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Pulled the latest changes from the trunk

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This commit is contained in:
Benoit Steiner 2015-02-06 05:25:03 -08:00
commit c739102ef9
345 changed files with 19503 additions and 15221 deletions
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2
.hgignore
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@ -30,3 +30,5 @@ log
patch
a
a.*
lapack/testing
lapack/reference

21
CMakeLists.txt
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@ -1,6 +1,6 @@
project(Eigen)
cmake_minimum_required(VERSION 2.8.2)
cmake_minimum_required(VERSION 2.8.4)
# guard against in-source builds
@ -219,12 +219,26 @@ if(NOT MSVC)
message(STATUS "Enabling AltiVec in tests/examples")
endif()
option(EIGEN_TEST_VSX "Enable/Disable VSX in tests/examples" OFF)
if(EIGEN_TEST_VSX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -m64 -mvsx")
message(STATUS "Enabling VSX in tests/examples")
endif()
option(EIGEN_TEST_NEON "Enable/Disable Neon in tests/examples" OFF)
if(EIGEN_TEST_NEON)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -mfpu=neon -mcpu=cortex-a8")
message(STATUS "Enabling NEON in tests/examples")
endif()
option(EIGEN_TEST_NEON64 "Enable/Disable Neon in tests/examples" OFF)
if(EIGEN_TEST_NEON64)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
message(STATUS "Enabling NEON in tests/examples")
endif()
check_cxx_compiler_flag("-fopenmp" COMPILER_SUPPORT_OPENMP)
if(COMPILER_SUPPORT_OPENMP)
option(EIGEN_TEST_OPENMP "Enable/Disable OpenMP in tests/examples" OFF)
@ -434,6 +448,7 @@ if(cmake_generator_tolower MATCHES "makefile")
message(STATUS "make check | Build and run the unit-tests. Read this page:")
message(STATUS " | http://eigen.tuxfamily.org/index.php?title=Tests")
message(STATUS "make blas | Build BLAS library (not the same thing as Eigen)")
message(STATUS "make uninstall| Removes files installed by make install")
message(STATUS "--------------+--------------------------------------------------------------")
else()
message(STATUS "To build/run the unit tests, read this page:")
@ -469,3 +484,7 @@ install ( FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/UseEigen3.cmake
${CMAKE_CURRENT_BINARY_DIR}/Eigen3Config.cmake
DESTINATION ${EIGEN_CONFIG_CMAKE_PATH}
)
# Add uninstall target
add_custom_target ( uninstall
COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_SOURCE_DIR}/cmake/EigenUninstall.cmake)

1
Eigen/Cholesky
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@ -21,7 +21,6 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE

4
Eigen/CholmodSupport
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@ -33,12 +33,8 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CHOLMODSUPPORT_MODULE_H

56
Eigen/Core
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@ -57,7 +57,7 @@
// 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 defined(__MINGW32__) && EIGEN_GNUC_AT_LEAST(4,6)
#if EIGEN_COMP_MINGW && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
@ -75,18 +75,18 @@
#endif
#endif
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
#include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
#if (_MSC_VER >= 1500) // 2008 or later
#if (EIGEN_COMP_MSVC >= 1500) // 2008 or later
// Remember that usage of defined() in a #define is undefined by the standard.
// a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || defined(_M_X64)
#if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
#define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
#endif
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || (defined __INTEL_COMPILER) || EIGEN_GNUC_AT_LEAST(4,2) )
#if (defined __SSE2__) && ( (!EIGEN_COMP_GNUC) || EIGEN_COMP_ICC || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
@ -125,9 +125,7 @@
#define EIGEN_VECTORIZE_SSE4_1
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __FMA__
#define EIGEN_VECTORIZE_FMA
#endif
// include files
// This extern "C" works around a MINGW-w64 compilation issue
@ -140,7 +138,7 @@
extern "C" {
// In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
// Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
#if defined(__INTEL_COMPILER) && __INTEL_COMPILER >= 1110
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <emmintrin.h>
@ -162,6 +160,15 @@
#endif
#endif
} // end extern "C"
#elif defined __VSX__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_VSX
#include <altivec.h>
// We need to #undef all these ugly tokens defined in <altivec.h>
// => use __vector instead of vector
#undef bool
#undef vector
#undef pixel
#elif defined __ALTIVEC__
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ALTIVEC
@ -171,7 +178,7 @@
#undef bool
#undef vector
#undef pixel
#elif defined __ARM_NEON__
#elif defined __ARM_NEON
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_NEON
#include <arm_neon.h>
@ -192,7 +199,7 @@
#endif
// MSVC for windows mobile does not have the errno.h file
#if !(defined(_MSC_VER) && defined(_WIN32_WCE)) && !defined(__ARMCC_VERSION)
#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
#define EIGEN_HAS_ERRNO
#endif
@ -218,7 +225,7 @@
#endif
// required for __cpuid, needs to be included after cmath
#if defined(_MSC_VER) && (defined(_M_IX86)||defined(_M_X64)) && (!defined(_WIN32_WCE))
#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
#include <intrin.h>
#endif
@ -240,6 +247,8 @@ inline static const char *SimdInstructionSetsInUse(void) {
return "SSE, SSE2";
#elif defined(EIGEN_VECTORIZE_ALTIVEC)
return "AltiVec";
#elif defined(EIGEN_VECTORIZE_VSX)
return "VSX";
#elif defined(EIGEN_VECTORIZE_NEON)
return "ARM NEON";
#else
@ -271,8 +280,8 @@ using std::ptrdiff_t;
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
@ -291,7 +300,7 @@ using std::ptrdiff_t;
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/SSE/Complex.h"
#elif defined EIGEN_VECTORIZE_ALTIVEC
#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
#include "src/Core/arch/AltiVec/PacketMath.h"
#include "src/Core/arch/AltiVec/Complex.h"
#elif defined EIGEN_VECTORIZE_NEON
@ -310,19 +319,16 @@ using std::ptrdiff_t;
#include "src/Core/functors/UnaryFunctors.h"
#include "src/Core/functors/NullaryFunctors.h"
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
#include "src/Core/EigenBase.h"
#ifdef EIGEN_ENABLE_EVALUATORS
#include "src/Core/functors/AssignmentFunctors.h"
#include "src/Core/Product.h"
#include "src/Core/CoreEvaluators.h"
#include "src/Core/AssignEvaluator.h"
#include "src/Core/ProductEvaluators.h"
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
// at least confirmed with Doxygen 1.5.5 and 1.5.6
@ -333,7 +339,10 @@ using std::ptrdiff_t;
#include "src/Core/util/BlasUtil.h"
#include "src/Core/DenseStorage.h"
#include "src/Core/NestByValue.h"
#include "src/Core/ForceAlignedAccess.h"
// #include "src/Core/ForceAlignedAccess.h"
// #include "src/Core/Flagged.h"
#include "src/Core/ReturnByValue.h"
#include "src/Core/NoAlias.h"
#include "src/Core/PlainObjectBase.h"
@ -346,12 +355,12 @@ using std::ptrdiff_t;
#include "src/Core/SelfCwiseBinaryOp.h"
#include "src/Core/Dot.h"
#include "src/Core/StableNorm.h"
#include "src/Core/MapBase.h"
#include "src/Core/Stride.h"
#include "src/Core/MapBase.h"
#include "src/Core/Map.h"
#include "src/Core/Ref.h"
#include "src/Core/Block.h"
#include "src/Core/VectorBlock.h"
#include "src/Core/Ref.h"
#include "src/Core/Transpose.h"
#include "src/Core/DiagonalMatrix.h"
#include "src/Core/Diagonal.h"
@ -364,14 +373,15 @@ using std::ptrdiff_t;
#include "src/Core/IO.h"
#include "src/Core/Swap.h"
#include "src/Core/CommaInitializer.h"
#include "src/Core/Flagged.h"
#include "src/Core/ProductBase.h"
#include "src/Core/GeneralProduct.h"
#include "src/Core/Solve.h"
#include "src/Core/Inverse.h"
#include "src/Core/TriangularMatrix.h"
#include "src/Core/SelfAdjointView.h"
#include "src/Core/products/GeneralBlockPanelKernel.h"
#include "src/Core/products/Parallelizer.h"
#include "src/Core/products/CoeffBasedProduct.h"
#include "src/Core/ProductEvaluators.h"
#include "src/Core/products/GeneralMatrixVector.h"
#include "src/Core/products/GeneralMatrixMatrix.h"
#include "src/Core/SolveTriangular.h"

