> > : functor_traits<scalar_abs_op<Scalar> > {}; + +/* Avoid recomputing abs when we know the score and they are the same. Not a true Eigen functor. */ +template<typename Scalar, typename=void> struct abs_knowing_score +{ + EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score) + typedef typename NumTraits<Scalar>::Real result_type; + template<typename Score> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a, const Score&) const { return numext::abs(a); } +}; +template<typename Scalar> struct abs_knowing_score<Scalar, typename scalar_score_coeff_op<Scalar>::Score_is_abs> +{ + EIGEN_EMPTY_STRUCT_CTOR(abs_knowing_score) + typedef typename NumTraits<Scalar>::Real result_type; + template<typename Scal> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scal&, const result_type& a) const { return a; } +}; + +/** \internal + * \brief Template functor to compute the squared absolute value of a scalar + * + * \sa class CwiseUnaryOp, Cwise::abs2 + */ +template<typename Scalar> struct scalar_abs2_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::pmul(a,a); } +}; +template<typename Scalar> +struct functor_traits<scalar_abs2_op<Scalar> > +{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; }; + +/** \internal + * \brief Template functor to compute the conjugate of a complex value + * + * \sa class CwiseUnaryOp, MatrixBase::conjugate() + */ +template<typename Scalar> struct scalar_conjugate_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op) + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::conj(a); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_conjugate_op<Scalar> > +{ + enum { + Cost = 0, + // Yes the cost is zero even for complexes because in most cases for which + // the cost is used, conjugation turns to be a no-op. Some examples: + // cost(a*conj(b)) == cost(a*b) + // cost(a+conj(b)) == cost(a+b) + // <etc. + // If we don't set it to zero, then: + // A.conjugate().lazyProduct(B.conjugate()) + // will bake its operands. We definitely don't want that! + PacketAccess = packet_traits<Scalar>::HasConj + }; +}; + +/** \internal + * \brief Template functor to compute the phase angle of a complex + * + * \sa class CwiseUnaryOp, Cwise::arg + */ +template<typename Scalar> struct scalar_arg_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_arg_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::arg(a); } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::parg(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_arg_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::IsComplex ? 5 * NumTraits<Scalar>::MulCost : NumTraits<Scalar>::AddCost, + PacketAccess = packet_traits<Scalar>::HasArg + }; +}; +/** \internal + * \brief Template functor to cast a scalar to another type + * + * \sa class CwiseUnaryOp, MatrixBase::cast() + */ +template<typename Scalar, typename NewType> +struct scalar_cast_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op) + typedef NewType result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); } +}; +template<typename Scalar, typename NewType> +struct functor_traits<scalar_cast_op<Scalar,NewType> > +{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; }; + +/** \internal + * \brief Template functor to arithmetically shift a scalar right by a number of bits + * + * \sa class CwiseUnaryOp, MatrixBase::shift_right() + */ +template<typename Scalar, int N> +struct scalar_shift_right_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_right_op) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const + { return a >> N; } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::parithmetic_shift_right<N>(a); } +}; +template<typename Scalar, int N> +struct functor_traits<scalar_shift_right_op<Scalar,N> > +{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; }; + +/** \internal + * \brief Template functor to logically shift a scalar left by a number of bits + * + * \sa class CwiseUnaryOp, MatrixBase::shift_left() + */ +template<typename Scalar, int N> +struct scalar_shift_left_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_shift_left_op) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const + { return a << N; } + template<typename Packet> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const + { return internal::plogical_shift_left<N>(a); } +}; +template<typename Scalar, int N> +struct functor_traits<scalar_shift_left_op<Scalar,N> > +{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; }; + +/** \internal + * \brief Template functor to extract the real part of a complex + * + * \sa class CwiseUnaryOp, MatrixBase::real() + */ +template<typename Scalar> +struct scalar_real_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_real_op<Scalar> > +{ enum { Cost = 0, PacketAccess = false }; }; + +/** \internal + * \brief Template functor to extract the imaginary part of a complex + * + * \sa class CwiseUnaryOp, MatrixBase::imag() + */ +template<typename Scalar> +struct scalar_imag_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_imag_op<Scalar> > +{ enum { Cost = 0, PacketAccess = false }; }; + +/** \internal + * \brief Template functor to extract the real part of a complex as a reference + * + * \sa class CwiseUnaryOp, MatrixBase::real() + */ +template<typename Scalar> +struct scalar_real_ref_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); } +}; +template<typename Scalar> +struct functor_traits<scalar_real_ref_op<Scalar> > +{ enum { Cost = 0, PacketAccess = false }; }; + +/** \internal + * \brief Template functor to extract the imaginary part of a complex as a reference + * + * \sa class CwiseUnaryOp, MatrixBase::imag() + */ +template<typename Scalar> +struct scalar_imag_ref_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op) + typedef typename NumTraits<Scalar>::Real result_type; + EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); } +}; +template<typename Scalar> +struct functor_traits<scalar_imag_ref_op<Scalar> > +{ enum { Cost = 0, PacketAccess = false }; }; + +/** \internal + * + * \brief Template functor to compute the exponential of a scalar + * + * \sa class CwiseUnaryOp, Cwise::exp() + */ +template<typename Scalar> struct scalar_exp_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::exp(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexp(a); } +}; +template <typename Scalar> +struct functor_traits<scalar_exp_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasExp, + // The following numbers are based on the AVX implementation. +#ifdef EIGEN_VECTORIZE_FMA + // Haswell can issue 2 add/mul/madd per cycle. + Cost = + (sizeof(Scalar) == 4 + // float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other + ? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost) + // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other + : (14 * NumTraits<Scalar>::AddCost + + 6 * NumTraits<Scalar>::MulCost + + scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)) +#else + Cost = + (sizeof(Scalar) == 4 + // float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other + ? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost) + // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other + : (23 * NumTraits<Scalar>::AddCost + + 12 * NumTraits<Scalar>::MulCost + + scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)) +#endif + }; +}; + +/** \internal + * + * \brief Template functor to compute the exponential of a scalar - 1. + * + * \sa class CwiseUnaryOp, ArrayBase::expm1() + */ +template<typename Scalar> struct scalar_expm1_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_expm1_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::expm1(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexpm1(a); } +}; +template <typename Scalar> +struct functor_traits<scalar_expm1_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasExpm1, + Cost = functor_traits<scalar_exp_op<Scalar> >::Cost // TODO measure cost of expm1 + }; +}; + +/** \internal + * + * \brief Template functor to compute the logarithm of a scalar + * + * \sa class CwiseUnaryOp, ArrayBase::log() + */ +template<typename Scalar> struct scalar_log_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog(a); } +}; +template <typename Scalar> +struct functor_traits<scalar_log_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasLog, + Cost = + (PacketAccess + // The following numbers are based on the AVX implementation. +#ifdef EIGEN_VECTORIZE_FMA + // 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle. + ? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost) +#else + // 8 pmadd, 6 pmul, 8 padd/psub, 20 other + ? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost) +#endif + // Measured cost of std::log. + : sizeof(Scalar)==4 ? 40 : 85) + }; +}; + +/** \internal + * + * \brief Template functor to compute the logarithm of 1 plus a scalar value + * + * \sa class CwiseUnaryOp, ArrayBase::log1p() + */ +template<typename Scalar> struct scalar_log1p_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_log1p_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log1p(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog1p(a); } +}; +template <typename Scalar> +struct functor_traits<scalar_log1p_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasLog1p, + Cost = functor_traits<scalar_log_op<Scalar> >::Cost // TODO measure cost of log1p + }; +}; + +/** \internal + * + * \brief Template functor to compute the base-10 logarithm of a scalar + * + * \sa class CwiseUnaryOp, Cwise::log10() + */ +template<typename Scalar> struct scalar_log10_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_log10_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { EIGEN_USING_STD(log10) return log10(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog10(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_log10_op<Scalar> > +{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog10 }; }; + +/** \internal + * + * \brief Template functor to compute the base-2 logarithm of a scalar + * + * \sa class CwiseUnaryOp, Cwise::log2() + */ +template<typename Scalar> struct scalar_log2_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_log2_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return Scalar(EIGEN_LOG2E) * numext::log(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog2(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_log2_op<Scalar> > +{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; }; + +/** \internal + * \brief Template functor to compute the square root of a scalar + * \sa class CwiseUnaryOp, Cwise::sqrt() + */ +template<typename Scalar> struct scalar_sqrt_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sqrt(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); } +}; +template <typename Scalar> +struct functor_traits<scalar_sqrt_op<Scalar> > { + enum { +#if EIGEN_FAST_MATH + // The following numbers are based on the AVX implementation. + Cost = (sizeof(Scalar) == 8 ? 