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ADD: added other eigen lib

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This commit is contained in:
Henry Winkel
2022-12-21 16:19:04 +01:00
parent a570766dc6
commit 9e56c7f2c0
832 changed files with 36586 additions and 20006 deletions
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50
libs/eigen/Eigen/AccelerateSupport Normal file
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@@ -0,0 +1,50 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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_ACCELERATESUPPORT_MODULE_H
#define EIGEN_ACCELERATESUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup AccelerateSupport_Module AccelerateSupport module
*
* This module provides an interface to the Apple Accelerate library.
* It provides the seven following main factorization classes:
* - class AccelerateLLT: a Cholesky (LL^T) factorization.
* - class AccelerateLDLT: the default LDL^T factorization.
* - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
* - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
* - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
* - class AccelerateQR: a QR factorization
* - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
*
* \code
* #include <Eigen/AccelerateSupport>
* \endcode
*
* In order to use this module, the Accelerate headers must be accessible from
* the include paths, and your binary must be linked to the Accelerate framework.
* The Accelerate library is only available on Apple hardware.
*
* Note that many of the algorithms can be influenced by the UpLo template
* argument. All matrices are assumed to be symmetric. For example, the following
* creates an LDLT factorization where your matrix is symmetric (implicit) and
* uses the lower triangle:
*
* \code
* AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
* \endcode
*/
#include "src/AccelerateSupport/AccelerateSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ACCELERATESUPPORT_MODULE_H

6
libs/eigen/Eigen/Cholesky
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@@ -32,11 +32,7 @@
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/misc/lapacke_helpers.h"
#include "src/Cholesky/LLT_LAPACKE.h"
#endif

2
libs/eigen/Eigen/CholmodSupport
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@@ -22,7 +22,7 @@ extern "C" {
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the two following main factorization classes:
* - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
* - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
*
* For the sake of completeness, this module also propose the two following classes:
* - class CholmodSimplicialLLT

42
libs/eigen/Eigen/Core
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@@ -8,8 +8,8 @@
// 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_CORE_H
#define EIGEN_CORE_H
#ifndef EIGEN_CORE_MODULE_H
#define EIGEN_CORE_MODULE_H
// first thing Eigen does: stop the compiler from reporting useless warnings.
#include "src/Core/util/DisableStupidWarnings.h"
@@ -36,7 +36,7 @@
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6) && EIGEN_GNUC_AT_MOST(5,5)
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_MOST(5,5)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
@@ -67,6 +67,7 @@
#endif
#ifdef EIGEN_HAS_OPENMP
#include <atomic>
#include <omp.h>
#endif
@@ -83,8 +84,8 @@
#include <cmath>
#include <cassert>
#include <functional>
#include <sstream>
#ifndef EIGEN_NO_IO
#include <sstream>
#include <iosfwd>
#endif
#include <cstring>
@@ -94,14 +95,10 @@
// for min/max:
#include <algorithm>
#if EIGEN_HAS_CXX11
#include <array>
#endif
// for std::is_nothrow_move_assignable
#ifdef EIGEN_INCLUDE_TYPE_TRAITS
#include <type_traits>
#endif
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
@@ -109,7 +106,8 @@
#endif
// required for __cpuid, needs to be included after cmath
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
// also required for _BitScanReverse on Windows on ARM
#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
@@ -165,6 +163,7 @@ using std::ptrdiff_t;
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/util/IntegralConstant.h"
#include "src/Core/util/Serializer.h"
#include "src/Core/util/SymbolicIndex.h"
#include "src/Core/NumTraits.h"
@@ -179,6 +178,9 @@ using std::ptrdiff_t;
#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
#if defined EIGEN_VECTORIZE_AVX512
#if defined EIGEN_VECTORIZE_AVX512FP16
#include "src/Core/arch/AVX512/PacketMathFP16.h"
#endif
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#include "src/Core/arch/SSE/Complex.h"
@@ -191,6 +193,7 @@ using std::ptrdiff_t;
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/AVX/MathFunctions.h"
#include "src/Core/arch/AVX512/MathFunctions.h"
#include "src/Core/arch/AVX512/TrsmKernel.h"
#elif defined EIGEN_VECTORIZE_AVX
// Use AVX for floats and doubles, SSE for integers
#include "src/Core/arch/SSE/PacketMath.h"
@@ -256,10 +259,14 @@ using std::ptrdiff_t;
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
// Specialized functors to enable the processing of complex numbers
// on CUDA devices
#ifdef EIGEN_CUDACC
#include "src/Core/arch/CUDA/Complex.h"
// Specialized functors for GPU.
#ifdef EIGEN_GPUCC
#include "src/Core/arch/GPU/Complex.h"
#endif
// Specializations of vectorized activation functions for NEON.
#ifdef EIGEN_VECTORIZE_NEON
#include "src/Core/arch/NEON/UnaryFunctors.h"
#endif
#include "src/Core/util/IndexedViewHelper.h"
@@ -314,6 +321,7 @@ using std::ptrdiff_t;
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/SkewSymmetricMatrix3.h"
#include "src/Core/Redux.h"
#include "src/Core/Visitor.h"
#include "src/Core/Fuzzy.h"
@@ -346,12 +354,16 @@ using std::ptrdiff_t;
#include "src/Core/CoreIterators.h"
#include "src/Core/ConditionEstimator.h"
#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
#if defined(EIGEN_VECTORIZE_VSX)
#include "src/Core/arch/AltiVec/MatrixProduct.h"
#elif defined EIGEN_VECTORIZE_NEON
#include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
#endif
#if defined(EIGEN_VECTORIZE_AVX512)
#include "src/Core/arch/AVX512/GemmKernel.h"
#endif
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
#include "src/Core/VectorwiseOp.h"
@@ -381,4 +393,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CORE_H
#endif // EIGEN_CORE_MODULE_H

2
libs/eigen/Eigen/IterativeLinearSolvers
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@@ -27,7 +27,7 @@
* - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteLUT - incomplete LU factorization with dual thresholding
*
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport, AccelerateSupport.
*
\code
#include <Eigen/IterativeLinearSolvers>

6
libs/eigen/Eigen/LU
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@@ -28,11 +28,7 @@
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/misc/lapacke_helpers.h"
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"

6
libs/eigen/Eigen/QR
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@@ -36,11 +36,7 @@
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/misc/lapacke_helpers.h"
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif

2
libs/eigen/Eigen/SPQRSupport
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@@ -28,7 +28,7 @@
*
*/
#include "src/CholmodSupport/CholmodSupport.h"
#include "Eigen/CholmodSupport"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif

5
libs/eigen/Eigen/SVD
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@@ -36,14 +36,17 @@
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#ifndef EIGEN_USE_LAPACKE_STRICT
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/SVD/BDCSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

1
libs/eigen/Eigen/SparseCore
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@@ -41,7 +41,6 @@
#include "src/SparseCore/SparseCompressedBase.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/SparseMap.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/SparseRef.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"

421
libs/eigen/Eigen/src/AccelerateSupport/AccelerateSupport.h Normal file
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@@ -0,0 +1,421 @@
#ifndef EIGEN_ACCELERATESUPPORT_H
#define EIGEN_ACCELERATESUPPORT_H
#include <Accelerate/Accelerate.h>
#include <Eigen/Sparse>
namespace Eigen {
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
class AccelerateImpl;
/** \ingroup AccelerateSupport_Module
* \class AccelerateLLT
* \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ additional information about the matrix structure. Default is Lower.
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
*/
template <typename MatrixType, int UpLo = Lower>
using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateLDLT
* \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ additional information about the matrix structure. Default is Lower.
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
*/
template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateLDLTUnpivoted
* \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ additional information about the matrix structure. Default is Lower.
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
*/
template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateLDLTSBK
* \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static pivoting
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ additional information about the matrix structure. Default is Lower.
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
*/
template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateLDLTTPP
* \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ additional information about the matrix structure. Default is Lower.
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
*/
template <typename MatrixType, int UpLo = Lower>
using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateQR
* \brief A QR factorization and solver based on Accelerate
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateQR
*/
template <typename MatrixType>
using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
/** \ingroup AccelerateSupport_Module
* \class AccelerateCholeskyAtA
* \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
*
* \warning Only single and double precision real scalar types are supported by Accelerate
*
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
*
* \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
*/
template <typename MatrixType>
using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
namespace internal {
template <typename T>
struct AccelFactorizationDeleter {
void operator()(T* sym) {
if (sym) {
SparseCleanup(*sym);
delete sym;
sym = nullptr;
}
}
};
template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
struct SparseTypesTraitBase {
typedef DenseVecT AccelDenseVector;
typedef DenseMatT AccelDenseMatrix;
typedef SparseMatT AccelSparseMatrix;
typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
typedef NumFactT NumericFactorization;
typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
};
template <typename Scalar>
struct SparseTypesTrait {};
template <>
struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
SparseOpaqueFactorization_Double> {};
template <>
struct SparseTypesTrait<float>
: SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
};
} // end namespace internal
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
protected:
using Base = SparseSolverBase<AccelerateImpl>;
using Base::derived;
using Base::m_isInitialized;
public:
using Base::_solve_impl;
typedef MatrixType_ MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::StorageIndex StorageIndex;
enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
enum { UpLo = UpLo_ };
using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
AccelerateImpl() {
m_isInitialized = false;
auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
if (check_flag_set(UpLo_, Symmetric)) {
m_sparseKind = SparseSymmetric;
m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
} else if (check_flag_set(UpLo_, UnitLower)) {
m_sparseKind = SparseUnitTriangular;
m_triType = SparseLowerTriangle;
} else if (check_flag_set(UpLo_, UnitUpper)) {
m_sparseKind = SparseUnitTriangular;
m_triType = SparseUpperTriangle;
} else if (check_flag_set(UpLo_, StrictlyLower)) {
m_sparseKind = SparseTriangular;
m_triType = SparseLowerTriangle;
} else if (check_flag_set(UpLo_, StrictlyUpper)) {
m_sparseKind = SparseTriangular;
m_triType = SparseUpperTriangle;
} else if (check_flag_set(UpLo_, Lower)) {
m_sparseKind = SparseTriangular;
m_triType = SparseLowerTriangle;
} else if (check_flag_set(UpLo_, Upper)) {
m_sparseKind = SparseTriangular;
m_triType = SparseUpperTriangle;
} else {
m_sparseKind = SparseOrdinary;
m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
}
m_order = SparseOrderDefault;
}
explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
~AccelerateImpl() {}
inline Index cols() const { return m_nCols; }
inline Index rows() const { return m_nRows; }
ComputationInfo info() const {
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
void analyzePattern(const MatrixType& matrix);
void factorize(const MatrixType& matrix);
void compute(const MatrixType& matrix);
template <typename Rhs, typename Dest>
void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
/** Sets the ordering algorithm to use. */
void setOrder(SparseOrder_t order) { m_order = order; }
private:
template <typename T>
void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
const Index nColumnsStarts = a.cols() + 1;
columnStarts.resize(nColumnsStarts);
for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
SparseAttributes_t attributes{};
attributes.transpose = false;
attributes.triangle = m_triType;
attributes.kind = m_sparseKind;
SparseMatrixStructure structure{};
structure.attributes = attributes;
structure.rowCount = static_cast<int>(a.rows());
structure.columnCount = static_cast<int>(a.cols());
structure.blockSize = 1;
structure.columnStarts = columnStarts.data();
structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
A.structure = structure;
A.data = const_cast<T*>(a.valuePtr());
}
void doAnalysis(AccelSparseMatrix& A) {
m_numericFactorization.reset(nullptr);
SparseSymbolicFactorOptions opts{};
opts.control = SparseDefaultControl;
opts.orderMethod = m_order;
opts.order = nullptr;
opts.ignoreRowsAndColumns = nullptr;
opts.malloc = malloc;
opts.free = free;
opts.reportError = nullptr;
m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
SparseStatus_t status = m_symbolicFactorization->status;
updateInfoStatus(status);
if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
}
void doFactorization(AccelSparseMatrix& A) {
SparseStatus_t status = SparseStatusReleased;
if (m_symbolicFactorization) {
m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
status = m_numericFactorization->status;
if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
}
updateInfoStatus(status);
}
protected:
void updateInfoStatus(SparseStatus_t status) const {
switch (status) {
case SparseStatusOK:
m_info = Success;
break;
case SparseFactorizationFailed:
case SparseMatrixIsSingular:
m_info = NumericalIssue;
break;
case SparseInternalError:
case SparseParameterError:
case SparseStatusReleased:
default:
m_info = InvalidInput;
break;
}
}
mutable ComputationInfo m_info;
Index m_nRows, m_nCols;
std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
SparseKind_t m_sparseKind;
SparseTriangle_t m_triType;
SparseOrder_t m_order;
};
/** Computes the symbolic and numeric decomposition of matrix \a a */
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
m_nRows = a.rows();
m_nCols = a.cols();
AccelSparseMatrix A{};
std::vector<long> columnStarts;
buildAccelSparseMatrix(a, A, columnStarts);
doAnalysis(A);
if (m_symbolicFactorization) doFactorization(A);
m_isInitialized = true;
}
/** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
*
* This function is particularly useful when solving for several problems having the same structure.
*
* \sa factorize()
*/
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
m_nRows = a.rows();
m_nCols = a.cols();
AccelSparseMatrix A{};
std::vector<long> columnStarts;
buildAccelSparseMatrix(a, A, columnStarts);
doAnalysis(A);
m_isInitialized = true;
}
/** Performs a numeric decomposition of matrix \a a.
*
* The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
AccelSparseMatrix A{};
std::vector<long> columnStarts;
buildAccelSparseMatrix(a, A, columnStarts);
doFactorization(A);
}
template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
template <typename Rhs, typename Dest>
void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
MatrixBase<Dest>& x) const {
if (!m_numericFactorization) {
m_info = InvalidInput;
return;
}
eigen_assert(m_nRows == b.rows());
eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
SparseStatus_t status = SparseStatusOK;
Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
AccelDenseMatrix xmat{};
xmat.attributes = SparseAttributes_t();
xmat.columnCount = static_cast<int>(x.cols());
xmat.rowCount = static_cast<int>(x.rows());
xmat.columnStride = xmat.rowCount;
xmat.data = x_ptr;
AccelDenseMatrix bmat{};
bmat.attributes = SparseAttributes_t();
bmat.columnCount = static_cast<int>(b.cols());
bmat.rowCount = static_cast<int>(b.rows());
bmat.columnStride = bmat.rowCount;
bmat.data = b_ptr;
SparseSolve(*m_numericFactorization, bmat, xmat);
updateInfoStatus(status);
}
} // end namespace Eigen
#endif // EIGEN_ACCELERATESUPPORT_H

3
libs/eigen/Eigen/src/AccelerateSupport/InternalHeaderCheck.h Normal file
View File

@@ -0,0 +1,3 @@
#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
#error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
#endif

3
libs/eigen/Eigen/src/Cholesky/InternalHeaderCheck.h Normal file
View File

@@ -0,0 +1,3 @@
#ifndef EIGEN_CHOLESKY_MODULE_H
#error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
#endif

55
libs/eigen/Eigen/src/Cholesky/LDLT.h
View File

@@ -13,11 +13,13 @@
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename _MatrixType, int _UpLo> struct traits<LDLT<_MatrixType, _UpLo> >
: traits<_MatrixType>
template<typename MatrixType_, int UpLo_> struct traits<LDLT<MatrixType_, UpLo_> >
: traits<MatrixType_>
{
typedef MatrixXpr XprKind;
typedef SolverStorage StorageKind;
@@ -37,8 +39,8 @@ namespace internal {
*
* \brief Robust Cholesky decomposition of a matrix with pivoting
*
* \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
* The other triangular part won't be read.
*
* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
@@ -56,11 +58,11 @@ namespace internal {
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
: public SolverBase<LDLT<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_> class LDLT
: public SolverBase<LDLT<MatrixType_, UpLo_> >
{
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
typedef SolverBase<LDLT> Base;
friend class SolverBase<LDLT>;
@@ -68,7 +70,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
enum {
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
UpLo = UpLo_
};
typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
@@ -244,7 +246,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LDLT& adjoint() const { return *this; };
const LDLT& adjoint() const { return *this; }
EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
@@ -270,10 +272,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
/** \internal
* Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
@@ -441,7 +440,7 @@ template<> struct ldlt_inplace<Lower>
// Update the terms of L
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
if(!numext::is_exactly_zero(gamma))
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
@@ -494,12 +493,10 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
template<typename MatrixType, int UpLo_>
template<typename InputType>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
LDLT<MatrixType,UpLo_>& LDLT<MatrixType,UpLo_>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
@@ -510,7 +507,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputTyp
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
if (UpLo_ == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
@@ -534,9 +531,9 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputTyp
* \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1.
* \sa setZero()
*/
template<typename MatrixType, int _UpLo>
template<typename MatrixType, int UpLo_>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
LDLT<MatrixType,UpLo_>& LDLT<MatrixType,UpLo_>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,UpLo_>::RealScalar& sigma)
{
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
@@ -562,16 +559,16 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Deri
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType, int _UpLo>
template<typename MatrixType_, int UpLo_>
template<typename RhsType, typename DstType>
void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
void LDLT<MatrixType_,UpLo_>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
_solve_impl_transposed<true>(rhs, dst);
}
template<typename _MatrixType,int _UpLo>
template<typename MatrixType_,int UpLo_>
template<bool Conjugate, typename RhsType, typename DstType>
void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
void LDLT<MatrixType_,UpLo_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
{
// dst = P b
dst = m_transpositions * rhs;
@@ -624,9 +621,9 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType
*
* \sa LDLT::solve(), MatrixBase::ldlt()
*/
template<typename MatrixType,int _UpLo>
template<typename MatrixType,int UpLo_>
template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
bool LDLT<MatrixType,UpLo_>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
eigen_assert(m_matrix.rows() == bAndX.rows());
@@ -639,8 +636,8 @@ bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: P^T L D L^* P.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
template<typename MatrixType, int UpLo_>
MatrixType LDLT<MatrixType,UpLo_>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();

59
libs/eigen/Eigen/src/Cholesky/LLT.h
View File

@@ -10,12 +10,14 @@
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal{
template<typename _MatrixType, int _UpLo> struct traits<LLT<_MatrixType, _UpLo> >
: traits<_MatrixType>
template<typename MatrixType_, int UpLo_> struct traits<LLT<MatrixType_, UpLo_> >
: traits<MatrixType_>
{
typedef MatrixXpr XprKind;
typedef SolverStorage StorageKind;
@@ -32,8 +34,8 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
*
* \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
* The other triangular part won't be read.
*
* This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
@@ -58,16 +60,16 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
* Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is considered.
* Therefore, the strict lower part does not have to store correct values.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
template<typename _MatrixType, int _UpLo> class LLT
: public SolverBase<LLT<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_> class LLT
: public SolverBase<LLT<MatrixType_, UpLo_> >
{
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
typedef SolverBase<LLT> Base;
friend class SolverBase<LLT>;
@@ -79,7 +81,7 @@ template<typename _MatrixType, int _UpLo> class LLT
enum {
PacketSize = internal::packet_traits<Scalar>::size,
AlignmentMask = int(PacketSize)-1,
UpLo = _UpLo
UpLo = UpLo_
};
typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
@@ -199,7 +201,7 @@ template<typename _MatrixType, int _UpLo> class LLT
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; };
const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; }
inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
@@ -217,10 +219,7 @@ template<typename _MatrixType, int _UpLo> class LLT
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
/** \internal
* Used to compute and store L
@@ -243,7 +242,7 @@ static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef internal::remove_all_t<ColXpr> ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
typedef Matrix<Scalar,Dynamic,1> TempVectorType;
typedef typename TempVectorType::SegmentReturnType TempVecSegment;
@@ -298,7 +297,7 @@ static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const
if(rs)
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
if(!numext::is_exactly_zero(gamma))
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
@@ -427,12 +426,10 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
* Example: \include TutorialLinAlgComputeTwice.cpp
* Output: \verbinclude TutorialLinAlgComputeTwice.out
*/
template<typename MatrixType, int _UpLo>
template<typename MatrixType, int UpLo_>
template<typename InputType>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
LLT<MatrixType,UpLo_>& LLT<MatrixType,UpLo_>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
@@ -444,7 +441,7 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
if (UpLo_ == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
@@ -464,9 +461,9 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
* then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
* of same dimension.
*/
template<typename _MatrixType, int _UpLo>
template<typename MatrixType_, int UpLo_>
template<typename VectorType>
LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma)
LLT<MatrixType_,UpLo_> & LLT<MatrixType_,UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
eigen_assert(v.size()==m_matrix.cols());
@@ -480,16 +477,16 @@ LLT<_MatrixType,_UpLo> & LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v,
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType,int _UpLo>
template<typename MatrixType_,int UpLo_>
template<typename RhsType, typename DstType>
void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
void LLT<MatrixType_,UpLo_>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
_solve_impl_transposed<true>(rhs, dst);
}
template<typename _MatrixType,int _UpLo>
template<typename MatrixType_,int UpLo_>
template<bool Conjugate, typename RhsType, typename DstType>
void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
void LLT<MatrixType_,UpLo_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
{
dst = rhs;
@@ -511,9 +508,9 @@ void LLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename MatrixType, int UpLo_>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
void LLT<MatrixType,UpLo_>::solveInPlace(const MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());
@@ -524,8 +521,8 @@ void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
/** \returns the matrix represented by the decomposition,
* i.e., it returns the product: L L^*.
* This function is provided for debug purpose. */
template<typename MatrixType, int _UpLo>
MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
template<typename MatrixType, int UpLo_>
MatrixType LLT<MatrixType,UpLo_>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
return matrixL() * matrixL().adjoint().toDenseMatrix();

128
libs/eigen/Eigen/src/Cholesky/LLT_LAPACKE.h
View File

@@ -33,64 +33,86 @@
#ifndef EIGEN_LLT_LAPACKE_H
#define EIGEN_LLT_LAPACKE_H
namespace Eigen {
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename Scalar> struct lapacke_llt;
namespace lapacke_helpers {
// -------------------------------------------------------------------------------------------------------------------
// Dispatch for rank update handling upper and lower parts
// -------------------------------------------------------------------------------------------------------------------
#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
template<> struct lapacke_llt<EIGTYPE> \
{ \
template<typename MatrixType> \
static inline Index potrf(MatrixType& m, char uplo) \
{ \
lapack_int matrix_order; \
lapack_int size, lda, info, StorageOrder; \
EIGTYPE* a; \
eigen_assert(m.rows()==m.cols()); \
/* Set up parameters for ?potrf */ \
size = convert_index<lapack_int>(m.rows()); \
StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
a = &(m.coeffRef(0,0)); \
lda = convert_index<lapack_int>(m.outerStride()); \
\
info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
}; \
template<> struct llt_inplace<EIGTYPE, Lower> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
}; \
template<> struct llt_inplace<EIGTYPE, Upper> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ \
Transpose<MatrixType> matt(mat); \
return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
} \
};
template<UpLoType Mode>
struct rank_update {};
EIGEN_LAPACKE_LLT(double, double, d)
EIGEN_LAPACKE_LLT(float, float, s)
EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
template<>
struct rank_update<Lower> {
template<typename MatrixType, typename VectorType>
static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
}
};
template<>
struct rank_update<Upper> {
template<typename MatrixType, typename VectorType>
static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
Transpose<MatrixType> matt(mat);
return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
}
};
// -------------------------------------------------------------------------------------------------------------------
// Generic lapacke llt implementation that hands of to the dispatches
// -------------------------------------------------------------------------------------------------------------------
template<typename Scalar, UpLoType Mode>
struct lapacke_llt {
template<typename MatrixType>
static Index blocked(MatrixType& m)
{
eigen_assert(m.rows() == m.cols());
if(m.rows() == 0) {
return -1;
}
/* Set up parameters for ?potrf */
lapack_int size = to_lapack(m.rows());
lapack_int matrix_order = lapack_storage_of(m);
Scalar* a = &(m.coeffRef(0,0));
lapack_int lda = to_lapack(m.outerStride());
lapack_int info = potrf(matrix_order, translate_mode<Mode>, size, to_lapack(a), lda );
info = (info==0) ? -1 : info>0 ? info-1 : size;
return info;
}
template<typename MatrixType, typename VectorType>
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
return rank_update<Mode>::run(mat, vec, sigma);
}
};
}
// end namespace lapacke_helpers
/*
* Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
* type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
* in LLT.h for double, float and complex double, complex float types.
*/
#define EIGEN_LAPACKE_LLT(EIGTYPE) \
template<> struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
template<> struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
EIGEN_LAPACKE_LLT(double)
EIGEN_LAPACKE_LLT(float)
EIGEN_LAPACKE_LLT(std::complex<double>)
EIGEN_LAPACKE_LLT(std::complex<float>)
#undef EIGEN_LAPACKE_LLT
} // end namespace internal

106
libs/eigen/Eigen/src/CholmodSupport/CholmodSupport.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_CHOLMODSUPPORT_H
#define EIGEN_CHOLMODSUPPORT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -54,8 +56,8 @@ template<> struct cholmod_configure_matrix<std::complex<double> > {
/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
* Note that the data are shared.
*/
template<typename _Scalar, int _Options, typename _StorageIndex>
cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
template<typename Scalar_, int Options_, typename StorageIndex_>
cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,StorageIndex_> > mat)
{
cholmod_sparse res;
res.nzmax = mat.nonZeros();
@@ -80,11 +82,11 @@ cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> >
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_StorageIndex,int>::value)
if (internal::is_same<StorageIndex_,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
else if (internal::is_same<StorageIndex_,SuiteSparse_long>::value)
{
res.itype = CHOLMOD_LONG;
}
@@ -94,39 +96,39 @@ cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> >
}
// setup res.xtype
internal::cholmod_configure_matrix<_Scalar>::run(res);
internal::cholmod_configure_matrix<Scalar_>::run(res);
res.stype = 0;
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
template<typename Scalar_, int Options_, typename Index_>
const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_,Options_,Index_>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.const_cast_derived()));
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
template<typename Scalar_, int Options_, typename Index_>
const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_,Options_,Index_>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.const_cast_derived()));
return res;
}
/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
* The data are not copied but shared. */
template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
template<typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_,Options_,Index_>, UpLo>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.matrix().const_cast_derived()));
if(UpLo==Upper) res.stype = 1;
if(UpLo==Lower) res.stype = -1;
// swap stype for rowmajor matrices (only works for real matrices)
EIGEN_STATIC_ASSERT((_Options & RowMajorBit) == 0 || NumTraits<_Scalar>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
if(_Options & RowMajorBit) res.stype *=-1;
EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
if(Options_ & RowMajorBit) res.stype *=-1;
return res;
}
@@ -155,9 +157,9 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
* The data are not copied but shared. */
template<typename Scalar, int Flags, typename StorageIndex>
MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
Map<SparseMatrix<Scalar,Flags,StorageIndex> > viewAsEigen(cholmod_sparse& cm)
{
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
return Map<SparseMatrix<Scalar,Flags,StorageIndex> >
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
@@ -167,11 +169,11 @@ namespace internal {
// template specializations for int and long that call the correct cholmod method
#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name) \
template<typename _StorageIndex> inline ret cm_ ## name (cholmod_common &Common) { return cholmod_ ## name (&Common); } \
template<typename StorageIndex_> inline ret cm_ ## name (cholmod_common &Common) { return cholmod_ ## name (&Common); } \
template<> inline ret cm_ ## name<SuiteSparse_long> (cholmod_common &Common) { return cholmod_l_ ## name (&Common); }
#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1) \
template<typename _StorageIndex> inline ret cm_ ## name (t1& a1, cholmod_common &Common) { return cholmod_ ## name (&a1, &Common); } \
template<typename StorageIndex_> inline ret cm_ ## name (t1& a1, cholmod_common &Common) { return cholmod_ ## name (&a1, &Common); } \
template<> inline ret cm_ ## name<SuiteSparse_long> (t1& a1, cholmod_common &Common) { return cholmod_l_ ## name (&a1, &Common); }
EIGEN_CHOLMOD_SPECIALIZE0(int, start)
@@ -183,14 +185,14 @@ EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
template<typename _StorageIndex> inline cholmod_dense* cm_solve (int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common &Common) { return cholmod_solve (sys, &L, &B, &Common); }
template<typename StorageIndex_> inline cholmod_dense* cm_solve (int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common &Common) { return cholmod_solve (sys, &L, &B, &Common); }
template<> inline cholmod_dense* cm_solve<SuiteSparse_long> (int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common &Common) { return cholmod_l_solve (sys, &L, &B, &Common); }
template<typename _StorageIndex> inline cholmod_sparse* cm_spsolve (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve (sys, &L, &B, &Common); }
template<typename StorageIndex_> inline cholmod_sparse* cm_spsolve (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve (sys, &L, &B, &Common); }
template<> inline cholmod_sparse* cm_spsolve<SuiteSparse_long> (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_l_spsolve (sys, &L, &B, &Common); }
template<typename _StorageIndex>
inline int cm_factorize_p (cholmod_sparse* A, double beta[2], _StorageIndex* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p (A, beta, fset, fsize, L, &Common); }
template<typename StorageIndex_>
inline int cm_factorize_p (cholmod_sparse* A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p (A, beta, fset, fsize, L, &Common); }
template<>
inline int cm_factorize_p<SuiteSparse_long> (cholmod_sparse* A, double beta[2], SuiteSparse_long* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_l_factorize_p (A, beta, fset, fsize, L, &Common); }
@@ -210,7 +212,7 @@ enum CholmodMode {
* \brief The base class for the direct Cholesky factorization of Cholmod
* \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
*/
template<typename _MatrixType, int _UpLo, typename Derived>
template<typename MatrixType_, int UpLo_, typename Derived>
class CholmodBase : public SparseSolverBase<Derived>
{
protected:
@@ -218,8 +220,8 @@ class CholmodBase : public SparseSolverBase<Derived>
using Base::derived;
using Base::m_isInitialized;
public:
typedef _MatrixType MatrixType;
enum { UpLo = _UpLo };
typedef MatrixType_ MatrixType;
enum { UpLo = UpLo_ };
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef MatrixType CholMatrixType;
@@ -436,7 +438,7 @@ class CholmodBase : public SparseSolverBase<Derived>
if (m_cholmodFactor->is_ll)
logDet *= 2.0;
return logDet;
};
}
template<typename Stream>
void dumpMemory(Stream& /*s*/)
@@ -461,8 +463,8 @@ class CholmodBase : public SparseSolverBase<Derived>
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
@@ -473,15 +475,15 @@ class CholmodBase : public SparseSolverBase<Derived>
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_ = Lower>
class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
CholmodSimplicialLLT() : Base() { init(); }
@@ -512,8 +514,8 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
@@ -524,15 +526,15 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_ = Lower>
class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
CholmodSimplicialLDLT() : Base() { init(); }
@@ -561,8 +563,8 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
@@ -573,15 +575,15 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_ = Lower>
class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
CholmodSupernodalLLT() : Base() { init(); }
@@ -612,8 +614,8 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* On the other hand, it does not provide access to the result of the factorization.
* The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
* \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
@@ -624,15 +626,15 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
template<typename MatrixType_, int UpLo_ = Lower>
class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> >
{
typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
using Base::m_cholmod;
public:
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
CholmodDecomposition() : Base() { init(); }

3
libs/eigen/Eigen/src/CholmodSupport/InternalHeaderCheck.h Normal file
View File

@@ -0,0 +1,3 @@
#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
#error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
#endif

197
libs/eigen/Eigen/src/Core/ArithmeticSequence.h
View File

@@ -10,69 +10,18 @@
#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
#define EIGEN_ARITHMETIC_SEQUENCE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
#if (!EIGEN_HAS_CXX11) || !((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
template<typename T> struct aseq_negate {};
template<> struct aseq_negate<Index> {
typedef Index type;
};
template<int N> struct aseq_negate<FixedInt<N> > {
typedef FixedInt<-N> type;
};
// Compilation error in the following case:
template<> struct aseq_negate<FixedInt<DynamicIndex> > {};
template<typename FirstType,typename SizeType,typename IncrType,
bool FirstIsSymbolic=symbolic::is_symbolic<FirstType>::value,
bool SizeIsSymbolic =symbolic::is_symbolic<SizeType>::value>
struct aseq_reverse_first_type {
typedef Index type;
};
template<typename FirstType,typename SizeType,typename IncrType>
struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,true> {
typedef symbolic::AddExpr<FirstType,
symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
symbolic::ValueExpr<IncrType> >
> type;
};
template<typename SizeType,typename IncrType,typename EnableIf = void>
struct aseq_reverse_first_type_aux {
typedef Index type;
};
template<typename SizeType,typename IncrType>
struct aseq_reverse_first_type_aux<SizeType,IncrType,typename internal::enable_if<bool((SizeType::value+IncrType::value)|0x1)>::type> {
typedef FixedInt<(SizeType::value-1)*IncrType::value> type;
};
template<typename FirstType,typename SizeType,typename IncrType>
struct aseq_reverse_first_type<FirstType,SizeType,IncrType,true,false> {
typedef typename aseq_reverse_first_type_aux<SizeType,IncrType>::type Aux;
typedef symbolic::AddExpr<FirstType,symbolic::ValueExpr<Aux> > type;
};
template<typename FirstType,typename SizeType,typename IncrType>
struct aseq_reverse_first_type<FirstType,SizeType,IncrType,false,true> {
typedef symbolic::AddExpr<symbolic::ProductExpr<symbolic::AddExpr<SizeType,symbolic::ValueExpr<FixedInt<-1> > >,
symbolic::ValueExpr<IncrType> >,
symbolic::ValueExpr<> > type;
};
#endif
// Helper to cleanup the type of the increment:
template<typename T> struct cleanup_seq_incr {
typedef typename cleanup_index_type<T,DynamicIndex>::type type;
};
}
} // namespace internal
//--------------------------------------------------------------------------------
// seq(first,last,incr) and seqN(first,size,incr)
@@ -137,21 +86,9 @@ protected:
IncrType m_incr;
public:
#if EIGEN_HAS_CXX11 && ((!EIGEN_COMP_GNUC) || EIGEN_COMP_GNUC>=48)
auto reverse() const -> decltype(Eigen::seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr)) {
return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
}
#else
protected:
typedef typename internal::aseq_negate<IncrType>::type ReverseIncrType;
typedef typename internal::aseq_reverse_first_type<FirstType,SizeType,IncrType>::type ReverseFirstType;
public:
ArithmeticSequence<ReverseFirstType,SizeType,ReverseIncrType>
reverse() const {
return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
}
#endif
};
/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
@@ -200,7 +137,6 @@ auto seq(FirstType f, LastType l);
#else // EIGEN_PARSED_BY_DOXYGEN
#if EIGEN_HAS_CXX11
template<typename FirstType,typename LastType>
auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
( typename internal::cleanup_index_type<LastType>::type(l)
@@ -226,101 +162,11 @@ auto seq(FirstType f, LastType l, IncrType incr)
CleanedIncrType(incr));
}
#else // EIGEN_HAS_CXX11
template<typename FirstType,typename LastType>
typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index> >::type
seq(FirstType f, LastType l)
{
return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())));
}
template<typename FirstTypeDerived,typename LastType>
typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
ArithmeticSequence<FirstTypeDerived, symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,symbolic::ValueExpr<> >,
symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l)
{
return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+fix<1>()));
}
template<typename FirstType,typename LastTypeDerived>
typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
symbolic::ValueExpr<internal::FixedInt<1> > > > >::type
seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l)
{
return seqN(typename internal::cleanup_index_type<FirstType>::type(f),(l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
}
template<typename FirstTypeDerived,typename LastTypeDerived>
ArithmeticSequence<FirstTypeDerived,
symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::NegateExpr<FirstTypeDerived> >,symbolic::ValueExpr<internal::FixedInt<1> > > >
seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l)
{
return seqN(f.derived(),(l.derived()-f.derived()+fix<1>()));
}
template<typename FirstType,typename LastType, typename IncrType>
typename internal::enable_if<!(symbolic::is_symbolic<FirstType>::value || symbolic::is_symbolic<LastType>::value),
ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,Index,typename internal::cleanup_seq_incr<IncrType>::type> >::type
seq(FirstType f, LastType l, IncrType incr)
{
typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
Index((typename internal::cleanup_index_type<LastType>::type(l)-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr)), incr);
}
template<typename FirstTypeDerived,typename LastType, typename IncrType>
typename internal::enable_if<!symbolic::is_symbolic<LastType>::value,
ArithmeticSequence<FirstTypeDerived,
symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<symbolic::NegateExpr<FirstTypeDerived>,
symbolic::ValueExpr<> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
typename internal::cleanup_seq_incr<IncrType>::type> >::type
seq(const symbolic::BaseExpr<FirstTypeDerived> &f, LastType l, IncrType incr)
{
typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
return seqN(f.derived(),(typename internal::cleanup_index_type<LastType>::type(l)-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
}
template<typename FirstType,typename LastTypeDerived, typename IncrType>
typename internal::enable_if<!symbolic::is_symbolic<FirstType>::value,
ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,symbolic::ValueExpr<> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
typename internal::cleanup_seq_incr<IncrType>::type> >::type
seq(FirstType f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
{
typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
(l.derived()-typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
}
template<typename FirstTypeDerived,typename LastTypeDerived, typename IncrType>
ArithmeticSequence<FirstTypeDerived,
symbolic::QuotientExpr<symbolic::AddExpr<symbolic::AddExpr<LastTypeDerived,
symbolic::NegateExpr<FirstTypeDerived> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
symbolic::ValueExpr<typename internal::cleanup_seq_incr<IncrType>::type> >,
typename internal::cleanup_seq_incr<IncrType>::type>
seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<LastTypeDerived> &l, IncrType incr)
{
typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
return seqN(f.derived(),(l.derived()-f.derived()+CleanedIncrType(incr))/CleanedIncrType(incr), incr);
}
#endif // EIGEN_HAS_CXX11
#endif // EIGEN_PARSED_BY_DOXYGEN
namespace placeholders {
#if EIGEN_HAS_CXX11 || defined(EIGEN_PARSED_BY_DOXYGEN)
/** \cpp11
* \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
*
@@ -329,9 +175,9 @@ seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<Last
* \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
template<typename SizeType,typename IncrType>
auto lastN(SizeType size, IncrType incr)
-> decltype(seqN(Eigen::last-(size-fix<1>())*incr, size, incr))
-> decltype(seqN(Eigen::placeholders::last-(size-fix<1>())*incr, size, incr))
{
return seqN(Eigen::last-(size-fix<1>())*incr, size, incr);
return seqN(Eigen::placeholders::last-(size-fix<1>())*incr, size, incr);
}
/** \cpp11
@@ -342,18 +188,19 @@ auto lastN(SizeType size, IncrType incr)
* \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
template<typename SizeType>
auto lastN(SizeType size)
-> decltype(seqN(Eigen::last+fix<1>()-size, size))
-> decltype(seqN(Eigen::placeholders::last+fix<1>()-size, size))
{
return seqN(Eigen::last+fix<1>()-size, size);
return seqN(Eigen::placeholders::last+fix<1>()-size, size);
}
#endif
} // namespace placeholders
namespace internal {
// Convert a symbolic span into a usable one (i.e., remove last/end "keywords")
template<typename T>
struct make_size_type {
typedef typename internal::conditional<symbolic::is_symbolic<T>::value, Index, T>::type type;
typedef std::conditional_t<symbolic::is_symbolic<T>::value, Index, T> type;
};
template<typename FirstType,typename SizeType,typename IncrType,int XprSize>
@@ -387,25 +234,23 @@ struct get_compile_time_incr<ArithmeticSequence<FirstType,SizeType,IncrType> > {
* \code using namespace Eigen::indexing; \endcode
* is equivalent to:
* \code
using Eigen::all;
using Eigen::fix;
using Eigen::seq;
using Eigen::seqN;
using Eigen::lastN; // c++11 only
using Eigen::last;
using Eigen::lastp1;
using Eigen::fix;
using Eigen::placeholders::all;
using Eigen::placeholders::last;
using Eigen::placeholders::lastN; // c++11 only
using Eigen::placeholders::lastp1;
\endcode
*/
namespace indexing {
using Eigen::all;
using Eigen::fix;
using Eigen::seq;
using Eigen::seqN;
#if EIGEN_HAS_CXX11
using Eigen::lastN;
#endif
using Eigen::last;
using Eigen::lastp1;
using Eigen::fix;
using Eigen::placeholders::all;
using Eigen::placeholders::last;
using Eigen::placeholders::lastN;
using Eigen::placeholders::lastp1;
}
} // end namespace Eigen