4
Eigen/IterativeLinearSolvers
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@ -26,9 +26,7 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/IterativeLinearSolvers/SolveWithGuess.h"
#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
#include "src/IterativeLinearSolvers/ConjugateGradient.h"

3
Eigen/LU
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@ -16,7 +16,6 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
@ -25,7 +24,7 @@
#include "src/LU/PartialPivLU_MKL.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#include "src/LU/InverseImpl.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled

4
Eigen/PaStiXSupport
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@ -35,12 +35,8 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/PaStiXSupport/PaStiXSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_PASTIXSUPPORT_MODULE_H

1
Eigen/QR
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@ -24,7 +24,6 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"

2
Eigen/SPQRSupport
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@ -21,8 +21,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"

10
Eigen/SVD
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@ -12,21 +12,25 @@
*
*
* This module provides SVD decomposition for matrices (both real and complex).
* This decomposition is accessible via the following MatrixBase method:
* Two decomposition algorithms are provided:
* - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
* - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
* These decompositions are accessible via the respective classes and following MatrixBase methods:
* - MatrixBase::jacobiSvd()
* - MatrixBase::bdcSvd()
*
* \code
* #include <Eigen/SVD>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/SVD/UpperBidiagonalization.h"
#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)
#include "src/SVD/JacobiSVD_MKL.h"
#endif
#include "src/SVD/UpperBidiagonalization.h"
#include "src/Core/util/ReenableStupidWarnings.h"

2
Eigen/SparseCholesky
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@ -34,8 +34,6 @@
#error The SparseCholesky module has nothing to offer in MPL2 only mode
#endif
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/SparseCholesky/SimplicialCholesky.h"
#ifndef EIGEN_MPL2_ONLY

23
Eigen/SparseCore
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@ -26,37 +26,32 @@
* This module depends on: Core.
*/
namespace Eigen {
/** The type used to identify a general sparse storage. */
struct Sparse {};
}
#include "src/SparseCore/SparseUtil.h"
#include "src/SparseCore/SparseMatrixBase.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/CompressedStorage.h"
#include "src/SparseCore/AmbiVector.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/SparseSelfAdjointView.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/TriangularSolver.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/SparseSolverBase.h"
#include "src/Core/util/ReenableStupidWarnings.h"

3
Eigen/SparseLU
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@ -20,9 +20,6 @@
* Please, see the documentation of the SparseLU class for more details.
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
// Ordering interface
#include "OrderingMethods"

3
Eigen/SparseQR
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@ -21,9 +21,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"

2
Eigen/StdDeque
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@ -14,7 +14,7 @@
#include "Core"
#include <deque>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)

2
Eigen/StdList
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@ -13,7 +13,7 @@
#include "Core"
#include <list>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)

2
Eigen/StdVector
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@ -14,7 +14,7 @@
#include "Core"
#include <vector>
#if (defined(_MSC_VER) && defined(_WIN64)) /* MSVC auto aligns in 64 bit builds */
#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 /* MSVC auto aligns in 64 bit builds */
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)

4
Eigen/SuperLUSupport
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@ -48,12 +48,8 @@ namespace Eigen { struct SluMatrix; }
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/SuperLUSupport/SuperLUSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SUPERLUSUPPORT_MODULE_H