28 + // 4 pmul, 1 pmadd, 3 other + : (3 * NumTraits<Scalar>::AddCost + + 5 * NumTraits<Scalar>::MulCost)), +#else + // The following numbers are based on min VSQRT throughput on Haswell. + Cost = (sizeof(Scalar) == 8 ? 28 : 14), +#endif + PacketAccess = packet_traits<Scalar>::HasSqrt + }; +}; + +// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings. +template<> struct scalar_sqrt_op<bool> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; } + template <typename Packet> + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return a; } +}; +template <> +struct functor_traits<scalar_sqrt_op<bool> > { + enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable }; +}; + +/** \internal + * \brief Template functor to compute the reciprocal square root of a scalar + * \sa class CwiseUnaryOp, Cwise::rsqrt() + */ +template<typename Scalar> struct scalar_rsqrt_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_rsqrt_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::rsqrt(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::prsqrt(a); } +}; + +template<typename Scalar> +struct functor_traits<scalar_rsqrt_op<Scalar> > +{ enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasRsqrt + }; +}; + +/** \internal + * \brief Template functor to compute the cosine of a scalar + * \sa class CwiseUnaryOp, ArrayBase::cos() + */ +template<typename Scalar> struct scalar_cos_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op) + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::cos(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcos(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_cos_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasCos + }; +}; + +/** \internal + * \brief Template functor to compute the sine of a scalar + * \sa class CwiseUnaryOp, ArrayBase::sin() + */ +template<typename Scalar> struct scalar_sin_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sin(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psin(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_sin_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasSin + }; +}; + + +/** \internal + * \brief Template functor to compute the tan of a scalar + * \sa class CwiseUnaryOp, ArrayBase::tan() + */ +template<typename Scalar> struct scalar_tan_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tan(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptan(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_tan_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasTan + }; +}; + +/** \internal + * \brief Template functor to compute the arc cosine of a scalar + * \sa class CwiseUnaryOp, ArrayBase::acos() + */ +template<typename Scalar> struct scalar_acos_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::acos(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pacos(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_acos_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasACos + }; +}; + +/** \internal + * \brief Template functor to compute the arc sine of a scalar + * \sa class CwiseUnaryOp, ArrayBase::asin() + */ +template<typename Scalar> struct scalar_asin_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::asin(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pasin(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_asin_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasASin + }; +}; + + +/** \internal + * \brief Template functor to compute the atan of a scalar + * \sa class CwiseUnaryOp, ArrayBase::atan() + */ +template<typename Scalar> struct scalar_atan_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_atan_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::atan(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::patan(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_atan_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasATan + }; +}; + +/** \internal + * \brief Template functor to compute the tanh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::tanh() + */ +template <typename Scalar> +struct scalar_tanh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::tanh(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return ptanh(x); } +}; + +template <typename Scalar> +struct functor_traits<scalar_tanh_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasTanh, + Cost = ( (EIGEN_FAST_MATH && is_same<Scalar,float>::value) +// The following numbers are based on the AVX implementation, +#ifdef EIGEN_VECTORIZE_FMA + // Haswell can issue 2 add/mul/madd per cycle. + // 9 pmadd, 2 pmul, 1 div, 2 other + ? (2 * NumTraits<Scalar>::AddCost + + 6 * NumTraits<Scalar>::MulCost + + scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value) +#else + ? (11 * NumTraits<Scalar>::AddCost + + 11 * NumTraits<Scalar>::MulCost + + scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value) +#endif + // This number assumes a naive implementation of tanh + : (6 * NumTraits<Scalar>::AddCost + + 3 * NumTraits<Scalar>::MulCost + + 2 * scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value + + functor_traits<scalar_exp_op<Scalar> >::Cost)) + }; +}; + +#if EIGEN_HAS_CXX11_MATH +/** \internal + * \brief Template functor to compute the atanh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::atanh() + */ +template <typename Scalar> +struct scalar_atanh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_atanh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::atanh(a); } +}; + +template <typename Scalar> +struct functor_traits<scalar_atanh_op<Scalar> > { + enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; +}; +#endif + +/** \internal + * \brief Template functor to compute the sinh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::sinh() + */ +template<typename Scalar> struct scalar_sinh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sinh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sinh(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psinh(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_sinh_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasSinh + }; +}; + +#if EIGEN_HAS_CXX11_MATH +/** \internal + * \brief Template functor to compute the asinh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::asinh() + */ +template <typename Scalar> +struct scalar_asinh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_asinh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::asinh(a); } +}; + +template <typename Scalar> +struct functor_traits<scalar_asinh_op<Scalar> > { + enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; +}; +#endif + +/** \internal + * \brief Template functor to compute the cosh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::cosh() + */ +template<typename Scalar> struct scalar_cosh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_cosh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::cosh(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcosh(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_cosh_op<Scalar> > +{ + enum { + Cost = 5 * NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasCosh + }; +}; + +#if EIGEN_HAS_CXX11_MATH +/** \internal + * \brief Template functor to compute the acosh of a scalar + * \sa class CwiseUnaryOp, ArrayBase::acosh() + */ +template <typename Scalar> +struct scalar_acosh_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_acosh_op) + EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::acosh(a); } +}; + +template <typename Scalar> +struct functor_traits<scalar_acosh_op<Scalar> > { + enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; +}; +#endif + +/** \internal + * \brief Template functor to compute the inverse of a scalar + * \sa class CwiseUnaryOp, Cwise::inverse() + */ +template<typename Scalar> +struct scalar_inverse_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op) + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; } + template<typename Packet> + EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const + { return internal::pdiv(pset1<Packet>(Scalar(1)),a); } +}; +template <typename Scalar> +struct functor_traits<scalar_inverse_op<Scalar> > { + enum { + PacketAccess = packet_traits<Scalar>::HasDiv, + Cost = scalar_div_cost<Scalar, PacketAccess>::value + }; +}; + +/** \internal + * \brief Template functor to compute the square of a scalar + * \sa class CwiseUnaryOp, Cwise::square() + */ +template<typename Scalar> +struct scalar_square_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op) + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a; } + template<typename Packet> + EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const + { return internal::pmul(a,a); } +}; +template<typename Scalar> +struct functor_traits<scalar_square_op<Scalar> > +{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; + +// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC. +template<> +struct scalar_square_op<bool> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op) + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; } + template<typename Packet> + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const + { return a; } +}; +template<> +struct functor_traits<scalar_square_op<bool> > +{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; }; + +/** \internal + * \brief Template functor to compute the cube of a scalar + * \sa class CwiseUnaryOp, Cwise::cube() + */ +template<typename Scalar> +struct scalar_cube_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op) + EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a*a; } + template<typename Packet> + EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const + { return internal::pmul(a,pmul(a,a)); } +}; +template<typename Scalar> +struct functor_traits<scalar_cube_op<Scalar> > +{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; + +// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC. +template<> +struct scalar_cube_op<bool> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op) + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; } + template<typename