38
libs/eigen/Eigen/src/Core/Array.h
View File

@@ -10,14 +10,16 @@
#ifndef EIGEN_ARRAY_H
#define EIGEN_ARRAY_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> > : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> > XprBase;
};
}
@@ -41,16 +43,16 @@ struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : tra
*
* \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
class Array
: public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
{
public:
typedef PlainObjectBase<Array> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Array)
enum { Options = _Options };
enum { Options = Options_ };
typedef typename Base::PlainObject PlainObject;
protected:
@@ -131,7 +133,6 @@ class Array
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
@@ -142,17 +143,14 @@ class Array
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
@@ -160,9 +158,7 @@ class Array
Base::operator=(std::move(other));
return *this;
}
#endif
#if EIGEN_HAS_CXX11
/** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
*
* Example: \include Array_variadic_ctor_cxx11.cpp
@@ -197,16 +193,15 @@ class Array
*
* \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
#endif // end EIGEN_HAS_CXX11
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Array(
const std::initializer_list<std::initializer_list<Scalar>>& list)
: Base(list) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
@@ -214,7 +209,6 @@ class Array
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(val0, val1);
}
@@ -249,7 +243,6 @@ class Array
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
@@ -261,7 +254,6 @@ class Array
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
@@ -283,8 +275,8 @@ class Array
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType>::type = PrivateType())
std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType> = PrivateType())
: Base(other.derived())
{ }
@@ -359,8 +351,6 @@ EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
#undef EIGEN_MAKE_ARRAY_TYPEDEFS
#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
#if EIGEN_HAS_CXX11
#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix) \
/** \ingroup arraytypedefs */ \
/** \brief \cpp11 */ \
@@ -392,8 +382,6 @@ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
#undef EIGEN_MAKE_ARRAY_TYPEDEFS
#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
#endif // EIGEN_HAS_CXX11
#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
using Eigen::Matrix##SizeSuffix##TypeSuffix; \
using Eigen::Vector##SizeSuffix##TypeSuffix; \

4
libs/eigen/Eigen/src/Core/ArrayBase.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_ARRAYBASE_H
#define EIGEN_ARRAYBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename ExpressionType> class MatrixWrapper;
@@ -21,7 +23,7 @@ template<typename ExpressionType> class MatrixWrapper;
*
* An array is similar to a dense vector or matrix. While matrices are mathematical
* objects with well defined linear algebra operators, an array is just a collection
* of scalar values arranged in a one or two dimensionnal fashion. As the main consequence,
* of scalar values arranged in a one or two dimensional fashion. As the main consequence,
* all operations applied to an array are performed coefficient wise. Furthermore,
* arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
* constructors allowing to easily write generic code working for both scalar values

32
libs/eigen/Eigen/src/Core/ArrayWrapper.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_ARRAYWRAPPER_H
#define EIGEN_ARRAYWRAPPER_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class ArrayWrapper
@@ -26,12 +28,12 @@ namespace Eigen {
namespace internal {
template<typename ExpressionType>
struct traits<ArrayWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
: public traits<remove_all_t<typename ExpressionType::Nested> >
{
typedef ArrayXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
@@ -45,13 +47,13 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
typedef ArrayBase<ArrayWrapper> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef internal::remove_all_t<ExpressionType> NestedExpression;
typedef typename internal::conditional<
typedef std::conditional_t<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
> ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
@@ -91,7 +93,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
inline void evalTo(Dest& dst) const { dst = m_expression; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
const internal::remove_all_t<NestedExpressionType>&
nestedExpression() const
{
return m_expression;
@@ -124,12 +126,12 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
namespace internal {
template<typename ExpressionType>
struct traits<MatrixWrapper<ExpressionType> >
: public traits<typename remove_all<typename ExpressionType::Nested>::type >
: public traits<remove_all_t<typename ExpressionType::Nested> >
{
typedef MatrixXpr XprKind;
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
@@ -143,13 +145,13 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
typedef typename internal::remove_all<ExpressionType>::type NestedExpression;
typedef internal::remove_all_t<ExpressionType> NestedExpression;
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef std::conditional_t<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
> ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
@@ -185,7 +187,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
const internal::remove_all_t<NestedExpressionType>&
nestedExpression() const
{
return m_expression;

2
libs/eigen/Eigen/src/Core/Assign.h
View File

@@ -12,6 +12,8 @@
#ifndef EIGEN_ASSIGN_H
#define EIGEN_ASSIGN_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename Derived>

59
libs/eigen/Eigen/src/Core/AssignEvaluator.h
View File

@@ -12,6 +12,8 @@
#ifndef EIGEN_ASSIGN_EVALUATOR_H
#define EIGEN_ASSIGN_EVALUATOR_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
// This implementation is based on Assign.h
@@ -40,7 +42,7 @@ public:
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
JointAlignment = plain_enum_min(DstAlignment, SrcAlignment)
};
private:
@@ -51,8 +53,8 @@ private:
InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
RestrictedInnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(InnerSize,MaxPacketSize),
RestrictedLinearSize = EIGEN_SIZE_MIN_PREFER_FIXED(Dst::SizeAtCompileTime,MaxPacketSize),
RestrictedInnerSize = min_size_prefer_fixed(InnerSize, MaxPacketSize),
RestrictedLinearSize = min_size_prefer_fixed(Dst::SizeAtCompileTime, MaxPacketSize),
OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime
};
@@ -111,7 +113,7 @@ public:
|| int(Traversal) == SliceVectorizedTraversal
};
typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
typedef std::conditional_t<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType> PacketType;
private:
enum {
@@ -216,7 +218,7 @@ struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop>
@@ -285,7 +287,7 @@ struct copy_using_evaluator_innervec_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
@@ -325,10 +327,9 @@ struct dense_assignment_loop;
template<typename Kernel, int Unrolling>
struct dense_assignment_loop<Kernel, AllAtOnceTraversal, Unrolling>
{
EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel& /*kernel*/)
EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE EIGEN_CONSTEXPR run(Kernel& /*kernel*/)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
EIGEN_STATIC_ASSERT(int(DstXprType::SizeAtCompileTime) == 0,
EIGEN_STATIC_ASSERT(int(Kernel::DstEvaluatorType::XprType::SizeAtCompileTime) == 0,
EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
}
};
@@ -386,7 +387,7 @@ struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&, Index, Index) {}
};
template <>
@@ -402,7 +403,7 @@ struct unaligned_dense_assignment_loop<false>
Index end)
#else
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel,
Index start,
Index end)
#endif
@@ -415,7 +416,7 @@ struct unaligned_dense_assignment_loop<false>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
const Index size = kernel.size();
typedef typename Kernel::Scalar Scalar;
@@ -443,7 +444,7 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
@@ -469,7 +470,7 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
@@ -511,7 +512,7 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
const Index size = kernel.size();
for(Index i = 0; i < size; ++i)
@@ -522,7 +523,7 @@ struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
@@ -536,7 +537,7 @@ struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
@@ -584,7 +585,7 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
@@ -766,7 +767,7 @@ void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::a
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
{
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
@@ -844,8 +845,8 @@ void call_assignment(const Dst& dst, const Src& src)
// Deal with "assume-aliasing"
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
void call_assignment(Dst& dst, const Src& src, const Func& func, std::enable_if_t< evaluator_assume_aliasing<Src>::value, void*> = 0)
{
typename plain_matrix_type<Src>::type tmp(src);
call_assignment_no_alias(dst, tmp, func);
@@ -853,7 +854,7 @@ void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
void call_assignment(Dst& dst, const Src& src, const Func& func, std::enable_if_t<!evaluator_assume_aliasing<Src>::value, void*> = 0)
{
call_assignment_no_alias(dst, src, func);
}
@@ -861,7 +862,7 @@ void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable
// by-pass "assume-aliasing"
// When there is no aliasing, we require that 'dst' has been properly resized
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
@@ -869,7 +870,7 @@ void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func&
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
@@ -878,8 +879,8 @@ void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
) && int(Dst::SizeAtCompileTime) != 1
};
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst> ActualDstTypeCleaned;
typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&> ActualDstType;
ActualDstType actualDst(dst);
// TODO check whether this is the right place to perform these checks:
@@ -911,14 +912,14 @@ void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src, const
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
// TODO check whether this is the right place to perform these checks:
@@ -929,7 +930,7 @@ void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func&
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
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,typename Src::Scalar>());

6
libs/eigen/Eigen/src/Core/Assign_MKL.h Executable file → Normal file
View File

@@ -34,6 +34,8 @@
#ifndef EIGEN_ASSIGN_VML_H
#define EIGEN_ASSIGN_VML_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -82,7 +84,7 @@ class vml_assign_traits
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
Dense2Dense, std::enable_if_t<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
@@ -142,7 +144,7 @@ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
template< typename DstXprType, typename SrcXprNested, typename Plain> \
struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
Dense2Dense, std::enable_if_t<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \

104
libs/eigen/Eigen/src/Core/BandMatrix.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_BANDMATRIX_H
#define EIGEN_BANDMATRIX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -41,7 +43,7 @@ class BandMatrixBase : public EigenBase<Derived>
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
? 1 + Supers + Subs
: Dynamic,
SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime,ColsAtCompileTime)
};
public:
@@ -96,13 +98,13 @@ class BandMatrixBase : public EigenBase<Derived>
DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
? Dynamic
: (ActualIndex<0
? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
: EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
: min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
};
typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
typedef typename internal::conditional<Conjugate,
typedef std::conditional_t<Conjugate,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
BuildType>::type Type;
BuildType> Type;
};
/** \returns a vector expression of the \a N -th sub or super diagonal */
@@ -161,12 +163,12 @@ class BandMatrixBase : public EigenBase<Derived>
*
* \brief Represents a rectangular matrix with a banded storage
*
* \tparam _Scalar Numeric type, i.e. float, double, int
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \tparam _Supers Number of super diagonal
* \tparam _Subs Number of sub diagonal
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* \tparam Scalar_ Numeric type, i.e. float, double, int
* \tparam Rows_ Number of rows, or \b Dynamic
* \tparam Cols_ Number of columns, or \b Dynamic
* \tparam Supers_ Number of super diagonal
* \tparam Subs_ Number of sub diagonal
* \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
@@ -174,29 +176,29 @@ class BandMatrixBase : public EigenBase<Derived>
* \sa class TridiagonalMatrix
*/
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
template<typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
struct traits<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
{
typedef _Scalar Scalar;
typedef Scalar_ Scalar;
typedef Dense StorageKind;
typedef Eigen::Index StorageIndex;
enum {
CoeffReadCost = NumTraits<Scalar>::ReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
RowsAtCompileTime = Rows_,
ColsAtCompileTime = Cols_,
MaxRowsAtCompileTime = Rows_,
MaxColsAtCompileTime = Cols_,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
Supers = Supers_,
Subs = Subs_,
Options = Options_,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor> CoefficientsType;
};
template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options>
class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> >
template<typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_,Rows,Cols,Supers,Subs,Options> >
{
public:
@@ -233,32 +235,32 @@ class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Sub
internal::variable_if_dynamic<Index, Subs> m_subs;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
class BandMatrixWrapper;
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
struct traits<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
{
typedef typename _CoefficientsType::Scalar Scalar;
typedef typename _CoefficientsType::StorageKind StorageKind;
typedef typename _CoefficientsType::StorageIndex StorageIndex;
typedef typename CoefficientsType_::Scalar Scalar;
typedef typename CoefficientsType_::StorageKind StorageKind;
typedef typename CoefficientsType_::StorageIndex StorageIndex;
enum {
CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _Rows,
MaxColsAtCompileTime = _Cols,
CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
RowsAtCompileTime = Rows_,
ColsAtCompileTime = Cols_,
MaxRowsAtCompileTime = Rows_,
MaxColsAtCompileTime = Cols_,
Flags = LvalueBit,
Supers = _Supers,
Subs = _Subs,
Options = _Options,
Supers = Supers_,
Subs = Subs_,
Options = Options_,
DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
};
typedef _CoefficientsType CoefficientsType;
typedef CoefficientsType_ CoefficientsType;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
{
public:
@@ -266,12 +268,12 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs)
explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=Rows_, Index cols=Cols_, Index supers=Supers_, Index subs=Subs_)
: m_coeffs(coeffs),
m_rows(rows), m_supers(supers), m_subs(subs)
{
EIGEN_UNUSED_VARIABLE(cols);
//internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
// eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
}
/** \returns the number of columns */
@@ -291,9 +293,9 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
protected:
const CoefficientsType& m_coeffs;
internal::variable_if_dynamic<Index, _Rows> m_rows;
internal::variable_if_dynamic<Index, _Supers> m_supers;
internal::variable_if_dynamic<Index, _Subs> m_subs;
internal::variable_if_dynamic<Index, Rows_> m_rows;
internal::variable_if_dynamic<Index, Supers_> m_supers;
internal::variable_if_dynamic<Index, Subs_> m_subs;
};
/**
@@ -330,16 +332,16 @@ class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint
struct BandShape {};
template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options>
struct evaluator_traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
template<typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
struct evaluator_traits<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
: public evaluator_traits_base<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
{
typedef BandShape Shape;
};
template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options>
struct evaluator_traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
: public evaluator_traits_base<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> >
template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
struct evaluator_traits<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
: public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
{
typedef BandShape Shape;
};

18
libs/eigen/Eigen/src/Core/Block.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_BLOCK_H
#define EIGEN_BLOCK_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -21,7 +23,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename ref_selector<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
@@ -110,7 +112,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
typedef internal::remove_all_t<XprType> NestedExpression;
/** Column or Row constructor
*/
@@ -260,19 +262,19 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
inline PacketScalar packet(Index index) const
EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const
{
return m_xpr.template packet<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
@@ -280,7 +282,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
@@ -295,7 +297,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
const internal::remove_all_t<XprTypeNested>& nestedExpression() const
{
return m_xpr;
}
@@ -378,7 +380,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const EIGEN_NOEXCEPT
const internal::remove_all_t<XprTypeNested>& nestedExpression() const EIGEN_NOEXCEPT
{
return m_xpr;
}

66
libs/eigen/Eigen/src/Core/BooleanRedux.h
View File

@@ -10,58 +10,62 @@
#ifndef EIGEN_ALLANDANY_H
#define EIGEN_ALLANDANY_H
namespace Eigen {
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename Derived, int UnrollCount, int Rows>
template<typename Derived, int UnrollCount, int InnerSize>
struct all_unroller
{
enum {
col = (UnrollCount-1) / Rows,
row = (UnrollCount-1) % Rows
IsRowMajor = (int(Derived::Flags) & int(RowMajor)),
i = (UnrollCount-1) / InnerSize,
j = (UnrollCount-1) % InnerSize
};
EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
{
return all_unroller<Derived, UnrollCount-1, Rows>::run(mat) && mat.coeff(row, col);
return all_unroller<Derived, UnrollCount-1, InnerSize>::run(mat) && mat.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i);
}
};
template<typename Derived, int Rows>
struct all_unroller<Derived, 0, Rows>
template<typename Derived, int InnerSize>
struct all_unroller<Derived, 0, InnerSize>
{
EIGEN_DEVICE_FUNC static inline bool run(const Derived &/*mat*/) { return true; }
};
template<typename Derived, int Rows>
struct all_unroller<Derived, Dynamic, Rows>
template<typename Derived, int InnerSize>
struct all_unroller<Derived, Dynamic, InnerSize>
{
EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
};
template<typename Derived, int UnrollCount, int Rows>
template<typename Derived, int UnrollCount, int InnerSize>
struct any_unroller
{
enum {
col = (UnrollCount-1) / Rows,
row = (UnrollCount-1) % Rows
IsRowMajor = (int(Derived::Flags) & int(RowMajor)),
i = (UnrollCount-1) / InnerSize,
j = (UnrollCount-1) % InnerSize
};
EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1, Rows>::run(mat) || mat.coeff(row, col);
return any_unroller<Derived, UnrollCount-1, InnerSize>::run(mat) || mat.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i);
}
};
template<typename Derived, int Rows>
struct any_unroller<Derived, 0, Rows>
template<typename Derived, int InnerSize>
struct any_unroller<Derived, 0, InnerSize>
{
EIGEN_DEVICE_FUNC static inline bool run(const Derived & /*mat*/) { return false; }
};
template<typename Derived, int Rows>
struct any_unroller<Derived, Dynamic, Rows>
template<typename Derived, int InnerSize>
struct any_unroller<Derived, Dynamic, InnerSize>
{
EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
};
@@ -81,16 +85,16 @@ EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT,
};
Evaluator evaluator(derived());
if(unroll)
return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, InnerSizeAtCompileTime>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (!evaluator.coeff(i, j)) return false;
for(Index i = 0; i < derived().outerSize(); ++i)
for(Index j = 0; j < derived().innerSize(); ++j)
if (!evaluator.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i)) return false;
return true;
}
}
@@ -105,16 +109,16 @@ EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (int(Evaluator::CoeffReadCost) + int(NumTraits<Scalar>::AddCost)) <= EIGEN_UNROLLING_LIMIT,
};
Evaluator evaluator(derived());
if(unroll)
return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, InnerSizeAtCompileTime>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (evaluator.coeff(i, j)) return true;
for(Index i = 0; i < derived().outerSize(); ++i)
for(Index j = 0; j < derived().innerSize(); ++j)
if (evaluator.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i)) return true;
return false;
}
}
@@ -134,7 +138,7 @@ EIGEN_DEVICE_FUNC inline Eigen::Index DenseBase<Derived>::count() const
* \sa allFinite()
*/
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::hasNaN() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isNaN().any();
@@ -148,7 +152,7 @@ inline bool DenseBase<Derived>::hasNaN() const
* \sa hasNaN()
*/
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::allFinite() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isFinite().all();
@@ -156,7 +160,7 @@ inline bool DenseBase<Derived>::allFinite() const
return !((derived()-derived()).hasNaN());
#endif
}
} // end namespace Eigen
#endif // EIGEN_ALLANDANY_H

4
libs/eigen/Eigen/src/Core/CommaInitializer.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_COMMAINITIALIZER_H
#define EIGEN_COMMAINITIALIZER_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class CommaInitializer
@@ -45,7 +47,7 @@ struct CommaInitializer
{
eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols()
&& "Cannot comma-initialize a 0x0 matrix (operator<<)");
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(), other.cols()) = other;
}
/* Copy/Move constructor which transfers ownership. This is crucial in

12
libs/eigen/Eigen/src/Core/ConditionEstimator.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_CONDITIONESTIMATOR_H
#define EIGEN_CONDITIONESTIMATOR_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -160,12 +162,12 @@ rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Deco
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
if (numext::is_exactly_zero(matrix_norm)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
return (numext::is_exactly_zero(inverse_matrix_norm) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
} // namespace internal

43
libs/eigen/Eigen/src/Core/CoreEvaluators.h
View File

@@ -13,6 +13,8 @@
#ifndef EIGEN_COREEVALUATORS_H
#define EIGEN_COREEVALUATORS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -498,7 +500,7 @@ struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
: evaluator_base<CwiseNullaryOp<NullaryOp,PlainObjectType> >
{
typedef CwiseNullaryOp<NullaryOp,PlainObjectType> XprType;
typedef typename internal::remove_all<PlainObjectType>::type PlainObjectTypeCleaned;
typedef internal::remove_all_t<PlainObjectType> PlainObjectTypeCleaned;
enum {
CoeffReadCost = internal::functor_traits<NullaryOp>::Cost,
@@ -655,9 +657,9 @@ struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased
)
),
Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
Alignment = EIGEN_PLAIN_ENUM_MIN(
EIGEN_PLAIN_ENUM_MIN(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
evaluator<Arg3>::Alignment)
Alignment = plain_enum_min(
plain_enum_min(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
evaluator<Arg3>::Alignment)
};
EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr) : m_d(xpr)
@@ -751,7 +753,7 @@ struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBase
)
),
Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<Lhs>::Alignment,evaluator<Rhs>::Alignment)
Alignment = plain_enum_min(evaluator<Lhs>::Alignment, evaluator<Rhs>::Alignment)
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -810,11 +812,11 @@ protected:
// -------------------- CwiseUnaryView --------------------
template<typename UnaryOp, typename ArgType>
struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType>, IndexBased>
: evaluator_base<CwiseUnaryView<UnaryOp, ArgType> >
template<typename UnaryOp, typename ArgType, typename StrideType>
struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType, StrideType>, IndexBased>
: evaluator_base<CwiseUnaryView<UnaryOp, ArgType, StrideType> >
{
typedef CwiseUnaryView<UnaryOp, ArgType> XprType;
typedef CwiseUnaryView<UnaryOp, ArgType, StrideType> XprType;
enum {
CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
@@ -900,7 +902,8 @@ struct mapbase_evaluator : evaluator_base<Derived>
m_innerStride(map.innerStride()),
m_outerStride(map.outerStride())
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(evaluator<Derived>::Flags&PacketAccessBit, internal::inner_stride_at_compile_time<Derived>::ret==1),
EIGEN_STATIC_ASSERT(check_implication((evaluator<Derived>::Flags & PacketAccessBit) != 0,
internal::inner_stride_at_compile_time<Derived>::ret == 1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
@@ -1072,7 +1075,7 @@ struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic)
&& (OuterStrideAtCompileTime!=0)
&& (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ArgType>::Alignment, Alignment0)
Alignment = plain_enum_min(evaluator<ArgType>::Alignment, Alignment0)
};
typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -1222,8 +1225,8 @@ struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAc
explicit block_evaluator(const XprType& block)
: mapbase_evaluator<XprType, typename XprType::PlainObject>(block)
{
// TODO: for the 3.3 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
eigen_assert(((internal::UIntPtr(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
eigen_internal_assert((internal::is_constant_evaluated() || (internal::UIntPtr(block.data()) % plain_enum_max(1,evaluator<XprType>::Alignment)) == 0) \
&& "data is not aligned");
}
};
@@ -1239,12 +1242,12 @@ struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
typedef Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> XprType;
enum {
CoeffReadCost = evaluator<ConditionMatrixType>::CoeffReadCost
+ EIGEN_PLAIN_ENUM_MAX(evaluator<ThenMatrixType>::CoeffReadCost,
evaluator<ElseMatrixType>::CoeffReadCost),
+ plain_enum_max(evaluator<ThenMatrixType>::CoeffReadCost,
evaluator<ElseMatrixType>::CoeffReadCost),
Flags = (unsigned int)evaluator<ThenMatrixType>::Flags & evaluator<ElseMatrixType>::Flags & HereditaryBits,
Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
Alignment = plain_enum_min(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -1295,7 +1298,7 @@ struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
};
typedef typename internal::nested_eval<ArgType,Factor>::type ArgTypeNested;
typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
enum {
CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
@@ -1379,7 +1382,7 @@ template<typename XprType>
struct evaluator_wrapper_base
: evaluator_base<XprType>
{
typedef typename remove_all<typename XprType::NestedExpressionType>::type ArgType;
typedef remove_all_t<typename XprType::NestedExpressionType> ArgType;
enum {
CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
Flags = evaluator<ArgType>::Flags,
@@ -1720,14 +1723,14 @@ struct evaluator<EvalToTemp<ArgType> >
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: m_result(xpr.arg())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
}
// This constructor is used when nesting an EvalTo evaluator in another evaluator
EIGEN_DEVICE_FUNC evaluator(const ArgType& arg)
: m_result(arg)
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
}
protected:

2
libs/eigen/Eigen/src/Core/CoreIterators.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_COREITERATORS_H
#define EIGEN_COREITERATORS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core

36
libs/eigen/Eigen/src/Core/CwiseBinaryOp.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_CWISE_BINARY_OP_H
#define EIGEN_CWISE_BINARY_OP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -19,7 +21,7 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
// we must not inherit from traits<Lhs> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Lhs>::type Ancestor;
typedef remove_all_t<Lhs> Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
@@ -43,10 +45,10 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
typename traits<Rhs>::StorageIndex>::type StorageIndex;
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
typedef std::remove_reference_t<LhsNested> LhsNested_;
typedef std::remove_reference_t<RhsNested> RhsNested_;
enum {
Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,LhsNested_::Flags & RowMajorBit,RhsNested_::Flags & RowMajorBit>::value
};
};
} // end namespace internal
@@ -84,9 +86,9 @@ class CwiseBinaryOp :
{
public:
typedef typename internal::remove_all<BinaryOp>::type Functor;
typedef typename internal::remove_all<LhsType>::type Lhs;
typedef typename internal::remove_all<RhsType>::type Rhs;
typedef internal::remove_all_t<BinaryOp> Functor;
typedef internal::remove_all_t<LhsType> Lhs;
typedef internal::remove_all_t<RhsType> Rhs;
typedef typename CwiseBinaryOpImpl<
BinaryOp, LhsType, RhsType,
@@ -95,12 +97,15 @@ class CwiseBinaryOp :
BinaryOp>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
typedef typename internal::ref_selector<LhsType>::type LhsNested;
typedef typename internal::ref_selector<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
typedef std::remove_reference_t<LhsNested> LhsNested_;
typedef std::remove_reference_t<RhsNested> RhsNested_;
#if EIGEN_COMP_MSVC && EIGEN_HAS_CXX11
#if EIGEN_COMP_MSVC
//Required for Visual Studio or the Copy constructor will probably not get inlined!
EIGEN_STRONG_INLINE
CwiseBinaryOp(const CwiseBinaryOp<BinaryOp,LhsType,RhsType>&) = default;
@@ -110,29 +115,26 @@ class CwiseBinaryOp :
CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
Index rows() const EIGEN_NOEXCEPT {
// return the fixed size type if available to enable compile time optimizations
return internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows();
return internal::traits<internal::remove_all_t<LhsNested>>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
Index cols() const EIGEN_NOEXCEPT {
// return the fixed size type if available to enable compile time optimizations
return internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols();
return internal::traits<internal::remove_all_t<LhsNested>>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols();
}
/** \returns the left hand side nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const _LhsNested& lhs() const { return m_lhs; }
const LhsNested_& lhs() const { return m_lhs; }
/** \returns the right hand side nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const _RhsNested& rhs() const { return m_rhs; }
const RhsNested_& rhs() const { return m_rhs; }
/** \returns the functor representing the binary operation */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const BinaryOp& functor() const { return m_functor; }

29
libs/eigen/Eigen/src/Core/CwiseNullaryOp.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_CWISE_NULLARY_OP_H
#define EIGEN_CWISE_NULLARY_OP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -304,6 +306,20 @@ DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
}
template <typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
DenseBase<Derived>::EqualSpaced(Index size, const Scalar& low, const Scalar& step) {
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::equalspaced_op<Scalar>(low, step));
}
template <typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
DenseBase<Derived>::EqualSpaced(const Scalar& low, const Scalar& step) {
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::equalspaced_op<Scalar>(low, step));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
@@ -453,6 +469,19 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(
return setLinSpaced(size(), low, high);
}
template <typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(Index newSize, const Scalar& low,
const Scalar& step) {
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::equalspaced_op<Scalar>(low, step));
}
template <typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(const Scalar& low,
const Scalar& step) {
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return setEqualSpaced(size(), low, step);
}
// zero:
/** \returns an expression of a zero matrix.

74
libs/eigen/Eigen/src/Core/CwiseTernaryOp.h
View File

@@ -12,6 +12,8 @@
#ifndef EIGEN_CWISE_TERNARY_OP_H
#define EIGEN_CWISE_TERNARY_OP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -19,7 +21,7 @@ template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
// we must not inherit from traits<Arg1> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Arg1>::type Ancestor;
typedef remove_all_t<Arg1> Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
@@ -41,10 +43,10 @@ struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
typedef typename Arg1::Nested Arg1Nested;
typedef typename Arg2::Nested Arg2Nested;
typedef typename Arg3::Nested Arg3Nested;
typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
enum { Flags = _Arg1Nested::Flags & RowMajorBit };
typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
enum { Flags = Arg1Nested_::Flags & RowMajorBit };
};
} // end namespace internal
@@ -87,9 +89,23 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
typedef typename internal::remove_all<Arg3Type>::type Arg3;
typedef internal::remove_all_t<Arg1Type> Arg1;
typedef internal::remove_all_t<Arg2Type> Arg2;
typedef internal::remove_all_t<Arg3Type> Arg3;
// 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)
typedef typename CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
@@ -99,29 +115,15 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
const Arg3& a3,
const TernaryOp& func = TernaryOp())
: m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
// 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());
}
@@ -130,14 +132,14 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
if (internal::traits<internal::remove_all_t<Arg1Nested>>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
internal::traits<internal::remove_all_t<Arg2Nested>>::
RowsAtCompileTime == Dynamic)
return m_arg3.rows();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
else if (internal::traits<internal::remove_all_t<Arg1Nested>>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
internal::traits<internal::remove_all_t<Arg3Nested>>::
RowsAtCompileTime == Dynamic)
return m_arg2.rows();
else
@@ -147,14 +149,14 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
if (internal::traits<internal::remove_all_t<Arg1Nested>>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
internal::traits<internal::remove_all_t<Arg2Nested>>::
ColsAtCompileTime == Dynamic)
return m_arg3.cols();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
else if (internal::traits<internal::remove_all_t<Arg1Nested>>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
internal::traits<internal::remove_all_t<Arg3Nested>>::
ColsAtCompileTime == Dynamic)
return m_arg2.cols();
else
@@ -163,13 +165,13 @@ class CwiseTernaryOp : public CwiseTernaryOpImpl<
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg1Nested& arg1() const { return m_arg1; }
const Arg1Nested_& arg1() const { return m_arg1; }
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg2Nested& arg2() const { return m_arg2; }
const Arg2Nested_& arg2() const { return m_arg2; }
/** \returns the third argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg3Nested& arg3() const { return m_arg3; }
const Arg3Nested_& arg3() const { return m_arg3; }
/** \returns the functor representing the ternary operation */
EIGEN_DEVICE_FUNC
const TernaryOp& functor() const { return m_functor; }

12
libs/eigen/Eigen/src/Core/CwiseUnaryOp.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_CWISE_UNARY_OP_H
#define EIGEN_CWISE_UNARY_OP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -22,9 +24,9 @@ struct traits<CwiseUnaryOp<UnaryOp, XprType> >
UnaryOp(const typename XprType::Scalar&)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
enum {
Flags = _XprTypeNested::Flags & RowMajorBit
Flags = XprTypeNested_::Flags & RowMajorBit
};
};
}
@@ -59,7 +61,7 @@ class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
typedef internal::remove_all_t<XprType> NestedExpression;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
@@ -76,12 +78,12 @@ class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type&
const internal::remove_all_t<XprTypeNested>&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_all<XprTypeNested>::type&
internal::remove_all_t<XprTypeNested>&
nestedExpression() { return m_xpr; }
protected:

65
libs/eigen/Eigen/src/Core/CwiseUnaryView.h
View File

@@ -10,35 +10,42 @@
#ifndef EIGEN_CWISE_UNARY_VIEW_H
#define EIGEN_CWISE_UNARY_VIEW_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename ViewOp, typename MatrixType>
struct traits<CwiseUnaryView<ViewOp, MatrixType> >
template<typename ViewOp, typename MatrixType, typename StrideType>
struct traits<CwiseUnaryView<ViewOp, MatrixType, StrideType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(const typename traits<MatrixType>::Scalar&)
>::type Scalar;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
typedef remove_all_t<MatrixTypeNested> MatrixTypeNested_;
enum {
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = traits<_MatrixTypeNested>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
Flags = traits<MatrixTypeNested_>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret,
// need to cast the sizeof's from size_t to int explicitly, otherwise:
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? (MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? (outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
: int(StrideType::OuterStrideAtCompileTime)
};
};
}
template<typename ViewOp, typename MatrixType, typename StorageKind>
template<typename ViewOp, typename MatrixType, typename StrideType, typename StorageKind>
class CwiseUnaryViewImpl;
/** \class CwiseUnaryView
@@ -54,15 +61,15 @@ class CwiseUnaryViewImpl;
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
template<typename ViewOp, typename MatrixType, typename StrideType>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
typedef internal::remove_all_t<MatrixType> NestedExpression;
explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
: m_matrix(mat), m_functor(func) {}
@@ -78,11 +85,11 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC const typename internal::remove_all<MatrixTypeNested>::type&
EIGEN_DEVICE_FUNC const internal::remove_all_t<MatrixTypeNested>&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC typename internal::remove_reference<MatrixTypeNested>::type&
EIGEN_DEVICE_FUNC std::remove_reference_t<MatrixTypeNested>&
nestedExpression() { return m_matrix; }
protected:
@@ -91,22 +98,22 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
};
// Generic API dispatcher
template<typename ViewOp, typename XprType, typename StorageKind>
template<typename ViewOp, typename XprType, typename StrideType, typename StorageKind>
class CwiseUnaryViewImpl
: public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
: public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type Base;
};
template<typename ViewOp, typename MatrixType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
template<typename ViewOp, typename MatrixType, typename StrideType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,StrideType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type
{
public:
typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
typedef CwiseUnaryView<ViewOp, MatrixType,StrideType> Derived;
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType,StrideType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
@@ -116,12 +123,16 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
return StrideType::InnerStrideAtCompileTime != 0
? int(StrideType::InnerStrideAtCompileTime)
: derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
{
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
return StrideType::OuterStrideAtCompileTime != 0
? int(StrideType::OuterStrideAtCompileTime)
: derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
protected:
EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)

83
libs/eigen/Eigen/src/Core/DenseBase.h
View File

@@ -11,17 +11,12 @@
#ifndef EIGEN_DENSEBASE_H
#define EIGEN_DENSEBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
}
} // end namespace internal
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
/** \class DenseBase
* \ingroup Core_Module
@@ -110,8 +105,7 @@ template<typename Derived> class DenseBase
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
@@ -138,8 +132,8 @@ template<typename Derived> class DenseBase
* \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
*/
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret),
MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime),
/**< This value is equal to the maximum possible number of coefficients that this expression
* might have. If this expression might have an arbitrarily high number of coefficients,
* this value is set to \a Dynamic.
@@ -206,13 +200,8 @@ template<typename Derived> class DenseBase
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
PlainMatrix, PlainArray>::type PlainObject;
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index nonZeros() const { return size(); }
typedef std::conditional_t<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
PlainMatrix, PlainArray> PlainObject;
/** \returns the outer size.
*
@@ -269,6 +258,8 @@ template<typename Derived> class DenseBase
EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> SequentialLinSpacedReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> RandomAccessLinSpacedReturnType;
/** \internal Represents a vector with equally spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::equalspaced_op<Scalar>, PlainObject> RandomAccessEqualSpacedReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
@@ -324,9 +315,9 @@ template<typename Derived> class DenseBase
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
TransposeReturnType transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
typedef Transpose<const Derived> ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
const ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC
void transposeInPlace();
@@ -347,6 +338,11 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType
EqualSpaced(Index size, const Scalar& low, const Scalar& step);
EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType
EqualSpaced(const Scalar& low, const Scalar& step);
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
@@ -368,6 +364,8 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setEqualSpaced(Index size, const Scalar& low, const Scalar& step);
EIGEN_DEVICE_FUNC Derived& setEqualSpaced(const Scalar& low, const Scalar& step);
EIGEN_DEVICE_FUNC Derived& setZero();
EIGEN_DEVICE_FUNC Derived& setOnes();
EIGEN_DEVICE_FUNC Derived& setRandom();
@@ -387,15 +385,15 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
inline bool hasNaN() const;
inline bool allFinite() const;
EIGEN_DEVICE_FUNC inline bool hasNaN() const;
EIGEN_DEVICE_FUNC inline bool allFinite() const;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
typedef internal::add_const_on_value_type_t<typename internal::eval<Derived>::type> EvalReturnType;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
@@ -439,9 +437,9 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> EIGEN_DEVICE_FUNC
inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
inline const std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&> forceAlignedAccessIf() const;
template<bool Enable> EIGEN_DEVICE_FUNC
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
inline std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&> forceAlignedAccessIf();
EIGEN_DEVICE_FUNC Scalar sum() const;
EIGEN_DEVICE_FUNC Scalar mean() const;
@@ -621,27 +619,21 @@ template<typename Derived> class DenseBase
/** This is the const version of iterator (aka read-only) */
typedef random_access_iterator_type const_iterator;
#else
typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
internal::pointer_based_stl_iterator<Derived>,
internal::generic_randaccess_stl_iterator<Derived>
>::type iterator_type;
typedef std::conditional_t< (Flags&DirectAccessBit)==DirectAccessBit,
internal::pointer_based_stl_iterator<Derived>,
internal::generic_randaccess_stl_iterator<Derived>
> iterator_type;
typedef typename internal::conditional< (Flags&DirectAccessBit)==DirectAccessBit,
internal::pointer_based_stl_iterator<const Derived>,
internal::generic_randaccess_stl_iterator<const Derived>
>::type const_iterator_type;
typedef std::conditional_t< (Flags&DirectAccessBit)==DirectAccessBit,
internal::pointer_based_stl_iterator<const Derived>,
internal::generic_randaccess_stl_iterator<const Derived>
> const_iterator_type;
// Stl-style iterators are supported only for vectors.
typedef typename internal::conditional< IsVectorAtCompileTime,
iterator_type,
void
>::type iterator;
typedef std::conditional_t<IsVectorAtCompileTime, iterator_type, void> iterator;
typedef typename internal::conditional< IsVectorAtCompileTime,
const_iterator_type,
void
>::type const_iterator;
typedef std::conditional_t<IsVectorAtCompileTime, const_iterator_type, void> const_iterator;
#endif
inline iterator begin();
@@ -678,14 +670,13 @@ template<typename Derived> class DenseBase
protected:
EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
/** Default constructor. Do nothing. */
EIGEN_DEVICE_FUNC DenseBase()
{
EIGEN_DEVICE_FUNC constexpr DenseBase() {
/* Just checks for self-consistency of the flags.
* Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
*/
#ifdef EIGEN_INTERNAL_DEBUGGING
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
&& EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
EIGEN_STATIC_ASSERT((internal::check_implication(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
&& internal::check_implication(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
#endif
}