3
Eigen/UmfPackSupport
View File

@ -26,9 +26,6 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/UmfPackSupport/UmfPackSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"

98
Eigen/src/Cholesky/LDLT.h
View File

@ -85,7 +85,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
* according to the specified problem \a size.
* \sa LDLT()
*/
LDLT(Index size)
explicit LDLT(Index size)
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
@ -98,7 +98,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
* This calculates the decomposition for the input \a matrix.
* \sa LDLT(Index size)
*/
LDLT(const MatrixType& matrix)
explicit LDLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
@ -175,13 +175,13 @@ template<typename _MatrixType, int _UpLo> class LDLT
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
*/
template<typename Rhs>
inline const internal::solve_retval<LDLT, Rhs>
inline const Solve<LDLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LDLT::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<LDLT, Rhs>(*this, b.derived());
return Solve<LDLT, Rhs>(*this, b.derived());
}
template<typename Derived>
@ -217,6 +217,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Success;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
@ -400,16 +406,16 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m; }
static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
};
template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); }
static inline MatrixU getU(const MatrixType& m) { return m; }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
};
} // end namespace internal
@ -427,6 +433,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
m_transpositions.resize(size);
m_isInitialized = false;
m_temporary.resize(size);
m_sign = internal::ZeroSign;
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign);
@ -466,52 +473,45 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Deri
return *this;
}
namespace internal {
template<typename _MatrixType, int _UpLo, typename Rhs>
struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
: solve_retval_base<LDLT<_MatrixType,_UpLo>, Rhs>
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType, int _UpLo>
template<typename RhsType, typename DstType>
void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
typedef LDLT<_MatrixType,_UpLo> LDLTType;
EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs)
eigen_assert(rhs.rows() == rows());
// dst = P b
dst = m_transpositions * rhs;
template<typename Dest> void evalTo(Dest& dst) const
// dst = L^-1 (P b)
matrixL().solveInPlace(dst);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
// as motivated by LAPACK's xGELSS:
// RealScalar tolerance = numext::maxi(vectorD.array().abs().maxCoeff() *NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
for (Index i = 0; i < vecD.size(); ++i)
{
eigen_assert(rhs().rows() == dec().matrixLDLT().rows());
// dst = P b
dst = dec().transpositionsP() * rhs();
// dst = L^-1 (P b)
dec().matrixL().solveInPlace(dst);
// dst = D^-1 (L^-1 P b)
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
EIGEN_USING_STD_MATH(max);
typedef typename LDLTType::MatrixType MatrixType;
typedef typename LDLTType::RealScalar RealScalar;
const typename Diagonal<const MatrixType>::RealReturnType vectorD(dec().vectorD());
// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
// as motivated by LAPACK's xGELSS:
// RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() *NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
for (Index i = 0; i < vectorD.size(); ++i) {
if(abs(vectorD(i)) > tolerance)
dst.row(i) /= vectorD(i);
else
dst.row(i).setZero();
}
// dst = L^-T (D^-1 L^-1 P b)
dec().matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = dec().transpositionsP().transpose() * dst;
if(abs(vecD(i)) > tolerance)
dst.row(i) /= vecD(i);
else
dst.row(i).setZero();
}
};
// dst = L^-T (D^-1 L^-1 P b)
matrixU().solveInPlace(dst);
// dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b
dst = m_transpositions.transpose() * dst;
}
#endif
/** \internal use x = ldlt_object.solve(x);
*

44
Eigen/src/Cholesky/LLT.h
View File

@ -83,10 +83,10 @@ template<typename _MatrixType, int _UpLo> class LLT
* according to the specified problem \a size.
* \sa LLT()
*/
LLT(Index size) : m_matrix(size, size),
explicit LLT(Index size) : m_matrix(size, size),
m_isInitialized(false) {}
LLT(const MatrixType& matrix)
explicit LLT(const MatrixType& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
@ -118,13 +118,13 @@ template<typename _MatrixType, int _UpLo> class LLT
* \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
*/
template<typename Rhs>
inline const internal::solve_retval<LLT, Rhs>
inline const Solve<LLT, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==b.rows()
&& "LLT::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<LLT, Rhs>(*this, b.derived());
return Solve<LLT, Rhs>(*this, b.derived());
}
template<typename Derived>
@ -161,6 +161,12 @@ template<typename _MatrixType, int _UpLo> class LLT
template<typename VectorType>
LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
/** \internal
@ -345,8 +351,8 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
{
typedef const TriangularView<const MatrixType, Lower> MatrixL;
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m; }
static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
};
@ -355,8 +361,8 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
{
typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
typedef const TriangularView<const MatrixType, Upper> MatrixU;
static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); }
static inline MatrixU getU(const MatrixType& m) { return m; }
static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
static bool inplace_decomposition(MatrixType& m)
{ return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
};
@ -404,22 +410,16 @@ LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, c
return *this;
}
namespace internal {
template<typename _MatrixType, int UpLo, typename Rhs>
struct solve_retval<LLT<_MatrixType, UpLo>, Rhs>
: solve_retval_base<LLT<_MatrixType, UpLo>, Rhs>
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename _MatrixType,int _UpLo>
template<typename RhsType, typename DstType>
void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
{
typedef LLT<_MatrixType,UpLo> LLTType;
EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dst = rhs();
dec().solveInPlace(dst);
}
};
dst = rhs;
solveInPlace(dst);
}
#endif
/** \internal use x = llt_object.solve(x);
*