Packet> + EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const + { return a; } +}; +template<> +struct functor_traits<scalar_cube_op<bool> > +{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; }; + +/** \internal + * \brief Template functor to compute the rounded value of a scalar + * \sa class CwiseUnaryOp, ArrayBase::round() + */ +template<typename Scalar> struct scalar_round_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_round_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::round(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pround(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_round_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasRound + }; +}; + +/** \internal + * \brief Template functor to compute the floor of a scalar + * \sa class CwiseUnaryOp, ArrayBase::floor() + */ +template<typename Scalar> struct scalar_floor_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_floor_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::floor(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pfloor(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_floor_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasFloor + }; +}; + +/** \internal + * \brief Template functor to compute the rounded (with current rounding mode) value of a scalar + * \sa class CwiseUnaryOp, ArrayBase::rint() + */ +template<typename Scalar> struct scalar_rint_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_rint_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::rint(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::print(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_rint_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasRint + }; +}; + +/** \internal + * \brief Template functor to compute the ceil of a scalar + * \sa class CwiseUnaryOp, ArrayBase::ceil() + */ +template<typename Scalar> struct scalar_ceil_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_ceil_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::ceil(a); } + template <typename Packet> + EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pceil(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_ceil_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = packet_traits<Scalar>::HasCeil + }; +}; + +/** \internal + * \brief Template functor to compute whether a scalar is NaN + * \sa class CwiseUnaryOp, ArrayBase::isnan() + */ +template<typename Scalar> struct scalar_isnan_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_isnan_op) + typedef bool result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { +#if defined(SYCL_DEVICE_ONLY) + return numext::isnan(a); +#else + return (numext::isnan)(a); +#endif + } +}; +template<typename Scalar> +struct functor_traits<scalar_isnan_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = false + }; +}; + +/** \internal + * \brief Template functor to check whether a scalar is +/-inf + * \sa class CwiseUnaryOp, ArrayBase::isinf() + */ +template<typename Scalar> struct scalar_isinf_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_isinf_op) + typedef bool result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { +#if defined(SYCL_DEVICE_ONLY) + return numext::isinf(a); +#else + return (numext::isinf)(a); +#endif + } +}; +template<typename Scalar> +struct functor_traits<scalar_isinf_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = false + }; +}; + +/** \internal + * \brief Template functor to check whether a scalar has a finite value + * \sa class CwiseUnaryOp, ArrayBase::isfinite() + */ +template<typename Scalar> struct scalar_isfinite_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_isfinite_op) + typedef bool result_type; + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { +#if defined(SYCL_DEVICE_ONLY) + return numext::isfinite(a); +#else + return (numext::isfinite)(a); +#endif + } +}; +template<typename Scalar> +struct functor_traits<scalar_isfinite_op<Scalar> > +{ + enum { + Cost = NumTraits<Scalar>::MulCost, + PacketAccess = false + }; +}; + +/** \internal + * \brief Template functor to compute the logical not of a boolean + * + * \sa class CwiseUnaryOp, ArrayBase::operator! + */ +template<typename Scalar> struct scalar_boolean_not_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_not_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a) const { return !a; } +}; +template<typename Scalar> +struct functor_traits<scalar_boolean_not_op<Scalar> > { + enum { + Cost = NumTraits<bool>::AddCost, + PacketAccess = false + }; +}; + +/** \internal + * \brief Template functor to compute the signum of a scalar + * \sa class CwiseUnaryOp, Cwise::sign() + */ +template<typename Scalar,bool is_complex=(NumTraits<Scalar>::IsComplex!=0), bool is_integer=(NumTraits<Scalar>::IsInteger!