14
libs/eigen/Eigen/src/Core/DenseCoeffsBase.h
View File

@@ -10,12 +10,14 @@
#ifndef EIGEN_DENSECOEFFSBASE_H
#define EIGEN_DENSECOEFFSBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename T> struct add_const_on_value_type_if_arithmetic
{
typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type;
typedef std::conditional_t<is_arithmetic<T>::value, T, add_const_on_value_type_t<T>> type;
};
}
@@ -43,13 +45,13 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
// - This is the return type of the coeff() method.
// - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
// to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
// - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems
// - The is_arithmetic check is required since "const int", "const double", etc. will cause warnings on some systems
// while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
// not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
const Scalar&,
typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type
>::type CoeffReturnType;
typedef std::conditional_t<bool(internal::traits<Derived>::Flags&LvalueBit),
const Scalar&,
std::conditional_t<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>
> CoeffReturnType;
typedef typename internal::add_const_on_value_type_if_arithmetic<
typename internal::packet_traits<Scalar>::type

490
libs/eigen/Eigen/src/Core/DenseStorage.h
View File

@@ -18,20 +18,20 @@
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
#endif
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size>
EIGEN_DEVICE_FUNC
void check_static_allocation_size()
{
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
template <typename T, int Size>
EIGEN_DEVICE_FUNC constexpr void check_static_allocation_size() {
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
#endif
#endif
}
/** \internal
@@ -45,35 +45,30 @@ struct plain_array
{
T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC constexpr plain_array() { check_static_allocation_size<T, Size>(); }
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
check_static_allocation_size<T, Size>();
}
};
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
#elif EIGEN_COMP_GNUC
// GCC 4.7 is too aggressive in its optimizations and remove the alignment test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
eigen_assert((internal::is_constant_evaluated() \
|| (internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0) \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
eigen_assert((internal::is_constant_evaluated() || (internal::UIntPtr(array) & (sizemask)) == 0) \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
@@ -84,17 +79,13 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_DEVICE_FUNC constexpr plain_array() {
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
check_static_allocation_size<T, Size>();
}
};
@@ -103,17 +94,13 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_DEVICE_FUNC constexpr plain_array() {
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
check_static_allocation_size<T, Size>();
}
};
@@ -122,17 +109,13 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 32>
{
EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_DEVICE_FUNC constexpr plain_array() {
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
check_static_allocation_size<T, Size>();
}
};
@@ -141,17 +124,13 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 64>
{
EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_DEVICE_FUNC constexpr plain_array() {
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
check_static_allocation_size<T, Size>();
}
};
@@ -159,8 +138,8 @@ template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC constexpr plain_array() {}
EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {}
};
struct plain_array_helper {
@@ -201,57 +180,32 @@ struct plain_array_helper {
*
* \sa Matrix
*/
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage;
template<typename T, int Size, int Rows_, int Cols_, int Options_> class DenseStorage;
// purely fixed-size matrix
template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage
template<typename T, int Size, int Rows_, int Cols_, int Options_> class DenseStorage
{
internal::plain_array<T,Size,_Options> m_data;
internal::plain_array<T,Size,Options_> m_data;
public:
EIGEN_DEVICE_FUNC DenseStorage() {
constexpr EIGEN_DEVICE_FUNC DenseStorage() {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
#if !EIGEN_HAS_CXX11 || defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
EIGEN_DEVICE_FUNC
#if defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
EIGEN_DEVICE_FUNC constexpr
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
#else
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) = default;
EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
#endif
#if !EIGEN_HAS_CXX11
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
}
#else
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) = default;
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
#if !EIGEN_HAS_CXX11
EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
{
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
if (this != &other)
m_data = std::move(other.m_data);
return *this;
}
#else
EIGEN_DEVICE_FUNC DenseStorage(DenseStorage&&) = default;
EIGEN_DEVICE_FUNC DenseStorage& operator=(DenseStorage&&) = default;
#endif
#endif
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&&) = default;
EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&&) = default;
EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
eigen_internal_assert(size == rows * cols && rows == Rows_ && cols == Cols_);
EIGEN_UNUSED_VARIABLE(size);
EIGEN_UNUSED_VARIABLE(rows);
EIGEN_UNUSED_VARIABLE(cols);
@@ -259,57 +213,148 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_data, other.m_data);
}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT { return Cols_; }
EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index) {}
EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index) {}
EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
template<typename T, int Rows_, int Cols_, int Options_>
class DenseStorage<T, 0, Rows_, Cols_, Options_>
{
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return 0; }
EIGEN_DEVICE_FUNC T *data() { return 0; }
static_assert(Rows_ * Cols_ == 0, "The fixed number of rows times columns must equal the storage size.");
EIGEN_DEVICE_FUNC constexpr DenseStorage() {}
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC constexpr DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT {return Rows_;}
EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT {return Cols_;}
EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC constexpr void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC constexpr const T *data() const { return 0; }
EIGEN_DEVICE_FUNC constexpr T *data() { return 0; }
};
// more specializations for null matrices; these are necessary to resolve ambiguities
template<typename T, int _Options> class DenseStorage<T, 0, Dynamic, Dynamic, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
template<typename T, int _Rows, int _Options> class DenseStorage<T, 0, _Rows, Dynamic, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic, _Cols, _Options>
: public DenseStorage<T, 0, 0, 0, _Options> { };
// dynamic-size matrix with fixed-size storage
template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
template<typename T, int Options_>
class DenseStorage<T, 0, Dynamic, Dynamic, Options_> {
Index m_rows;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols)
{
internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
m_rows = other.m_rows;
m_cols = other.m_cols;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {
eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_rows,other.m_rows);
numext::swap(m_cols,other.m_cols);
}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT {return m_rows;}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) {
m_rows = rows;
m_cols = cols;
eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) {
m_rows = rows;
m_cols = cols;
eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
EIGEN_DEVICE_FUNC T *data() { return nullptr; }
};
template<typename T, int Rows_, int Options_>
class DenseStorage<T, 0, Rows_, Dynamic, Options_> {
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
m_cols = other.m_cols;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {
eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_cols, other.m_cols);
}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return Rows_;}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) {
m_cols = cols;
eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) {
m_cols = cols;
eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
EIGEN_DEVICE_FUNC T *data() { return nullptr; }
};
template<typename T, int Cols_, int Options_>
class DenseStorage<T, 0, Dynamic, Cols_, Options_> {
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
m_rows = other.m_rows;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {
eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_rows, other.m_rows);
}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return Cols_;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) {
m_rows = rows;
eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) {
m_rows = rows;
eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
}
EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
EIGEN_DEVICE_FUNC T *data() { return nullptr; }
};
// dynamic-size matrix with fixed-size storage
template<typename T, int Size, int Options_>
class DenseStorage<T, Size, Dynamic, Dynamic, Options_>
{
internal::plain_array<T,Size,Options_> m_data;
Index m_rows;
Index m_cols;
public:
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols) {
internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
@@ -320,113 +365,121 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{
internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols);
numext::swap(m_rows,other.m_rows);
numext::swap(m_cols,other.m_cols);
}
EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index cols) {
m_rows = rows;
m_cols = cols;
}
EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index cols) {
m_rows = rows;
m_cols = cols;
}
EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
template<typename T, int Size, int Cols_, int Options_>
class DenseStorage<T, Size, Dynamic, Cols_, Options_>
{
internal::plain_array<T,Size,_Options> m_data;
internal::plain_array<T,Size,Options_> m_data;
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows)
{
internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
}
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows) {
internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_rows = other.m_rows;
internal::plain_array_helper::copy(other.m_data, m_rows * _Cols, m_data);
internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{
internal::plain_array_helper::swap(m_data, m_rows * _Cols, other.m_data, other.m_rows * _Cols);
internal::plain_array_helper::swap(m_data, m_rows * Cols_, other.m_data, other.m_rows * Cols_);
numext::swap(m_rows, other.m_rows);
}
EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return Cols_; }
EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
template<typename T, int Size, int Rows_, int Options_>
class DenseStorage<T, Size, Rows_, Dynamic, Options_>
{
internal::plain_array<T,Size,_Options> m_data;
internal::plain_array<T,Size,Options_> m_data;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols)
{
internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
}
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols) {
internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_cols = other.m_cols;
internal::plain_array_helper::copy(other.m_data, _Rows * m_cols, m_data);
internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
}
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
internal::plain_array_helper::swap(m_data, _Rows * m_cols, other.m_data, _Rows * other.m_cols);
internal::plain_array_helper::swap(m_data, Rows_ * m_cols, other.m_data, Rows_ * other.m_cols);
numext::swap(m_cols, other.m_cols);
}
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return Rows_; }
EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
template<typename T, int Options_>
class DenseStorage<T, Dynamic, Dynamic, Dynamic, Options_>
{
T *m_data;
Index m_rows;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)),
m_rows(rows),
m_cols(cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
}
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(other.m_rows*other.m_cols))
, m_rows(other.m_rows)
, m_cols(other.m_cols)
{
@@ -442,7 +495,6 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
@@ -461,8 +513,7 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
numext::swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{
numext::swap(m_data,other.m_data);
@@ -473,7 +524,7 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
}
@@ -481,9 +532,9 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
{
if(size != m_rows*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols);
internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, m_rows*m_cols);
if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
@@ -496,25 +547,25 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
template<typename T, int Rows_, int Options_>
class DenseStorage<T, Dynamic, Rows_, Dynamic, Options_> {
T *m_data;
Index m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_cols(0) {}
explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_cols(cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
eigen_internal_assert(size==rows*cols && rows==Rows_ && cols >=0);
EIGEN_UNUSED_VARIABLE(rows);
}
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(Rows_*other.m_cols))
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*Rows_)
internal::smart_copy(other.m_data, other.m_data+Rows_*m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
@@ -525,7 +576,6 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
@@ -541,26 +591,25 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
numext::swap(m_cols, other.m_cols);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Rows_*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_data,other.m_data);
numext::swap(m_cols,other.m_cols);
}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return _Rows;}
EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, Rows_*m_cols);
m_cols = cols;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
if(size != Rows_*m_cols)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols);
internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Rows_*m_cols);
if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
@@ -572,25 +621,26 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
template<typename T, int Cols_, int Options_>
class DenseStorage<T, Dynamic, Dynamic, Cols_, Options_>
{
T *m_data;
Index m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{
EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0) {}
explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_rows(rows) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
eigen_internal_assert(size==rows*cols && rows>=0 && cols == Cols_);
EIGEN_UNUSED_VARIABLE(cols);
}
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(other.m_rows*Cols_))
, m_rows(other.m_rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*Cols_)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*Cols_, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
@@ -601,7 +651,6 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
@@ -617,26 +666,25 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
numext::swap(m_rows, other.m_rows);
return *this;
}
#endif
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Cols_*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
numext::swap(m_data,other.m_data);
numext::swap(m_rows,other.m_rows);
}
EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC static constexpr Index cols(void) { return Cols_; }
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, m_rows*Cols_);
m_rows = rows;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
if(size != m_rows*Cols_)
{
internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows);
internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Cols_*m_rows);
if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})

59
libs/eigen/Eigen/src/Core/Diagonal.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_DIAGONAL_H
#define EIGEN_DIAGONAL_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class Diagonal
@@ -18,8 +20,8 @@ namespace Eigen {
*
* \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
*
* \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* \tparam MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \tparam DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* A positive value means a superdiagonal, a negative value means a subdiagonal.
* You can also use DynamicIndex so the index can be set at runtime.
*
@@ -38,21 +40,21 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
typedef typename MatrixType::StorageKind StorageKind;
enum {
RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
: (plain_enum_min(MatrixType::RowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - plain_enum_max( DiagIndex, 0))),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
: DiagIndex == DynamicIndex ? min_size_prefer_fixed(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (plain_enum_min(MatrixType::MaxRowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - plain_enum_max( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
Flags = (unsigned int)MatrixTypeNested_::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
OuterStrideAtCompileTime = 0
@@ -60,12 +62,12 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
};
}
template<typename MatrixType, int _DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
template<typename MatrixType, int DiagIndex_> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex_> >::type
{
public:
enum { DiagIndex = _DiagIndex };
enum { DiagIndex = DiagIndex_ };
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
@@ -95,11 +97,11 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index outerStride() const EIGEN_NOEXCEPT { return 0; }
typedef typename internal::conditional<
internal::is_lvalue<MatrixType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef std::conditional_t<
internal::is_lvalue<MatrixType>::value,
Scalar,
const Scalar
> ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
@@ -145,7 +147,7 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
}
EIGEN_DEVICE_FUNC
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
inline const internal::remove_all_t<typename MatrixType::Nested>&
nestedExpression() const
{
return m_matrix;
@@ -191,7 +193,8 @@ MatrixBase<Derived>::diagonal()
/** This is the const version of diagonal(). */
template<typename Derived>
EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalReturnType
EIGEN_DEVICE_FUNC inline
const typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
@@ -209,18 +212,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
EIGEN_DEVICE_FUNC inline Diagonal<Derived, DynamicIndex>
MatrixBase<Derived>::diagonal(Index index)
{
return DiagonalDynamicIndexReturnType(derived(), index);
return Diagonal<Derived, DynamicIndex>(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
EIGEN_DEVICE_FUNC inline const Diagonal<const Derived, DynamicIndex>
MatrixBase<Derived>::diagonal(Index index) const
{
return ConstDiagonalDynamicIndexReturnType(derived(), index);
return Diagonal<const Derived, DynamicIndex>(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -237,20 +240,20 @@ MatrixBase<Derived>::diagonal(Index index) const
template<typename Derived>
template<int Index_>
EIGEN_DEVICE_FUNC
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
inline Diagonal<Derived, Index_>
MatrixBase<Derived>::diagonal()
{
return typename DiagonalIndexReturnType<Index_>::Type(derived());
return Diagonal<Derived, Index_>(derived());
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index_>
EIGEN_DEVICE_FUNC
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
inline const Diagonal<const Derived, Index_>
MatrixBase<Derived>::diagonal() const
{
return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
return Diagonal<const Derived, Index_>(derived());
}
} // end namespace Eigen

200
libs/eigen/Eigen/src/Core/DiagonalMatrix.h
View File

@@ -11,9 +11,23 @@
#ifndef EIGEN_DIAGONALMATRIX_H
#define EIGEN_DIAGONALMATRIX_H
namespace Eigen {
#include "./InternalHeaderCheck.h"
#ifndef EIGEN_PARSED_BY_DOXYGEN
namespace Eigen {
/** \class DiagonalBase
* \ingroup Core_Module
*
* \brief Base class for diagonal matrices and expressions
*
* This is the base class that is inherited by diagonal matrix and related expression
* types, which internally use a vector for storing the diagonal entries. Diagonal
* types always represent square matrices.
*
* \tparam Derived is the derived type, a DiagonalMatrix or DiagonalWrapper.
*
* \sa class DiagonalMatrix, class DiagonalWrapper
*/
template<typename Derived>
class DiagonalBase : public EigenBase<Derived>
{
@@ -37,24 +51,35 @@ class DiagonalBase : public EigenBase<Derived>
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
/** \returns a reference to the derived object. */
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
/** \returns a const reference to the derived object. */
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
/**
* Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
* not an expression.
* \returns A dense matrix, with its diagonal entries set from the the derived object. */
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
/** \returns a reference to the derived object's vector of diagonal coefficients. */
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
/** \returns a const reference to the derived object's vector of diagonal coefficients. */
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
/** \returns the number of rows. */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index rows() const { return diagonal().size(); }
EIGEN_DEVICE_FUNC
/** \returns the number of columns. */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index cols() const { return diagonal().size(); }
/** \returns the diagonal matrix product of \c *this by the dense matrix, \a matrix */
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<Derived,MatrixDerived,LazyProduct>
@@ -63,88 +88,99 @@ class DiagonalBase : public EigenBase<Derived>
return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const InverseReturnType
inverse() const
{
return InverseReturnType(diagonal().cwiseInverse());
}
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
template <typename OtherDerived>
using DiagonalProductReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
DiagonalVectorType, typename OtherDerived::DiagonalVectorType, product)>;
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a other */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC const DiagonalProductReturnType<OtherDerived> operator*(
const DiagonalBase<OtherDerived>& other) const {
return diagonal().cwiseProduct(other.diagonal()).asDiagonal();
}
template<typename OtherDerived>
using DiagonalInverseReturnType =
DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType>>;
/** \returns the inverse \c *this. Computed as the coefficient-wise inverse of the diagonal. */
EIGEN_DEVICE_FUNC
#ifdef EIGEN_PARSED_BY_DOXYGEN
inline unspecified_expression_type
#else
inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,sum) >
#endif
operator+(const DiagonalBase<OtherDerived>& other) const
{
inline const DiagonalInverseReturnType inverse() const { return diagonal().cwiseInverse().asDiagonal(); }
using DiagonalScaleReturnType =
DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType, Scalar, product)>;
/** \returns the product of \c *this by the scalar \a scalar */
EIGEN_DEVICE_FUNC
inline const DiagonalScaleReturnType operator*(const Scalar& scalar) const {
return (diagonal() * scalar).asDiagonal();
}
using ScaleDiagonalReturnType =
DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, DiagonalVectorType, product)>;
/** \returns the product of a scalar and the diagonal matrix \a other */
EIGEN_DEVICE_FUNC
friend inline const ScaleDiagonalReturnType operator*(const Scalar& scalar, const DiagonalBase& other) {
return (scalar * other.diagonal()).asDiagonal();
}
template <typename OtherDerived>
using DiagonalSumReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
DiagonalVectorType, typename OtherDerived::DiagonalVectorType, sum)>;
/** \returns the sum of \c *this and the diagonal matrix \a other */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC inline const DiagonalSumReturnType<OtherDerived> operator+(
const DiagonalBase<OtherDerived>& other) const {
return (diagonal() + other.diagonal()).asDiagonal();
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
#ifdef EIGEN_PARSED_BY_DOXYGEN
inline unspecified_expression_type
#else
inline const DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(DiagonalVectorType,typename OtherDerived::DiagonalVectorType,difference) >
#endif
operator-(const DiagonalBase<OtherDerived>& other) const
{
template <typename OtherDerived>
using DiagonalDifferenceReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
DiagonalVectorType, typename OtherDerived::DiagonalVectorType, difference)>;
/** \returns the difference of \c *this and the diagonal matrix \a other */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC inline const DiagonalDifferenceReturnType<OtherDerived> operator-(
const DiagonalBase<OtherDerived>& other) const {
return (diagonal() - other.diagonal()).asDiagonal();
}
};
#endif
/** \class DiagonalMatrix
* \ingroup Core_Module
*
* \brief Represents a diagonal matrix with its storage
*
* \param _Scalar the type of coefficients
* \param SizeAtCompileTime the dimension of the matrix, or Dynamic
* \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
* to SizeAtCompileTime. Most of the time, you do not need to specify it.
*
* \sa class DiagonalWrapper
*/
* \ingroup Core_Module
*
* \brief Represents a diagonal matrix with its storage
*
* \tparam Scalar_ the type of coefficients
* \tparam SizeAtCompileTime the dimension of the matrix, or Dynamic
* \tparam MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
* to SizeAtCompileTime. Most of the time, you do not need to specify it.
*
* \sa class DiagonalBase, class DiagonalWrapper
*/
namespace internal {
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
: traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
struct traits<DiagonalMatrix<Scalar_,SizeAtCompileTime,MaxSizeAtCompileTime> >
: traits<Matrix<Scalar_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
typedef Matrix<Scalar_,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
typedef DiagonalShape StorageKind;
enum {
Flags = LvalueBit | NoPreferredStorageOrderBit
Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit
};
};
}
template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime>
template<typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
class DiagonalMatrix
: public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
: public DiagonalBase<DiagonalMatrix<Scalar_,SizeAtCompileTime,MaxSizeAtCompileTime> >
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
typedef const DiagonalMatrix& Nested;
typedef _Scalar Scalar;
typedef Scalar_ Scalar;
typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
#endif
@@ -178,10 +214,7 @@ class DiagonalMatrix
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
#if EIGEN_HAS_CXX11
/** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients. \cpp11
*
* There exists C++98 anologue constructors for fixed-size diagonal matrices having 2 or 3 coefficients.
/** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients.
*
* \warning To construct a diagonal matrix of fixed size, the number of values passed to this
* constructor must match the fixed dimension of \c *this.
@@ -200,7 +233,10 @@ class DiagonalMatrix
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE DiagonalMatrix(const std::initializer_list<std::initializer_list<Scalar>>& list)
: m_diagonal(list) {}
#endif // EIGEN_HAS_CXX11
/** \brief Constructs a DiagonalMatrix from an r-value diagonal vector type */
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(DiagonalVectorType&& diag) : m_diagonal(std::move(diag)) {}
/** Copy constructor. */
template<typename OtherDerived>
@@ -239,6 +275,22 @@ class DiagonalMatrix
}
#endif
typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, DiagonalVectorType>>
InitializeReturnType;
/** Initializes a diagonal matrix of size SizeAtCompileTime with coefficients set to zero */
EIGEN_DEVICE_FUNC
static const InitializeReturnType Zero() { return DiagonalVectorType::Zero().asDiagonal(); }
/** Initializes a diagonal matrix of size dim with coefficients set to zero */
EIGEN_DEVICE_FUNC
static const InitializeReturnType Zero(Index size) { return DiagonalVectorType::Zero(size).asDiagonal(); }
/** Initializes a identity matrix of size SizeAtCompileTime */
EIGEN_DEVICE_FUNC
static const InitializeReturnType Identity() { return DiagonalVectorType::Ones().asDiagonal(); }
/** Initializes a identity matrix of size dim */
EIGEN_DEVICE_FUNC
static const InitializeReturnType Identity(Index size) { return DiagonalVectorType::Ones(size).asDiagonal(); }
/** Resizes to given size. */
EIGEN_DEVICE_FUNC
inline void resize(Index size) { m_diagonal.resize(size); }
@@ -261,7 +313,7 @@ class DiagonalMatrix
*
* \brief Expression of a diagonal matrix
*
* \param _DiagonalVectorType the type of the vector of diagonal coefficients
* \tparam DiagonalVectorType_ the type of the vector of diagonal coefficients
*
* This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
* instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
@@ -271,10 +323,10 @@ class DiagonalMatrix
*/
namespace internal {
template<typename _DiagonalVectorType>
struct traits<DiagonalWrapper<_DiagonalVectorType> >
template<typename DiagonalVectorType_>
struct traits<DiagonalWrapper<DiagonalVectorType_> >
{
typedef _DiagonalVectorType DiagonalVectorType;
typedef DiagonalVectorType_ DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::StorageIndex StorageIndex;
typedef DiagonalShape StorageKind;
@@ -289,13 +341,13 @@ struct traits<DiagonalWrapper<_DiagonalVectorType> >
};
}
template<typename _DiagonalVectorType>
template<typename DiagonalVectorType_>
class DiagonalWrapper
: public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator
: public DiagonalBase<DiagonalWrapper<DiagonalVectorType_> >, internal::no_assignment_operator
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef _DiagonalVectorType DiagonalVectorType;
typedef DiagonalVectorType_ DiagonalVectorType;
typedef DiagonalWrapper Nested;
#endif
@@ -386,6 +438,6 @@ struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
} // namespace internal
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

2
libs/eigen/Eigen/src/Core/DiagonalProduct.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_DIAGONALPRODUCT_H
#define EIGEN_DIAGONALPRODUCT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.

21
libs/eigen/Eigen/src/Core/Dot.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_DOT_H
#define EIGEN_DOT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -18,14 +20,9 @@ namespace internal {
// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
// looking at the static assertions. Thus this is a trick to get better compile errors.
template<typename T, typename U,
// the NeedToTranspose condition here is taken straight from Assign.h
bool NeedToTranspose = T::IsVectorAtCompileTime
&& U::IsVectorAtCompileTime
&& ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
>
bool NeedToTranspose = T::IsVectorAtCompileTime && U::IsVectorAtCompileTime &&
((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1) ||
(int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))>
struct dot_nocheck
{
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
@@ -123,8 +120,8 @@ template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested_eval<Derived,2>::type _Nested;
_Nested n(derived());
typedef typename internal::nested_eval<Derived,2>::type Nested_;
Nested_ n(derived());
RealScalar z = n.squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
@@ -166,8 +163,8 @@ template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
_Nested n(derived());
typedef typename internal::nested_eval<Derived,3>::type Nested_;
Nested_ n(derived());
RealScalar w = n.cwiseAbs().maxCoeff();
RealScalar z = (n/w).squaredNorm();
if(z>RealScalar(0))

2
libs/eigen/Eigen/src/Core/EigenBase.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_EIGENBASE_H
#define EIGEN_EIGENBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class EigenBase

6
libs/eigen/Eigen/src/Core/ForceAlignedAccess.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_FORCEALIGNEDACCESS_H
#define EIGEN_FORCEALIGNEDACCESS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class ForceAlignedAccess
@@ -128,7 +130,7 @@ MatrixBase<Derived>::forceAlignedAccess()
*/
template<typename Derived>
template<bool Enable>
inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
inline add_const_on_value_type_t<std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&>>
MatrixBase<Derived>::forceAlignedAccessIf() const
{
return derived(); // FIXME This should not work but apparently is never used
@@ -139,7 +141,7 @@ MatrixBase<Derived>::forceAlignedAccessIf() const
*/
template<typename Derived>
template<bool Enable>
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
inline std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&>
MatrixBase<Derived>::forceAlignedAccessIf()
{
return derived(); // FIXME This should not work but apparently is never used

2
libs/eigen/Eigen/src/Core/Fuzzy.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_FUZZY_H
#define EIGEN_FUZZY_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal

40
libs/eigen/Eigen/src/Core/GeneralProduct.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_GENERAL_PRODUCT_H
#define EIGEN_GENERAL_PRODUCT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
enum {
@@ -50,17 +52,17 @@ template<int Size, int MaxSize> struct product_size_category
template<typename Lhs, typename Rhs> struct product_type
{
typedef typename remove_all<Lhs>::type _Lhs;
typedef typename remove_all<Rhs>::type _Rhs;
typedef remove_all_t<Lhs> Lhs_;
typedef remove_all_t<Rhs> Rhs_;
enum {
MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
Rows = traits<_Lhs>::RowsAtCompileTime,
MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
Cols = traits<_Rhs>::ColsAtCompileTime,
MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime,
traits<_Rhs>::MaxRowsAtCompileTime),
Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime,
traits<_Rhs>::RowsAtCompileTime)
MaxRows = traits<Lhs_>::MaxRowsAtCompileTime,
Rows = traits<Lhs_>::RowsAtCompileTime,
MaxCols = traits<Rhs_>::MaxColsAtCompileTime,
Cols = traits<Rhs_>::ColsAtCompileTime,
MaxDepth = min_size_prefer_fixed(traits<Lhs_>::MaxColsAtCompileTime,
traits<Rhs_>::MaxRowsAtCompileTime),
Depth = min_size_prefer_fixed(traits<Lhs_>::ColsAtCompileTime,
traits<Rhs_>::RowsAtCompileTime)
};
// the splitting into different lines of code here, introducing the _select enums and the typedef below,
@@ -180,12 +182,13 @@ struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
PacketSize = internal::packet_traits<Scalar>::size
};
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0,
internal::plain_enum_min(AlignedMax, PacketSize)> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() {
return ForceAlignment
? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
@@ -216,14 +219,13 @@ template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef typename Dest::RealScalar RealScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
typedef Map<Matrix<ResScalar,Dynamic,1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)> MappedDest;
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
@@ -231,7 +233,7 @@ template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
// make sure Dest is a compile-time vector type (bug 1166)
typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;
typedef std::conditional_t<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr> ActualDest;
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
@@ -261,7 +263,7 @@ template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
{
gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
const bool alphaIsCompatible = (!ComplexByReal) || (numext::is_exactly_zero(numext::imag(actualAlpha)));
const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
@@ -314,10 +316,10 @@ template<> struct gemv_dense_selector<OnTheRight,RowMajor,true>
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);

272
libs/eigen/Eigen/src/Core/GenericPacketMath.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_GENERIC_PACKET_MATH_H
#define EIGEN_GENERIC_PACKET_MATH_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -57,12 +59,14 @@ struct default_packet_traits
HasMax = 1,
HasConj = 1,
HasSetLinear = 1,
HasSign = 1,
HasBlend = 0,
// This flag is used to indicate whether packet comparison is supported.
// pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
HasCmp = 0,
HasDiv = 0,
HasReciprocal = 0,
HasSqrt = 0,
HasRsqrt = 0,
HasExp = 0,
@@ -98,8 +102,7 @@ struct default_packet_traits
HasRound = 0,
HasRint = 0,
HasFloor = 0,
HasCeil = 0,
HasSign = 0
HasCeil = 0
};
};
@@ -160,7 +163,7 @@ struct eigen_packet_wrapper
{
EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {}
EIGEN_ALWAYS_INLINE eigen_packet_wrapper() = default;
EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {}
EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) {
m_val = v;
@@ -176,7 +179,7 @@ struct eigen_packet_wrapper
*/
template<typename Packet>
struct is_scalar {
typedef typename unpacket_traits<Packet>::type Scalar;
using Scalar = typename unpacket_traits<Packet>::type;
enum {
value = internal::is_same<Packet, Scalar>::value
};
@@ -217,6 +220,15 @@ padd(const Packet& a, const Packet& b) { return a+b; }
template<> EIGEN_DEVICE_FUNC inline bool
padd(const bool& a, const bool& b) { return a || b; }
/** \internal \returns a packet version of \a *from, (un-aligned masked add)
* There is no generic implementation. We only have implementations for specialized
* cases. Generic case should not be called.
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline
std::enable_if_t<unpacket_traits<Packet>::masked_fpops_available, Packet>
padd(const Packet& a, const Packet& b, typename unpacket_traits<Packet>::mask_t umask);
/** \internal \returns a - b (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
psub(const Packet& a, const Packet& b) { return a-b; }
@@ -259,7 +271,7 @@ struct ptrue_impl {
// have another option, since the scalar type requires initialization.
template<typename T>
struct ptrue_impl<T,
typename internal::enable_if<is_scalar<T>::value && NumTraits<T>::RequireInitialization>::type > {
std::enable_if_t<is_scalar<T>::value && NumTraits<T>::RequireInitialization> > {
static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){
return T(1);
}
@@ -285,7 +297,7 @@ struct pzero_impl {
// for zero may not consist of all-zero bits.
template<typename T>
struct pzero_impl<T,
typename internal::enable_if<is_scalar<T>::value>::type> {
std::enable_if_t<is_scalar<T>::value>> {
static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) {
return T(0);
}
@@ -356,16 +368,16 @@ struct bytewise_bitwise_helper {
EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
return binary(a, b, bit_and<unsigned char>());
}
EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) {
EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) {
return binary(a, b, bit_or<unsigned char>());
}
EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
return binary(a, b, bit_xor<unsigned char>());
}
EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) {
EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) {
return unary(a,bit_not<unsigned char>());
}
private:
template<typename Op>
EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
@@ -398,8 +410,8 @@ struct bitwise_helper : public bytewise_bitwise_helper<T> {};
// For integers or non-trivial scalars, use binary operators.
template<typename T>
struct bitwise_helper<T,
typename internal::enable_if<
is_scalar<T>::value && (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>::type
typename std::enable_if_t<
is_scalar<T>::value && (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>
> : public operator_bitwise_helper<T> {};
/** \internal \returns the bitwise and of \a a and \a b */
@@ -441,7 +453,7 @@ struct pselect_impl {
// For scalars, use ternary select.
template<typename Packet>
struct pselect_impl<Packet,
typename internal::enable_if<is_scalar<Packet>::value>::type > {
std::enable_if_t<is_scalar<Packet>::value> > {
static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
return numext::equal_strict(mask, Packet(0)) ? b : a;
}
@@ -551,13 +563,13 @@ template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
parg(const Packet& a) { using numext::arg; return arg(a); }
/** \internal \returns \a a logically shifted by N bits to the right */
/** \internal \returns \a a arithmetically shifted by N bits to the right */
template<int N> EIGEN_DEVICE_FUNC inline int
parithmetic_shift_right(const int& a) { return a >> N; }
template<int N> EIGEN_DEVICE_FUNC inline long int
parithmetic_shift_right(const long int& a) { return a >> N; }
/** \internal \returns \a a arithmetically shifted by N bits to the right */
/** \internal \returns \a a logically shifted by N bits to the right */
template<int N> EIGEN_DEVICE_FUNC inline int
plogical_shift_right(const int& a) { return static_cast<int>(static_cast<unsigned int>(a) >> N); }
template<int N> EIGEN_DEVICE_FUNC inline long int
@@ -594,20 +606,52 @@ pldexp(const Packet &a, const Packet &exponent) {
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pabsdiff(const Packet& a, const Packet& b) { return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b)); }
/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
/** \internal \returns a packet version of \a *from, from must be properly aligned */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns n elements of a packet version of \a *from, from must be properly aligned
* offset indicates the starting element in which to load and
* offset + n <= unpacket_traits::size
* All elements before offset and after the last element loaded will initialized with zero */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload_partial(const typename unpacket_traits<Packet>::type* from, const Index n, const Index offset = 0)
{
const Index packet_size = unpacket_traits<Packet>::size;
eigen_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
typedef typename unpacket_traits<Packet>::type Scalar;
EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
for (Index i = offset; i < numext::mini(n+offset,packet_size); i++) {
elements[i] = from[i-offset];
}
return pload<Packet>(elements);
}
/** \internal \returns a packet version of \a *from, (un-aligned load) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns n elements of a packet version of \a *from, (un-aligned load)
* All elements after the last element loaded will initialized with zero */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadu_partial(const typename unpacket_traits<Packet>::type* from, const Index n)
{
const Index packet_size = unpacket_traits<Packet>::size;
eigen_assert(n <= packet_size && "number of elements will read past end of packet");
typedef typename unpacket_traits<Packet>::type Scalar;
EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
for (Index i = 0; i < numext::mini(n,packet_size); i++) {
elements[i] = from[i];
}
return pload<Packet>(elements);
}
/** \internal \returns a packet version of \a *from, (un-aligned masked load)
* There is no generic implementation. We only have implementations for specialized
* cases. Generic case should not be called.
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline
typename enable_if<unpacket_traits<Packet>::masked_load_available, Packet>::type
std::enable_if_t<unpacket_traits<Packet>::masked_load_available, Packet>
ploadu(const typename unpacket_traits<Packet>::type* from, typename unpacket_traits<Packet>::mask_t umask);
/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
@@ -692,28 +736,74 @@ peven_mask(const Packet& /*a*/) {
}
/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
/** \internal copy the packet \a from to \a *to, \a to must be properly aligned */
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
{ (*to) = from; }
/** \internal copy n elements of the packet \a from to \a *to, \a to must be properly aligned
* offset indicates the starting element in which to store and
* offset + n <= unpacket_traits::size */
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0)
{
const Index packet_size = unpacket_traits<Packet>::size;
eigen_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
EIGEN_ALIGN_MAX Scalar elements[packet_size];
pstore<Scalar>(elements, from);
for (Index i = 0; i < numext::mini(n,packet_size-offset); i++) {
to[i] = elements[i + offset];
}
}
/** \internal copy the packet \a from to \a *to, (un-aligned store) */
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
{ (*to) = from; }
/** \internal copy n elements of the packet \a from to \a *to, (un-aligned store) */
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu_partial(Scalar* to, const Packet& from, const Index n)
{
const Index packet_size = unpacket_traits<Packet>::size;
eigen_assert(n <= packet_size && "number of elements will write past end of packet");
EIGEN_ALIGN_MAX Scalar elements[packet_size];
pstore<Scalar>(elements, from);
for (Index i = 0; i < numext::mini(n,packet_size); i++) {
to[i] = elements[i];
}
}
/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask)
* There is no generic implementation. We only have implementations for specialized
* cases. Generic case should not be called.
*/
template<typename Scalar, typename Packet>
EIGEN_DEVICE_FUNC inline
typename enable_if<unpacket_traits<Packet>::masked_store_available, void>::type
std::enable_if_t<unpacket_traits<Packet>::masked_store_available, void>
pstoreu(Scalar* to, const Packet& from, typename unpacket_traits<Packet>::mask_t umask);
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
{ return ploadu<Packet>(from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
{ return ploadu<Packet>(from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
{ pstore(to, from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_partial(const Scalar* from, Index stride, const Index n)
{
const Index packet_size = unpacket_traits<Packet>::size;
EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
for (Index i = 0; i < numext::mini(n,packet_size); i++) {
elements[i] = from[i*stride];
}
return pload<Packet>(elements);
}
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
{ pstore(to, from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_partial(Scalar* to, const Packet& from, Index stride, const Index n)
{
const Index packet_size = unpacket_traits<Packet>::size;
EIGEN_ALIGN_MAX Scalar elements[packet_size];
pstore<Scalar>(elements, from);
for (Index i = 0; i < numext::mini(n,packet_size); i++) {
to[i*stride] = elements[i];
}
}
/** \internal tries to do cache prefetching of \a addr */
template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
@@ -807,20 +897,13 @@ Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog2(const Packet& a) {
typedef typename internal::unpacket_traits<Packet>::type Scalar;
return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a));
return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a));
}
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { return numext::sqrt(a); }
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
typedef typename internal::unpacket_traits<Packet>::type Scalar;
return pdiv(pset1<Packet>(Scalar(1)), psqrt(a));
}
/** \internal \returns the rounded value of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pround(const Packet& a) { using numext::round; return round(a); }
@@ -838,6 +921,24 @@ Packet print(const Packet& a) { using numext::rint; return rint(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
template<typename Packet, typename EnableIf = void>
struct psign_impl {
static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
return numext::sign(a);
}
};
/** \internal \returns the sign of \a a (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
psign(const Packet& a) {
return psign_impl<Packet>::run(a);
}
template<> EIGEN_DEVICE_FUNC inline bool
psign(const bool& a) {
return a;
}
/** \internal \returns the first element of a packet */
template<typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
@@ -849,7 +950,7 @@ pfirst(const Packet& a)
* For packet-size smaller or equal to 4, this boils down to a noop.
*/
template<typename Packet>
EIGEN_DEVICE_FUNC inline typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type
EIGEN_DEVICE_FUNC inline std::conditional_t<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>
predux_half_dowto4(const Packet& a)
{ return a; }
@@ -881,7 +982,7 @@ predux(const Packet& a)
template <typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
const Packet& a) {
typedef typename unpacket_traits<Packet>::type Scalar;
typedef typename unpacket_traits<Packet>::type Scalar;
return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
}
@@ -889,14 +990,14 @@ EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
template <typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
const Packet &a) {
typedef typename unpacket_traits<Packet>::type Scalar;
typedef typename unpacket_traits<Packet>::type Scalar;
return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<PropagateFast, Scalar>)));
}
template <int NaNPropagation, typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
const Packet& a) {
typedef typename unpacket_traits<Packet>::type Scalar;
typedef typename unpacket_traits<Packet>::type Scalar;
return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
}
@@ -904,14 +1005,14 @@ EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
template <typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
const Packet &a) {
typedef typename unpacket_traits<Packet>::type Scalar;
typedef typename unpacket_traits<Packet>::type Scalar;
return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<PropagateFast, Scalar>)));
}
template <int NaNPropagation, typename Packet>
EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
const Packet& a) {
typedef typename unpacket_traits<Packet>::type Scalar;
typedef typename unpacket_traits<Packet>::type Scalar;
return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
}
@@ -943,6 +1044,35 @@ template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_any(const Packet&
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/
// FMA instructions.
/** \internal \returns a * b + c (coeff-wise) */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pmadd(const Packet& a, const Packet& b,
const Packet& c) {
return padd(pmul(a, b), c);
}
/** \internal \returns a * b - c (coeff-wise) */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pmsub(const Packet& a, const Packet& b,
const Packet& c) {
return psub(pmul(a, b), c);
}
/** \internal \returns -(a * b) + c (coeff-wise) */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pnmadd(const Packet& a, const Packet& b,
const Packet& c) {
return padd(pnegate(pmul(a, b)), c);
}
/** \internal \returns -(a * b) - c (coeff-wise) */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet pnmsub(const Packet& a, const Packet& b,
const Packet& c) {
return psub(pnegate(pmul(a, b)), c);
}
/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
template<typename Packet>
@@ -951,13 +1081,6 @@ inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename u
pstore(to, pset1<Packet>(a));
}
/** \internal \returns a * b + c (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmadd(const Packet& a,
const Packet& b,
const Packet& c)
{ return padd(pmul(a, b),c); }
/** \internal \returns a packet version of \a *from.
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Packet, int Alignment>
@@ -969,6 +1092,17 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_trai
return ploadu<Packet>(from);
}
/** \internal \returns n elements of a packet version of \a *from.
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_partial(const typename unpacket_traits<Packet>::type* from, const Index n, const Index offset = 0)
{
if(Alignment >= unpacket_traits<Packet>::alignment)
return pload_partial<Packet>(from, n, offset);
else
return ploadu_partial<Packet>(from, n);
}
/** \internal copy the packet \a from to \a *to.
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Scalar, typename Packet, int Alignment>
@@ -980,6 +1114,17 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& fro
pstoreu(to, from);
}
/** \internal copy n elements of the packet \a from to \a *to.
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Scalar, typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0)
{
if(Alignment >= unpacket_traits<Packet>::alignment)
pstore_partial(to, from, n, offset);
else
pstoreu_partial(to, from, n);
}
/** \internal \returns a packet version of \a *from.
* Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
* hardware if available to speedup the loading of data that won't be modified
@@ -1033,6 +1178,47 @@ pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& th
return ifPacket.select[0] ? thenPacket : elsePacket;
}
/** \internal \returns 1 / a (coeff-wise) */
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet preciprocal(const Packet& a) {
using Scalar = typename unpacket_traits<Packet>::type;
return pdiv(pset1<Packet>(Scalar(1)), a);
}
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
return preciprocal<Packet>(psqrt(a));
}
template <typename Packet, bool IsScalar = is_scalar<Packet>::value,
bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
struct psignbit_impl;
template <typename Packet, bool IsInteger>
struct psignbit_impl<Packet, true, IsInteger> {
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return numext::signbit(a); }
};
template <typename Packet>
struct psignbit_impl<Packet, false, false> {
// generic implementation if not specialized in PacketMath.h
// slower than arithmetic shift
typedef typename unpacket_traits<Packet>::type Scalar;
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Packet run(const Packet& a) {
const Packet cst_pos_one = pset1<Packet>(Scalar(1));
const Packet cst_neg_one = pset1<Packet>(Scalar(-1));
return pcmp_eq(por(pand(a, cst_neg_one), cst_pos_one), cst_neg_one);
}
};
template <typename Packet>
struct psignbit_impl<Packet, false, true> {
// generic implementation for integer packets
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return pcmp_lt(a, pzero(a)); }
};
/** \internal \returns the sign bit of \a a as a bitmask*/
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr Packet
psignbit(const Packet& a) { return psignbit_impl<Packet>::run(a); }
} // end namespace internal
} // end namespace Eigen