80
Eigen/src/CholmodSupport/CholmodSupport.h
View File

@ -105,7 +105,7 @@ const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>&
/** 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<SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
{
cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived());
@ -157,8 +157,12 @@ enum CholmodMode {
* \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
*/
template<typename _MatrixType, int _UpLo, typename Derived>
class CholmodBase : internal::noncopyable
class CholmodBase : public SparseSolverBase<Derived>
{
protected:
typedef SparseSolverBase<Derived> Base;
using Base::derived;
using Base::m_isInitialized;
public:
typedef _MatrixType MatrixType;
enum { UpLo = _UpLo };
@ -170,14 +174,14 @@ class CholmodBase : internal::noncopyable
public:
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
: m_cholmodFactor(0), m_info(Success)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
cholmod_start(&m_cholmod);
}
CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
explicit CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
cholmod_start(&m_cholmod);
@ -194,9 +198,6 @@ class CholmodBase : internal::noncopyable
inline Index cols() const { return m_cholmodFactor->n; }
inline Index rows() const { return m_cholmodFactor->n; }
Derived& derived() { return *static_cast<Derived*>(this); }
const Derived& derived() const { return *static_cast<const Derived*>(this); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
@ -216,34 +217,6 @@ class CholmodBase : internal::noncopyable
return derived();
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::solve_retval<CholmodBase, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
*/
template<typename Rhs>
inline const internal::sparse_solve_retval<CholmodBase, Rhs>
solve(const SparseMatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(rows()==b.rows()
&& "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@ -290,7 +263,7 @@ class CholmodBase : internal::noncopyable
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal */
template<typename Rhs,typename Dest>
void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
@ -312,7 +285,7 @@ class CholmodBase : internal::noncopyable
/** \internal */
template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
void _solve_impl(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
@ -357,7 +330,6 @@ class CholmodBase : internal::noncopyable
cholmod_factor* m_cholmodFactor;
RealScalar m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@ -572,36 +544,6 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
}
};
namespace internal {
template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
struct solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
: solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
{
typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dec()._solve(rhs(),dst);
}
};
template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
struct sparse_solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
: sparse_solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
{
typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dec()._solve(rhs(),dst);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CHOLMODSUPPORT_H

25
Eigen/src/Core/Array.h
View File

@ -74,6 +74,21 @@ class Array
{
return Base::operator=(other);
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill()
*/
/* This overload is needed because the usage of
* using Base::operator=;
* fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
* the usage of 'using'. This should be done only for operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
{
Base::setConstant(value);
return *this;
}
/** Copies the value of the expression \a other into \c *this with automatic resizing.
*
@ -99,7 +114,7 @@ class Array
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
@ -144,7 +159,6 @@ class Array
}
#endif
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
@ -244,13 +258,6 @@ class Array
*this = other;
}
/** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
*/
template<typename OtherDerived>
void swap(ArrayBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }

28
Eigen/src/Core/ArrayBase.h
View File

@ -64,8 +64,7 @@ template<typename Derived> class ArrayBase
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@ -121,8 +120,15 @@ template<typename Derived> class ArrayBase
EIGEN_DEVICE_FUNC
Derived& operator=(const ArrayBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill() */
EIGEN_DEVICE_FUNC
Derived& operator=(const Scalar &value)
{ Base::setConstant(value); return derived(); }
EIGEN_DEVICE_FUNC
Derived& operator+=(const Scalar& scalar);
@ -153,9 +159,9 @@ template<typename Derived> class ArrayBase
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return derived(); }
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
@ -186,8 +192,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
return derived();
}
@ -200,8 +205,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
return derived();
}
@ -214,8 +218,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -228,8 +231,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar>());
return derived();
}

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

@ -44,6 +44,7 @@ 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 typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
@ -54,7 +55,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
@ -186,6 +187,7 @@ 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 typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
@ -196,7 +198,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
EIGEN_DEVICE_FUNC
inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_matrix) {}
explicit inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }

558
Eigen/src/Core/Assign.h
View File

@ -14,485 +14,6 @@
namespace Eigen {
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
***************************************************************************/
template <typename Derived, typename OtherDerived>
struct assign_traits
{
public:
enum {
DstIsAligned = Derived::Flags & AlignedBit,
DstHasDirectAccess = Derived::Flags & DirectAccessBit,
SrcIsAligned = OtherDerived::Flags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
};
private:
enum {
InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime)
: int(Derived::RowsAtCompileTime),
InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime)
: int(Derived::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Derived::SizeAtCompileTime,
PacketSize = packet_traits<typename Derived::Scalar>::size
};
enum {
StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(DstIsAligned) && int(SrcIsAligned),
MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = MightVectorize && DstHasDirectAccess
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize)
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix */
};
public:
enum {
Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
: int(DefaultTraversal),
Vectorized = int(Traversal) == InnerVectorizedTraversal
|| int(Traversal) == LinearVectorizedTraversal
|| int(Traversal) == SliceVectorizedTraversal
};
private:
enum {
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
};
public:
enum {
Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
? (
int(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) )
: int(NoUnrolling)
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
EIGEN_DEBUG_VAR(DstIsAligned)
EIGEN_DEBUG_VAR(SrcIsAligned)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(Derived::SizeAtCompileTime)
EIGEN_DEBUG_VAR(OtherDerived::CoeffReadCost)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
EIGEN_DEBUG_VAR(Traversal)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
EIGEN_DEBUG_VAR(Unrolling)
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime
};
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeffByOuterInner(outer, inner, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer)
{
dst.copyCoeffByOuterInner(outer, Index, src);
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_LinearTraversal_CompleteUnrolling
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.copyCoeff(Index, src);
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_CompleteUnrolling
{
enum {
outer = Index / Derived1::InnerSizeAtCompileTime,
inner = Index % Derived1::InnerSizeAtCompileTime,
JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src);
assign_innervec_CompleteUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer)
{
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src);
assign_innervec_InnerUnrolling<Derived1, Derived2,
Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer);
}
};
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
template<typename Derived1, typename Derived2,
int Traversal = assign_traits<Derived1, Derived2>::Traversal,
int Unrolling = assign_traits<Derived1, Derived2>::Unrolling,
int Version = Specialized>
struct assign_impl;
/************************
*** Default traversal ***
************************/
template<typename Derived1, typename Derived2, int Unrolling, int Version>
struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling, Version>
{
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &, const Derived2 &) { }
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling, Version>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
for(Index i = 0; i < size; ++i)
dst.copyCoeff(i, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling, Version>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index packetSize = packet_traits<typename Derived1::Scalar>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling, Version>
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime>
::run(dst, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime>
::run(dst, src, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
template <bool IsAligned = false>
struct unaligned_assign_impl
{
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {}
};
template <>
struct unaligned_assign_impl<false>
{
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
#ifdef _MSC_VER
template <typename Derived, typename OtherDerived>
static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#else
template <typename Derived, typename OtherDerived>
static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end)
#endif
{
for (typename Derived::Index index = start; index < end; ++index)
dst.copyCoeff(index, src);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
const Index size = dst.size();
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0
: internal::first_aligned(&dst.coeffRef(0), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
{
dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src);
}
unaligned_assign_impl<>::run(src,dst,alignedEnd,size);
}
};
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling, Version>
{
typedef typename Derived1::Index Index;
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src)
{
enum { size = Derived1::SizeAtCompileTime,
packetSize = packet_traits<typename Derived1::Scalar>::size,
alignedSize = (size/packetSize)*packetSize };
assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src);
assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Derived1, typename Derived2, int Version>
struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling, Version>
{
typedef typename Derived1::Index Index;
static inline void run(Derived1 &dst, const Derived2 &src)
{
typedef packet_traits<typename Derived1::Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) ,
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0
: internal::first_aligned(&dst.coeffRef(0,0), innerSize);
for(Index outer = 0; outer < outerSize; ++outer)
{
const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
// do the non-vectorizable part of the assignment
for(Index inner = 0; inner<alignedStart ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
dst.copyCoeffByOuterInner(outer, inner, src);
alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
} // end namespace internal
/***************************************************************************
* Part 4 : implementation of DenseBase methods
***************************************************************************/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
@ -506,91 +27,35 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
#ifdef EIGEN_TEST_EVALUATORS
#ifdef EIGEN_DEBUG_ASSIGN
internal::copy_using_evaluator_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::call_dense_assignment_loop(derived(),other.derived());
internal::call_assignment_no_alias(derived(),other.derived());
#else // EIGEN_TEST_EVALUATORS
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<Derived, OtherDerived>::debug();
#endif
eigen_assert(rows() == other.rows() && cols() == other.cols());
internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal)
: int(InvalidTraversal)>::run(derived(),other.derived());
#endif // EIGEN_TEST_EVALUATORS
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
return derived();
}
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(internal::traits<OtherDerived>::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::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(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
template<typename ActualDerived, typename ActualOtherDerived>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { other.evalTo(dst); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,true> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
template<typename ActualDerived, typename ActualOtherDerived>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { Transpose<ActualDerived> dstTrans(dst); other.evalTo(dstTrans); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); }
};
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
{
return internal::assign_selector<Derived,Derived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
@ -598,7 +63,8 @@ template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
@ -606,7 +72,8 @@ template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
internal::call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
@ -614,7 +81,8 @@ template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
other.derived().evalTo(derived());
return derived();
}
} // end namespace Eigen