=0) > struct scalar_sign_op; +template<typename Scalar> +struct scalar_sign_op<Scalar, false, true> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const + { + return Scalar( (a>Scalar(0)) - (a<Scalar(0)) ); + } + //TODO + //template <typename Packet> + //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); } +}; + +template<typename Scalar> +struct scalar_sign_op<Scalar, false, false> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const + { + return (numext::isnan)(a) ? a : Scalar( (a>Scalar(0)) - (a<Scalar(0)) ); + } + //TODO + //template <typename Packet> + //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); } +}; + +template<typename Scalar, bool is_integer> +struct scalar_sign_op<Scalar,true, is_integer> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op) + EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const + { + typedef typename NumTraits<Scalar>::Real real_type; + real_type aa = numext::abs(a); + if (aa==real_type(0)) + return Scalar(0); + aa = real_type(1)/aa; + return Scalar(a.real()*aa, a.imag()*aa ); + } + //TODO + //template <typename Packet> + //EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psign(a); } +}; +template<typename Scalar> +struct functor_traits<scalar_sign_op<Scalar> > +{ enum { + Cost = + NumTraits<Scalar>::IsComplex + ? ( 8*NumTraits<Scalar>::MulCost ) // roughly + : ( 3*NumTraits<Scalar>::AddCost), + PacketAccess = packet_traits<Scalar>::HasSign + }; +}; + +/** \internal + * \brief Template functor to compute the logistic function of a scalar + * \sa class CwiseUnaryOp, ArrayBase::logistic() + */ +template <typename T> +struct scalar_logistic_op { + EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { + return packetOp(x); + } + + template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Packet packetOp(const Packet& x) const { + const Packet one = pset1<Packet>(T(1)); + return pdiv(one, padd(one, pexp(pnegate(x)))); + } +}; + +#ifndef EIGEN_GPU_COMPILE_PHASE +/** \internal + * \brief Template specialization of the logistic function for float. + * + * Uses just a 9/10-degree rational interpolant which + * interpolates 1/(1+exp(-x)) - 0.5 up to a couple of ulps in the range + * [-9, 18]. Below -9 we use the more accurate approximation + * 1/(1+exp(-x)) ~= exp(x), and above 18 the logistic function is 1 withing + * one ulp. The shifted logistic is interpolated because it was easier to + * make the fit converge. + * + */ +template <> +struct scalar_logistic_op<float> { + EIGEN_EMPTY_STRUCT_CTOR(scalar_logistic_op) + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator()(const float& x) const { + return packetOp(x); + } + + template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + Packet packetOp(const Packet& _x) const { + const Packet cutoff_lower = pset1<Packet>(-9.f); + const Packet lt_mask = pcmp_lt<Packet>(_x, cutoff_lower); + const bool any_small = predux_any(lt_mask); + + // The upper cut-off is the smallest x for which the rational approximation evaluates to 1. + // Choosing this value saves us a few instructions clamping the results at the end. +#ifdef EIGEN_VECTORIZE_FMA + const Packet cutoff_upper = pset1<Packet>(15.7243833541870117f); +#else + const Packet cutoff_upper = pset1<Packet>(15.6437711715698242f); +#endif + const Packet x = pmin(_x, cutoff_upper); + + // The monomial coefficients of the numerator polynomial (odd). + const Packet alpha_1 = pset1<Packet>(2.48287947061529e-01f); + const Packet alpha_3 = pset1<Packet>(8.51377133304701e-03f); + const Packet alpha_5 = pset1<Packet>(6.08574864600143e-05f); + const Packet alpha_7 = pset1<Packet>(1.15627324459942e-07f); + const Packet alpha_9 = pset1<Packet>(4.37031012579801e-11f); + + // The monomial coefficients of the denominator polynomial (even). + const Packet beta_0 = pset1<Packet>(9.93151921023180e-01f); + const Packet beta_2 = pset1<Packet>(1.16817656904453e-01f); + const Packet beta_4 = pset1<Packet>(1.70198817374094e-03f); + const Packet beta_6 = pset1<Packet>(6.29106785017040e-06f); + const Packet beta_8 = pset1<Packet>(5.76102136993427e-09f); + const Packet beta_10 = pset1<Packet>(6.10247389755681e-13f); + + // Since the polynomials are odd/even, we need x^2. + const Packet x2 = pmul(x, x); + + // Evaluate the numerator polynomial p. + Packet p = pmadd(x2, alpha_9, alpha_7); + p = pmadd(x2, p, alpha_5); + p = pmadd(x2, p, alpha_3); + p = pmadd(x2, p, alpha_1); + p = pmul(x, p); + + // Evaluate the denominator polynomial q. + Packet q = pmadd(x2, beta_10, beta_8); + q = pmadd(x2, q, beta_6); + q = pmadd(x2, q, beta_4); + q = pmadd(x2, q, beta_2); + q = pmadd(x2, q, beta_0); + // Divide the numerator by the denominator and shift it up. + const Packet logistic = padd(pdiv(p, q), pset1<Packet>(0.5f)); + if (EIGEN_PREDICT_FALSE(any_small)) { + const Packet exponential = pexp(_x); + return pselect(lt_mask, exponential, logistic); + } else { + return logistic; + } + } +}; +#endif // #ifndef EIGEN_GPU_COMPILE_PHASE + +template <typename T> +struct functor_traits<scalar_logistic_op<T> > { + enum { + // The cost estimate for float here here is for the common(?) case where + // all arguments are greater than -9. + Cost = scalar_div_cost<T, packet_traits<T>::HasDiv>::value + + (internal::is_same<T, float>::value + ? NumTraits<T>::AddCost * 15 + NumTraits<T>::MulCost * 11 + : NumTraits<T>::AddCost * 2 + + functor_traits<scalar_exp_op<T> >::Cost), + PacketAccess = + packet_traits<T>::HasAdd && packet_traits<T>::HasDiv && + (internal::is_same<T, float>::value + ? packet_traits<T>::HasMul && packet_traits<T>::HasMax && + packet_traits<T>::HasMin + : packet_traits<T>::HasNegate && packet_traits<T>::HasExp) + }; +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_FUNCTORS_H |