70
libs/eigen/Eigen/src/Core/GlobalFunctions.h
View File

@@ -51,6 +51,8 @@
} \
};
#include "./InternalHeaderCheck.h"
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
@@ -66,11 +68,9 @@ namespace Eigen
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
#if EIGEN_HAS_CXX11_MATH
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh,scalar_asinh_op,inverse hyperbolic sine,\sa ArrayBase::asinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh,scalar_acosh_op,inverse hyperbolic cosine,\sa ArrayBase::acosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh,scalar_atanh_op,inverse hyperbolic tangent,\sa ArrayBase::atanh)
#endif
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
@@ -99,31 +99,31 @@ namespace Eigen
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
template <typename Derived, typename ScalarExponent>
using GlobalUnaryPowReturnType = std::enable_if_t<
!internal::is_arithmetic<typename NumTraits<Derived>::Real>::value &&
internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
CwiseUnaryOp<internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>, const Derived> >;
/** \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
*/
*
* \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);
template <typename Derived, typename ScalarExponent>
EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
#else
template <typename Derived,typename ScalarExponent>
EIGEN_DEVICE_FUNC inline
EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<typename Derived::Scalar
EIGEN_COMMA ScalarExponent EIGEN_COMMA
EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type,pow))
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent)
{
typedef typename internal::promote_scalar_arg<typename Derived::Scalar,ScalarExponent,
EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent)>::type PromotedExponent;
return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedExponent,pow)(x.derived(),
typename internal::plain_constant_type<Derived,PromotedExponent>::type(x.derived().rows(), x.derived().cols(), internal::scalar_constant_op<PromotedExponent>(exponent)));
template <typename Derived, typename ScalarExponent>
EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
return GlobalUnaryPowReturnType<Derived, ScalarExponent>(
x.derived(), internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>(exponent));
}
#endif
@@ -168,10 +168,9 @@ namespace Eigen
#else
template <typename Scalar, typename Derived>
EIGEN_DEVICE_FUNC inline
EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<typename Derived::Scalar
EIGEN_COMMA Scalar EIGEN_COMMA
EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type,Derived,pow))
EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type,Derived,pow)
pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
typedef typename internal::promote_scalar_arg<typename Derived::Scalar,Scalar,
EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type PromotedScalar;
@@ -180,6 +179,25 @@ namespace Eigen
}
#endif
/** \returns an expression of the coefficient-wise atan2(\a x, \a y). \a x and \a y must be of the same type.
*
* This function computes the coefficient-wise atan2().
*
* \sa ArrayBase::atan2()
*
* \relates ArrayBase
*/
template <typename LhsDerived, typename RhsDerived>
inline const std::enable_if_t<
std::is_same<typename LhsDerived::Scalar, typename RhsDerived::Scalar>::value,
Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived, const RhsDerived>
>
atan2(const Eigen::ArrayBase<LhsDerived>& x, const Eigen::ArrayBase<RhsDerived>& exponents) {
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived, const RhsDerived>(
x.derived(),
exponents.derived()
);
}
namespace internal
{

12
libs/eigen/Eigen/src/Core/IO.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_IO_H
#define EIGEN_IO_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
enum { DontAlignCols = 1 };
@@ -131,7 +133,6 @@ template<typename Derived>
std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
{
using internal::is_same;
using internal::conditional;
if(_m.size() == 0)
{
@@ -141,22 +142,21 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
typename Derived::Nested m = _m;
typedef typename Derived::Scalar Scalar;
typedef typename
conditional<
typedef std::conditional_t<
is_same<Scalar, char>::value ||
is_same<Scalar, unsigned char>::value ||
is_same<Scalar, numext::int8_t>::value ||
is_same<Scalar, numext::uint8_t>::value,
int,
typename conditional<
std::conditional_t<
is_same<Scalar, std::complex<char> >::value ||
is_same<Scalar, std::complex<unsigned char> >::value ||
is_same<Scalar, std::complex<numext::int8_t> >::value ||
is_same<Scalar, std::complex<numext::uint8_t> >::value,
std::complex<int>,
const Scalar&
>::type
>::type PrintType;
>
> PrintType;
Index width = 0;

34
libs/eigen/Eigen/src/Core/IndexedView.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_INDEXED_VIEW_H
#define EIGEN_INDEXED_VIEW_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -21,8 +23,8 @@ struct traits<IndexedView<XprType, RowIndices, ColIndices> >
enum {
RowsAtCompileTime = int(array_size<RowIndices>::value),
ColsAtCompileTime = int(array_size<ColIndices>::value),
MaxRowsAtCompileTime = RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) : Dynamic,
MaxColsAtCompileTime = ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) : Dynamic,
MaxRowsAtCompileTime = RowsAtCompileTime,
MaxColsAtCompileTime = ColsAtCompileTime,
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
@@ -40,10 +42,10 @@ struct traits<IndexedView<XprType, RowIndices, ColIndices> >
InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime,
IsBlockAlike = InnerIncr==1 && OuterIncr==1,
IsInnerPannel = HasSameStorageOrderAsXprType && is_same<AllRange<InnerSize>,typename conditional<XprTypeIsRowMajor,ColIndices,RowIndices>::type>::value,
IsInnerPannel = HasSameStorageOrderAsXprType && is_same<AllRange<InnerSize>,std::conditional_t<XprTypeIsRowMajor,ColIndices,RowIndices>>::value,
InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic ? Dynamic : XprInnerStride * InnerIncr,
OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic ? Dynamic : XprOuterstride * OuterIncr,
InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic || InnerIncr==UndefinedIncr ? Dynamic : XprInnerStride * InnerIncr,
OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic || OuterIncr==UndefinedIncr ? Dynamic : XprOuterstride * OuterIncr,
ReturnAsScalar = is_same<RowIndices,SingleRange>::value && is_same<ColIndices,SingleRange>::value,
ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike,
@@ -96,7 +98,7 @@ class IndexedViewImpl;
* - decltype(ArrayXi::LinSpaced(...))
* - Any view/expressions of the previous types
* - Eigen::ArithmeticSequence
* - Eigen::internal::AllRange (helper for Eigen::all)
* - Eigen::internal::AllRange (helper for Eigen::placeholders::all)
* - Eigen::internal::SingleRange (helper for single index)
* - etc.
*
@@ -114,7 +116,7 @@ public:
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView)
typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
typedef internal::remove_all_t<XprType> NestedExpression;
template<typename T0, typename T1>
IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices)
@@ -122,17 +124,17 @@ public:
{}
/** \returns number of rows */
Index rows() const { return internal::size(m_rowIndices); }
Index rows() const { return internal::index_list_size(m_rowIndices); }
/** \returns number of columns */
Index cols() const { return internal::size(m_colIndices); }
Index cols() const { return internal::index_list_size(m_colIndices); }
/** \returns the nested expression */
const typename internal::remove_all<XprType>::type&
const internal::remove_all_t<XprType>&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
typename internal::remove_reference<XprType>::type&
std::remove_reference_t<XprType>&
nestedExpression() { return m_xpr; }
/** \returns a const reference to the object storing/generating the row indices */
@@ -189,12 +191,16 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
&& m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
&& m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
}
@@ -204,6 +210,8 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
EIGEN_STATIC_ASSERT_LVALUE(XprType)
Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
&& m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
}
@@ -212,6 +220,8 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
{
Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
&& m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
}
@@ -220,6 +230,8 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
{
Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
&& m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
}

3
libs/eigen/Eigen/src/Core/InternalHeaderCheck.h Normal file
View File

@@ -0,0 +1,3 @@
#ifndef EIGEN_CORE_MODULE_H
#error "Please include Eigen/Core instead of including headers inside the src directory directly."
#endif

8
libs/eigen/Eigen/src/Core/Inverse.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_INVERSE_H
#define EIGEN_INVERSE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename XprType,typename StorageKind> class InverseImpl;
@@ -46,9 +48,9 @@ public:
typedef typename XprType::StorageIndex StorageIndex;
typedef typename XprType::Scalar Scalar;
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprTypeNested>::type XprTypeNestedCleaned;
typedef internal::remove_all_t<XprTypeNested> XprTypeNestedCleaned;
typedef typename internal::ref_selector<Inverse>::type Nested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
typedef internal::remove_all_t<XprType> NestedExpression;
explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
: m_xpr(xpr)
@@ -102,7 +104,7 @@ struct unary_evaluator<Inverse<ArgType> >
unary_evaluator(const InverseType& inv_xpr)
: m_result(inv_xpr.rows(), inv_xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
internal::call_assignment_no_alias(m_result, inv_xpr);
}

5
libs/eigen/Eigen/src/Core/Map.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_MAP_H
#define EIGEN_MAP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -129,7 +131,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
@@ -142,7 +143,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size matrix case.
@@ -156,7 +156,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)

20
libs/eigen/Eigen/src/Core/MapBase.h
View File

@@ -15,6 +15,8 @@
EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \ingroup Core_Module
@@ -51,11 +53,11 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename internal::conditional<
bool(internal::is_lvalue<Derived>::value),
Scalar *,
const Scalar *>::type
PointerType;
typedef std::conditional_t<
bool(internal::is_lvalue<Derived>::value),
Scalar *,
const Scalar *>
PointerType;
using Base::derived;
// using Base::RowsAtCompileTime;
@@ -189,7 +191,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
void checkSanity(std::enable_if_t<(internal::traits<T>::Alignment>0),void*> = 0) const
{
#if EIGEN_MAX_ALIGN_BYTES>0
// innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
@@ -202,7 +204,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<internal::traits<T>::Alignment==0,void*>::type = 0) const
void checkSanity(std::enable_if_t<internal::traits<T>::Alignment==0,void*> = 0) const
{}
PointerType m_data;
@@ -245,11 +247,11 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
using Base::rowStride;
using Base::colStride;
typedef typename internal::conditional<
typedef std::conditional_t<
internal::is_lvalue<Derived>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
> ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return this->m_data; }

236
libs/eigen/Eigen/src/Core/MathFunctions.h
View File

@@ -17,16 +17,9 @@
#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L
#define EIGEN_LN2 0.693147180559945309417232121458176568075500134360255254120680009493393621L
namespace Eigen {
#include "./InternalHeaderCheck.h"
// On WINCE, std::abs is defined for int only, so let's defined our own overloads:
// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too.
#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500
long abs(long x) { return (labs(x)); }
double abs(double x) { return (fabs(x)); }
float abs(float x) { return (fabsf(x)); }
long double abs(long double x) { return (fabsl(x)); }
#endif
namespace Eigen {
namespace internal {
@@ -236,6 +229,63 @@ struct imag_ref_retval
typedef typename NumTraits<Scalar>::Real & type;
};
/****************************************************************************
* Implementation of sign *
****************************************************************************/
template<typename Scalar, bool IsComplex = (NumTraits<Scalar>::IsComplex!=0),
bool IsInteger = (NumTraits<Scalar>::IsInteger!=0)>
struct sign_impl
{
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& a)
{
return Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
}
};
template<typename Scalar>
struct sign_impl<Scalar, false, false>
{
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& a)
{
return (std::isnan)(a) ? a : Scalar( (a>Scalar(0)) - (a<Scalar(0)) );
}
};
template<typename Scalar, bool IsInteger>
struct sign_impl<Scalar, true, IsInteger>
{
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& a)
{
using real_type = typename NumTraits<Scalar>::Real;
real_type aa = std::abs(a);
if (aa==real_type(0))
return Scalar(0);
aa = real_type(1)/aa;
return Scalar(a.real()*aa, a.imag()*aa );
}
};
// The sign function for bool is the identity.
template<>
struct sign_impl<bool, false, true>
{
EIGEN_DEVICE_FUNC
static inline bool run(const bool& a)
{
return a;
}
};
template<typename Scalar>
struct sign_retval
{
typedef Scalar type;
};
/****************************************************************************
* Implementation of conj *
****************************************************************************/
@@ -441,9 +491,9 @@ struct cast_impl
// generating warnings on clang. Here we explicitly cast the real component.
template<typename OldType, typename NewType>
struct cast_impl<OldType, NewType,
typename internal::enable_if<
typename std::enable_if_t<
!NumTraits<OldType>::IsComplex && NumTraits<NewType>::IsComplex
>::type>
>>
{
EIGEN_DEVICE_FUNC
static inline NewType run(const OldType& x)
@@ -469,57 +519,16 @@ inline NewType cast(const OldType& x)
template<typename Scalar>
struct round_impl
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
#if EIGEN_HAS_CXX11_MATH
EIGEN_USING_STD(round);
#endif
return Scalar(round(x));
}
};
#if !EIGEN_HAS_CXX11_MATH
#if EIGEN_HAS_C99_MATH
// Use ::roundf for float.
template<>
struct round_impl<float> {
EIGEN_DEVICE_FUNC
static inline float run(const float& x)
{
return ::roundf(x);
}
};
#else
template<typename Scalar>
struct round_using_floor_ceil_impl
{
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
// Without C99 round/roundf, resort to floor/ceil.
EIGEN_USING_STD(floor);
EIGEN_USING_STD(ceil);
// If not enough precision to resolve a decimal at all, return the input.
// Otherwise, adding 0.5 can trigger an increment by 1.
const Scalar limit = Scalar(1ull << (NumTraits<Scalar>::digits() - 1));
if (x >= limit || x <= -limit) {
return x;
}
return (x > Scalar(0)) ? Scalar(floor(x + Scalar(0.5))) : Scalar(ceil(x - Scalar(0.5)));
}
};
template<>
struct round_impl<float> : round_using_floor_ceil_impl<float> {};
template<>
struct round_impl<double> : round_using_floor_ceil_impl<double> {};
#endif // EIGEN_HAS_C99_MATH
#endif // !EIGEN_HAS_CXX11_MATH
template<typename Scalar>
struct round_retval
{
@@ -532,36 +541,16 @@ struct round_retval
template<typename Scalar>
struct rint_impl {
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
EIGEN_DEVICE_FUNC
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL)
#if EIGEN_HAS_CXX11_MATH
EIGEN_USING_STD(rint);
#endif
EIGEN_USING_STD(rint);
return rint(x);
}
};
#if !EIGEN_HAS_CXX11_MATH
template<>
struct rint_impl<double> {
EIGEN_DEVICE_FUNC
static inline double run(const double& x)
{
return ::rint(x);
}
};
template<>
struct rint_impl<float> {
EIGEN_DEVICE_FUNC
static inline float run(const float& x)
{
return ::rintf(x);
}
};
#endif
template<typename Scalar>
struct rint_retval
{
@@ -574,7 +563,7 @@ struct rint_retval
// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs.
// This seems to be fixed in VS 2019.
#if EIGEN_HAS_CXX11_MATH && (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
#if (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
// std::arg is only defined for types of std::complex, or integer types or float/double/long double
template<typename Scalar,
bool HasStdImpl = NumTraits<Scalar>::IsComplex || is_integral<Scalar>::value
@@ -675,11 +664,7 @@ struct expm1_impl {
EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#if EIGEN_HAS_CXX11_MATH
using std::expm1;
#else
using std_fallback::expm1;
#endif
return expm1(x);
}
};
@@ -736,14 +721,11 @@ namespace std_fallback {
template<typename Scalar>
struct log1p_impl {
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#if EIGEN_HAS_CXX11_MATH
using std::log1p;
#else
using std_fallback::log1p;
#endif
return log1p(x);
}
};
@@ -751,9 +733,10 @@ struct log1p_impl {
// Specialization for complex types that are not supported by std::log1p.
template <typename RealScalar>
struct log1p_impl<std::complex<RealScalar> > {
EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
const std::complex<RealScalar>& x) {
EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
return std_fallback::log1p(x);
}
};
@@ -893,7 +876,7 @@ struct random_default_impl<Scalar, false, true>
// ScalarX is the widest of ScalarU and unsigned int.
// We'll deal only with ScalarX and unsigned int below thus avoiding signed
// types and arithmetic and signed overflows (which are undefined behavior).
typedef typename conditional<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned>::type ScalarX;
typedef std::conditional_t<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned> ScalarX;
// The following difference doesn't overflow, provided our integer types are two's
// complement and have the same number of padding bits in signed and unsigned variants.
// This is the case in most modern implementations of C++.
@@ -918,8 +901,8 @@ struct random_default_impl<Scalar, false, true>
#else
enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value,
scalar_bits = sizeof(Scalar) * CHAR_BIT,
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
shift = plain_enum_max(0, int(rand_bits) - int(scalar_bits)),
offset = NumTraits<Scalar>::IsSigned ? (1 << (plain_enum_min(rand_bits, scalar_bits)-1)) : 0
};
return Scalar((std::rand() >> shift) - offset);
#endif
@@ -956,7 +939,7 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
// Implementation of is* functions
// std::is* do not work with fast-math and gcc, std::is* are available on MSVC 2013 and newer, as well as in clang.
#if (EIGEN_HAS_CXX11_MATH && !(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC>=1800) || (EIGEN_COMP_CLANG)
#if (!(EIGEN_COMP_GNUC_STRICT && __FINITE_MATH_ONLY__)) || (EIGEN_COMP_MSVC) || (EIGEN_COMP_CLANG)
#define EIGEN_USE_STD_FPCLASSIFY 1
#else
#define EIGEN_USE_STD_FPCLASSIFY 0
@@ -964,22 +947,22 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<internal::is_integral<T>::value,bool>::type
std::enable_if_t<internal::is_integral<T>::value,bool>
isnan_impl(const T&) { return false; }
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<internal::is_integral<T>::value,bool>::type
std::enable_if_t<internal::is_integral<T>::value,bool>
isinf_impl(const T&) { return false; }
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<internal::is_integral<T>::value,bool>::type
std::enable_if_t<internal::is_integral<T>::value,bool>
isfinite_impl(const T&) { return true; }
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
std::enable_if_t<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>
isfinite_impl(const T& x)
{
#if defined(EIGEN_GPU_COMPILE_PHASE)
@@ -994,7 +977,7 @@ isfinite_impl(const T& x)
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
std::enable_if_t<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>
isinf_impl(const T& x)
{
#if defined(EIGEN_GPU_COMPILE_PHASE)
@@ -1009,7 +992,7 @@ isinf_impl(const T& x)
template<typename T>
EIGEN_DEVICE_FUNC
typename internal::enable_if<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>::type
std::enable_if_t<(!internal::is_integral<T>::value)&&(!NumTraits<T>::IsComplex),bool>
isnan_impl(const T& x)
{
#if defined(EIGEN_GPU_COMPILE_PHASE)
@@ -1042,7 +1025,7 @@ EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x) { return isinf_ms
#elif (defined __FINITE_MATH_ONLY__ && __FINITE_MATH_ONLY__ && EIGEN_COMP_GNUC)
#if EIGEN_GNUC_AT_LEAST(5,0)
#if EIGEN_COMP_GNUC
#define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((optimize("no-finite-math-only")))
#else
// NOTE the inline qualifier and noinline attribute are both needed: the former is to avoid linking issue (duplicate symbol),
@@ -1234,7 +1217,7 @@ inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
inline internal::add_const_on_value_type_t< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) > real_ref(const Scalar& x)
{
return internal::real_ref_impl<Scalar>::run(x);
}
@@ -1262,7 +1245,7 @@ inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x)
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
inline internal::add_const_on_value_type_t< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) > imag_ref(const Scalar& x)
{
return internal::imag_ref_impl<Scalar>::run(x);
}
@@ -1281,6 +1264,13 @@ inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(sign, Scalar) sign(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(sign, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
@@ -1505,7 +1495,7 @@ double log(const double &x) { return ::log(x); }
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
typename internal::enable_if<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>::type
std::enable_if_t<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>
abs(const T &x) {
EIGEN_USING_STD(abs);
return abs(x);
@@ -1513,7 +1503,7 @@ abs(const T &x) {
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
typename internal::enable_if<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>::type
std::enable_if_t<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>
abs(const T &x) {
return x;
}
@@ -1541,6 +1531,37 @@ double abs(const std::complex<double>& x) {
}
#endif
template <typename Scalar, bool IsInteger = NumTraits<Scalar>::IsInteger, bool IsSigned = NumTraits<Scalar>::IsSigned>
struct signbit_impl;
template <typename Scalar>
struct signbit_impl<Scalar, false, true> {
static constexpr size_t Size = sizeof(Scalar);
static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
using intSize_t = typename get_integer_by_size<Size>::signed_type;
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Scalar run(const Scalar& x) {
intSize_t a = bit_cast<intSize_t, Scalar>(x);
a = a >> Shift;
Scalar result = bit_cast<Scalar, intSize_t>(a);
return result;
}
};
template <typename Scalar>
struct signbit_impl<Scalar, true, true> {
static constexpr size_t Size = sizeof(Scalar);
static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar& x) { return x >> Shift; }
};
template <typename Scalar>
struct signbit_impl<Scalar, true, false> {
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar& ) {
return Scalar(0);
}
};
template <typename Scalar>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar signbit(const Scalar& x) {
return signbit_impl<Scalar>::run(x);
}
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T exp(const T &x) {
@@ -1659,14 +1680,12 @@ T acos(const T &x) {
return acos(x);
}
#if EIGEN_HAS_CXX11_MATH
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T acosh(const T &x) {
EIGEN_USING_STD(acosh);
return static_cast<T>(acosh(x));
}
#endif
#if defined(SYCL_DEVICE_ONLY)
SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos)
@@ -1688,14 +1707,12 @@ T asin(const T &x) {
return asin(x);
}
#if EIGEN_HAS_CXX11_MATH
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T asinh(const T &x) {
EIGEN_USING_STD(asinh);
return static_cast<T>(asinh(x));
}
#endif
#if defined(SYCL_DEVICE_ONLY)
SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin)
@@ -1717,14 +1734,12 @@ T atan(const T &x) {
return static_cast<T>(atan(x));
}
#if EIGEN_HAS_CXX11_MATH
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T atanh(const T &x) {
EIGEN_USING_STD(atanh);
return static_cast<T>(atanh(x));
}
#endif
#if defined(SYCL_DEVICE_ONLY)
SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan)
@@ -2006,9 +2021,10 @@ namespace internal {
// Specialization for complex types that are not supported by std::expm1.
template <typename RealScalar>
struct expm1_impl<std::complex<RealScalar> > {
EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
const std::complex<RealScalar>& x) {
EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
RealScalar xr = x.real();
RealScalar xi = x.imag();
// expm1(z) = exp(z) - 1

151
libs/eigen/Eigen/src/Core/MathFunctionsImpl.h
View File

@@ -11,17 +11,152 @@
#ifndef EIGEN_MATHFUNCTIONSIMPL_H
#define EIGEN_MATHFUNCTIONSIMPL_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
/** \internal Fast reciprocal using Newton-Raphson's method.
Preconditions:
1. The starting guess provided in approx_a_recip must have at least half
the leading mantissa bits in the correct result, such that a single
Newton-Raphson step is sufficient to get within 1-2 ulps of the currect
result.
2. If a is zero, approx_a_recip must be infinite with the same sign as a.
3. If a is infinite, approx_a_recip must be zero with the same sign as a.
If the preconditions are satisfied, which they are for for the _*_rcp_ps
instructions on x86, the result has a maximum relative error of 2 ulps,
and correctly handles reciprocals of zero, infinity, and NaN.
*/
template <typename Packet, int Steps>
struct generic_reciprocal_newton_step {
static_assert(Steps > 0, "Steps must be at least 1.");
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
run(const Packet& a, const Packet& approx_a_recip) {
using Scalar = typename unpacket_traits<Packet>::type;
const Packet two = pset1<Packet>(Scalar(2));
// Refine the approximation using one Newton-Raphson step:
// x_{i} = x_{i-1} * (2 - a * x_{i-1})
const Packet x =
generic_reciprocal_newton_step<Packet,Steps - 1>::run(a, approx_a_recip);
const Packet tmp = pnmadd(a, x, two);
// If tmp is NaN, it means that a is either +/-0 or +/-Inf.
// In this case return the approximation directly.
const Packet is_not_nan = pcmp_eq(tmp, tmp);
return pselect(is_not_nan, pmul(x, tmp), x);
}
};
template<typename Packet>
struct generic_reciprocal_newton_step<Packet, 0> {
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
return approx_rsqrt;
}
};
/** \internal Fast reciprocal sqrt using Newton-Raphson's method.
Preconditions:
1. The starting guess provided in approx_a_recip must have at least half
the leading mantissa bits in the correct result, such that a single
Newton-Raphson step is sufficient to get within 1-2 ulps of the currect
result.
2. If a is zero, approx_a_recip must be infinite with the same sign as a.
3. If a is infinite, approx_a_recip must be zero with the same sign as a.
If the preconditions are satisfied, which they are for for the _*_rcp_ps
instructions on x86, the result has a maximum relative error of 2 ulps,
and correctly handles zero, infinity, and NaN. Positive denormals are
treated as zero.
*/
template <typename Packet, int Steps>
struct generic_rsqrt_newton_step {
static_assert(Steps > 0, "Steps must be at least 1.");
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
run(const Packet& a, const Packet& approx_rsqrt) {
using Scalar = typename unpacket_traits<Packet>::type;
const Packet one_point_five = pset1<Packet>(Scalar(1.5));
const Packet minus_half = pset1<Packet>(Scalar(-0.5));
// Refine the approximation using one Newton-Raphson step:
// x_{n+1} = x_n * (1.5 + (-0.5 * x_n) * (a * x_n)).
// The approximation is expressed this way to avoid over/under-flows.
Packet x_newton = pmul(approx_rsqrt, pmadd(pmul(minus_half, approx_rsqrt), pmul(a, approx_rsqrt), one_point_five));
for (int step = 1; step < Steps; ++step) {
x_newton = pmul(x_newton, pmadd(pmul(minus_half, x_newton), pmul(a, x_newton), one_point_five));
}
// If approx_rsqrt is 0 or +/-inf, we should return it as is. Note:
// on intel, approx_rsqrt can be inf for small denormal values.
const Packet return_approx = por(pcmp_eq(approx_rsqrt, pzero(a)),
pcmp_eq(pabs(approx_rsqrt), pset1<Packet>(NumTraits<Scalar>::infinity())));
return pselect(return_approx, approx_rsqrt, x_newton);
}
};
template<typename Packet>
struct generic_rsqrt_newton_step<Packet, 0> {
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
return approx_rsqrt;
}
};
/** \internal Fast sqrt using Newton-Raphson's method.
Preconditions:
1. The starting guess for the reciprocal sqrt provided in approx_rsqrt must
have at least half the leading mantissa bits in the correct result, such
that a single Newton-Raphson step is sufficient to get within 1-2 ulps of
the currect result.
2. If a is zero, approx_rsqrt must be infinite.
3. If a is infinite, approx_rsqrt must be zero.
If the preconditions are satisfied, which they are for for the _*_rsqrt_ps
instructions on x86, the result has a maximum relative error of 2 ulps,
and correctly handles zero and infinity, and NaN. Positive denormal inputs
are treated as zero.
*/
template <typename Packet, int Steps=1>
struct generic_sqrt_newton_step {
static_assert(Steps > 0, "Steps must be at least 1.");
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
run(const Packet& a, const Packet& approx_rsqrt) {
using Scalar = typename unpacket_traits<Packet>::type;
const Packet one_point_five = pset1<Packet>(Scalar(1.5));
const Packet minus_half = pset1<Packet>(Scalar(-0.5));
// If a is inf or zero, return a directly.
const Packet inf_mask = pcmp_eq(a, pset1<Packet>(NumTraits<Scalar>::infinity()));
const Packet return_a = por(pcmp_eq(a, pzero(a)), inf_mask);
// Do a single step of Newton's iteration for reciprocal square root:
// x_{n+1} = x_n * (1.5 + (-0.5 * x_n) * (a * x_n))).
// The Newton's step is computed this way to avoid over/under-flows.
Packet rsqrt = pmul(approx_rsqrt, pmadd(pmul(minus_half, approx_rsqrt), pmul(a, approx_rsqrt), one_point_five));
for (int step = 1; step < Steps; ++step) {
rsqrt = pmul(rsqrt, pmadd(pmul(minus_half, rsqrt), pmul(a, rsqrt), one_point_five));
}
// Return sqrt(x) = x * rsqrt(x) for non-zero finite positive arguments.
// Return a itself for 0 or +inf, NaN for negative arguments.
return pselect(return_a, a, pmul(a, rsqrt));
}
};
/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
Doesn't do anything fancy, just a 13/6-degree rational interpolant which
is accurate up to a couple of ulps in the (approximate) range [-8, 8],
outside of which tanh(x) = +/-1 in single precision. The input is clamped
to the range [-c, c]. The value c is chosen as the smallest value where
the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004]
the approxmation tanh(x) ~= x is used for better accuracy as x tends to zero.
the approximation tanh(x) ~= x is used for better accuracy as x tends to zero.
This implementation works on both scalars and packets.
*/
@@ -88,7 +223,7 @@ RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
EIGEN_USING_STD(sqrt);
RealScalar p, qp;
p = numext::maxi(x,y);
if(p==RealScalar(0)) return RealScalar(0);
if(numext::is_exactly_zero(p)) return RealScalar(0);
qp = numext::mini(y,x) / p;
return p * sqrt(RealScalar(1) + qp*qp);
}
@@ -138,8 +273,8 @@ EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) {
return
(numext::isinf)(y) ? std::complex<T>(NumTraits<T>::infinity(), y)
: x == zero ? std::complex<T>(w, y < zero ? -w : w)
: x > zero ? std::complex<T>(w, y / (2 * w))
: numext::is_exactly_zero(x) ? std::complex<T>(w, y < zero ? -w : w)
: x > zero ? std::complex<T>(w, y / (2 * w))
: std::complex<T>(numext::abs(y) / (2 * w), y < zero ? -w : w );
}
@@ -177,10 +312,10 @@ EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& z) {
const T woz = w / abs_z;
// Corner cases consistent with 1/sqrt(z) on gcc/clang.
return
abs_z == zero ? std::complex<T>(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
: ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex<T>(zero, zero)
: x == zero ? std::complex<T>(woz, y < zero ? woz : -woz)
: x > zero ? std::complex<T>(woz, -y / (2 * w * abs_z))
numext::is_exactly_zero(abs_z) ? std::complex<T>(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
: ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex<T>(zero, zero)
: numext::is_exactly_zero(x) ? std::complex<T>(woz, y < zero ? woz : -woz)
: x > zero ? std::complex<T>(woz, -y / (2 * w * abs_z))
: std::complex<T>(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz );
}