500
Eigen/src/Core/AssignEvaluator.h
View File

@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2011-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// This Source Code Form is subject to the terms of the Mozilla
@ -24,37 +24,46 @@ namespace internal {
// copy_using_evaluator_traits is based on assign_traits
template <typename Derived, typename OtherDerived>
template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc>
struct copy_using_evaluator_traits
{
typedef typename DstEvaluator::XprType Dst;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags
};
public:
enum {
DstIsAligned = Derived::Flags & AlignedBit,
DstHasDirectAccess = Derived::Flags & DirectAccessBit,
SrcIsAligned = OtherDerived::Flags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned,
SrcEvalBeforeAssign = (evaluator_traits<OtherDerived>::HasEvalTo == 1)
DstIsAligned = DstFlags & AlignedBit,
DstHasDirectAccess = DstFlags & DirectAccessBit,
SrcIsAligned = SrcFlags & AlignedBit,
JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned
};
private:
enum {
InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime)
: int(Derived::RowsAtCompileTime),
InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime)
: int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime)
: int(Derived::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Derived::SizeAtCompileTime,
PacketSize = packet_traits<typename Derived::Scalar>::size
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
: int(Dst::RowsAtCompileTime),
InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
: int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
PacketSize = packet_traits<typename Dst::Scalar>::size
};
enum {
StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)),
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = StorageOrdersAgree
&& (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit),
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& (functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(DstIsAligned) && int(SrcIsAligned),
MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit),
MayLinearize = StorageOrdersAgree && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& (DstIsAligned || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
@ -68,8 +77,7 @@ private:
public:
enum {
Traversal = int(SrcEvalBeforeAssign) ? int(AllAtOnceTraversal)
: int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
@ -82,12 +90,12 @@ public:
private:
enum {
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(OtherDerived::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit)
&& int(SrcEvaluator::CoeffReadCost) != Dynamic
&& int(InnerSize) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit)
};
public:
@ -110,6 +118,12 @@ public:
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
std::cerr.setf(std::ios::hex, std::ios::basefield);
EIGEN_DEBUG_VAR(DstFlags)
EIGEN_DEBUG_VAR(SrcFlags)
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstIsAligned)
EIGEN_DEBUG_VAR(SrcIsAligned)
EIGEN_DEBUG_VAR(JointAlignment)
@ -127,6 +141,7 @@ public:
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
EIGEN_DEBUG_VAR(Unrolling)
std::cerr << std::endl;
}
#endif
};
@ -142,6 +157,7 @@ public:
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
@ -150,7 +166,7 @@ struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
inner = Index % DstXprType::InnerSizeAtCompileTime
};
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.assignCoeffByOuterInner(outer, inner);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
@ -160,13 +176,13 @@ struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, typename Kernel::Index outer)
{
kernel.assignCoeffByOuterInner(outer, Index);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index+1, Stop>::run(kernel, outer);
@ -176,7 +192,7 @@ struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&, int) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, typename Kernel::Index) { }
};
/***********************
@ -186,7 +202,7 @@ struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel& kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
{
kernel.assignCoeff(Index);
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
@ -196,7 +212,7 @@ struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
/**************************
@ -206,16 +222,17 @@ struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling
{
// FIXME: this is not very clean, perhaps this information should be provided by the kernel?
typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
typedef typename DstEvaluatorType::XprType DstXprType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime,
JointAlignment = Kernel::AssignmentTraits::JointAlignment
};
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<Aligned, JointAlignment>(outer, inner);
enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size };
@ -226,17 +243,16 @@ struct copy_using_evaluator_innervec_CompleteUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel&) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, typename Kernel::Index outer)
{
kernel.template assignPacketByOuterInner<Aligned, Aligned>(outer, Index);
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size };
enum { NextIndex = Index + packet_traits<typename Kernel::Scalar>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop>::run(kernel, outer);
}
};
@ -244,7 +260,7 @@ struct copy_using_evaluator_innervec_InnerUnrolling
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Kernel &, int) { }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, typename Kernel::Index) { }
};
/***************************************************************************
@ -265,7 +281,7 @@ struct dense_assignment_loop;
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
{
static void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
@ -280,7 +296,7 @@ struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
@ -291,7 +307,7 @@ template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
@ -314,7 +330,7 @@ struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
static EIGEN_STRONG_INLINE void run(Kernel&, typename Kernel::Index, typename Kernel::Index) {}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, typename Kernel::Index, typename Kernel::Index) {}
};
template <>
@ -323,14 +339,14 @@ struct unaligned_dense_assignment_loop<false>
// MSVC must not inline this functions. If it does, it fails to optimize the
// packet access path.
// FIXME check which version exhibits this issue
#ifdef _MSC_VER
#if EIGEN_COMP_MSVC
template <typename Kernel>
static EIGEN_DONT_INLINE void run(Kernel &kernel,
typename Kernel::Index start,
typename Kernel::Index end)
#else
template <typename Kernel>
static EIGEN_STRONG_INLINE void run(Kernel &kernel,
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
typename Kernel::Index start,
typename Kernel::Index end)
#endif
@ -343,7 +359,7 @@ struct unaligned_dense_assignment_loop<false>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
@ -371,7 +387,7 @@ template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
@ -391,7 +407,7 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrollin
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
@ -407,7 +423,7 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
@ -418,7 +434,7 @@ template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
{
typedef typename Kernel::Index Index;
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
const Index outerSize = kernel.outerSize();
@ -434,7 +450,7 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
const Index size = kernel.size();
@ -446,7 +462,7 @@ struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
static EIGEN_STRONG_INLINE void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
@ -460,7 +476,7 @@ struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
static inline void run(Kernel &kernel)
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::Index Index;
typedef packet_traits<typename Kernel::Scalar> PacketTraits;
@ -496,25 +512,8 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
}
};
/****************************
*** All-at-once traversal ***
****************************/
// TODO: this 'AllAtOnceTraversal' should be dropped or caught earlier (Gael)
// Indeed, what to do with the kernel's functor??
template<typename Kernel>
struct dense_assignment_loop<Kernel, AllAtOnceTraversal, NoUnrolling>
{
static inline void run(Kernel & kernel)
{
// Evaluate rhs in temporary to prevent aliasing problems in a = a * a;
// TODO: Do not pass the xpr object to evalTo() (Jitse)
kernel.srcEvaluator().evalTo(kernel.dstEvaluator(), kernel.dstExpression());
}
};
/***************************************************************************
* Part 4 : Generic Assignment routine
* Part 4 : Generic dense assignment kernel
***************************************************************************/
// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
@ -523,7 +522,7 @@ struct dense_assignment_loop<Kernel, AllAtOnceTraversal, NoUnrolling>
// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
// One can customize the assignment using this generic dense_assignment_kernel with different
// functors, or by completely overloading it, by-passing a functor.
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
class generic_dense_assignment_kernel
{
protected:
@ -535,35 +534,44 @@ public:
typedef SrcEvaluatorTypeT SrcEvaluatorType;
typedef typename DstEvaluatorType::Scalar Scalar;
typedef typename DstEvaluatorType::Index Index;
typedef copy_using_evaluator_traits<DstXprType, SrcXprType> AssignmentTraits;
typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
EIGEN_DEVICE_FUNC generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
: m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
{}
{
#ifdef EIGEN_DEBUG_ASSIGN
AssignmentTraits::debug();
#endif
}
Index size() const { return m_dstExpr.size(); }
Index innerSize() const { return m_dstExpr.innerSize(); }
Index outerSize() const { return m_dstExpr.outerSize(); }
Index outerStride() const { return m_dstExpr.outerStride(); }
EIGEN_DEVICE_FUNC Index size() const { return m_dstExpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_dstExpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_dstExpr.outerSize(); }
EIGEN_DEVICE_FUNC Index rows() const { return m_dstExpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_dstExpr.cols(); }
EIGEN_DEVICE_FUNC Index outerStride() const { return m_dstExpr.outerStride(); }
// TODO get rid of this one:
DstXprType& dstExpression() const { return m_dstExpr; }
EIGEN_DEVICE_FUNC DstXprType& dstExpression() const { return m_dstExpr; }
DstEvaluatorType& dstEvaluator() { return m_dst; }
const SrcEvaluatorType& srcEvaluator() const { return m_src; }
EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() { return m_dst; }
EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const { return m_src; }
void assignCoeff(Index row, Index col)
/// Assign src(row,col) to dst(row,col) through the assignment functor.
EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
{
m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
}
void assignCoeff(Index index)
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
void assignCoeffByOuterInner(Index outer, Index inner)
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC void assignCoeffByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
@ -572,40 +580,40 @@ public:
template<int StoreMode, int LoadMode>
void assignPacket(Index row, Index col)
EIGEN_DEVICE_FUNC void assignPacket(Index row, Index col)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode>(row,col));
}
template<int StoreMode, int LoadMode>
void assignPacket(Index index)
EIGEN_DEVICE_FUNC void assignPacket(Index index)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode>(index));
}
template<int StoreMode, int LoadMode>
void assignPacketByOuterInner(Index outer, Index inner)
EIGEN_DEVICE_FUNC void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode>(row, col);
}
static Index rowIndexByOuterInner(Index outer, Index inner)
EIGEN_DEVICE_FUNC static Index rowIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::RowsAtCompileTime) == 1 ? 0
: int(Traits::ColsAtCompileTime) == 1 ? inner
: int(Traits::Flags)&RowMajorBit ? outer
: int(DstEvaluatorType::Flags)&RowMajorBit ? outer
: inner;
}
static Index colIndexByOuterInner(Index outer, Index inner)
EIGEN_DEVICE_FUNC static Index colIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::ColsAtCompileTime) == 1 ? 0
: int(Traits::RowsAtCompileTime) == 1 ? inner
: int(Traits::Flags)&RowMajorBit ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? inner
: outer;
}
@ -617,13 +625,13 @@ protected:
DstXprType& m_dstExpr;
};
/***************************************************************************
* Part 5 : Entry point for dense rectangular assignment
***************************************************************************/
template<typename DstXprType, typename SrcXprType, typename Functor>
void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
{
#ifdef EIGEN_DEBUG_ASSIGN
// TODO these traits should be computed from information provided by the evaluators
internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug();
#endif
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
typedef typename evaluator<DstXprType>::type DstEvaluatorType;
@ -639,201 +647,147 @@ void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, co
}
template<typename DstXprType, typename SrcXprType>
void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar>());
}
/***************************************************************************
* Part 5 : Entry points
* Part 6 : Generic assignment
***************************************************************************/
// Based on DenseBase::LazyAssign()
// The following functions are just for testing and they are meant to be moved to operator= and the likes.
// Based on the respective shapes of the destination and source,
// the class AssignmentKind determine the kind of assignment mechanism.
// AssignmentKind must define a Kind typedef.
template<typename DstShape, typename SrcShape> struct AssignmentKind;
template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst,
const EigenBase<SrcXprType>& src)
{
return noalias_copy_using_evaluator(dst.expression(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
}
// Assignement kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
template<typename XprType, int AssumeAliasing = evaluator_traits<XprType>::AssumeAliasing>
struct AddEvalIfAssumingAliasing;
template<typename XprType>
struct AddEvalIfAssumingAliasing<XprType, 0>
{
static const XprType& run(const XprType& xpr)
{
return xpr;
}
};
template<typename XprType>
struct AddEvalIfAssumingAliasing<XprType, 1>
{
static const EvalToTemp<XprType> run(const XprType& xpr)
{
return EvalToTemp<XprType>(xpr);
}
};
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
return noalias_copy_using_evaluator(dst.const_cast_derived(),
AddEvalIfAssumingAliasing<SrcXprType>::run(src.derived()),
func
);
}
// this mimics operator=
template<typename DstXprType, typename SrcXprType>
EIGEN_STRONG_INLINE
const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src)
{
return copy_using_evaluator(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>());
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& noalias_copy_using_evaluator(const PlainObjectBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
#ifdef EIGEN_DEBUG_ASSIGN
internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug();
#endif
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size())
: (dst.rows() == src.rows() && dst.cols() == src.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
#else
dst.const_cast_derived().resizeLike(src.derived());
#endif
call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func);
return dst.derived();
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_STRONG_INLINE
const DstXprType& noalias_copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func)
{
call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func);
return dst.derived();
}
// Based on DenseBase::swap()
// TODO: Check whether we need to do something special for swapping two
// Arrays or Matrices. (Jitse)
// Overload default assignPacket behavior for swapping them
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
class swap_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> >
{
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> > Base;
typedef typename DstEvaluatorTypeT::PacketScalar PacketScalar;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::DstXprType DstXprType;
swap_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, DstXprType& dstExpr)
: Base(dst, src, swap_assign_op<Scalar>(), dstExpr)
{}
template<int StoreMode, int LoadMode>
void assignPacket(Index row, Index col)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(row,col), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(row,col));
}
template<int StoreMode, int LoadMode>
void assignPacket(Index index)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(index), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(index));
}
// TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
template<int StoreMode, int LoadMode>
void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = Base::rowIndexByOuterInner(outer, inner);
Index col = Base::colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode>(row, col);
}
};
template<typename DstXprType, typename SrcXprType>
void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src)
{
// TODO there is too much redundancy with call_dense_assignment_loop
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
typedef typename evaluator<DstXprType>::type DstEvaluatorType;
typedef typename evaluator<SrcXprType>::type SrcEvaluatorType;
DstEvaluatorType dstEvaluator(dst);
SrcEvaluatorType srcEvaluator(src);
template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
typedef swap_kernel<DstEvaluatorType,SrcEvaluatorType> Kernel;
Kernel kernel(dstEvaluator, srcEvaluator, dst.const_cast_derived());
// This is the main assignment class
template< typename DstXprType, typename SrcXprType, typename Functor,
typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
typename Scalar = typename DstXprType::Scalar>
struct Assignment;
// The only purpose of this call_assignment() function is to deal with noalias() / AssumeAliasing and automatic transposition.
// Indeed, I (Gael) think that this concept of AssumeAliasing was a mistake, and it makes thing quite complicated.
// So this intermediate function removes everything related to AssumeAliasing such that Assignment
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
}
// Deal with AssumeAliasing
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<evaluator_traits<Src>::AssumeAliasing==1, void*>::type = 0)
{
typename plain_matrix_type<Src>::type tmp(src);
call_assignment_no_alias(dst, tmp, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<evaluator_traits<Src>::AssumeAliasing==0, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
// by-pass AssumeAliasing
// FIXME the const version should probably not be needed
// 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 void call_assignment(const NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::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(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1))
&& int(Dst::SizeAtCompileTime) != 1
};
typename Dst::Index dstRows = NeedToTranspose ? src.cols() : src.rows();
typename Dst::Index dstCols = NeedToTranspose ? src.rows() : src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
dense_assignment_loop<Kernel>::run(kernel);
}
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst>::type ActualDstTypeCleaned;
typedef typename internal::conditional<NeedToTranspose, Transpose<Dst>, Dst&>::type ActualDstType;
ActualDstType actualDst(dst);
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
// Based on MatrixBase::operator+= (in CwiseBinaryOp.h)
template<typename DstXprType, typename SrcXprType>
void add_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& src)
// TODO this line is commented to allow matrix = permutation
// Actually, the "Scalar" type for a permutation matrix does not really make sense,
// perhaps it could be void, and EIGEN_CHECK_BINARY_COMPATIBILIY could allow micing void with anything...?
// EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment_no_alias(Dst& dst, const Src& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>());
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar>());
}
// Based on ArrayBase::operator+=
template<typename DstXprType, typename SrcXprType>
void add_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Scalar>
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>());
}
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
#ifndef EIGEN_NO_DEBUG
internal::check_for_aliasing(dst, src);
#endif
call_dense_assignment_loop(dst, src, func);
}
};
// TODO: Add add_assign_using_evaluator for EigenBase ? (Jitse)
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& src)
// Generic assignment through evalTo.
// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Scalar>
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void subtract_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void multiply_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), mul_assign_op<Scalar>());
}
template<typename DstXprType, typename SrcXprType>
void divide_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src)
{
typedef typename DstXprType::Scalar Scalar;
copy_using_evaluator(dst.derived(), src.derived(), div_assign_op<Scalar>());
}
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);
}
};
} // namespace internal