134
libs/eigen/Eigen/src/Core/Matrix.h
View File

@@ -11,37 +11,39 @@
#ifndef EIGEN_MATRIX_H
#define EIGEN_MATRIX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
struct traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
{
private:
enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret };
typedef typename find_best_packet<_Scalar,size>::type PacketScalar;
constexpr static int size = internal::size_at_compile_time(Rows_,Cols_);
typedef typename find_best_packet<Scalar_,size>::type PacketScalar;
enum {
row_major_bit = _Options&RowMajor ? RowMajorBit : 0,
is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic,
max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols,
default_alignment = compute_default_alignment<_Scalar,max_size>::value,
actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
row_major_bit = Options_&RowMajor ? RowMajorBit : 0,
is_dynamic_size_storage = MaxRows_==Dynamic || MaxCols_==Dynamic,
max_size = is_dynamic_size_storage ? Dynamic : MaxRows_*MaxCols_,
default_alignment = compute_default_alignment<Scalar_,max_size>::value,
actual_alignment = ((Options_&DontAlign)==0) ? default_alignment : 0,
required_alignment = unpacket_traits<PacketScalar>::alignment,
packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
packet_access_bit = (packet_traits<Scalar_>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
};
public:
typedef _Scalar Scalar;
typedef Scalar_ Scalar;
typedef Dense StorageKind;
typedef Eigen::Index StorageIndex;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
Options = _Options,
RowsAtCompileTime = Rows_,
ColsAtCompileTime = Cols_,
MaxRowsAtCompileTime = MaxRows_,
MaxColsAtCompileTime = MaxCols_,
Flags = compute_matrix_flags(Options_),
Options = Options_,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
@@ -63,18 +65,18 @@ public:
* The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
*
* The first three template parameters are required:
* \tparam _Scalar Numeric type, e.g. float, double, int or std::complex<float>.
* \tparam Scalar_ Numeric type, e.g. float, double, int or std::complex<float>.
* User defined scalar types are supported as well (see \ref user_defined_scalars "here").
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \tparam Rows_ Number of rows, or \b Dynamic
* \tparam Cols_ Number of columns, or \b Dynamic
*
* The remaining template parameters are optional -- in most cases you don't have to worry about them.
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either
* \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of either
* \b #AutoAlign or \b #DontAlign.
* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
* for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
* \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note").
* \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note").
* \tparam MaxRows_ Maximum number of rows. Defaults to \a Rows_ (\ref maxrows "note").
* \tparam MaxCols_ Maximum number of columns. Defaults to \a Cols_ (\ref maxrows "note").
*
* Eigen provides a number of typedefs covering the usual cases. Here are some examples:
*
@@ -128,12 +130,12 @@ public:
* Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map.
* If you want this behavior, see the Sparse module.</dd>
*
* <dt><b>\anchor maxrows _MaxRows and _MaxCols:</b></dt>
* <dt><b>\anchor maxrows MaxRows_ and MaxCols_:</b></dt>
* <dd>In most cases, one just leaves these parameters to the default values.
* These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
* when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot
* exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols
* are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
* exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case MaxRows_ and MaxCols_
* are the dimensions of the original matrix, while Rows_ and Cols_ are Dynamic.</dd>
* </dl>
*
* <i><b>ABI and storage layout</b></i>
@@ -174,9 +176,9 @@ public:
* \ref TopicStorageOrders
*/
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
class Matrix
: public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public PlainObjectBase<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
{
public:
@@ -185,7 +187,7 @@ class Matrix
*/
typedef PlainObjectBase<Matrix> Base;
enum { Options = _Options };
enum { Options = Options_ };
EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
@@ -258,7 +260,6 @@ class Matrix
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Matrix() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
@@ -266,24 +267,18 @@ class Matrix
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
{ EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
}
: Base(std::move(other)) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
Base::operator=(std::move(other));
return *this;
}
#endif
#if EIGEN_HAS_CXX11
/** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&, const Scalar&, const ArgTypes&... args)
*
* Example: \include Matrix_variadic_ctor_cxx11.cpp
@@ -317,9 +312,9 @@ class Matrix
*
* \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
*/
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE Matrix(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
#endif // end EIGEN_HAS_CXX11
EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE Matrix(
const std::initializer_list<std::initializer_list<Scalar>>& list)
: Base(list) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@@ -328,7 +323,6 @@ class Matrix
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit Matrix(const T& x)
{
Base::_check_template_params();
Base::template _init1<T>(x);
}
@@ -336,7 +330,6 @@ class Matrix
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Matrix(const T0& x, const T1& y)
{
Base::_check_template_params();
Base::template _init2<T0,T1>(x, y);
}
@@ -388,7 +381,6 @@ class Matrix
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
@@ -400,7 +392,6 @@ class Matrix
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
@@ -480,16 +471,21 @@ class Matrix
#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \
/** \ingroup matrixtypedefs */ \
/** \brief `Size`&times;`Size` matrix of type `Type`. */ \
typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix; \
/** \ingroup matrixtypedefs */ \
/** \brief `Size`&times;`1` vector of type `Type`. */ \
typedef Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix; \
/** \ingroup matrixtypedefs */ \
/** \brief `1`&times;`Size` vector of type `Type`. */ \
typedef Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix;
#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \
/** \ingroup matrixtypedefs */ \
/** \brief `Size`&times;`Dynamic` matrix of type `Type`. */ \
typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix; \
/** \ingroup matrixtypedefs */ \
/** \brief `Dynamic`&times;`Size` matrix of type `Type`. */ \
typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
@@ -511,30 +507,28 @@ EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
#undef EIGEN_MAKE_TYPEDEFS
#undef EIGEN_MAKE_FIXED_TYPEDEFS
#if EIGEN_HAS_CXX11
#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix) \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 */ \
template <typename Type> \
using Matrix##SizeSuffix = Matrix<Type, Size, Size>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 */ \
template <typename Type> \
using Vector##SizeSuffix = Matrix<Type, Size, 1>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 */ \
template <typename Type> \
#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix) \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 `Size`&times;`Size` matrix of type `Type`.*/ \
template <typename Type> \
using Matrix##SizeSuffix = Matrix<Type, Size, Size>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 `Size`&times;`1` vector of type `Type`.*/ \
template <typename Type> \
using Vector##SizeSuffix = Matrix<Type, Size, 1>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 `1`&times;`Size` vector of type `Type`.*/ \
template <typename Type> \
using RowVector##SizeSuffix = Matrix<Type, 1, Size>;
#define EIGEN_MAKE_FIXED_TYPEDEFS(Size) \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 */ \
template <typename Type> \
using Matrix##Size##X = Matrix<Type, Size, Dynamic>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 */ \
template <typename Type> \
#define EIGEN_MAKE_FIXED_TYPEDEFS(Size) \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 `Size`&times;`Dynamic` matrix of type `Type` */ \
template <typename Type> \
using Matrix##Size##X = Matrix<Type, Size, Dynamic>; \
/** \ingroup matrixtypedefs */ \
/** \brief \cpp11 `Dynamic`&times;`Size` matrix of type `Type`. */ \
template <typename Type> \
using Matrix##X##Size = Matrix<Type, Dynamic, Size>;
EIGEN_MAKE_TYPEDEFS(2, 2)
@@ -546,20 +540,18 @@ EIGEN_MAKE_FIXED_TYPEDEFS(3)
EIGEN_MAKE_FIXED_TYPEDEFS(4)
/** \ingroup matrixtypedefs
* \brief \cpp11 */
* \brief \cpp11 `Size`&times;`1` vector of type `Type`. */
template <typename Type, int Size>
using Vector = Matrix<Type, Size, 1>;
/** \ingroup matrixtypedefs
* \brief \cpp11 */
* \brief \cpp11 `1`&times;`Size` vector of type `Type`. */
template <typename Type, int Size>
using RowVector = Matrix<Type, 1, Size>;
#undef EIGEN_MAKE_TYPEDEFS
#undef EIGEN_MAKE_FIXED_TYPEDEFS
#endif // EIGEN_HAS_CXX11
} // end namespace Eigen
#endif // EIGEN_MATRIX_H

69
libs/eigen/Eigen/src/Core/MatrixBase.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_MATRIXBASE_H
#define EIGEN_MATRIXBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class MatrixBase
@@ -92,8 +94,8 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** type of the equivalent square matrix */
typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)> SquareMatrixType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
/** \returns the size of the main diagonal, which is min(rows(),cols()).
@@ -107,10 +109,10 @@ template<typename Derived> class MatrixBase
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
ConstTransposeReturnType
>::type AdjointReturnType;
typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
ConstTransposeReturnType
> AdjointReturnType;
/** \internal Return type of eigenvalues() */
typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
/** \internal the return type of identity */
@@ -184,6 +186,11 @@ template<typename Derived> class MatrixBase
const Product<Derived, DiagonalDerived, LazyProduct>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename SkewDerived>
EIGEN_DEVICE_FUNC
const Product<Derived, SkewDerived, LazyProduct>
operator*(const SkewSymmetricBase<SkewDerived> &skew) const;
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
@@ -206,28 +213,22 @@ template<typename Derived> class MatrixBase
EIGEN_DEVICE_FUNC
DiagonalReturnType diagonal();
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
typedef Diagonal<const Derived> ConstDiagonalReturnType;
EIGEN_DEVICE_FUNC
ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
const ConstDiagonalReturnType diagonal() const;
template<int Index>
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<Index>::Type diagonal();
Diagonal<Derived, Index> diagonal();
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
const Diagonal<const Derived, Index> diagonal() const;
EIGEN_DEVICE_FUNC
DiagonalDynamicIndexReturnType diagonal(Index index);
Diagonal<Derived, DynamicIndex> diagonal(Index index);
EIGEN_DEVICE_FUNC
ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
const Diagonal<const Derived, DynamicIndex> diagonal(Index index) const;
template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
@@ -263,6 +264,8 @@ template<typename Derived> class MatrixBase
EIGEN_DEVICE_FUNC
const DiagonalWrapper<const Derived> asDiagonal() const;
const PermutationWrapper<const Derived> asPermutation() const;
EIGEN_DEVICE_FUNC
const SkewSymmetricWrapper<const Derived> asSkewSymmetric() const;
EIGEN_DEVICE_FUNC
Derived& setIdentity();
@@ -277,6 +280,8 @@ template<typename Derived> class MatrixBase
bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isSkewSymmetric(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isOrthogonal(const MatrixBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
@@ -368,25 +373,23 @@ template<typename Derived> class MatrixBase
/////////// SVD module ///////////
inline JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
inline BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
template<int Options = 0>
inline JacobiSVD<PlainObject, Options> jacobiSvd() const;
template<int Options = 0>
EIGEN_DEPRECATED
inline JacobiSVD<PlainObject, Options> jacobiSvd(unsigned int computationOptions) const;
template<int Options = 0>
inline BDCSVD<PlainObject, Options> bdcSvd() const;
template<int Options = 0>
EIGEN_DEPRECATED
inline BDCSVD<PlainObject, Options> bdcSvd(unsigned int computationOptions) const;
/////////// Geometry module ///////////
#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 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
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename cross_product_return_type<OtherDerived>::type
#else
inline PlainObject
#endif
inline typename internal::cross_impl<Derived, OtherDerived>::return_type
cross(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
@@ -468,11 +471,9 @@ template<typename Derived> class MatrixBase
const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
#if EIGEN_HAS_CXX11_MATH
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine)
#endif
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)

16
libs/eigen/Eigen/src/Core/NestByValue.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_NESTBYVALUE_H
#define EIGEN_NESTBYVALUE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -41,6 +43,8 @@ template<typename ExpressionType> class NestByValue
public:
typedef typename internal::dense_xpr_base<NestByValue>::type Base;
static constexpr bool HasDirectAccess = internal::has_direct_access<ExpressionType>::ret;
EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
@@ -52,6 +56,18 @@ template<typename ExpressionType> class NestByValue
EIGEN_DEVICE_FUNC const ExpressionType& nestedExpression() const { return m_expression; }
EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, const Scalar*>::type data() const {
return m_expression.data();
}
EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, Index>::type innerStride() const {
return m_expression.innerStride();
}
EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, Index>::type outerStride() const {
return m_expression.outerStride();
}
protected:
const ExpressionType m_expression;
};

2
libs/eigen/Eigen/src/Core/NoAlias.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_NOALIAS_H
#define EIGEN_NOALIAS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class NoAlias

32
libs/eigen/Eigen/src/Core/NumTraits.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_NUMTRAITS_H
#define EIGEN_NUMTRAITS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -61,10 +63,10 @@ struct default_digits_impl<T,false,false> // Floating point
{
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
static int run() {
using std::log;
using std::log2;
using std::ceil;
typedef typename NumTraits<T>::Real Real;
return int(ceil(-log(NumTraits<Real>::epsilon())/log(static_cast<Real>(2))));
return int(ceil(-log2(NumTraits<Real>::epsilon())));
}
};
@@ -83,17 +85,17 @@ namespace numext {
// TODO: Replace by std::bit_cast (available in C++20)
template <typename Tgt, typename Src>
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
#if EIGEN_HAS_TYPE_TRAITS
// The behaviour of memcpy is not specified for non-trivially copyable types
EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED);
EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value && std::is_default_constructible<Tgt>::value,
THIS_TYPE_IS_NOT_SUPPORTED);
#endif
EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED);
Tgt tgt;
// Load src into registers first. This allows the memcpy to be elided by CUDA.
const Src staged = src;
EIGEN_USING_STD(memcpy)
memcpy(&tgt, &src, sizeof(Tgt));
memcpy(static_cast<void*>(&tgt),static_cast<const void*>(&staged), sizeof(Tgt));
return tgt;
}
} // namespace numext
@@ -162,11 +164,7 @@ template<typename T> struct GenericNumTraits
};
typedef T Real;
typedef typename internal::conditional<
IsInteger,
typename internal::conditional<sizeof(T)<=2, float, double>::type,
T
>::type NonInteger;
typedef std::conditional_t<IsInteger, std::conditional_t<sizeof(T)<=2, float, double>, T> NonInteger;
typedef T Nested;
typedef T Literal;
@@ -252,15 +250,15 @@ template<> struct NumTraits<long double>
static inline long double dummy_precision() { return 1e-15l; }
};
template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
template<typename Real_> struct NumTraits<std::complex<Real_> >
: GenericNumTraits<std::complex<Real_> >
{
typedef _Real Real;
typedef typename NumTraits<_Real>::Literal Literal;
typedef Real_ Real;
typedef typename NumTraits<Real_>::Literal Literal;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
ReadCost = 2 * NumTraits<_Real>::ReadCost,
RequireInitialization = NumTraits<Real_>::RequireInitialization,
ReadCost = 2 * NumTraits<Real_>::ReadCost,
AddCost = 2 * NumTraits<Real>::AddCost,
MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
};

25
libs/eigen/Eigen/src/Core/PartialReduxEvaluator.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_PARTIALREDUX_H
#define EIGEN_PARTIALREDUX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -29,7 +31,7 @@ namespace internal {
* some (optional) processing of the outcome, e.g., division by n for mean.
*
* For the vectorized path let's observe that the packet-size and outer-unrolling
* are both decided by the assignement logic. So all we have to do is to decide
* are both decided by the assignment logic. So all we have to do is to decide
* on the inner unrolling.
*
* For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h,
@@ -54,12 +56,17 @@ struct packetwise_redux_traits
/* Value to be returned when size==0 , by default let's return 0 */
template<typename PacketType,typename Func>
EIGEN_DEVICE_FUNC
PacketType packetwise_redux_empty_value(const Func& ) { return pset1<PacketType>(0); }
PacketType packetwise_redux_empty_value(const Func& ) {
const typename unpacket_traits<PacketType>::type zero(0);
return pset1<PacketType>(zero);
}
/* For products the default is 1 */
template<typename PacketType,typename Scalar>
EIGEN_DEVICE_FUNC
PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) { return pset1<PacketType>(1); }
PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) {
return pset1<PacketType>(Scalar(1));
}
/* Perform the actual reduction */
template<typename Func, typename Evaluator,
@@ -134,8 +141,8 @@ struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
{
typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType;
typedef typename internal::nested_eval<ArgType,1>::type ArgTypeNested;
typedef typename internal::add_const_on_value_type<ArgTypeNested>::type ConstArgTypeNested;
typedef typename internal::remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
typedef add_const_on_value_type_t<ArgTypeNested> ConstArgTypeNested;
typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
typedef typename ArgType::Scalar InputScalar;
typedef typename XprType::Scalar Scalar;
enum {
@@ -147,16 +154,16 @@ struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
: TraversalSize==0 ? 1
: int(TraversalSize) * int(evaluator<ArgType>::CoeffReadCost) + int(CostOpType::value),
_ArgFlags = evaluator<ArgType>::Flags,
ArgFlags_ = evaluator<ArgType>::Flags,
_Vectorizable = bool(int(_ArgFlags)&PacketAccessBit)
Vectorizable_ = bool(int(ArgFlags_)&PacketAccessBit)
&& bool(MemberOp::Vectorizable)
&& (Direction==int(Vertical) ? bool(_ArgFlags&RowMajorBit) : (_ArgFlags&RowMajorBit)==0)
&& (Direction==int(Vertical) ? bool(ArgFlags_&RowMajorBit) : (ArgFlags_&RowMajorBit)==0)
&& (TraversalSize!=0),
Flags = (traits<XprType>::Flags&RowMajorBit)
| (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit)))
| (_Vectorizable ? PacketAccessBit : 0)
| (Vectorizable_ ? PacketAccessBit : 0)
| LinearAccessBit,
Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized

56
libs/eigen/Eigen/src/Core/PermutationMatrix.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_PERMUTATIONMATRIX_H
#define EIGEN_PERMUTATIONMATRIX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -269,13 +271,13 @@ class PermutationBase : public EigenBase<Derived>
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
: traits<Matrix<StorageIndex_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef _StorageIndex StorageIndex;
typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef StorageIndex_ StorageIndex;
typedef void Scalar;
};
}
@@ -287,14 +289,14 @@ struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _Storag
*
* \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
* \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \tparam _StorageIndex the integer type of the indices
* \tparam StorageIndex_ the integer type of the indices
*
* This class represents a permutation matrix, internally stored as a vector of integers.
*
* \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
*/
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
{
typedef PermutationBase<PermutationMatrix> Base;
typedef internal::traits<PermutationMatrix> Traits;
@@ -389,20 +391,20 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_> >
: traits<Matrix<StorageIndex_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef Map<const Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, PacketAccess_> IndicesType;
typedef StorageIndex_ StorageIndex;
typedef void Scalar;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_> >
{
typedef PermutationBase<Map> Base;
typedef internal::traits<Map> Traits;
@@ -452,18 +454,18 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageInd
IndicesType m_indices;
};
template<typename _IndicesType> class TranspositionsWrapper;
template<typename IndicesType_> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
struct traits<PermutationWrapper<_IndicesType> >
template<typename IndicesType_>
struct traits<PermutationWrapper<IndicesType_> >
{
typedef PermutationStorage StorageKind;
typedef void Scalar;
typedef typename _IndicesType::Scalar StorageIndex;
typedef _IndicesType IndicesType;
typedef typename IndicesType_::Scalar StorageIndex;
typedef IndicesType_ IndicesType;
enum {
RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
RowsAtCompileTime = IndicesType_::SizeAtCompileTime,
ColsAtCompileTime = IndicesType_::SizeAtCompileTime,
MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
Flags = 0
@@ -476,14 +478,14 @@ struct traits<PermutationWrapper<_IndicesType> >
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \tparam _IndicesType the type of the vector of integer (can be any compatible expression)
* \tparam IndicesType_ the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
template<typename _IndicesType>
class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
template<typename IndicesType_>
class PermutationWrapper : public PermutationBase<PermutationWrapper<IndicesType_> >
{
typedef PermutationBase<PermutationWrapper> Base;
typedef internal::traits<PermutationWrapper> Traits;
@@ -498,7 +500,7 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
{}
/** const version of indices(). */
const typename internal::remove_all<typename IndicesType::Nested>::type&
const internal::remove_all_t<typename IndicesType::Nested>&
indices() const { return m_indices; }
protected:

204
libs/eigen/Eigen/src/Core/PlainObjectBase.h
View File

@@ -22,23 +22,20 @@
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
template <typename Index>
EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index, Index) {}
};
template<> struct check_rows_cols_for_overflow<Dynamic> {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
{
template <typename Index>
EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index rows, Index cols) {
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
@@ -64,18 +61,18 @@ namespace doxygen {
// This is a workaround to doxygen not being able to understand the inheritance logic
// when it is hidden by the dense_xpr_base helper struct.
// Moreover, doxygen fails to include members that are not documented in the declaration body of
// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
// MatrixBase if we inherits MatrixBase<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >,
// this is why we simply inherits MatrixBase, though this does not make sense.
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename Derived> struct dense_xpr_base_dispatcher;
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
struct dense_xpr_base_dispatcher<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
: public MatrixBase {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
struct dense_xpr_base_dispatcher<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
: public ArrayBase {};
} // namespace doxygen
@@ -134,6 +131,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
EIGEN_STATIC_ASSERT(internal::check_implication(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT(internal::check_implication(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_STATIC_ASSERT(((Options & (DontAlign|RowMajor)) == Options), INVALID_MATRIX_TEMPLATE_PARAMETERS)
EIGEN_DEVICE_FUNC
Base& base() { return *static_cast<Base*>(this); }
EIGEN_DEVICE_FUNC
@@ -148,12 +155,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeff(Index rowId, Index colId) const {
if (Flags & RowMajorBit)
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
else // column-major
return m_storage.data()[rowId + colId * m_storage.rows()];
}
@@ -171,12 +176,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
if(Flags & RowMajorBit)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index rowId, Index colId) {
if (Flags & RowMajorBit)
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
else // column-major
return m_storage.data()[rowId + colId * m_storage.rows()];
}
@@ -184,28 +187,20 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index index) { return m_storage.data()[index]; }
/** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeffRef(Index rowId, Index colId) const {
if (Flags & RowMajorBit)
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
else // column-major
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeffRef(Index index) const {
return m_storage.data()[index];
}
@@ -267,13 +262,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index rows, Index cols) {
eigen_assert(internal::check_implication(RowsAtCompileTime!=Dynamic, rows==RowsAtCompileTime)
&& internal::check_implication(ColsAtCompileTime!=Dynamic, cols==ColsAtCompileTime)
&& internal::check_implication(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic, rows<=MaxRowsAtCompileTime)
&& internal::check_implication(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic, cols<=MaxColsAtCompileTime)
&& rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
#ifdef EIGEN_INITIALIZE_COEFFS
@@ -297,12 +290,13 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
#ifdef EIGEN_INITIALIZE_COEFFS
EIGEN_DEVICE_FUNC inline constexpr void resize(Index size) {
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(
((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime == Dynamic || size <= MaxSizeAtCompileTime)) ||
SizeAtCompileTime == size) &&
size >= 0);
#ifdef EIGEN_INITIALIZE_COEFFS
bool size_changed = size != this->size();
#endif
if(RowsAtCompileTime == 1)
@@ -322,11 +316,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index cols)
{
resize(rows(), cols);
}
EIGEN_DEVICE_FUNC inline constexpr void resize(NoChange_t, Index cols) { resize(rows(), cols); }
/** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
* as in the example below.
@@ -336,11 +326,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index rows, NoChange_t)
{
resize(rows, cols());
}
EIGEN_DEVICE_FUNC inline constexpr void resize(Index rows, NoChange_t) { resize(rows, cols()); }
/** Resizes \c *this to have the same dimensions as \a other.
* Takes care of doing all the checking that's needed.
@@ -475,7 +461,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
@@ -486,11 +471,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
: m_storage( std::move(other.m_storage) )
@@ -500,11 +484,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
{
_check_template_params();
m_storage = std::move(other.m_storage);
return *this;
}
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
@@ -514,17 +496,14 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
{
// _check_template_params();
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#if EIGEN_HAS_CXX11
/** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11
/** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
*
* \only_for_vectors
*
* This constructor is for 1D array or vectors with more than 4 coefficients.
* There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients.
*
* \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
* constructor must match the the fixed number of rows (resp. columns) of \c *this.
@@ -534,7 +513,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
: m_storage()
{
_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4);
m_storage.data()[0] = a0;
m_storage.data()[1] = a1;
@@ -546,14 +524,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
/** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer
* lists \cpp11
* lists
*/
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE PlainObjectBase(const std::initializer_list<std::initializer_list<Scalar>>& list)
: m_storage()
{
_check_template_params();
EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE PlainObjectBase(
const std::initializer_list<std::initializer_list<Scalar>>& list)
: m_storage() {
size_t list_size = 0;
if (list.begin() != list.end()) {
list_size = list.begin()->size();
@@ -581,7 +556,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
}
}
#endif // end EIGEN_HAS_CXX11
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
@@ -589,7 +563,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
_set_noalias(other);
}
@@ -600,7 +573,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
*this = other.derived();
}
@@ -609,7 +581,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
{
_check_template_params();
// FIXME this does not automatically transpose vectors if necessary
resize(other.rows(), other.cols());
other.evalTo(this->derived());
@@ -640,7 +611,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \see class Map
*/
//@{
///@{
static inline ConstMapType Map(const Scalar* data)
{ return ConstMapType(data); }
static inline MapType Map(Scalar* data)
@@ -704,7 +675,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<int Outer, int Inner>
static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
{ return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
//@}
///@}
using Base::setConstant;
EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
@@ -800,7 +771,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, std::enable_if_t<Base::SizeAtCompileTime!=2,T0>* = 0)
{
const bool t0_is_integer_alike = internal::is_valid_index_type<T0>::value;
const bool t1_is_integer_alike = internal::is_valid_index_type<T1>::value;
@@ -812,7 +783,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, std::enable_if_t<Base::SizeAtCompileTime==2,T0>* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = Scalar(val0);
@@ -822,10 +793,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<T0,Index>::value)
&& (internal::is_same<T1,Index>::value)
&& Base::SizeAtCompileTime==2,T1>::type* = 0)
std::enable_if_t< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<T0,Index>::value)
&& (internal::is_same<T1,Index>::value)
&& Base::SizeAtCompileTime==2,T1>* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = Scalar(val0);
@@ -836,8 +807,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
// then the argument is meant to be the size of the object.
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if< (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
&& ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>::type* = 0)
EIGEN_STRONG_INLINE void _init1(Index size, std::enable_if_t< (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
&& ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>* = 0)
{
// NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
const bool is_integer_alike = internal::is_valid_index_type<T>::value;
@@ -850,7 +821,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>::type* = 0)
EIGEN_STRONG_INLINE void _init1(const Scalar& val0, std::enable_if_t<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
m_storage.data()[0] = val0;
@@ -860,10 +831,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Index& val0,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime==1
&& internal::is_convertible<T, Scalar>::value,T*>::type* = 0)
std::enable_if_t< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime==1
&& internal::is_convertible<T, Scalar>::value,T*>* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
m_storage.data()[0] = Scalar(val0);
@@ -916,10 +887,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar& val0,
typename internal::enable_if< Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>::type* = 0)
std::enable_if_t< Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>* = 0)
{
Base::setConstant(val0);
}
@@ -928,12 +899,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Index& val0,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>::type* = 0)
std::enable_if_t< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime!=Dynamic
&& Base::SizeAtCompileTime!=1
&& internal::is_convertible<T, Scalar>::value
&& internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>* = 0)
{
Base::setConstant(val0);
}
@@ -964,21 +935,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
void swap(DenseBase<OtherDerived> const & other)
{ Base::swap(other.derived()); }
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void _check_template_params()
{
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor)
&& EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0)
&& ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0))
&& ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0))
&& ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0))
&& ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0))
&& (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic)
&& (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic)
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
enum { IsPlainObjectBase = 1 };
#endif
public:
@@ -999,11 +955,7 @@ namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct conservative_resize_like_impl
{
#if EIGEN_HAS_TYPE_TRAITS
static const bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
#else
static const bool IsRelocatable = !NumTraits<typename Derived::Scalar>::RequireInitialization;
#endif
static constexpr bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
{
if (_this.rows() == rows && _this.cols() == cols) return;

30
libs/eigen/Eigen/src/Core/Product.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_PRODUCT_H
#define EIGEN_PRODUCT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
@@ -19,8 +21,8 @@ namespace internal {
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef remove_all_t<Lhs> LhsCleaned;
typedef remove_all_t<Rhs> RhsCleaned;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
@@ -40,7 +42,7 @@ struct traits<Product<Lhs, Rhs, Option> >
MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
// FIXME: only needed by GeneralMatrixMatrixTriangular
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
InnerSize = min_size_prefer_fixed(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
// The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit
@@ -58,8 +60,8 @@ struct traits<Product<Lhs, Rhs, Option> >
*
* \brief Expression of the product of two arbitrary matrices or vectors
*
* \tparam _Lhs the type of the left-hand side expression
* \tparam _Rhs the type of the right-hand side expression
* \tparam Lhs_ the type of the left-hand side expression
* \tparam Rhs_ the type of the right-hand side expression
*
* This class represents an expression of the product of two arbitrary matrices.
*
@@ -67,16 +69,16 @@ struct traits<Product<Lhs, Rhs, Option> >
* \tparam Option can be DefaultProduct, AliasFreeProduct, or LazyProduct
*
*/
template<typename _Lhs, typename _Rhs, int Option>
class Product : public ProductImpl<_Lhs,_Rhs,Option,
typename internal::product_promote_storage_type<typename internal::traits<_Lhs>::StorageKind,
typename internal::traits<_Rhs>::StorageKind,
internal::product_type<_Lhs,_Rhs>::ret>::ret>
template<typename Lhs_, typename Rhs_, int Option>
class Product : public ProductImpl<Lhs_,Rhs_,Option,
typename internal::product_promote_storage_type<typename internal::traits<Lhs_>::StorageKind,
typename internal::traits<Rhs_>::StorageKind,
internal::product_type<Lhs_,Rhs_>::ret>::ret>
{
public:
typedef _Lhs Lhs;
typedef _Rhs Rhs;
typedef Lhs_ Lhs;
typedef Rhs_ Rhs;
typedef typename ProductImpl<
Lhs, Rhs, Option,
@@ -87,8 +89,8 @@ class Product : public ProductImpl<_Lhs,_Rhs,Option,
typedef typename internal::ref_selector<Lhs>::type LhsNested;
typedef typename internal::ref_selector<Rhs>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)

116
libs/eigen/Eigen/src/Core/ProductEvaluators.h
View File

@@ -13,6 +13,8 @@
#ifndef EIGEN_PRODUCTEVALUATORS_H
#define EIGEN_PRODUCTEVALUATORS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -107,14 +109,14 @@ struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsSh
explicit product_evaluator(const XprType& xpr)
: m_result(xpr.rows(), xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
// FIXME shall we handle nested_eval here?,
// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
// typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
// typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
// typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
// typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
// typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
// typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
//
// const LhsNested lhs(xpr.lhs());
// const RhsNested rhs(xpr.rhs());
@@ -134,7 +136,7 @@ protected:
// Dense = Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -152,7 +154,7 @@ 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,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -167,7 +169,7 @@ 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,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -296,7 +298,7 @@ void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, cons
template<typename Lhs, typename Rhs>
struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
{
template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
template<typename T> struct is_row_major : std::conditional_t<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type> {};
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
// TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
@@ -370,7 +372,7 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
typedef typename nested_eval<Rhs,1>::type RhsNested;
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::remove_all<typename internal::conditional<int(Side)==OnTheRight,LhsNested,RhsNested>::type>::type MatrixType;
typedef internal::remove_all_t<std::conditional_t<int(Side)==OnTheRight,LhsNested,RhsNested>> MatrixType;
template<typename Dest>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
@@ -427,8 +429,8 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
// 3 - it makes this fallback consistent with the heavy GEMM routine.
// 4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
// (see https://stackoverflow.com/questions/54738495)
// For small fixed sizes matrices, howver, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
// and the behavior depends also a lot on the compiler... This is why this re-writting strategy is currently
// For small fixed sizes matrices, however, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
// and the behavior depends also a lot on the compiler... This is why this re-writing strategy is currently
// enabled only when falling back from the main GEMM.
template<typename Dst, typename Func>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@@ -448,7 +450,7 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(),
func,
actualAlpha,
typename conditional<HasScalarFactor,true_type,false_type>::type());
std::conditional_t<HasScalarFactor,true_type,false_type>());
}
protected:
@@ -458,7 +460,7 @@ protected:
void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s /* == 1 */, false_type)
{
EIGEN_UNUSED_VARIABLE(s);
eigen_internal_assert(s==Scalar(1));
eigen_internal_assert(numext::is_exactly_one(s));
call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
}
@@ -526,8 +528,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
typedef evaluator<LhsNestedCleaned> LhsEtorType;
typedef evaluator<RhsNestedCleaned> RhsEtorType;
@@ -535,7 +537,7 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
enum {
RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
InnerSize = min_size_prefer_fixed(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
};
@@ -564,8 +566,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
// Here, we don't care about alignment larger than the usable packet size.
LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
LhsAlignment = plain_enum_min(LhsEtorType::Alignment, LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
RhsAlignment = plain_enum_min(RhsEtorType::Alignment, RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
@@ -585,8 +587,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
: bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % plain_enum_max(1, LhsAlignment))!=0 ? 0 : LhsAlignment)
: bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % plain_enum_max(1, RhsAlignment))!=0 ? 0 : RhsAlignment)
: 0,
/* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
@@ -640,8 +642,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
}
protected:
typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
add_const_on_value_type_t<LhsNested> m_lhs;
add_const_on_value_type_t<RhsNested> m_rhs;
LhsEtorType m_lhsImpl;
RhsEtorType m_rhsImpl;
@@ -836,22 +838,22 @@ public:
MatrixFlags = evaluator<MatrixType>::Flags,
DiagFlags = evaluator<DiagonalType>::Flags,
_StorageOrder = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
StorageOrder_ = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
: (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor
: MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
_SameStorageOrder = _StorageOrder == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
SameStorageOrder_ = StorageOrder_ == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
_ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
ScalarAccessOnDiag_ = !((int(StorageOrder_) == ColMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder_) == RowMajor && int(ProductOrder) == OnTheRight)),
SameTypes_ = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit)
&& _SameTypes
&& (_SameStorageOrder || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
&& (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
_LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
//Vectorizable_ = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (SameTypes_ && bool(int(DiagFlags)&PacketAccessBit))),
Vectorizable_ = bool(int(MatrixFlags)&PacketAccessBit)
&& SameTypes_
&& (SameStorageOrder_ || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
&& (ScalarAccessOnDiag_ || (bool(int(DiagFlags)&PacketAccessBit))),
LinearAccessMask_ = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|LinearAccessMask_) & (unsigned int)(MatrixFlags)) | (Vectorizable_ ? PacketAccessBit : 0),
Alignment = evaluator<MatrixType>::Alignment,
AsScalarProduct = (DiagonalType::SizeAtCompileTime==1)
@@ -887,7 +889,7 @@ protected:
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
DiagonalPacketLoadMode = plain_enum_min(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
};
return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
@@ -913,7 +915,7 @@ struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalSha
typedef typename Lhs::DiagonalVectorType DiagonalType;
enum { StorageOrder = Base::_StorageOrder };
enum { StorageOrder = Base::StorageOrder_ };
EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
: Base(xpr.rhs(), xpr.lhs().diagonal())
@@ -932,7 +934,7 @@ struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalSha
// FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
// See also similar calls below.
return this->template packet_impl<LoadMode,PacketType>(row,col, row,
typename internal::conditional<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>::type());
std::conditional_t<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>());
}
template<int LoadMode,typename PacketType>
@@ -957,7 +959,7 @@ struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape,
typedef Product<Lhs, Rhs, ProductKind> XprType;
typedef typename XprType::PlainObject PlainObject;
enum { StorageOrder = Base::_StorageOrder };
enum { StorageOrder = Base::StorageOrder_ };
EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
: Base(xpr.lhs(), xpr.rhs().diagonal())
@@ -974,7 +976,7 @@ struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape,
EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
{
return this->template packet_impl<LoadMode,PacketType>(row,col, col,
typename internal::conditional<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>::type());
std::conditional_t<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>());
}
template<int LoadMode,typename PacketType>
@@ -1001,7 +1003,7 @@ template<typename ExpressionType, int Side, bool Transposed>
struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
{
typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
typedef remove_all_t<MatrixType> MatrixTypeCleaned;
template<typename Dest, typename PermutationType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
@@ -1109,7 +1111,7 @@ template<typename ExpressionType, int Side, bool Transposed, typename Expression
struct transposition_matrix_product
{
typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
typedef remove_all_t<MatrixType> MatrixTypeCleaned;
template<typename Dest, typename TranspositionType>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
@@ -1172,6 +1174,40 @@ struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShap
}
};
/***************************************************************************
* skew symmetric products
* for now we just call the generic implementation
***************************************************************************/
template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, MatrixShape, ProductTag>
{
template<typename Dest>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
{
generic_product_impl<typename Lhs::DenseMatrixType , Rhs, DenseShape, MatrixShape, ProductTag>::evalTo(dst, lhs, rhs);
}
};
template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
struct generic_product_impl<Lhs, Rhs, MatrixShape, SkewSymmetricShape, ProductTag>
{
template<typename Dest>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
{
generic_product_impl<Lhs, typename Rhs::DenseMatrixType, MatrixShape, DenseShape, ProductTag>::evalTo(dst, lhs, rhs);
}
};
template<typename Lhs, typename Rhs, int ProductTag>
struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, SkewSymmetricShape, ProductTag>
{
template<typename Dest>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
{
generic_product_impl<typename Lhs::DenseMatrixType, typename Rhs::DenseMatrixType, DenseShape, DenseShape, ProductTag>::evalTo(dst, lhs, rhs);
}
};
} // end namespace internal
} // end namespace Eigen

3
libs/eigen/Eigen/src/Core/Random.h
View File

@@ -10,12 +10,13 @@
#ifndef EIGEN_RANDOM_H
#define EIGEN_RANDOM_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename Scalar> struct scalar_random_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
inline const Scalar operator() () const { return random<Scalar>(); }
};

15
libs/eigen/Eigen/src/Core/Redux.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_REDUX_H
#define EIGEN_REDUX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -198,8 +200,7 @@ struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
{
eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
Scalar res;
res = eval.coeffByOuterInner(0, 0);
Scalar res = eval.coeffByOuterInner(0, 0);
for(Index i = 1; i < xpr.innerSize(); ++i)
res = func(res, eval.coeffByOuterInner(0, i));
for(Index i = 1; i < xpr.outerSize(); ++i)
@@ -238,7 +239,7 @@ struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
enum {
alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Evaluator::Alignment)
alignment = plain_enum_max(alignment0, Evaluator::Alignment)
};
const Index alignedStart = internal::first_default_aligned(xpr);
const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
@@ -353,12 +354,12 @@ struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling>
};
// evaluator adaptor
template<typename _XprType>
class redux_evaluator : public internal::evaluator<_XprType>
template<typename XprType_>
class redux_evaluator : public internal::evaluator<XprType_>
{
typedef internal::evaluator<_XprType> Base;
typedef internal::evaluator<XprType_> Base;
public:
typedef _XprType XprType;
typedef XprType_ XprType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit redux_evaluator(const XprType &xpr) : Base(xpr) {}

34
libs/eigen/Eigen/src/Core/Ref.h
View File

@@ -10,20 +10,22 @@
#ifndef EIGEN_REF_H
#define EIGEN_REF_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename _PlainObjectType, int _Options, typename _StrideType>
struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
: public traits<Map<_PlainObjectType, _Options, _StrideType> >
template<typename PlainObjectType_, int Options_, typename StrideType_>
struct traits<Ref<PlainObjectType_, Options_, StrideType_> >
: public traits<Map<PlainObjectType_, Options_, StrideType_> >
{
typedef _PlainObjectType PlainObjectType;
typedef _StrideType StrideType;
typedef PlainObjectType_ PlainObjectType;
typedef StrideType_ StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
Options = Options_,
Flags = traits<Map<PlainObjectType_, Options_, StrideType_> >::Flags | NestByRefBit,
Alignment = traits<Map<PlainObjectType_, Options_, StrideType_> >::Alignment
};
template<typename Derived> struct match {
@@ -46,7 +48,7 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
};
typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
typedef std::conditional_t<MatchAtCompileTime,internal::true_type,internal::false_type> type;
};
};
@@ -197,8 +199,8 @@ protected:
return false;
}
::new (static_cast<Base*>(this)) Base(expr.data(), rows, cols);
::new (&m_stride) StrideBase(
internal::construct_at<Base>(this, expr.data(), rows, cols);
internal::construct_at(&m_stride,
(StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride,
(StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride );
return true;
@@ -285,7 +287,7 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
typedef internal::traits<Ref> Traits;
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0);
public:
typedef RefBase<Ref> Base;
@@ -295,17 +297,17 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0)
{
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
// Construction must pass since we will not create temprary storage in the non-const case.
// Construction must pass since we will not create temporary storage in the non-const case.
const bool success = Base::construct(expr.derived());
EIGEN_UNUSED_VARIABLE(success)
eigen_assert(success);
}
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0)
#else
/** Implicit constructor from any dense expression */
template<typename Derived>
@@ -337,7 +339,7 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
std::enable_if_t<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>* = 0)
{
// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";

14
libs/eigen/Eigen/src/Core/Replicate.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_REPLICATE_H
#define EIGEN_REPLICATE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -21,7 +23,7 @@ struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
enum {
RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
? Dynamic
@@ -62,19 +64,19 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
: public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
{
typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<Replicate>::_MatrixTypeNested _MatrixTypeNested;
typedef typename internal::traits<Replicate>::MatrixTypeNested_ MatrixTypeNested_;
public:
typedef typename internal::dense_xpr_base<Replicate>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
typedef internal::remove_all_t<MatrixType> NestedExpression;
template<typename OriginalMatrixType>
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic);
}
@@ -84,7 +86,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
}
@@ -94,7 +96,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const
const MatrixTypeNested_& nestedExpression() const
{
return m_matrix;
}

23
libs/eigen/Eigen/src/Core/Reshaped.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_RESHAPED_H
#define EIGEN_RESHAPED_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class Reshaped
@@ -27,10 +29,9 @@ namespace Eigen {
* It is the return type of DenseBase::reshaped(NRowsType,NColsType) and
* most of the time this is the only way it is used.
*
* However, in C++98, if you want to directly maniputate reshaped expressions,
* for instance if you want to write a function returning such an expression, you
* will need to use this class. In C++11, it is advised to use the \em auto
* keyword for such use cases.
* If you want to directly manipulate reshaped expressions,
* for instance if you want to write a function returning such an expression,
* it is advised to use the \em auto keyword for such use cases.
*
* Here is an example illustrating the dynamic case:
* \include class_Reshaped.cpp
@@ -156,7 +157,7 @@ class ReshapedImpl_dense<XprType,Rows,Cols,Order,false>
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
typedef internal::remove_all_t<XprType> NestedExpression;
class InnerIterator;
@@ -186,12 +187,12 @@ class ReshapedImpl_dense<XprType,Rows,Cols,Order,false>
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprType>::type&
const internal::remove_all_t<XprType>&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
typename internal::remove_reference<XprType>::type&
std::remove_reference_t<XprType>&
nestedExpression() { return m_xpr; }
protected:
@@ -231,7 +232,7 @@ class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
{}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
const internal::remove_all_t<XprTypeNested>& nestedExpression() const
{
return m_xpr;
}
@@ -250,7 +251,7 @@ class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index outerStride() const
{
return ((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows();
return (((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows()) * m_xpr.innerStride();
}
protected:
@@ -324,7 +325,7 @@ struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ fals
typedef std::pair<Index, Index> RowCol;
inline RowCol index_remap(Index rowId, Index colId) const
EIGEN_DEVICE_FUNC inline RowCol index_remap(Index rowId, Index colId) const
{
if(Order==ColMajor)
{
@@ -443,7 +444,7 @@ struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ true
: mapbase_evaluator<XprType, typename XprType::PlainObject>(xpr)
{
// TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
eigen_assert(((internal::UIntPtr(xpr.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
eigen_assert(((internal::UIntPtr(xpr.data()) % plain_enum_max(1, evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
}
};

4
libs/eigen/Eigen/src/Core/ReturnByValue.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_RETURNBYVALUE_H
#define EIGEN_RETURNBYVALUE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -104,7 +106,7 @@ struct evaluator<ReturnByValue<Derived> >
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: m_result(xpr.rows(), xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
xpr.evalTo(m_result);
}

22
libs/eigen/Eigen/src/Core/Reverse.h
View File

@@ -12,6 +12,8 @@
#ifndef EIGEN_REVERSE_H
#define EIGEN_REVERSE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -24,13 +26,13 @@ struct traits<Reverse<MatrixType, Direction> >
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit)
Flags = MatrixTypeNested_::Flags & (RowMajorBit | LvalueBit)
};
};
@@ -67,7 +69,7 @@ template<typename MatrixType, int Direction> class Reverse
typedef typename internal::dense_xpr_base<Reverse>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
typedef internal::remove_all_t<MatrixType> NestedExpression;
using Base::IsRowMajor;
protected:
@@ -99,7 +101,7 @@ template<typename MatrixType, int Direction> class Reverse
return -m_matrix.innerStride();
}
EIGEN_DEVICE_FUNC const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC const internal::remove_all_t<typename MatrixType::Nested>&
nestedExpression() const
{
return m_matrix;
@@ -173,10 +175,10 @@ struct vectorwise_reverse_inplace_impl<Vertical>
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
const int HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2;
constexpr Index HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2;
Index half = xpr.rows()/2;
xpr.topRows(fix<HalfAtCompileTime>(half))
.swap(xpr.bottomRows(fix<HalfAtCompileTime>(half)).colwise().reverse());
xpr.template topRows<HalfAtCompileTime>(half)
.swap(xpr.template bottomRows<HalfAtCompileTime>(half).colwise().reverse());
}
};
@@ -186,10 +188,10 @@ struct vectorwise_reverse_inplace_impl<Horizontal>
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
const int HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2;
constexpr Index HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2;
Index half = xpr.cols()/2;
xpr.leftCols(fix<HalfAtCompileTime>(half))
.swap(xpr.rightCols(fix<HalfAtCompileTime>(half)).rowwise().reverse());
xpr.template leftCols<HalfAtCompileTime>(half)
.swap(xpr.template rightCols<HalfAtCompileTime>(half).rowwise().reverse());
}
};

8
libs/eigen/Eigen/src/Core/Select.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SELECT_H
#define EIGEN_SELECT_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class Select
@@ -17,9 +19,9 @@ namespace Eigen {
*
* \brief Expression of a coefficient wise version of the C++ ternary operator ?:
*
* \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
* \param ThenMatrixType the type of the \em then expression
* \param ElseMatrixType the type of the \em else expression
* \tparam ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
* \tparam ThenMatrixType the type of the \em then expression
* \tparam ElseMatrixType the type of the \em else expression
*
* This class represents an expression of a coefficient wise version of the C++ ternary operator ?:.
* It is the return type of DenseBase::select() and most of the time this is the only way it is used.

48
libs/eigen/Eigen/src/Core/SelfAdjointView.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SELFADJOINTMATRIX_H
#define EIGEN_SELFADJOINTMATRIX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class SelfAdjointView
@@ -18,8 +20,8 @@ namespace Eigen {
*
* \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
*
* \param MatrixType the type of the dense matrix storing the coefficients
* \param TriangularPart can be either \c #Lower or \c #Upper
* \tparam MatrixType the type of the dense matrix storing the coefficients
* \tparam TriangularPart can be either \c #Lower or \c #Upper
*
* This class is an expression of a sefladjoint matrix from a triangular part of a matrix
* with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
@@ -33,7 +35,7 @@ template<typename MatrixType, unsigned int UpLo>
struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
typedef MatrixType ExpressionType;
typedef typename MatrixType::PlainObject FullMatrixType;
enum {
@@ -46,12 +48,13 @@ struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
}
template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
: public TriangularBase<SelfAdjointView<_MatrixType, UpLo> >
template<typename MatrixType_, unsigned int UpLo> class SelfAdjointView
: public TriangularBase<SelfAdjointView<MatrixType_, UpLo> >
{
public:
EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY)
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
typedef TriangularBase<SelfAdjointView> Base;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
@@ -60,8 +63,8 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
/** \brief The type of coefficients in this matrix */
typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
typedef SelfAdjointView<typename internal::add_const<MatrixType>::type, UpLo> ConstSelfAdjointView;
typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
typedef SelfAdjointView<std::add_const_t<MatrixType>, UpLo> ConstSelfAdjointView;
enum {
Mode = internal::traits<SelfAdjointView>::Mode,
@@ -71,10 +74,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
typedef typename MatrixType::PlainObject PlainObject;
EIGEN_DEVICE_FUNC
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{
EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY);
}
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix) { }
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
@@ -180,16 +180,16 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
*/
template<unsigned int TriMode>
EIGEN_DEVICE_FUNC
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type
std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >
triangularView() const
{
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType>::type tmp1(m_matrix);
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType>::type tmp2(tmp1);
return typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType> tmp1(m_matrix);
std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType> tmp2(tmp1);
return std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >(tmp2);
}
typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
@@ -203,10 +203,10 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
*/
template<bool Cond>
EIGEN_DEVICE_FUNC
inline typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type
inline std::conditional_t<Cond,ConjugateReturnType,ConstSelfAdjointView>
conjugateIf() const
{
typedef typename internal::conditional<Cond,ConjugateReturnType,ConstSelfAdjointView>::type ReturnType;
typedef std::conditional_t<Cond,ConjugateReturnType,ConstSelfAdjointView> ReturnType;
return ReturnType(m_matrix.template conjugateIf<Cond>());
}
@@ -218,10 +218,10 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
/** \sa MatrixBase::transpose() */
template<class Dummy=int>
EIGEN_DEVICE_FUNC
inline TransposeReturnType transpose()
inline TransposeReturnType transpose(std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
typename MatrixType::TransposeReturnType tmp(m_matrix);
return TransposeReturnType(tmp);
}

2
libs/eigen/Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SELFCWISEBINARYOP_H
#define EIGEN_SELFCWISEBINARYOP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
// TODO generalize the scalar type of 'other'

412
libs/eigen/Eigen/src/Core/SkewSymmetricMatrix3.h Normal file
View File

@@ -0,0 +1,412 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SKEWSYMMETRICMATRIX3_H
#define EIGEN_SKEWSYMMETRICMATRIX3_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class SkewSymmetricBase
* \ingroup Core_Module
*
* \brief Base class for skew symmetric matrices and expressions
*
* This is the base class that is inherited by SkewSymmetricMatrix3 and related expression
* types, which internally use a three vector for storing the entries. SkewSymmetric
* types always represent square three times three matrices.
*
* This implementations follows class DiagonalMatrix
*
* \tparam Derived is the derived type, a SkewSymmetricMatrix3 or SkewSymmetricWrapper.
*
* \sa class SkewSymmetricMatrix3, class SkewSymmetricWrapper
*/
template<typename Derived>
class SkewSymmetricBase : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::SkewSymmetricVectorType SkewSymmetricVectorType;
typedef typename SkewSymmetricVectorType::Scalar Scalar;
typedef typename SkewSymmetricVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = NoPreferredStorageOrderBit
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
typedef DenseMatrixType DenseType;
typedef SkewSymmetricMatrix3<Scalar> PlainObject;
/** \returns a reference to the derived object. */
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
/** \returns a const reference to the derived object. */
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
/**
* Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
* not an expression.
* \returns A dense matrix, with its entries set from the the derived object. */
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
/** Determinant vanishes */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Scalar determinant() const { return 0; }
/** A.transpose() = -A */
EIGEN_DEVICE_FUNC
PlainObject transpose() const { return (-vector()).asSkewSymmetric(); }
/** \returns the exponential of this matrix using Rodrigues’ formula */
EIGEN_DEVICE_FUNC
DenseMatrixType exponential() const {
DenseMatrixType retVal = DenseMatrixType::Identity();
const SkewSymmetricVectorType& v = vector();
if (v.isZero()) {
return retVal;
}
const Scalar norm2 = v.squaredNorm();
const Scalar norm = numext::sqrt(norm2);
retVal += ((((1 - numext::cos(norm))/norm2)*derived())*derived()) + (numext::sin(norm)/norm)*derived().toDenseMatrix();
return retVal;
}
/** \returns a reference to the derived object's vector of coefficients. */
EIGEN_DEVICE_FUNC
inline const SkewSymmetricVectorType& vector() const { return derived().vector(); }
/** \returns a const reference to the derived object's vector of coefficients. */
EIGEN_DEVICE_FUNC
inline SkewSymmetricVectorType& vector() { return derived().vector(); }
/** \returns the number of rows. */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index rows() const { return 3; }
/** \returns the number of columns. */
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
inline Index cols() const { return 3; }
/** \returns the matrix product of \c *this by the dense matrix, \a matrix */
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
}
/** \returns the matrix product of \c *this by the skew symmetric matrix, \a matrix */
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
Product<Derived,MatrixDerived,LazyProduct>
operator*(const SkewSymmetricBase<MatrixDerived> &matrix) const
{
return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
}
template <typename OtherDerived>
using SkewSymmetricProductReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, product)>;
/** \returns the wedge product of \c *this by the skew symmetric matrix \a other
* A wedge B = AB - BA */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC SkewSymmetricProductReturnType<OtherDerived> wedge(
const SkewSymmetricBase<OtherDerived>& other) const {
return vector().cross(other.vector()).asSkewSymmetric();
}
using SkewSymmetricScaleReturnType =
SkewSymmetricWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SkewSymmetricVectorType, Scalar, product)>;
/** \returns the product of \c *this by the scalar \a scalar */
EIGEN_DEVICE_FUNC
inline SkewSymmetricScaleReturnType operator*(const Scalar& scalar) const {
return (vector() * scalar).asSkewSymmetric();
}
using ScaleSkewSymmetricReturnType =
SkewSymmetricWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, SkewSymmetricVectorType, product)>;
/** \returns the product of a scalar and the skew symmetric matrix \a other */
EIGEN_DEVICE_FUNC
friend inline ScaleSkewSymmetricReturnType operator*(const Scalar& scalar, const SkewSymmetricBase& other) {
return (scalar * other.vector()).asSkewSymmetric();
}
template <typename OtherDerived>
using SkewSymmetricSumReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, sum)>;
/** \returns the sum of \c *this and the skew symmetric matrix \a other */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC inline SkewSymmetricSumReturnType<OtherDerived> operator+(
const SkewSymmetricBase<OtherDerived>& other) const {
return (vector() + other.vector()).asSkewSymmetric();
}
template <typename OtherDerived>
using SkewSymmetricDifferenceReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, difference)>;
/** \returns the difference of \c *this and the skew symmetric matrix \a other */
template <typename OtherDerived>
EIGEN_DEVICE_FUNC inline SkewSymmetricDifferenceReturnType<OtherDerived> operator-(
const SkewSymmetricBase<OtherDerived>& other) const {
return (vector() - other.vector()).asSkewSymmetric();
}
};
/** \class SkewSymmetricMatrix3
* \ingroup Core_Module
*
* \brief Represents a 3x3 skew symmetric matrix with its storage
*
* \tparam Scalar_ the type of coefficients
*
* \sa class SkewSymmetricBase, class SkewSymmetricWrapper
*/
namespace internal {
template<typename Scalar_>
struct traits<SkewSymmetricMatrix3<Scalar_> >
: traits<Matrix<Scalar_,3,3,0,3,3> >
{
typedef Matrix<Scalar_,3,1,0,3,1> SkewSymmetricVectorType;
typedef SkewSymmetricShape StorageKind;
enum {
Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit
};
};
}
template<typename Scalar_>
class SkewSymmetricMatrix3
: public SkewSymmetricBase<SkewSymmetricMatrix3<Scalar_> >
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename internal::traits<SkewSymmetricMatrix3>::SkewSymmetricVectorType SkewSymmetricVectorType;
typedef const SkewSymmetricMatrix3& Nested;
typedef Scalar_ Scalar;
typedef typename internal::traits<SkewSymmetricMatrix3>::StorageKind StorageKind;
typedef typename internal::traits<SkewSymmetricMatrix3>::StorageIndex StorageIndex;
#endif
protected:
SkewSymmetricVectorType m_vector;
public:
/** const version of vector(). */
EIGEN_DEVICE_FUNC
inline const SkewSymmetricVectorType& vector() const { return m_vector; }
/** \returns a reference to the stored vector of coefficients. */
EIGEN_DEVICE_FUNC
inline SkewSymmetricVectorType& vector() { return m_vector; }
/** Default constructor without initialization */
EIGEN_DEVICE_FUNC
inline SkewSymmetricMatrix3() {}
/** Constructor from three scalars */
EIGEN_DEVICE_FUNC
inline SkewSymmetricMatrix3(const Scalar& x, const Scalar& y, const Scalar& z) : m_vector(x,y,z) {}
/** \brief Constructs a SkewSymmetricMatrix3 from an r-value vector type */
EIGEN_DEVICE_FUNC
explicit inline SkewSymmetricMatrix3(SkewSymmetricVectorType&& vec) : m_vector(std::move(vec)) {}
/** generic constructor from expression of the coefficients */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit inline SkewSymmetricMatrix3(const MatrixBase<OtherDerived>& other) : m_vector(other)
{}
/** Copy constructor. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline SkewSymmetricMatrix3(const SkewSymmetricBase<OtherDerived>& other) : m_vector(other.vector()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
inline SkewSymmetricMatrix3(const SkewSymmetricMatrix3& other) : m_vector(other.vector()) {}
#endif
/** Copy operator. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
SkewSymmetricMatrix3& operator=(const SkewSymmetricBase<OtherDerived>& other)
{
m_vector = other.vector();
return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
SkewSymmetricMatrix3& operator=(const SkewSymmetricMatrix3& other)
{
m_vector = other.vector();
return *this;
}
#endif
typedef SkewSymmetricWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, SkewSymmetricVectorType>>
InitializeReturnType;
/** Initializes a skew symmetric matrix with coefficients set to zero */
EIGEN_DEVICE_FUNC
static InitializeReturnType Zero() { return SkewSymmetricVectorType::Zero().asSkewSymmetric(); }
/** Sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero() { m_vector.setZero(); }
};
/** \class SkewSymmetricWrapper
* \ingroup Core_Module
*
* \brief Expression of a skew symmetric matrix
*
* \tparam SkewSymmetricVectorType_ the type of the vector of coefficients
*
* This class is an expression of a skew symmetric matrix, but not storing its own vector of coefficients,
* instead wrapping an existing vector expression. It is the return type of MatrixBase::asSkewSymmetric()
* and most of the time this is the only way that it is used.
*
* \sa class SkewSymmetricMatrix3, class SkewSymmetricBase, MatrixBase::asSkewSymmetric()
*/
namespace internal {
template<typename SkewSymmetricVectorType_>
struct traits<SkewSymmetricWrapper<SkewSymmetricVectorType_> >
{
typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
typedef typename SkewSymmetricVectorType::Scalar Scalar;
typedef typename SkewSymmetricVectorType::StorageIndex StorageIndex;
typedef SkewSymmetricShape StorageKind;
typedef typename traits<SkewSymmetricVectorType>::XprKind XprKind;
enum {
RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
Flags = (traits<SkewSymmetricVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
};
};
}
template<typename SkewSymmetricVectorType_>
class SkewSymmetricWrapper
: public SkewSymmetricBase<SkewSymmetricWrapper<SkewSymmetricVectorType_> >, internal::no_assignment_operator
{
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
typedef SkewSymmetricWrapper Nested;
#endif
/** Constructor from expression of coefficients to wrap. */
EIGEN_DEVICE_FUNC
explicit inline SkewSymmetricWrapper(SkewSymmetricVectorType& a_vector) : m_vector(a_vector) {}
/** \returns a const reference to the wrapped expression of coefficients. */
EIGEN_DEVICE_FUNC
const SkewSymmetricVectorType& vector() const { return m_vector; }
protected:
typename SkewSymmetricVectorType::Nested m_vector;
};
/** \returns a pseudo-expression of a skew symmetric matrix with *this as vector of coefficients
*
* \only_for_vectors
*
* \sa class SkewSymmetricWrapper, class SkewSymmetricMatrix3, vector(), isSkewSymmetric()
**/
template<typename Derived>
EIGEN_DEVICE_FUNC inline const SkewSymmetricWrapper<const Derived>
MatrixBase<Derived>::asSkewSymmetric() const
{
return SkewSymmetricWrapper<const Derived>(derived());
}
/** \returns true if *this is approximately equal to a skew symmetric matrix,
* within the precision given by \a prec.
*/
template<typename Derived>
bool MatrixBase<Derived>::isSkewSymmetric(const RealScalar& prec) const
{
if(cols() != rows()) return false;
return (this->transpose() + *this).isZero(prec);
}
/** \returns the matrix product of \c *this by the skew symmetric matrix \skew.
*/
template<typename Derived>
template<typename SkewDerived>
EIGEN_DEVICE_FUNC inline const Product<Derived, SkewDerived, LazyProduct>
MatrixBase<Derived>::operator*(const SkewSymmetricBase<SkewDerived> &skew) const
{
return Product<Derived, SkewDerived, LazyProduct>(derived(), skew.derived());
}
namespace internal {
template<> struct storage_kind_to_shape<SkewSymmetricShape> { typedef SkewSymmetricShape Shape; };
struct SkewSymmetric2Dense {};
template<> struct AssignmentKind<DenseShape,SkewSymmetricShape> { typedef SkewSymmetric2Dense Kind; };
// SkewSymmetric matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, SkewSymmetric2Dense>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
if((dst.rows()!=3) || (dst.cols()!=3)) {
dst.resize(3, 3);
}
dst.diagonal().setZero();
const typename SrcXprType::SkewSymmetricVectorType v = src.vector();
dst(0, 1) = -v(2);
dst(1, 0) = v(2);
dst(0, 2) = v(1);
dst(2, 0) = -v(1);
dst(1, 2) = -v(0);
dst(2, 1) = v(0);
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.vector() += src.vector(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.vector() -= src.vector(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_SKEWSYMMETRICMATRIX3_H

6
libs/eigen/Eigen/src/Core/Solve.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SOLVE_H
#define EIGEN_SOLVE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
@@ -77,7 +79,7 @@ public:
protected:
const Decomposition &m_dec;
const RhsType &m_rhs;
const typename internal::ref_selector<RhsType>::type m_rhs;
};
@@ -123,7 +125,7 @@ struct evaluator<Solve<Decomposition,RhsType> >
EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
: m_result(solve.rows(), solve.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::construct_at<Base>(this, m_result);
solve.dec()._solve_impl(solve.rhs(), m_result);
}

12
libs/eigen/Eigen/src/Core/SolveTriangular.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SOLVETRIANGULAR_H
#define EIGEN_SOLVETRIANGULAR_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -87,7 +89,7 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
{
typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsProductTraits::extract(lhs);
add_const_on_value_type_t<ActualLhsType> actualLhs = LhsProductTraits::extract(lhs);
const Index size = lhs.rows();
const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows();
@@ -174,11 +176,11 @@ EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(c
return;
enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit) && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
typedef typename internal::conditional<copy,
typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
typedef std::conditional_t<copy,
typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&> OtherCopy;
OtherCopy otherCopy(other);
internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
internal::triangular_solver_selector<MatrixType, std::remove_reference_t<OtherCopy>,
Side, Mode>::run(derived().nestedExpression(), otherCopy);
if (copy)
@@ -206,7 +208,7 @@ struct traits<triangular_solve_retval<Side, TriangularType, Rhs> >
template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval
: public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> >
{
typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
typedef remove_all_t<typename Rhs::Nested> RhsNestedCleaned;
typedef ReturnByValue<triangular_solve_retval> Base;
triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)

29
libs/eigen/Eigen/src/Core/SolverBase.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SOLVERBASE_H
#define EIGEN_SOLVERBASE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -28,7 +30,7 @@ struct solve_assertion<Transpose<Derived> >
template<bool Transpose_, typename Rhs>
static void run(const type& transpose, const Rhs& b)
{
internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<true>(transpose.nestedExpression(), b);
internal::solve_assertion<internal::remove_all_t<Derived>>::template run<true>(transpose.nestedExpression(), b);
}
};
@@ -40,7 +42,7 @@ struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>
template<bool Transpose_, typename Rhs>
static void run(const type& adjoint, const Rhs& b)
{
internal::solve_assertion<typename internal::remove_all<Transpose<Derived> >::type>::template run<true>(adjoint.nestedExpression(), b);
internal::solve_assertion<internal::remove_all_t<Transpose<Derived> >>::template run<true>(adjoint.nestedExpression(), b);
}
};
} // end namespace internal
@@ -79,12 +81,11 @@ class SolverBase : public EigenBase<Derived>
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret),
MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime),
IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
|| internal::traits<Derived>::MaxColsAtCompileTime == 1,
NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2
@@ -105,12 +106,12 @@ class SolverBase : public EigenBase<Derived>
inline const Solve<Derived, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
internal::solve_assertion<typename internal::remove_all<Derived>::type>::template run<false>(derived(), b);
internal::solve_assertion<internal::remove_all_t<Derived>>::template run<false>(derived(), b);
return Solve<Derived, Rhs>(derived(), b.derived());
}
/** \internal the return type of transpose() */
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
typedef Transpose<const Derived> ConstTransposeReturnType;
/** \returns an expression of the transposed of the factored matrix.
*
* A typical usage is to solve for the transposed problem A^T x = b:
@@ -118,16 +119,16 @@ class SolverBase : public EigenBase<Derived>
*
* \sa adjoint(), solve()
*/
inline ConstTransposeReturnType transpose() const
inline const ConstTransposeReturnType transpose() const
{
return ConstTransposeReturnType(derived());
}
/** \internal the return type of adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
ConstTransposeReturnType
>::type AdjointReturnType;
typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const ConstTransposeReturnType>,
const ConstTransposeReturnType
> AdjointReturnType;
/** \returns an expression of the adjoint of the factored matrix
*
* A typical usage is to solve for the adjoint problem A' x = b:
@@ -137,7 +138,7 @@ class SolverBase : public EigenBase<Derived>
*
* \sa transpose(), solve()
*/
inline AdjointReturnType adjoint() const
inline const AdjointReturnType adjoint() const
{
return AdjointReturnType(derived().transpose());
}

12
libs/eigen/Eigen/src/Core/StableNorm.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_STABLENORM_H
#define EIGEN_STABLENORM_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -57,7 +59,7 @@ void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealSca
const Index blockSize = 4096;
typedef typename internal::nested_eval<VectorType,2>::type VectorTypeCopy;
typedef typename internal::remove_all<VectorTypeCopy>::type VectorTypeCopyClean;
typedef internal::remove_all_t<VectorTypeCopy> VectorTypeCopyClean;
const VectorTypeCopy copy(vec);
enum {
@@ -66,8 +68,8 @@ void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealSca
) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
&& (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
};
typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
typename VectorTypeCopyClean::ConstSegmentReturnType>::type SegmentWrapper;
typedef std::conditional_t<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
typename VectorTypeCopyClean::ConstSegmentReturnType> SegmentWrapper;
Index n = vec.size();
Index bi = internal::first_default_aligned(copy);
@@ -79,7 +81,7 @@ void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealSca
template<typename VectorType>
typename VectorType::RealScalar
stable_norm_impl(const VectorType &vec, typename enable_if<VectorType::IsVectorAtCompileTime>::type* = 0 )
stable_norm_impl(const VectorType &vec, std::enable_if_t<VectorType::IsVectorAtCompileTime>* = 0 )
{
using std::sqrt;
using std::abs;
@@ -101,7 +103,7 @@ stable_norm_impl(const VectorType &vec, typename enable_if<VectorType::IsVectorA
template<typename MatrixType>
typename MatrixType::RealScalar
stable_norm_impl(const MatrixType &mat, typename enable_if<!MatrixType::IsVectorAtCompileTime>::type* = 0 )
stable_norm_impl(const MatrixType &mat, std::enable_if_t<!MatrixType::IsVectorAtCompileTime>* = 0 )
{
using std::sqrt;

68
libs/eigen/Eigen/src/Core/StlIterators.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_STLITERATORS_H
#define EIGEN_STLITERATORS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -25,7 +27,7 @@ protected:
typedef typename traits::XprType XprType;
typedef indexed_based_stl_iterator_base<typename traits::non_const_iterator> non_const_iterator;
typedef indexed_based_stl_iterator_base<typename traits::const_iterator> const_iterator;
typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
// NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
friend class indexed_based_stl_iterator_base<typename traits::const_iterator>;
friend class indexed_based_stl_iterator_base<typename traits::non_const_iterator>;
@@ -104,7 +106,7 @@ protected:
typedef typename traits::XprType XprType;
typedef indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator> non_const_iterator;
typedef indexed_based_stl_reverse_iterator_base<typename traits::const_iterator> const_iterator;
typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
// NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
friend class indexed_based_stl_reverse_iterator_base<typename traits::const_iterator>;
friend class indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator>;
@@ -179,18 +181,18 @@ template<typename XprType>
class pointer_based_stl_iterator
{
enum { is_lvalue = internal::is_lvalue<XprType>::value };
typedef pointer_based_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
typedef pointer_based_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
typedef typename internal::conditional<internal::is_const<XprType>::value,non_const_iterator,const_iterator>::type other_iterator;
typedef pointer_based_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
typedef pointer_based_stl_iterator<std::add_const_t<XprType>> const_iterator;
typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
// NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
friend class pointer_based_stl_iterator<typename internal::add_const<XprType>::type>;
friend class pointer_based_stl_iterator<typename internal::remove_const<XprType>::type>;
friend class pointer_based_stl_iterator<std::add_const_t<XprType>>;
friend class pointer_based_stl_iterator<std::remove_const_t<XprType>>;
public:
typedef Index difference_type;
typedef typename XprType::Scalar value_type;
typedef std::random_access_iterator_tag iterator_category;
typedef typename internal::conditional<bool(is_lvalue), value_type*, const value_type*>::type pointer;
typedef typename internal::conditional<bool(is_lvalue), value_type&, const value_type&>::type reference;
typedef std::conditional_t<bool(is_lvalue), value_type*, const value_type*> pointer;
typedef std::conditional_t<bool(is_lvalue), value_type&, const value_type&> reference;
pointer_based_stl_iterator() EIGEN_NO_THROW : m_ptr(0) {}
@@ -256,12 +258,12 @@ protected:
internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_incr;
};
template<typename _XprType>
struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<_XprType> >
template<typename XprType_>
struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<XprType_> >
{
typedef _XprType XprType;
typedef generic_randaccess_stl_iterator<typename internal::remove_const<XprType>::type> non_const_iterator;
typedef generic_randaccess_stl_iterator<typename internal::add_const<XprType>::type> const_iterator;
typedef XprType_ XprType;
typedef generic_randaccess_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
typedef generic_randaccess_stl_iterator<std::add_const_t<XprType>> const_iterator;
};
template<typename XprType>
@@ -283,13 +285,13 @@ protected:
// TODO currently const Transpose/Reshape expressions never returns const references,
// so lets return by value too.
//typedef typename internal::conditional<bool(has_direct_access), const value_type&, const value_type>::type read_only_ref_t;
//typedef std::conditional_t<bool(has_direct_access), const value_type&, const value_type> read_only_ref_t;
typedef const value_type read_only_ref_t;
public:
typedef typename internal::conditional<bool(is_lvalue), value_type *, const value_type *>::type pointer;
typedef typename internal::conditional<bool(is_lvalue), value_type&, read_only_ref_t>::type reference;
typedef std::conditional_t<bool(is_lvalue), value_type *, const value_type *> pointer;
typedef std::conditional_t<bool(is_lvalue), value_type&, read_only_ref_t> reference;
generic_randaccess_stl_iterator() : Base() {}
generic_randaccess_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
@@ -301,12 +303,12 @@ public:
pointer operator->() const { return &((*mp_xpr)(m_index)); }
};
template<typename _XprType, DirectionType Direction>
struct indexed_based_stl_iterator_traits<subvector_stl_iterator<_XprType,Direction> >
template<typename XprType_, DirectionType Direction>
struct indexed_based_stl_iterator_traits<subvector_stl_iterator<XprType_,Direction> >
{
typedef _XprType XprType;
typedef subvector_stl_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
typedef subvector_stl_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
typedef XprType_ XprType;
typedef subvector_stl_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
typedef subvector_stl_iterator<std::add_const_t<XprType>, Direction> const_iterator;
};
template<typename XprType, DirectionType Direction>
@@ -320,12 +322,12 @@ protected:
using Base::m_index;
using Base::mp_xpr;
typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
typedef std::conditional_t<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr> SubVectorType;
typedef std::conditional_t<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr> ConstSubVectorType;
public:
typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
typedef typename reference::PlainObject value_type;
private:
@@ -349,12 +351,12 @@ public:
pointer operator->() const { return (*mp_xpr).template subVector<Direction>(m_index); }
};
template<typename _XprType, DirectionType Direction>
struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<_XprType,Direction> >
template<typename XprType_, DirectionType Direction>
struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<XprType_,Direction> >
{
typedef _XprType XprType;
typedef subvector_stl_reverse_iterator<typename internal::remove_const<XprType>::type, Direction> non_const_iterator;
typedef subvector_stl_reverse_iterator<typename internal::add_const<XprType>::type, Direction> const_iterator;
typedef XprType_ XprType;
typedef subvector_stl_reverse_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
typedef subvector_stl_reverse_iterator<std::add_const_t<XprType>, Direction> const_iterator;
};
template<typename XprType, DirectionType Direction>
@@ -368,12 +370,12 @@ protected:
using Base::m_index;
using Base::mp_xpr;
typedef typename internal::conditional<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr>::type SubVectorType;
typedef typename internal::conditional<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr>::type ConstSubVectorType;
typedef std::conditional_t<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr> SubVectorType;
typedef std::conditional_t<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr> ConstSubVectorType;
public:
typedef typename internal::conditional<bool(is_lvalue), SubVectorType, ConstSubVectorType>::type reference;
typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
typedef typename reference::PlainObject value_type;
private:

18
libs/eigen/Eigen/src/Core/Stride.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_STRIDE_H
#define EIGEN_STRIDE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class Stride
@@ -31,27 +33,31 @@ namespace Eigen {
* arguments to the constructor.
*
* Indeed, this class takes two template parameters:
* \tparam _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
* \tparam _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
* \tparam OuterStrideAtCompileTime_ the outer stride, or Dynamic if you want to specify it at runtime.
* \tparam InnerStrideAtCompileTime_ the inner stride, or Dynamic if you want to specify it at runtime.
*
* Here is an example:
* \include Map_general_stride.cpp
* Output: \verbinclude Map_general_stride.out
*
* Both strides can be negative, however, a negative stride of -1 cannot be specified at compiletime
* Both strides can be negative. However, a negative stride of -1 cannot be specified at compile time
* because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
* not allowed).
*
* Note that for compile-time vectors (ColsAtCompileTime==1 or RowsAtCompile==1),
* the inner stride is the pointer increment between two consecutive elements,
* regardless of storage layout.
*
* \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
*/
template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
template<int OuterStrideAtCompileTime_, int InnerStrideAtCompileTime_>
class Stride
{
public:
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
enum {
InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
OuterStrideAtCompileTime = _OuterStrideAtCompileTime
InnerStrideAtCompileTime = InnerStrideAtCompileTime_,
OuterStrideAtCompileTime = OuterStrideAtCompileTime_
};
/** Default constructor, for use when strides are fixed at compile time */

2
libs/eigen/Eigen/src/Core/Swap.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_SWAP_H
#define EIGEN_SWAP_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {

24
libs/eigen/Eigen/src/Core/Transpose.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_TRANSPOSE_H
#define EIGEN_TRANSPOSE_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -18,7 +20,7 @@ template<typename MatrixType>
struct traits<Transpose<MatrixType> > : public traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedPlain;
enum {
RowsAtCompileTime = MatrixType::ColsAtCompileTime,
ColsAtCompileTime = MatrixType::RowsAtCompileTime,
@@ -58,7 +60,7 @@ template<typename MatrixType> class Transpose
typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
typedef internal::remove_all_t<MatrixType> NestedExpression;
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE Transpose(MatrixType& matrix) : m_matrix(matrix) {}
@@ -72,12 +74,12 @@ template<typename MatrixType> class Transpose
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<MatrixTypeNested>::type&
const internal::remove_all_t<MatrixTypeNested>&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_reference<MatrixTypeNested>::type&
std::remove_reference_t<MatrixTypeNested>&
nestedExpression() { return m_matrix; }
/** \internal */
@@ -130,11 +132,11 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index outerStride() const { return derived().nestedExpression().outerStride(); }
typedef typename internal::conditional<
internal::is_lvalue<MatrixType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef std::conditional_t<
internal::is_lvalue<MatrixType>::value,
Scalar,
const Scalar
> ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
@@ -178,7 +180,7 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
* \sa transposeInPlace(), adjoint() */
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Transpose<Derived>
typename DenseBase<Derived>::TransposeReturnType
DenseBase<Derived>::transpose()
{
return TransposeReturnType(derived());
@@ -191,7 +193,7 @@ DenseBase<Derived>::transpose()
* \sa transposeInPlace(), adjoint() */
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename DenseBase<Derived>::ConstTransposeReturnType
const typename DenseBase<Derived>::ConstTransposeReturnType
DenseBase<Derived>::transpose() const
{
return ConstTransposeReturnType(derived());

40
libs/eigen/Eigen/src/Core/Transpositions.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_TRANSPOSITIONS_H
#define EIGEN_TRANSPOSITIONS_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
template<typename Derived>
@@ -113,11 +115,11 @@ class TranspositionsBase
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
{
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef TranspositionsStorage StorageKind;
};
}
@@ -151,8 +153,8 @@ struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageInde
* \sa class PermutationMatrix
*/
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
{
typedef internal::traits<Transpositions> Traits;
public:
@@ -199,19 +201,19 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,_PacketAccess> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess_> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
{
typedef Map<const Matrix<_StorageIndex,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef Map<const Matrix<StorageIndex_,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, PacketAccess_> IndicesType;
typedef StorageIndex_ StorageIndex;
typedef TranspositionsStorage StorageKind;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess> >
{
typedef internal::traits<Map> Traits;
public:
@@ -260,17 +262,17 @@ class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,P
};
namespace internal {
template<typename _IndicesType>
struct traits<TranspositionsWrapper<_IndicesType> >
: traits<PermutationWrapper<_IndicesType> >
template<typename IndicesType_>
struct traits<TranspositionsWrapper<IndicesType_> >
: traits<PermutationWrapper<IndicesType_> >
{
typedef TranspositionsStorage StorageKind;
};
}
template<typename _IndicesType>
template<typename IndicesType_>
class TranspositionsWrapper
: public TranspositionsBase<TranspositionsWrapper<_IndicesType> >
: public TranspositionsBase<TranspositionsWrapper<IndicesType_> >
{
typedef internal::traits<TranspositionsWrapper> Traits;
public:

62
libs/eigen/Eigen/src/Core/TriangularMatrix.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_TRIANGULARMATRIX_H
#define EIGEN_TRIANGULARMATRIX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -35,14 +37,13 @@ template<typename Derived> class TriangularBase : public EigenBase<Derived>
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret)
MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime)
};
typedef typename internal::traits<Derived>::Scalar Scalar;
@@ -153,8 +154,8 @@ template<typename Derived> class TriangularBase : public EigenBase<Derived>
*
* \brief Expression of a triangular part in a matrix
*
* \param MatrixType the type of the object in which we are taking the triangular part
* \param Mode the kind of triangular matrix expression to construct. Can be #Upper,
* \tparam MatrixType the type of the object in which we are taking the triangular part
* \tparam Mode the kind of triangular matrix expression to construct. Can be #Upper,
* #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower.
* This is in fact a bit field; it must have either #Upper or #Lower,
* and additionally it may have #UnitDiag or #ZeroDiag or neither.
@@ -166,39 +167,39 @@ template<typename Derived> class TriangularBase : public EigenBase<Derived>
* \sa MatrixBase::triangularView()
*/
namespace internal {
template<typename MatrixType, unsigned int _Mode>
struct traits<TriangularView<MatrixType, _Mode> > : traits<MatrixType>
template<typename MatrixType, unsigned int Mode_>
struct traits<TriangularView<MatrixType, Mode_> > : traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedNonRef;
typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
typedef typename MatrixType::PlainObject FullMatrixType;
typedef MatrixType ExpressionType;
enum {
Mode = _Mode,
Mode = Mode_,
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)))
};
};
}
template<typename _MatrixType, unsigned int _Mode, typename StorageKind> class TriangularViewImpl;
template<typename MatrixType_, unsigned int Mode_, typename StorageKind> class TriangularViewImpl;
template<typename _MatrixType, unsigned int _Mode> class TriangularView
: public TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind >
template<typename MatrixType_, unsigned int Mode_> class TriangularView
: public TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind >
{
public:
typedef TriangularViewImpl<_MatrixType, _Mode, typename internal::traits<_MatrixType>::StorageKind > Base;
typedef TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind > Base;
typedef typename internal::traits<TriangularView>::Scalar Scalar;
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
protected:
typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef;
typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
typedef TriangularView<typename internal::add_const<MatrixType>::type, _Mode> ConstTriangularView;
typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
typedef TriangularView<std::add_const_t<MatrixType>, Mode_> ConstTriangularView;
public:
@@ -206,7 +207,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned NestedExpression;
enum {
Mode = _Mode,
Mode = Mode_,
Flags = internal::traits<TriangularView>::Flags,
TransposeMode = (Mode & Upper ? Lower : 0)
| (Mode & Lower ? Upper : 0)
@@ -247,10 +248,10 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
*/
template<bool Cond>
EIGEN_DEVICE_FUNC
inline typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type
inline std::conditional_t<Cond,ConjugateReturnType,ConstTriangularView>
conjugateIf() const
{
typedef typename internal::conditional<Cond,ConjugateReturnType,ConstTriangularView>::type ReturnType;
typedef std::conditional_t<Cond,ConjugateReturnType,ConstTriangularView> ReturnType;
return ReturnType(m_matrix.template conjugateIf<Cond>());
}
@@ -262,10 +263,10 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
typedef TriangularView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
/** \sa MatrixBase::transpose() */
template<class Dummy=int>
EIGEN_DEVICE_FUNC
inline TransposeReturnType transpose()
inline TransposeReturnType transpose(std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
typename MatrixType::TransposeReturnType tmp(m_matrix);
return TransposeReturnType(tmp);
}
@@ -342,16 +343,17 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
*
* \sa class TriangularView, MatrixBase::triangularView()
*/
template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_MatrixType,_Mode,Dense>
: public TriangularBase<TriangularView<_MatrixType, _Mode> >
template<typename MatrixType_, unsigned int Mode_> class TriangularViewImpl<MatrixType_,Mode_,Dense>
: public TriangularBase<TriangularView<MatrixType_, Mode_> >
{
public:
typedef TriangularView<_MatrixType, _Mode> TriangularViewType;
typedef TriangularView<MatrixType_, Mode_> TriangularViewType;
typedef TriangularBase<TriangularViewType> Base;
typedef typename internal::traits<TriangularViewType>::Scalar Scalar;
typedef _MatrixType MatrixType;
typedef MatrixType_ MatrixType;
typedef typename MatrixType::PlainObject DenseMatrixType;
typedef DenseMatrixType PlainObject;
@@ -362,7 +364,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
typedef typename internal::traits<TriangularViewType>::StorageKind StorageKind;
enum {
Mode = _Mode,
Mode = Mode_,
Flags = internal::traits<TriangularViewType>::Flags
};
@@ -728,10 +730,10 @@ struct evaluator_traits<TriangularView<MatrixType,Mode> >
template<typename MatrixType, unsigned int Mode>
struct unary_evaluator<TriangularView<MatrixType,Mode>, IndexBased>
: evaluator<typename internal::remove_all<MatrixType>::type>
: evaluator<internal::remove_all_t<MatrixType>>
{
typedef TriangularView<MatrixType,Mode> XprType;
typedef evaluator<typename internal::remove_all<MatrixType>::type> Base;
typedef evaluator<internal::remove_all_t<MatrixType>> Base;
EIGEN_DEVICE_FUNC
unary_evaluator(const XprType &xpr) : Base(xpr.nestedExpression()) {}
};

11
libs/eigen/Eigen/src/Core/VectorBlock.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_VECTORBLOCK_H
#define EIGEN_VECTORBLOCK_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -66,6 +68,7 @@ template<typename VectorType, int Size> class VectorBlock
};
public:
EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock)
using Base::operator=;
@@ -76,18 +79,14 @@ template<typename VectorType, int Size> class VectorBlock
: Base(vector,
IsColVector ? start : 0, IsColVector ? 0 : start,
IsColVector ? size : 1, IsColVector ? 1 : size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
}
{ }
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
VectorBlock(VectorType& vector, Index start)
: Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock);
}
{ }
};

17
libs/eigen/Eigen/src/Core/VectorwiseOp.h
View File

@@ -11,6 +11,8 @@
#ifndef EIGEN_PARTIAL_REDUX_H
#define EIGEN_PARTIAL_REDUX_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class PartialReduxExpr
@@ -86,7 +88,6 @@ template<typename A,typename B> struct partial_redux_dummy_func;
#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP) \
template <typename ResultType,typename Scalar> \
struct member_##MEMBER { \
EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \
typedef ResultType result_type; \
typedef BINARYOP<Scalar,Scalar> BinaryOp; \
template<int Size> struct Cost { enum { value = COST }; }; \
@@ -191,7 +192,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename ExpressionType::RealScalar RealScalar;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
typedef internal::remove_all_t<ExpressionTypeNested> ExpressionTypeNestedCleaned;
template<template<typename OutScalar,typename InputScalar> class Functor,
typename ReturnScalar=Scalar> struct ReturnType
@@ -230,9 +231,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typename ExtendedType<OtherDerived>::Type
extendedTo(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename ExtendedType<OtherDerived>::Type
(other.derived(),
@@ -253,9 +254,9 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typename OppositeExtendedType<OtherDerived>::Type
extendedToOpposite(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename OppositeExtendedType<OtherDerived>::Type
(other.derived(),
@@ -594,7 +595,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
return m_matrix += extendedTo(other.derived());
}
/** Substracts the vector \a other to each subvector of \c *this */
/** Subtracts the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
@@ -604,7 +605,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
return m_matrix -= extendedTo(other.derived());
}
/** Multiples each subvector of \c *this by the vector \a other */
/** Multiplies each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator*=(const DenseBase<OtherDerived>& other)

342
libs/eigen/Eigen/src/Core/Visitor.h
View File

@@ -10,16 +10,23 @@
#ifndef EIGEN_VISITOR_H
#define EIGEN_VISITOR_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template<typename Visitor, typename Derived, int UnrollCount, bool Vectorize=((Derived::PacketAccess!=0) && functor_traits<Visitor>::PacketAccess)>
struct visitor_impl;
template<typename Visitor, typename Derived, int UnrollCount>
struct visitor_impl
struct visitor_impl<Visitor, Derived, UnrollCount, false>
{
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = Derived::IsRowMajor ? (UnrollCount-1) % Derived::ColsAtCompileTime
: (UnrollCount-1) / Derived::RowsAtCompileTime,
row = Derived::IsRowMajor ? (UnrollCount-1) / Derived::ColsAtCompileTime
: (UnrollCount-1) % Derived::RowsAtCompileTime
};
EIGEN_DEVICE_FUNC
@@ -31,7 +38,7 @@ struct visitor_impl
};
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, 1>
struct visitor_impl<Visitor, Derived, 1, false>
{
EIGEN_DEVICE_FUNC
static inline void run(const Derived &mat, Visitor& visitor)
@@ -42,24 +49,73 @@ struct visitor_impl<Visitor, Derived, 1>
// This specialization enables visitors on empty matrices at compile-time
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, 0> {
struct visitor_impl<Visitor, Derived, 0, false> {
EIGEN_DEVICE_FUNC
static inline void run(const Derived &/*mat*/, Visitor& /*visitor*/)
{}
};
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, Dynamic>
struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/false>
{
EIGEN_DEVICE_FUNC
static inline void run(const Derived& mat, Visitor& visitor)
{
visitor.init(mat.coeff(0,0), 0, 0);
for(Index i = 1; i < mat.rows(); ++i)
visitor(mat.coeff(i, 0), i, 0);
for(Index j = 1; j < mat.cols(); ++j)
for(Index i = 0; i < mat.rows(); ++i)
visitor(mat.coeff(i, j), i, j);
if (Derived::IsRowMajor) {
for(Index i = 1; i < mat.cols(); ++i) {
visitor(mat.coeff(0, i), 0, i);
}
for(Index j = 1; j < mat.rows(); ++j) {
for(Index i = 0; i < mat.cols(); ++i) {
visitor(mat.coeff(j, i), j, i);
}
}
} else {
for(Index i = 1; i < mat.rows(); ++i) {
visitor(mat.coeff(i, 0), i, 0);
}
for(Index j = 1; j < mat.cols(); ++j) {
for(Index i = 0; i < mat.rows(); ++i) {
visitor(mat.coeff(i, j), i, j);
}
}
}
}
};
template<typename Visitor, typename Derived, int UnrollSize>
struct visitor_impl<Visitor, Derived, UnrollSize, /*Vectorize=*/true>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC
static inline void run(const Derived& mat, Visitor& visitor)
{
const Index PacketSize = packet_traits<Scalar>::size;
visitor.init(mat.coeff(0,0), 0, 0);
if (Derived::IsRowMajor) {
for(Index i = 0; i < mat.rows(); ++i) {
Index j = i == 0 ? 1 : 0;
for(; j+PacketSize-1 < mat.cols(); j += PacketSize) {
Packet p = mat.packet(i, j);
visitor.packet(p, i, j);
}
for(; j < mat.cols(); ++j)
visitor(mat.coeff(i, j), i, j);
}
} else {
for(Index j = 0; j < mat.cols(); ++j) {
Index i = j == 0 ? 1 : 0;
for(; i+PacketSize-1 < mat.rows(); i += PacketSize) {
Packet p = mat.packet(i, j);
visitor.packet(p, i, j);
}
for(; i < mat.rows(); ++i)
visitor(mat.coeff(i, j), i, j);
}
}
}
};
@@ -68,28 +124,38 @@ template<typename XprType>
class visitor_evaluator
{
public:
EIGEN_DEVICE_FUNC
explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef internal::evaluator<XprType> Evaluator;
enum {
PacketAccess = Evaluator::Flags & PacketAccessBit,
IsRowMajor = XprType::IsRowMajor,
RowsAtCompileTime = XprType::RowsAtCompileTime,
CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
ColsAtCompileTime = XprType::ColsAtCompileTime,
CoeffReadCost = Evaluator::CoeffReadCost
};
EIGEN_DEVICE_FUNC
explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) { }
typedef typename XprType::Scalar Scalar;
typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
typedef std::remove_const_t<typename XprType::PacketReturnType> PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_xpr.size(); }
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
EIGEN_DEVICE_FUNC PacketReturnType packet(Index row, Index col) const
{ return m_evaluator.template packet<Unaligned,PacketReturnType>(row, col); }
protected:
internal::evaluator<XprType> m_evaluator;
Evaluator m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
@@ -152,123 +218,131 @@ struct coeff_visitor
}
};
/** \internal
* \brief Visitor computing the min coefficient with its value and coordinates
*
* \sa DenseBase::minCoeff(Index*, Index*)
*/
template <typename Derived, int NaNPropagation>
struct min_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value < this->res)
{
this->res = value;
this->row = i;
this->col = j;
}
}
};
template <typename Derived>
struct min_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value < this->res))
{
this->res = value;
this->row = i;
this->col = j;
}
}
};
template <typename Derived>
struct min_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if((numext::isnan)(value) || value < this->res)
{
this->res = value;
this->row = i;
this->col = j;
}
}
template<typename Scalar, int NaNPropagation, bool is_min=true>
struct minmax_compare {
typedef typename packet_traits<Scalar>::type Packet;
static EIGEN_DEVICE_FUNC inline bool compare(Scalar a, Scalar b) { return a < b; }
static EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& p) { return predux_min<NaNPropagation>(p);}
};
template<typename Scalar, int NaNPropagation>
struct functor_traits<min_coeff_visitor<Scalar, NaNPropagation> > {
struct minmax_compare<Scalar, NaNPropagation, false> {
typedef typename packet_traits<Scalar>::type Packet;
static EIGEN_DEVICE_FUNC inline bool compare(Scalar a, Scalar b) { return a > b; }
static EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& p) { return predux_max<NaNPropagation>(p);}
};
template <typename Derived, bool is_min, int NaNPropagation>
struct minmax_coeff_visitor : coeff_visitor<Derived>
{
using Scalar = typename Derived::Scalar;
using Packet = typename packet_traits<Scalar>::type;
using Comparator = minmax_compare<Scalar, NaNPropagation, is_min>;
EIGEN_DEVICE_FUNC inline
void operator() (const Scalar& value, Index i, Index j)
{
if(Comparator::compare(value, this->res)) {
this->res = value;
this->row = i;
this->col = j;
}
}
EIGEN_DEVICE_FUNC inline
void packet(const Packet& p, Index i, Index j) {
const Index PacketSize = packet_traits<Scalar>::size;
Scalar value = Comparator::predux(p);
if (Comparator::compare(value, this->res)) {
const Packet range = preverse(plset<Packet>(Scalar(1)));
Packet mask = pcmp_eq(pset1<Packet>(value), p);
Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
this->res = value;
this->row = Derived::IsRowMajor ? i : i + max_idx;;
this->col = Derived::IsRowMajor ? j + max_idx : j;
}
}
};
// Suppress NaN. The only case in which we return NaN is if the matrix is all NaN, in which case,
// the row=0, col=0 is returned for the location.
template <typename Derived, bool is_min>
struct minmax_coeff_visitor<Derived, is_min, PropagateNumbers> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
using Packet = typename packet_traits<Scalar>::type;
using Comparator = minmax_compare<Scalar, PropagateNumbers, is_min>;
EIGEN_DEVICE_FUNC inline
void operator() (const Scalar& value, Index i, Index j)
{
if ((!(numext::isnan)(value) && (numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
this->res = value;
this->row = i;
this->col = j;
}
}
EIGEN_DEVICE_FUNC inline
void packet(const Packet& p, Index i, Index j) {
const Index PacketSize = packet_traits<Scalar>::size;
Scalar value = Comparator::predux(p);
if ((!(numext::isnan)(value) && (numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
const Packet range = preverse(plset<Packet>(Scalar(1)));
/* mask will be zero for NaNs, so they will be ignored. */
Packet mask = pcmp_eq(pset1<Packet>(value), p);
Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
this->res = value;
this->row = Derived::IsRowMajor ? i : i + max_idx;;
this->col = Derived::IsRowMajor ? j + max_idx : j;
}
}
};
// Propagate NaN. If the matrix contains NaN, the location of the first NaN will be returned in
// row and col.
template <typename Derived, bool is_min>
struct minmax_coeff_visitor<Derived, is_min, PropagateNaN> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
using Packet = typename packet_traits<Scalar>::type;
using Comparator = minmax_compare<Scalar, PropagateNaN, is_min>;
EIGEN_DEVICE_FUNC inline
void operator() (const Scalar& value, Index i, Index j)
{
const bool value_is_nan = (numext::isnan)(value);
if ((value_is_nan && !(numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
this->res = value;
this->row = i;
this->col = j;
}
}
EIGEN_DEVICE_FUNC inline
void packet(const Packet& p, Index i, Index j) {
const Index PacketSize = packet_traits<Scalar>::size;
Scalar value = Comparator::predux(p);
const bool value_is_nan = (numext::isnan)(value);
if ((value_is_nan && !(numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
const Packet range = preverse(plset<Packet>(Scalar(1)));
// If the value is NaN, pick the first position of a NaN, otherwise pick the first extremal value.
Packet mask = value_is_nan ? pnot(pcmp_eq(p, p)) : pcmp_eq(pset1<Packet>(value), p);
Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
this->res = value;
this->row = Derived::IsRowMajor ? i : i + max_idx;;
this->col = Derived::IsRowMajor ? j + max_idx : j;
}
}
};
template<typename Scalar, bool is_min, int NaNPropagation>
struct functor_traits<minmax_coeff_visitor<Scalar, is_min, NaNPropagation> > {
enum {
Cost = NumTraits<Scalar>::AddCost
};
};
/** \internal
* \brief Visitor computing the max coefficient with its value and coordinates
*
* \sa DenseBase::maxCoeff(Index*, Index*)
*/
template <typename Derived, int NaNPropagation>
struct max_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value > this->res)
{
this->res = value;
this->row = i;
this->col = j;
}
}
};
template <typename Derived>
struct max_coeff_visitor<Derived, PropagateNumbers> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if((numext::isnan)(this->res) || (!(numext::isnan)(value) && value > this->res))
{
this->res = value;
this->row = i;
this->col = j;
}
}
};
template <typename Derived>
struct max_coeff_visitor<Derived, PropagateNaN> : coeff_visitor<Derived>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if((numext::isnan)(value) || value > this->res)
{
this->res = value;
this->row = i;
this->col = j;
}
}
};
template<typename Scalar, int NaNPropagation>
struct functor_traits<max_coeff_visitor<Scalar, NaNPropagation> > {
enum {
Cost = NumTraits<Scalar>::AddCost
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = true
};
};
@@ -293,7 +367,7 @@ DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
{
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
internal::minmax_coeff_visitor<Derived, true, NaNPropagation> minVisitor;
this->visit(minVisitor);
*rowId = minVisitor.row;
if (colId) *colId = minVisitor.col;
@@ -319,7 +393,7 @@ DenseBase<Derived>::minCoeff(IndexType* index) const
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
internal::min_coeff_visitor<Derived, NaNPropagation> minVisitor;
internal::minmax_coeff_visitor<Derived, true, NaNPropagation> minVisitor;
this->visit(minVisitor);
*index = IndexType((RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row);
return minVisitor.res;
@@ -344,7 +418,7 @@ DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
{
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
internal::minmax_coeff_visitor<Derived, false, NaNPropagation> maxVisitor;
this->visit(maxVisitor);
*rowPtr = maxVisitor.row;
if (colPtr) *colPtr = maxVisitor.col;
@@ -370,7 +444,7 @@ DenseBase<Derived>::maxCoeff(IndexType* index) const
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
internal::max_coeff_visitor<Derived, NaNPropagation> maxVisitor;
internal::minmax_coeff_visitor<Derived, false, NaNPropagation> maxVisitor;
this->visit(maxVisitor);
*index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row;
return maxVisitor.res;

18
libs/eigen/Eigen/src/Core/arch/AVX/Complex.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_COMPLEX_AVX_H
#define EIGEN_COMPLEX_AVX_H
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -99,7 +101,9 @@ template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<fl
template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
{
return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from)));
const float re = std::real(from);
const float im = std::imag(from);
return Packet4cf(_mm256_set_ps(im, re, im, re, im, re, im, re));
}
template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
@@ -167,15 +171,12 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const P
Packet2cf(_mm256_extractf128_ps(a.v, 1))));
}
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
{
Packet4cf num = pmul(a, pconj(b));
__m256 tmp = _mm256_mul_ps(b.v, b.v);
__m256 tmp2 = _mm256_shuffle_ps(tmp,tmp,0xB1);
__m256 denom = _mm256_add_ps(tmp, tmp2);
return Packet4cf(_mm256_div_ps(num.v, denom));
return pdiv_complex(a, b);
}
template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
@@ -321,10 +322,7 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
{
Packet2cd num = pmul(a, pconj(b));
__m256d tmp = _mm256_mul_pd(b.v, b.v);
__m256d denom = _mm256_hadd_pd(tmp, tmp);
return Packet2cd(_mm256_div_pd(num.v, denom));
return pdiv_complex(a, b);
}
template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)

141
libs/eigen/Eigen/src/Core/arch/AVX/MathFunctions.h
View File

@@ -14,52 +14,78 @@
* Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
*/
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
psin<Packet8f>(const Packet8f& _x) {
return psin_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
pcos<Packet8f>(const Packet8f& _x) {
return pcos_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
pasin<Packet8f>(const Packet8f& _x) {
return pasin_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
pacos<Packet8f>(const Packet8f& _x) {
return pacos_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
patan<Packet8f>(const Packet8f& _x) {
return patan_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d
patan<Packet4d>(const Packet4d& _x) {
return patan_double(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
plog<Packet8f>(const Packet8f& _x) {
return plog_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d
plog<Packet4d>(const Packet4d& _x) {
return plog_double(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
plog2<Packet8f>(const Packet8f& _x) {
return plog2_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d
plog2<Packet4d>(const Packet4d& _x) {
return plog2_double(_x);
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet8f plog1p<Packet8f>(const Packet8f& _x) {
return generic_plog1p(_x);
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet8f pexpm1<Packet8f>(const Packet8f& _x) {
return generic_expm1(_x);
}
@@ -68,110 +94,59 @@ Packet8f pexpm1<Packet8f>(const Packet8f& _x) {
// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
pexp<Packet8f>(const Packet8f& _x) {
return pexp_float(_x);
}
// Hyperbolic Tangent function.
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8f
ptanh<Packet8f>(const Packet8f& _x) {
return internal::generic_fast_tanh_float(_x);
}
// Exponential function for doubles.
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4d
pexp<Packet4d>(const Packet4d& _x) {
return pexp_double(_x);
}
// Functions for sqrt.
// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
// exact solution. It does not handle +inf, or denormalized numbers correctly.
// The main advantage of this approach is not just speed, but also the fact that
// it can be inlined and pipelined with other computations, further reducing its
// effective latency. This is similar to Quake3's fast inverse square root.
// For detail see here: http://www.beyond3d.com/content/articles/8/
#if EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f psqrt<Packet8f>(const Packet8f& _x) {
Packet8f minus_half_x = pmul(_x, pset1<Packet8f>(-0.5f));
Packet8f denormal_mask = pandnot(
pcmp_lt(_x, pset1<Packet8f>((std::numeric_limits<float>::min)())),
pcmp_lt(_x, pzero(_x)));
// Compute approximate reciprocal sqrt.
Packet8f x = _mm256_rsqrt_ps(_x);
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(minus_half_x, pmul(x,x), pset1<Packet8f>(1.5f)));
// Flush results for denormals to zero.
return pandnot(pmul(_x,x), denormal_mask);
}
#else
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
// Notice that for newer processors, it is counterproductive to use Newton
// iteration for square root. In particular, Skylake and Zen2 processors
// have approximately doubled throughput of the _mm_sqrt_ps instruction
// compared to their predecessors.
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet8f psqrt<Packet8f>(const Packet8f& _x) {
return _mm256_sqrt_ps(_x);
}
#endif
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet4d psqrt<Packet4d>(const Packet4d& _x) {
return _mm256_sqrt_pd(_x);
}
// Even on Skylake, using Newton iteration is a win for reciprocal square root.
#if EIGEN_FAST_MATH
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000);
_EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
_EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
Packet8f neg_half = pmul(_x, p8f_minus_half);
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
Packet8f lt_min_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_LT_OQ);
Packet8f inf_mask = _mm256_cmp_ps(_x, p8f_inf, _CMP_EQ_OQ);
Packet8f not_normal_finite_mask = _mm256_or_ps(lt_min_mask, inf_mask);
// Compute an approximate result using the rsqrt intrinsic.
Packet8f y_approx = _mm256_rsqrt_ps(_x);
// Do a single step of Newton-Raphson iteration to improve the approximation.
// This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
// It is essential to evaluate the inner term like this because forming
// y_n^2 may over- or underflow.
Packet8f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p8f_one_point_five));
// Select the result of the Newton-Raphson step for positive normal arguments.
// For other arguments, choose the output of the intrinsic. This will
// return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(x) = +inf if
// x is zero or a positive denormalized float (equivalent to flushing positive
// denormalized inputs to zero).
return pselect<Packet8f>(not_normal_finite_mask, y_approx, y_newton);
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet8f prsqrt<Packet8f>(const Packet8f& a) {
// _mm256_rsqrt_ps returns -inf for negative denormals.
// _mm512_rsqrt**_ps returns -NaN for negative denormals. We may want
// consistency here.
// const Packet8f rsqrt = pselect(pcmp_lt(a, pzero(a)),
// pset1<Packet8f>(-NumTraits<float>::quiet_NaN()),
// _mm256_rsqrt_ps(a));
return generic_rsqrt_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rsqrt_ps(a));
}
#else
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet8f prsqrt<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f(one, 1.0f);
return _mm256_div_ps(p8f_one, _mm256_sqrt_ps(_x));
template<> EIGEN_STRONG_INLINE Packet8f preciprocal<Packet8f>(const Packet8f& a) {
return generic_reciprocal_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rcp_ps(a));
}
#endif
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4d prsqrt<Packet4d>(const Packet4d& _x) {
_EIGEN_DECLARE_CONST_Packet4d(one, 1.0);
return _mm256_div_pd(p4d_one, _mm256_sqrt_pd(_x));
}
F16_PACKET_FUNCTION(Packet8f, Packet8h, psin)
F16_PACKET_FUNCTION(Packet8f, Packet8h, pcos)
@@ -183,6 +158,7 @@ F16_PACKET_FUNCTION(Packet8f, Packet8h, pexp)
F16_PACKET_FUNCTION(Packet8f, Packet8h, ptanh)
F16_PACKET_FUNCTION(Packet8f, Packet8h, psqrt)
F16_PACKET_FUNCTION(Packet8f, Packet8h, prsqrt)
F16_PACKET_FUNCTION(Packet8f, Packet8h, preciprocal)
template <>
EIGEN_STRONG_INLINE Packet8h pfrexp(const Packet8h& a, Packet8h& exponent) {
@@ -207,6 +183,7 @@ BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexp)
BF16_PACKET_FUNCTION(Packet8f, Packet8bf, ptanh)
BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psqrt)
BF16_PACKET_FUNCTION(Packet8f, Packet8bf, prsqrt)
BF16_PACKET_FUNCTION(Packet8f, Packet8bf, preciprocal)
template <>
EIGEN_STRONG_INLINE Packet8bf pfrexp(const Packet8bf& a, Packet8bf& exponent) {

655
libs/eigen/Eigen/src/Core/arch/AVX/PacketMath.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_PACKET_MATH_AVX_H
#define EIGEN_PACKET_MATH_AVX_H
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -29,28 +31,29 @@ namespace internal {
#endif
typedef __m256 Packet8f;
typedef __m256i Packet8i;
typedef eigen_packet_wrapper<__m256i, 0> Packet8i;
typedef __m256d Packet4d;
#ifndef EIGEN_VECTORIZE_AVX512FP16
typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
#endif
typedef eigen_packet_wrapper<__m128i, 3> Packet8bf;
#ifdef EIGEN_VECTORIZE_AVX2
// Start from 3 to be compatible with AVX512
typedef eigen_packet_wrapper<__m256i, 3> Packet4l;
#endif
template<> struct is_arithmetic<__m256> { enum { value = true }; };
template<> struct is_arithmetic<__m256i> { enum { value = true }; };
template<> struct is_arithmetic<__m256d> { enum { value = true }; };
template<> struct is_arithmetic<Packet8i> { enum { value = true }; };
#ifndef EIGEN_VECTORIZE_AVX512FP16
template<> struct is_arithmetic<Packet8h> { enum { value = true }; };
#endif
template<> struct is_arithmetic<Packet8bf> { enum { value = true }; };
#define _EIGEN_DECLARE_CONST_Packet8f(NAME,X) \
const Packet8f p8f_##NAME = pset1<Packet8f>(X)
#define _EIGEN_DECLARE_CONST_Packet4d(NAME,X) \
const Packet4d p4d_##NAME = pset1<Packet4d>(X)
#define _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(NAME,X) \
const Packet8f p8f_##NAME = _mm256_castsi256_ps(pset1<Packet8i>(X))
#define _EIGEN_DECLARE_CONST_Packet8i(NAME,X) \
const Packet8i p8i_##NAME = pset1<Packet8i>(X)
#ifdef EIGEN_VECTORIZE_AVX2
template<> struct is_arithmetic<Packet4l> { enum { value = true }; };
#endif
// Use the packet_traits defined in AVX512/PacketMath.h instead if we're going
// to leverage AVX512 instructions.
@@ -67,8 +70,12 @@ template<> struct packet_traits<float> : default_packet_traits
HasCmp = 1,
HasDiv = 1,
HasReciprocal = EIGEN_FAST_MATH,
HasSin = EIGEN_FAST_MATH,
HasCos = EIGEN_FAST_MATH,
HasACos = 1,
HasASin = 1,
HasATan = 1,
HasLog = 1,
HasLog1p = 1,
HasExpm1 = 1,
@@ -102,6 +109,7 @@ template<> struct packet_traits<double> : default_packet_traits
HasExp = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasATan = 1,
HasBlend = 1,
HasRound = 1,
HasFloor = 1,
@@ -196,38 +204,74 @@ struct packet_traits<bfloat16> : default_packet_traits {
HasNdtri = 1
};
};
template<> struct packet_traits<int> : default_packet_traits
{
typedef Packet8i type;
typedef Packet4i half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
HasCmp = 1,
HasDiv = 1,
size=8
};
};
#ifdef EIGEN_VECTORIZE_AVX2
template<> struct packet_traits<int64_t> : default_packet_traits
{
typedef Packet4l type;
// There is no half-size packet for current Packet4l.
// TODO: support as SSE path.
typedef Packet4l half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
HasCmp = 1,
size=4
};
};
#endif
#endif
template<> struct scalar_div_cost<float,true> { enum { value = 14 }; };
template<> struct scalar_div_cost<double,true> { enum { value = 16 }; };
/* Proper support for integers is only provided by AVX2. In the meantime, we'll
use SSE instructions and packets to deal with integers.
template<> struct packet_traits<int> : default_packet_traits
{
typedef Packet8i type;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size=8
};
};
*/
template<> struct unpacket_traits<Packet8f> {
typedef float type;
typedef Packet4f half;
typedef Packet8i integer_packet;
typedef uint8_t mask_t;
enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=true, masked_store_available=true};
enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=true, masked_store_available=true
#ifdef EIGEN_VECTORIZE_AVX512
, masked_fpops_available=true
#endif
};
};
template<> struct unpacket_traits<Packet4d> {
typedef double type;
typedef Packet2d half;
enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
};
template<> struct unpacket_traits<Packet8i> { typedef int type; typedef Packet4i half; enum {size=8, alignment=Aligned32, vectorizable=false, masked_load_available=false, masked_store_available=false}; };
template<> struct unpacket_traits<Packet8bf> { typedef bfloat16 type; typedef Packet8bf half; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; };
template<> struct unpacket_traits<Packet8i> {
typedef int type;
typedef Packet4i half;
enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
};
#ifdef EIGEN_VECTORIZE_AVX2
template<> struct unpacket_traits<Packet4l> {
typedef int64_t type;
typedef Packet4l half;
enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
};
#endif
template<> struct unpacket_traits<Packet8bf> {
typedef bfloat16 type;
typedef Packet8bf half;
enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
};
// Helper function for bit packing snippet of low precision comparison.
// It packs the flags from 16x16 to 8x16.
@@ -236,6 +280,210 @@ EIGEN_STRONG_INLINE __m128i Pack16To8(Packet8f rf) {
_mm256_extractf128_si256(_mm256_castps_si256(rf), 1));
}
#ifdef EIGEN_VECTORIZE_AVX2
template <>
EIGEN_STRONG_INLINE Packet4l pset1<Packet4l>(const int64_t& from) {
return _mm256_set1_epi64x(from);
}
template <>
EIGEN_STRONG_INLINE Packet4l pzero(const Packet4l& /*a*/) {
return _mm256_setzero_si256();
}
template <>
EIGEN_STRONG_INLINE Packet4l peven_mask(const Packet4l& /*a*/) {
return _mm256_set_epi64x(0ll, -1ll, 0ll, -1ll);
}
template <>
EIGEN_STRONG_INLINE Packet4l pload1<Packet4l>(const int64_t* from) {
return _mm256_set1_epi64x(*from);
}
template <>
EIGEN_STRONG_INLINE Packet4l padd<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_add_epi64(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l plset<Packet4l>(const int64_t& a) {
return padd(pset1<Packet4l>(a), Packet4l(_mm256_set_epi64x(3ll, 2ll, 1ll, 0ll)));
}
template <>
EIGEN_STRONG_INLINE Packet4l psub<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_sub_epi64(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l pnegate(const Packet4l& a) {
return psub(pzero(a), a);
}
template <>
EIGEN_STRONG_INLINE Packet4l pconj(const Packet4l& a) {
return a;
}
template <>
EIGEN_STRONG_INLINE Packet4l pcmp_le(const Packet4l& a, const Packet4l& b) {
return _mm256_xor_si256(_mm256_cmpgt_epi64(a, b), _mm256_set1_epi32(-1));
}
template <>
EIGEN_STRONG_INLINE Packet4l pcmp_lt(const Packet4l& a, const Packet4l& b) {
return _mm256_cmpgt_epi64(b, a);
}
template <>
EIGEN_STRONG_INLINE Packet4l pcmp_eq(const Packet4l& a, const Packet4l& b) {
return _mm256_cmpeq_epi64(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l ptrue<Packet4l>(const Packet4l& a) {
return _mm256_cmpeq_epi64(a, a);
}
template <>
EIGEN_STRONG_INLINE Packet4l pand<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_and_si256(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l por<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_or_si256(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l pxor<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_xor_si256(a, b);
}
template <>
EIGEN_STRONG_INLINE Packet4l pandnot<Packet4l>(const Packet4l& a, const Packet4l& b) {
return _mm256_andnot_si256(b, a);
}
template <int N>
EIGEN_STRONG_INLINE Packet4l plogical_shift_right(Packet4l a) {
return _mm256_srli_epi64(a, N);
}
template <int N>
EIGEN_STRONG_INLINE Packet4l plogical_shift_left(Packet4l a) {
return _mm256_slli_epi64(a, N);
}
#ifdef EIGEN_VECTORIZE_AVX512FP16
template <int N>
EIGEN_STRONG_INLINE Packet4l parithmetic_shift_right(Packet4l a) { return _mm256_srai_epi64(a, N); }
#else
template <int N>
EIGEN_STRONG_INLINE std::enable_if_t< (N == 0), Packet4l> parithmetic_shift_right(Packet4l a) {
return a;
}
template <int N>
EIGEN_STRONG_INLINE std::enable_if_t< (N > 0) && (N < 32), Packet4l> parithmetic_shift_right(Packet4l a) {
__m256i hi_word = _mm256_srai_epi32(a, N);
__m256i lo_word = _mm256_srli_epi64(a, N);
return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
}
template <int N>
EIGEN_STRONG_INLINE std::enable_if_t< (N >= 32) && (N < 63), Packet4l> parithmetic_shift_right(Packet4l a) {
__m256i hi_word = _mm256_srai_epi32(a, 31);
__m256i lo_word = _mm256_shuffle_epi32(_mm256_srai_epi32(a, N - 32), (shuffle_mask<1, 1, 3, 3>::mask));
return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
}
template <int N>
EIGEN_STRONG_INLINE std::enable_if_t< (N == 63), Packet4l> parithmetic_shift_right(Packet4l a) {
return _mm256_shuffle_epi32(_mm256_srai_epi32(a, 31), (shuffle_mask<1, 1, 3, 3>::mask));
}
template <int N>
EIGEN_STRONG_INLINE std::enable_if_t< (N < 0) || (N > 63), Packet4l> parithmetic_shift_right(Packet4l a) {
return parithmetic_shift_right<int(N&63)>(a);
}
#endif
template <>
EIGEN_STRONG_INLINE Packet4l pload<Packet4l>(const int64_t* from) {
EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
}
template <>
EIGEN_STRONG_INLINE Packet4l ploadu<Packet4l>(const int64_t* from) {
EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
}
// Loads 2 int64_ts from memory a returns the packet {a0, a0, a1, a1}
template <>
EIGEN_STRONG_INLINE Packet4l ploaddup<Packet4l>(const int64_t* from) {
const Packet4l a = _mm256_castsi128_si256(_mm_loadu_si128(reinterpret_cast<const __m128i*>(from)));
return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 1, 0, 1, 2, 3, 2, 3));
}
template <>
EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet4l& from) {
EIGEN_DEBUG_ALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
}
template <>
EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet4l& from) {
EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
}
template <>
EIGEN_DEVICE_FUNC inline Packet4l pgather<int64_t, Packet4l>(const int64_t* from, Index stride) {
return _mm256_set_epi64x(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
}
template <>
EIGEN_DEVICE_FUNC inline void pscatter<int64_t, Packet4l>(int64_t* to, const Packet4l& from, Index stride) {
__m128i low = _mm256_extractf128_si256(from, 0);
to[stride * 0] = _mm_extract_epi64(low, 0);
to[stride * 1] = _mm_extract_epi64(low, 1);
__m128i high = _mm256_extractf128_si256(from, 1);
to[stride * 2] = _mm_extract_epi64(high, 0);
to[stride * 3] = _mm_extract_epi64(high, 1);
}
template <>
EIGEN_STRONG_INLINE void pstore1<Packet4l>(int64_t* to, const int64_t& a) {
Packet4l pa = pset1<Packet4l>(a);
pstore(to, pa);
}
template <>
EIGEN_STRONG_INLINE int64_t pfirst<Packet4l>(const Packet4l& a) {
return _mm_cvtsi128_si64(_mm256_castsi256_si128(a));
}
template <>
EIGEN_STRONG_INLINE int64_t predux<Packet4l>(const Packet4l& a) {
__m128i r = _mm_add_epi64(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
return _mm_extract_epi64(r, 0) + _mm_extract_epi64(r, 1);
}
#define MM256_SHUFFLE_EPI64(A, B, M) _mm256_shuffle_pd(_mm256_castsi256_pd(A), _mm256_castsi256_pd(B), M)
EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4l, 4>& kernel) {
__m256d T0 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 15);
__m256d T1 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 0);
__m256d T2 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 15);
__m256d T3 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 0);
kernel.packet[1] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 32));
kernel.packet[3] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 49));
kernel.packet[0] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 32));
kernel.packet[2] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 49));
}
template <>
EIGEN_STRONG_INLINE Packet4l pmin<Packet4l>(const Packet4l& a, const Packet4l& b) {
__m256i cmp = _mm256_cmpgt_epi64(a, b);
__m256i a_min = _mm256_andnot_si256(cmp, a);
__m256i b_min = _mm256_and_si256(cmp, b);
return Packet4l(_mm256_or_si256(a_min, b_min));
}
template <>
EIGEN_STRONG_INLINE Packet4l pmax<Packet4l>(const Packet4l& a, const Packet4l& b) {
__m256i cmp = _mm256_cmpgt_epi64(a, b);
__m256i a_min = _mm256_and_si256(cmp, a);
__m256i b_min = _mm256_andnot_si256(cmp, b);
return Packet4l(_mm256_or_si256(a_min, b_min));
}
template <>
EIGEN_STRONG_INLINE Packet4l pabs<Packet4l>(const Packet4l& a) {
Packet4l pz = pzero<Packet4l>(a);
Packet4l cmp = _mm256_cmpgt_epi64(a, pz);
return psub(cmp, pxor(a, cmp));
}
template <>
EIGEN_STRONG_INLINE Packet4l pmul<Packet4l>(const Packet4l& a, const Packet4l& b) {
// 64-bit mul requires avx512, so do this with 32-bit multiplication
__m256i upper32_a = _mm256_srli_epi64(a, 32);
__m256i upper32_b = _mm256_srli_epi64(b, 32);
// upper * lower
__m256i mul1 = _mm256_mul_epu32(upper32_a, b);
__m256i mul2 = _mm256_mul_epu32(upper32_b, a);
// Gives us both upper*upper and lower*lower
__m256i mul3 = _mm256_mul_epu32(a, b);
__m256i high = _mm256_slli_epi64(_mm256_add_epi64(mul1, mul2), 32);
return _mm256_add_epi64(high, mul3);
}
#endif
template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float& from) { return _mm256_set1_ps(from); }
template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); }
@@ -256,10 +504,17 @@ template<> EIGEN_STRONG_INLINE Packet4d peven_mask(const Packet4d& /*a*/) { retu
template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float* from) { return _mm256_broadcast_ss(from); }
template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); }
template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); }
#ifdef EIGEN_VECTORIZE_AVX512
template <>
EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b, uint8_t umask) {
__mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
return _mm512_castps512_ps256(_mm512_maskz_add_ps(
mask,
_mm512_castps256_ps512(a),
_mm512_castps256_ps512(b)));
}
#endif
template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
@@ -271,6 +526,10 @@ template<> EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const
#endif
}
template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return padd(pset1<Packet8f>(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return padd(pset1<Packet4d>(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
template<> EIGEN_STRONG_INLINE Packet8i plset<Packet8i>(const int& a) { return padd(pset1<Packet8i>(a), (Packet8i)_mm256_set_epi32(7,6,5,4,3,2,1,0)); }
template<> EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_sub_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_sub_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const Packet8i& b) {
@@ -285,11 +544,17 @@ template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const
template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a)
{
return _mm256_sub_ps(_mm256_set1_ps(0.0),a);
const Packet8f mask = _mm256_castsi256_ps(_mm256_set1_epi32(0x80000000));
return _mm256_xor_ps(a, mask);
}
template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a)
{
return _mm256_sub_pd(_mm256_set1_pd(0.0),a);
const Packet4d mask = _mm256_castsi256_pd(_mm256_set1_epi64x(0x8000000000000000ULL));
return _mm256_xor_pd(a, mask);
}
template<> EIGEN_STRONG_INLINE Packet8i pnegate(const Packet8i& a)
{
return psub(pzero(a), a);
}
template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; }
@@ -310,36 +575,58 @@ template<> EIGEN_STRONG_INLINE Packet8i pmul<Packet8i>(const Packet8i& a, const
template<> EIGEN_STRONG_INLINE Packet8f pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_div_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet4d pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_div_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, const Packet8i& /*b*/)
{ eigen_assert(false && "packet integer division are not supported by AVX");
return pset1<Packet8i>(0);
template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& a, const Packet8i& b)
{
#ifdef EIGEN_VECTORIZE_AVX512
return _mm512_cvttpd_epi32(_mm512_div_pd(_mm512_cvtepi32_pd(a), _mm512_cvtepi32_pd(b)));
#else
Packet4i lo = pdiv<Packet4i>(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
Packet4i hi = pdiv<Packet4i>(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1);
#endif
}
#ifdef EIGEN_VECTORIZE_FMA
template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
// Clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and even register spilling with clang>=6.0 (bug 1637).
// Gcc stupidly generates a vfmadd132ps instruction.
// So let's enforce it to generate a vfmadd231ps instruction since the most common use
// case is to accumulate the result of the product.
Packet8f res = c;
__asm__("vfmadd231ps %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
return res;
#else
return _mm256_fmadd_ps(a,b,c);
#endif
template <>
EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
return _mm256_fmadd_ps(a, b, c);
}
template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
// see above
Packet4d res = c;
__asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
return res;
#else
return _mm256_fmadd_pd(a,b,c);
#endif
template <>
EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
return _mm256_fmadd_pd(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet8f pmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
return _mm256_fmsub_ps(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet4d pmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
return _mm256_fmsub_pd(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet8f pnmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
return _mm256_fnmadd_ps(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet4d pnmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
return _mm256_fnmadd_pd(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet8f pnmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
return _mm256_fnmsub_ps(a, b, c);
}
template <>
EIGEN_STRONG_INLINE Packet4d pnmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
return _mm256_fnmsub_pd(a, b, c);
}
#endif
template<> EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LE_OQ); }
@@ -352,7 +639,26 @@ template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt(const Packet4d& a, const Packet4
template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt_or_nan(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a, b, _CMP_NGE_UQ); }
template<> EIGEN_STRONG_INLINE Packet4d pcmp_eq(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_EQ_OQ); }
template<> EIGEN_STRONG_INLINE Packet8i pcmp_le(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_xor_si256(_mm256_cmpgt_epi32(a,b), _mm256_set1_epi32(-1));
#else
__m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
lo = _mm_xor_si128(lo, _mm_set1_epi32(-1));
__m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
hi = _mm_xor_si128(hi, _mm_set1_epi32(-1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8i pcmp_lt(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_cmpgt_epi32(b,a);
#else
__m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 0), _mm256_extractf128_si256(a, 0));
__m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 1), _mm256_extractf128_si256(a, 1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8i pcmp_eq(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_cmpeq_epi32(a,b);
@@ -388,6 +694,15 @@ template<> EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const
return _mm256_min_pd(b,a);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8i pmin<Packet8i>(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_min_epi32(a, b);
#else
__m128i lo = _mm_min_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
__m128i hi = _mm_min_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) {
#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
@@ -411,6 +726,21 @@ template<> EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const
return _mm256_max_pd(b,a);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8i pmax<Packet8i>(const Packet8i& a, const Packet8i& b) {
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_max_epi32(a, b);
#else
__m128i lo = _mm_max_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
__m128i hi = _mm_max_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
#endif
}
#ifdef EIGEN_VECTORIZE_AVX2
template<> EIGEN_STRONG_INLINE Packet8i psign(const Packet8i& a) {
return _mm256_sign_epi32(_mm256_set1_epi32(1), a);
}
#endif
// Add specializations for min/max with prescribed NaN progation.
template<>
@@ -583,11 +913,16 @@ template<> EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) { E
template<> EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); }
template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from, uint8_t umask) {
#ifdef EIGEN_VECTORIZE_AVX512
__mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_castps512_ps256(_mm512_maskz_loadu_ps(mask, from));
#else
Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
mask = por<Packet8i>(mask, bit_mask);
mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_maskload_ps(from, mask);
#endif
}
// Loads 4 floats from memory a returns the packet {a0, a0 a1, a1, a2, a2, a3, a3}
@@ -605,12 +940,26 @@ template<> EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from)
// then we can perform a consistent permutation on the global register to get everything in shape:
return _mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2));
}
// Loads 2 doubles from memory a returns the packet {a0, a0 a1, a1}
// Loads 2 doubles from memory a returns the packet {a0, a0, a1, a1}
template<> EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from)
{
Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from);
return _mm256_permute_pd(tmp, 3<<2);
}
// Loads 4 integers from memory a returns the packet {a0, a0, a1, a1, a2, a2, a3, a3}
template<> EIGEN_STRONG_INLINE Packet8i ploaddup<Packet8i>(const int* from)
{
#ifdef EIGEN_VECTORIZE_AVX2
const Packet8i a = _mm256_castsi128_si256(ploadu<Packet4i>(from));
return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 0, 1, 1, 2, 2, 3, 3));
#else
__m256 tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
// mimic an "inplace" permutation of the lower 128bits using a blend
tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15);
// then we can perform a consistent permutation on the global register to get everything in shape:
return _mm256_castps_si256(_mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2)));
#endif
}
// Loads 2 floats from memory a returns the packet {a0, a0 a0, a0, a1, a1, a1, a1}
template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from)
@@ -618,6 +967,10 @@ template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from)
Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from));
return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from+1), 1);
}
template<> EIGEN_STRONG_INLINE Packet8i ploadquad<Packet8i>(const int* from)
{
return _mm256_insertf128_si256(_mm256_set1_epi32(*from), _mm_set1_epi32(*(from+1)), 1);
}
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet8f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from); }
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from); }
@@ -628,11 +981,16 @@ template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d&
template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet8f& from, uint8_t umask) {
#ifdef EIGEN_VECTORIZE_AVX512
__mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_ps(to, mask, _mm512_castps256_ps512(from));
#else
Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
mask = por<Packet8i>(mask, bit_mask);
mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
EIGEN_DEBUG_UNALIGNED_STORE return _mm256_maskstore_ps(to, mask, from);
#endif
}
// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
@@ -646,6 +1004,11 @@ template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const dou
{
return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
}
template<> EIGEN_DEVICE_FUNC inline Packet8i pgather<int, Packet8i>(const int* from, Index stride)
{
return _mm256_set_epi32(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride)
{
@@ -670,6 +1033,20 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to,
to[stride*2] = _mm_cvtsd_f64(high);
to[stride*3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet8i>(int* to, const Packet8i& from, Index stride)
{
__m128i low = _mm256_extractf128_si256(from, 0);
to[stride*0] = _mm_extract_epi32(low, 0);
to[stride*1] = _mm_extract_epi32(low, 1);
to[stride*2] = _mm_extract_epi32(low, 2);
to[stride*3] = _mm_extract_epi32(low, 3);
__m128i high = _mm256_extractf128_si256(from, 1);
to[stride*4] = _mm_extract_epi32(high, 0);
to[stride*5] = _mm_extract_epi32(high, 1);
to[stride*6] = _mm_extract_epi32(high, 2);
to[stride*7] = _mm_extract_epi32(high, 3);
}
template<> EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a)
{
@@ -720,6 +1097,17 @@ template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a)
return _mm256_permute_pd(swap_halves,5);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8i preverse(const Packet8i& a)
{
return _mm256_castps_si256(preverse(_mm256_castsi256_ps(a)));
}
#ifdef EIGEN_VECTORIZE_AVX2
template<> EIGEN_STRONG_INLINE Packet4l preverse(const Packet4l& a)
{
return _mm256_castpd_si256(preverse(_mm256_castsi256_pd(a)));
}
#endif
// pabs should be ok
template<> EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a)
@@ -732,6 +1120,23 @@ template<> EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a)
const Packet4d mask = _mm256_castsi256_pd(_mm256_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
return _mm256_and_pd(a,mask);
}
template<> EIGEN_STRONG_INLINE Packet8i pabs(const Packet8i& a)
{
#ifdef EIGEN_VECTORIZE_AVX2
return _mm256_abs_epi32(a);
#else
__m128i lo = _mm_abs_epi32(_mm256_extractf128_si256(a, 0));
__m128i hi = _mm_abs_epi32(_mm256_extractf128_si256(a, 1));
return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
#endif
}
template<> EIGEN_STRONG_INLINE Packet8h psignbit(const Packet8h& a) { return _mm_srai_epi16(a, 15); }
template<> EIGEN_STRONG_INLINE Packet8bf psignbit(const Packet8bf& a) { return _mm_srai_epi16(a, 15); }
template<> EIGEN_STRONG_INLINE Packet8f psignbit(const Packet8f& a) { return _mm256_castsi256_ps(parithmetic_shift_right<31>((Packet8i)_mm256_castps_si256(a))); }
#ifdef EIGEN_VECTORIZE_AVX2
template<> EIGEN_STRONG_INLINE Packet4d psignbit(const Packet4d& a) { return _mm256_castsi256_pd(parithmetic_shift_right<63>((Packet4l)_mm256_castpd_si256(a))); }
#endif
template<> EIGEN_STRONG_INLINE Packet8f pfrexp<Packet8f>(const Packet8f& a, Packet8f& exponent) {
return pfrexp_generic(a,exponent);
@@ -803,11 +1208,19 @@ template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a)
{
return predux(Packet2d(_mm_add_pd(_mm256_castpd256_pd128(a),_mm256_extractf128_pd(a,1))));
}
template<> EIGEN_STRONG_INLINE int predux<Packet8i>(const Packet8i& a)
{
return predux(Packet4i(_mm_add_epi32(_mm256_castsi256_si128(a),_mm256_extractf128_si256(a,1))));
}
template<> EIGEN_STRONG_INLINE Packet4f predux_half_dowto4<Packet8f>(const Packet8f& a)
{
return _mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1));
}
template<> EIGEN_STRONG_INLINE Packet4i predux_half_dowto4<Packet8i>(const Packet8i& a)
{
return _mm_add_epi32(_mm256_castsi256_si128(a),_mm256_extractf128_si256(a,1));
}
template<> EIGEN_STRONG_INLINE float predux_mul<Packet8f>(const Packet8f& a)
{
@@ -856,7 +1269,12 @@ template<> EIGEN_STRONG_INLINE double predux_max<Packet4d>(const Packet4d& a)
template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8f& x)
{
return _mm256_movemask_ps(x)!=0;
return _mm256_movemask_ps(x) != 0;
}
template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8i& x)
{
return _mm256_movemask_ps(_mm256_castsi256_ps(x)) != 0;
}
EIGEN_DEVICE_FUNC inline void
@@ -905,6 +1323,66 @@ ptranspose(PacketBlock<Packet8f,4>& kernel) {
kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31);
}
#define MM256_SHUFFLE_EPI32(A, B, M) \
_mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B), M))
#ifndef EIGEN_VECTORIZE_AVX2
#define MM256_UNPACKLO_EPI32(A, B) \
_mm256_castps_si256(_mm256_unpacklo_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
#define MM256_UNPACKHI_EPI32(A, B) \
_mm256_castps_si256(_mm256_unpackhi_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
#else
#define MM256_UNPACKLO_EPI32(A, B) _mm256_unpacklo_epi32(A, B)
#define MM256_UNPACKHI_EPI32(A, B) _mm256_unpackhi_epi32(A, B)
#endif
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet8i,8>& kernel) {
__m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
__m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
__m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
__m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
__m256i T4 = MM256_UNPACKLO_EPI32(kernel.packet[4], kernel.packet[5]);
__m256i T5 = MM256_UNPACKHI_EPI32(kernel.packet[4], kernel.packet[5]);
__m256i T6 = MM256_UNPACKLO_EPI32(kernel.packet[6], kernel.packet[7]);
__m256i T7 = MM256_UNPACKHI_EPI32(kernel.packet[6], kernel.packet[7]);
__m256i S0 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(1,0,1,0));
__m256i S1 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(3,2,3,2));
__m256i S2 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(1,0,1,0));
__m256i S3 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(3,2,3,2));
__m256i S4 = MM256_SHUFFLE_EPI32(T4,T6,_MM_SHUFFLE(1,0,1,0));
__m256i S5 = MM256_SHUFFLE_EPI32(T4,T6,_MM_SHUFFLE(3,2,3,2));
__m256i S6 = MM256_SHUFFLE_EPI32(T5,T7,_MM_SHUFFLE(1,0,1,0));
__m256i S7 = MM256_SHUFFLE_EPI32(T5,T7,_MM_SHUFFLE(3,2,3,2));
kernel.packet[0] = _mm256_permute2f128_si256(S0, S4, 0x20);
kernel.packet[1] = _mm256_permute2f128_si256(S1, S5, 0x20);
kernel.packet[2] = _mm256_permute2f128_si256(S2, S6, 0x20);
kernel.packet[3] = _mm256_permute2f128_si256(S3, S7, 0x20);
kernel.packet[4] = _mm256_permute2f128_si256(S0, S4, 0x31);
kernel.packet[5] = _mm256_permute2f128_si256(S1, S5, 0x31);
kernel.packet[6] = _mm256_permute2f128_si256(S2, S6, 0x31);
kernel.packet[7] = _mm256_permute2f128_si256(S3, S7, 0x31);
}
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet8i,4>& kernel) {
__m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
__m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
__m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
__m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
__m256i S0 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(1,0,1,0));
__m256i S1 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(3,2,3,2));
__m256i S2 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(1,0,1,0));
__m256i S3 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(3,2,3,2));
kernel.packet[0] = _mm256_permute2f128_si256(S0, S1, 0x20);
kernel.packet[1] = _mm256_permute2f128_si256(S2, S3, 0x20);
kernel.packet[2] = _mm256_permute2f128_si256(S0, S1, 0x31);
kernel.packet[3] = _mm256_permute2f128_si256(S2, S3, 0x31);
}
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet4d,4>& kernel) {
__m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15);
@@ -919,21 +1397,37 @@ ptranspose(PacketBlock<Packet4d,4>& kernel) {
}
template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) {
#ifdef EIGEN_VECTORIZE_AVX2
const __m256i zero = _mm256_setzero_si256();
const __m256i select = _mm256_set_epi32(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256i false_mask = _mm256_cmpeq_epi32(zero, select);
return _mm256_blendv_ps(thenPacket, elsePacket, _mm256_castsi256_ps(false_mask));
#else
const __m256 zero = _mm256_setzero_ps();
const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ);
return _mm256_blendv_ps(thenPacket, elsePacket, false_mask);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) {
#ifdef EIGEN_VECTORIZE_AVX2
const __m256i zero = _mm256_setzero_si256();
const __m256i select = _mm256_set_epi64x(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256i false_mask = _mm256_cmpeq_epi64(select, zero);
return _mm256_blendv_pd(thenPacket, elsePacket, _mm256_castsi256_pd(false_mask));
#else
const __m256d zero = _mm256_setzero_pd();
const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
__m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ);
return _mm256_blendv_pd(thenPacket, elsePacket, false_mask);
#endif
}
// Packet math for Eigen::half
#ifndef EIGEN_VECTORIZE_AVX512FP16
template<> struct unpacket_traits<Packet8h> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet8h half; };
#endif
template<> EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
@@ -989,18 +1483,9 @@ EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
#ifdef EIGEN_HAS_FP16_C
return _mm256_cvtph_ps(a);
#else
EIGEN_ALIGN32 Eigen::half aux[8];
pstore(aux, a);
float f0(aux[0]);
float f1(aux[1]);
float f2(aux[2]);
float f3(aux[3]);
float f4(aux[4]);
float f5(aux[5]);
float f6(aux[6]);
float f7(aux[7]);
return _mm256_set_ps(f7, f6, f5, f4, f3, f2, f1, f0);
Eigen::internal::Packet8f pp = _mm256_castsi256_ps(_mm256_insertf128_si256(
_mm256_castsi128_si256(half2floatsse(a)), half2floatsse(_mm_srli_si128(a, 8)), 1));
return pp;
#endif
}
@@ -1008,17 +1493,9 @@ EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
#ifdef EIGEN_HAS_FP16_C
return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
#else
EIGEN_ALIGN32 float aux[8];
pstore(aux, a);
const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[0]));
const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[1]));
const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[2]));
const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[3]));
const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[4]));
const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[5]));
const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[6]));
const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(Eigen::half(aux[7]));
return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
__m128i lo = float2half(_mm256_extractf128_ps(a, 0));
__m128i hi = float2half(_mm256_extractf128_ps(a, 1));
return _mm_packus_epi32(lo, hi);
#endif
}
@@ -1097,6 +1574,7 @@ template<> EIGEN_STRONG_INLINE Packet8h pnegate(const Packet8h& a) {
return _mm_xor_si128(a, sign_mask);
}
#ifndef EIGEN_VECTORIZE_AVX512FP16
template<> EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
Packet8f af = half2float(a);
Packet8f bf = half2float(b);
@@ -1124,6 +1602,7 @@ template<> EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const
Packet8f rf = pdiv(af, bf);
return float2half(rf);
}
#endif
template<> EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride)
{
@@ -1152,11 +1631,14 @@ template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half*
to[stride*7] = aux[7];
}
#ifndef EIGEN_VECTORIZE_AVX512FP16
template<> EIGEN_STRONG_INLINE Eigen::half predux<Packet8h>(const Packet8h& a) {
Packet8f af = half2float(a);
float reduced = predux<Packet8f>(af);
return Eigen::half(reduced);
}
#endif
template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8h>(const Packet8h& a) {
Packet8f af = half2float(a);
@@ -1272,7 +1754,6 @@ EIGEN_STRONG_INLINE Packet8f Bf16ToF32(const Packet8bf& a) {
// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
EIGEN_STRONG_INLINE Packet8bf F32ToBf16(const Packet8f& a) {
Packet8bf r;
__m256i input = _mm256_castps_si256(a);

2
libs/eigen/Eigen/src/Core/arch/AVX/TypeCasting.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_TYPE_CASTING_AVX_H
#define EIGEN_TYPE_CASTING_AVX_H
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {

54
libs/eigen/Eigen/src/Core/arch/AVX512/Complex.h
View File

@@ -10,6 +10,8 @@
#ifndef EIGEN_COMPLEX_AVX512_H
#define EIGEN_COMPLEX_AVX512_H
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
@@ -97,7 +99,9 @@ template<> EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<fl
template<> EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from)
{
return Packet8cf(_mm512_castpd_ps(pload1<Packet8d>((const double*)(const void*)&from)));
const float re = std::real(from);
const float im = std::imag(from);
return Packet8cf(_mm512_set_ps(im, re, im, re, im, re, im, re, im, re, im, re, im, re, im, re));
}
template<> EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from)
@@ -157,11 +161,7 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf,Packet16f)
template<> EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
{
Packet8cf num = pmul(a, pconj(b));
__m512 tmp = _mm512_mul_ps(b.v, b.v);
__m512 tmp2 = _mm512_shuffle_ps(tmp,tmp,0xB1);
__m512 denom = _mm512_add_ps(tmp, tmp2);
return Packet8cf(_mm512_div_ps(num.v, denom));
return pdiv_complex(a, b);
}
template<> EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x)
@@ -253,11 +253,7 @@ template<> EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<do
template<> EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from)
{
#ifdef EIGEN_VECTORIZE_AVX512DQ
return Packet4cd(_mm512_broadcast_f64x2(pset1<Packet1cd>(from).v));
#else
return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4( _mm_castpd_ps(pset1<Packet1cd>(from).v))));
#endif
}
template<> EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
@@ -309,47 +305,11 @@ template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const
Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
}
template<> struct conj_helper<Packet4cd, Packet4cd, false,true>
{
EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet4cd, Packet4cd, true,false>
{
EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet4cd, Packet4cd, true,true>
{
EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
{
return pconj(internal::pmul(a, b));
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd,Packet8d)
template<> EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
{
Packet4cd num = pmul(a, pconj(b));
__m512d tmp = _mm512_mul_pd(b.v, b.v);
__m512d denom = padd(_mm512_permute_pd(tmp,0x55), tmp);
return Packet4cd(_mm512_div_pd(num.v, denom));
return pdiv_complex(a, b);
}
template<> EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x)

1235
libs/eigen/Eigen/src/Core/arch/AVX512/GemmKernel.h Normal file
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File diff suppressed because it is too large Load Diff

220
libs/eigen/Eigen/src/Core/arch/AVX512/MathFunctions.h
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@@ -10,39 +10,40 @@
#ifndef THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
#define THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
#include "../../InternalHeaderCheck.h"
namespace Eigen {
namespace internal {
// Disable the code for older versions of gcc that don't support many of the required avx512 instrinsics.
#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC >= 1923
#if EIGEN_HAS_AVX512_MATH
#define _EIGEN_DECLARE_CONST_Packet16f(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet16f(NAME, X) \
const Packet16f p16f_##NAME = pset1<Packet16f>(X)
#define _EIGEN_DECLARE_CONST_Packet16f_FROM_INT(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet16f_FROM_INT(NAME, X) \
const Packet16f p16f_##NAME = preinterpret<Packet16f,Packet16i>(pset1<Packet16i>(X))
#define _EIGEN_DECLARE_CONST_Packet8d(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet8d(NAME, X) \
const Packet8d p8d_##NAME = pset1<Packet8d>(X)
#define _EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(NAME, X) \
const Packet8d p8d_##NAME = _mm512_castsi512_pd(_mm512_set1_epi64(X))
#define _EIGEN_DECLARE_CONST_Packet16bf(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet16bf(NAME, X) \
const Packet16bf p16bf_##NAME = pset1<Packet16bf>(X)
#define _EIGEN_DECLARE_CONST_Packet16bf_FROM_INT(NAME, X) \
#define EIGEN_DECLARE_CONST_Packet16bf_FROM_INT(NAME, X) \
const Packet16bf p16bf_##NAME = preinterpret<Packet16bf,Packet16i>(pset1<Packet16i>(X))
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
plog<Packet16f>(const Packet16f& _x) {
return plog_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
plog<Packet8d>(const Packet8d& _x) {
return plog_double(_x);
}
@@ -51,13 +52,13 @@ F16_PACKET_FUNCTION(Packet16f, Packet16h, plog)
BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog)
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
plog2<Packet16f>(const Packet16f& _x) {
return plog2_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
plog2<Packet8d>(const Packet8d& _x) {
return plog2_double(_x);
}
@@ -69,23 +70,23 @@ BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog2)
// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
pexp<Packet16f>(const Packet16f& _x) {
_EIGEN_DECLARE_CONST_Packet16f(1, 1.0f);
_EIGEN_DECLARE_CONST_Packet16f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet16f(127, 127.0f);
EIGEN_DECLARE_CONST_Packet16f(1, 1.0f);
EIGEN_DECLARE_CONST_Packet16f(half, 0.5f);
EIGEN_DECLARE_CONST_Packet16f(127, 127.0f);
_EIGEN_DECLARE_CONST_Packet16f(exp_hi, 88.3762626647950f);
_EIGEN_DECLARE_CONST_Packet16f(exp_lo, -88.3762626647949f);
EIGEN_DECLARE_CONST_Packet16f(exp_hi, 88.3762626647950f);
EIGEN_DECLARE_CONST_Packet16f(exp_lo, -88.3762626647949f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_LOG2EF, 1.44269504088896341f);
EIGEN_DECLARE_CONST_Packet16f(cephes_LOG2EF, 1.44269504088896341f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p0, 1.9875691500E-4f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p1, 1.3981999507E-3f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p2, 8.3334519073E-3f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p3, 4.1665795894E-2f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p4, 1.6666665459E-1f);
_EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p5, 5.0000001201E-1f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p0, 1.9875691500E-4f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p1, 1.3981999507E-3f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p2, 8.3334519073E-3f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p3, 4.1665795894E-2f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p4, 1.6666665459E-1f);
EIGEN_DECLARE_CONST_Packet16f(cephes_exp_p5, 5.0000001201E-1f);
// Clamp x.
Packet16f x = pmax(pmin(_x, p16f_exp_hi), p16f_exp_lo);
@@ -96,7 +97,7 @@ pexp<Packet16f>(const Packet16f& _x) {
// Get r = x - m*ln(2). Note that we can do this without losing more than one
// ulp precision due to the FMA instruction.
_EIGEN_DECLARE_CONST_Packet16f(nln2, -0.6931471805599453f);
EIGEN_DECLARE_CONST_Packet16f(nln2, -0.6931471805599453f);
Packet16f r = _mm512_fmadd_ps(m, p16f_nln2, x);
Packet16f r2 = pmul(r, r);
Packet16f r3 = pmul(r2, r);
@@ -120,7 +121,7 @@ pexp<Packet16f>(const Packet16f& _x) {
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
pexp<Packet8d>(const Packet8d& _x) {
return pexp_double(_x);
}
@@ -154,49 +155,18 @@ EIGEN_STRONG_INLINE Packet16bf pldexp(const Packet16bf& a, const Packet16bf& exp
return F32ToBf16(pldexp<Packet16f>(Bf16ToF32(a), Bf16ToF32(exponent)));
}
// Functions for sqrt.
// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
// exact solution. The main advantage of this approach is not just speed, but
// also the fact that it can be inlined and pipelined with other computations,
// further reducing its effective latency.
#if EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
psqrt<Packet16f>(const Packet16f& _x) {
Packet16f neg_half = pmul(_x, pset1<Packet16f>(-.5f));
__mmask16 denormal_mask = _mm512_kand(
_mm512_cmp_ps_mask(_x, pset1<Packet16f>((std::numeric_limits<float>::min)()),
_CMP_LT_OQ),
_mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_GE_OQ));
Packet16f x = _mm512_rsqrt14_ps(_x);
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet16f>(1.5f)));
// Flush results for denormals to zero.
return _mm512_mask_blend_ps(denormal_mask, pmul(_x,x), _mm512_setzero_ps());
return generic_sqrt_newton_step<Packet16f>::run(_x, _mm512_rsqrt14_ps(_x));
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
psqrt<Packet8d>(const Packet8d& _x) {
Packet8d neg_half = pmul(_x, pset1<Packet8d>(-.5));
__mmask16 denormal_mask = _mm512_kand(
_mm512_cmp_pd_mask(_x, pset1<Packet8d>((std::numeric_limits<double>::min)()),
_CMP_LT_OQ),
_mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_GE_OQ));
Packet8d x = _mm512_rsqrt14_pd(_x);
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
// Do a second step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), pset1<Packet8d>(1.5)));
return _mm512_mask_blend_pd(denormal_mask, pmul(_x,x), _mm512_setzero_pd());
// Double requires 2 Newton-Raphson steps for convergence.
return generic_sqrt_newton_step<Packet8d, /*Steps=*/2>::run(_x, _mm512_rsqrt14_pd(_x));
}
#else
template <>
@@ -223,40 +193,9 @@ EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
#elif EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
prsqrt<Packet16f>(const Packet16f& _x) {
_EIGEN_DECLARE_CONST_Packet16f_FROM_INT(inf, 0x7f800000);
_EIGEN_DECLARE_CONST_Packet16f(one_point_five, 1.5f);
_EIGEN_DECLARE_CONST_Packet16f(minus_half, -0.5f);
Packet16f neg_half = pmul(_x, p16f_minus_half);
// Identity infinite, negative and denormal arguments.
__mmask16 inf_mask = _mm512_cmp_ps_mask(_x, p16f_inf, _CMP_EQ_OQ);
__mmask16 not_pos_mask = _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_LE_OQ);
__mmask16 not_finite_pos_mask = not_pos_mask | inf_mask;
// Compute an approximate result using the rsqrt intrinsic, forcing +inf
// for denormals for consistency with AVX and SSE implementations.
Packet16f y_approx = _mm512_rsqrt14_ps(_x);
// Do a single step of Newton-Raphson iteration to improve the approximation.
// This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
// It is essential to evaluate the inner term like this because forming
// y_n^2 may over- or underflow.
Packet16f y_newton = pmul(y_approx, pmadd(y_approx, pmul(neg_half, y_approx), p16f_one_point_five));
// Select the result of the Newton-Raphson step for positive finite arguments.
// For other arguments, choose the output of the intrinsic. This will
// return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
return _mm512_mask_blend_ps(not_finite_pos_mask, y_newton, y_approx);
}
#else
template <>
EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
_EIGEN_DECLARE_CONST_Packet16f(one, 1.0f);
return _mm512_div_ps(p16f_one, _mm512_sqrt_ps(x));
return generic_rsqrt_newton_step<Packet16f, /*Steps=*/1>::run(_x, _mm512_rsqrt14_ps(_x));
}
#endif
@@ -266,51 +205,28 @@ BF16_PACKET_FUNCTION(Packet16f, Packet16bf, prsqrt)
// prsqrt for double.
#if EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8d
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
prsqrt<Packet8d>(const Packet8d& _x) {
_EIGEN_DECLARE_CONST_Packet8d(one_point_five, 1.5);
_EIGEN_DECLARE_CONST_Packet8d(minus_half, -0.5);
_EIGEN_DECLARE_CONST_Packet8d_FROM_INT64(inf, 0x7ff0000000000000LL);
Packet8d neg_half = pmul(_x, p8d_minus_half);
// Identity infinite, negative and denormal arguments.
__mmask8 inf_mask = _mm512_cmp_pd_mask(_x, p8d_inf, _CMP_EQ_OQ);
__mmask8 not_pos_mask = _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_LE_OQ);
__mmask8 not_finite_pos_mask = not_pos_mask | inf_mask;
// Compute an approximate result using the rsqrt intrinsic, forcing +inf
// for denormals for consistency with AVX and SSE implementations.
#if defined(EIGEN_VECTORIZE_AVX512ER)
Packet8d y_approx = _mm512_rsqrt28_pd(_x);
#else
Packet8d y_approx = _mm512_rsqrt14_pd(_x);
#endif
// Do one or two steps of Newton-Raphson's to improve the approximation, depending on the
// starting accuracy (either 2^-14 or 2^-28, depending on whether AVX512ER is available).
// The Newton-Raphson algorithm has quadratic convergence and roughly doubles the number
// of correct digits for each step.
// This uses the formula y_{n+1} = y_n * (1.5 - y_n * (0.5 * x) * y_n).
// It is essential to evaluate the inner term like this because forming
// y_n^2 may over- or underflow.
Packet8d y_newton = pmul(y_approx, pmadd(neg_half, pmul(y_approx, y_approx), p8d_one_point_five));
#if !defined(EIGEN_VECTORIZE_AVX512ER)
y_newton = pmul(y_newton, pmadd(y_newton, pmul(neg_half, y_newton), p8d_one_point_five));
#endif
// Select the result of the Newton-Raphson step for positive finite arguments.
// For other arguments, choose the output of the intrinsic. This will
// return rsqrt(+inf) = 0, rsqrt(x) = NaN if x < 0, and rsqrt(0) = +inf.
return _mm512_mask_blend_pd(not_finite_pos_mask, y_newton, y_approx);
#ifdef EIGEN_VECTORIZE_AVX512ER
return generic_rsqrt_newton_step<Packet8d, /*Steps=*/1>::run(_x, _mm512_rsqrt28_pd(_x));
#else
return generic_rsqrt_newton_step<Packet8d, /*Steps=*/2>::run(_x, _mm512_rsqrt14_pd(_x));
#endif
}
template<> EIGEN_STRONG_INLINE Packet16f preciprocal<Packet16f>(const Packet16f& a) {
#ifdef EIGEN_VECTORIZE_AVX512ER
return _mm512_rcp28_ps(a);
#else
template <>
EIGEN_STRONG_INLINE Packet8d prsqrt<Packet8d>(const Packet8d& x) {
_EIGEN_DECLARE_CONST_Packet8d(one, 1.0f);
return _mm512_div_pd(p8d_one, _mm512_sqrt_pd(x));
return generic_reciprocal_newton_step<Packet16f, /*Steps=*/1>::run(a, _mm512_rcp14_ps(a));
#endif
}
F16_PACKET_FUNCTION(Packet16f, Packet16h, preciprocal)
BF16_PACKET_FUNCTION(Packet16f, Packet16bf, preciprocal)
#endif
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet16f plog1p<Packet16f>(const Packet16f& _x) {
return generic_plog1p(_x);
}
@@ -318,7 +234,7 @@ Packet16f plog1p<Packet16f>(const Packet16f& _x) {
F16_PACKET_FUNCTION(Packet16f, Packet16h, plog1p)
BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog1p)
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet16f pexpm1<Packet16f>(const Packet16f& _x) {
return generic_expm1(_x);
}
@@ -326,23 +242,47 @@ Packet16f pexpm1<Packet16f>(const Packet16f& _x) {
F16_PACKET_FUNCTION(Packet16f, Packet16h, pexpm1)
BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexpm1)
#endif
#endif // EIGEN_HAS_AVX512_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
psin<Packet16f>(const Packet16f& _x) {
return psin_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
pcos<Packet16f>(const Packet16f& _x) {
return pcos_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet16f
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
pacos<Packet16f>(const Packet16f& _x) {
return pacos_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
pasin<Packet16f>(const Packet16f& _x) {
return pasin_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
patan<Packet16f>(const Packet16f& _x) {
return patan_float(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
patan<Packet8d>(const Packet8d& _x) {
return patan_double(_x);
}
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
ptanh<Packet16f>(const Packet16f& _x) {
return internal::generic_fast_tanh_float(_x);
}

731
libs/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h
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