25
Eigen/src/Core/BandMatrix.h
View File

@ -204,7 +204,7 @@ class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Sub
typedef typename internal::traits<BandMatrix>::Index Index;
typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
: m_coeffs(1+supers+subs,cols),
m_rows(rows), m_supers(supers), m_subs(subs)
{
@ -266,7 +266,7 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::Index Index;
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)
{
@ -314,7 +314,7 @@ class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint
typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
typedef typename Base::Index Index;
public:
TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
inline typename Base::template DiagonalIntReturnType<1>::Type super()
{ return Base::template diagonal<1>(); }
@ -327,6 +327,25 @@ class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint
protected:
};
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> >
{
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> >
{
typedef BandShape Shape;
};
template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen

34
Eigen/src/Core/Block.h
View File

@ -68,6 +68,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
@ -80,18 +81,14 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprTyp
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
&& (InnerStrideAtCompileTime == 1)
? PacketAccessBit : 0,
MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % EIGEN_ALIGN_BYTES) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (traits<XprType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
// IsAligned is needed by MapBase's assertions
// We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
IsAligned = 0,
// FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
DirectAccessBit |
MaskPacketAccessBit |
MaskAlignedBit),
Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
Flags = (traits<XprType>::Flags & DirectAccessBit) | FlagsLvalueBit | FlagsRowMajorBit
// FIXME DirectAccessBit should not be handled by expressions
};
};
@ -111,6 +108,8 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef typename internal::remove_all<XprType>::type NestedExpression;
/** Column or Row constructor
*/
@ -179,7 +178,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
@ -333,6 +332,9 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
enum {
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
};
public:
typedef MapBase<BlockType> Base;
@ -343,9 +345,8 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: Base(internal::const_cast_ptr(&xpr.coeffRef(
(BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0,
(BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)),
: Base(xpr.data() + i * ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
|| ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
BlockRows==1 ? 1 : xpr.rows(),
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
@ -357,7 +358,8 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
m_xpr(xpr)
{
init();
}
@ -368,7 +370,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
m_xpr(xpr)
{
init();

32
Eigen/src/Core/BooleanRedux.h
View File

@ -17,9 +17,10 @@ namespace internal {
template<typename Derived, int UnrollCount>
struct all_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
@ -43,11 +44,12 @@ struct all_unroller<Derived, Dynamic>
template<typename Derived, int UnrollCount>
struct any_unroller
{
typedef typename Derived::ExpressionTraits Traits;
enum {
col = (UnrollCount-1) / Derived::RowsAtCompileTime,
row = (UnrollCount-1) % Derived::RowsAtCompileTime
col = (UnrollCount-1) / Traits::RowsAtCompileTime,
row = (UnrollCount-1) % Traits::RowsAtCompileTime
};
static inline bool run(const Derived &mat)
{
return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col);
@ -78,19 +80,21 @@ struct any_unroller<Derived, Dynamic>
template<typename Derived>
inline bool DenseBase<Derived>::all() const
{
typedef typename internal::evaluator<Derived>::type Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& Evaluator::CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::all_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived());
return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (!coeff(i, j)) return false;
if (!evaluator.coeff(i, j)) return false;
return true;
}
}
@ -102,19 +106,21 @@ inline bool DenseBase<Derived>::all() const
template<typename Derived>
inline bool DenseBase<Derived>::any() const
{
typedef typename internal::evaluator<Derived>::type Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& Evaluator::CoeffReadCost != Dynamic
&& NumTraits<Scalar>::AddCost != Dynamic
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
&& SizeAtCompileTime * (Evaluator::CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
Evaluator evaluator(derived());
if(unroll)
return internal::any_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived());
return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(evaluator);
else
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if (coeff(i, j)) return true;
if (evaluator.coeff(i, j)) return true;
return false;
}
}

884
Eigen/src/Core/CoreEvaluators.h
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File diff suppressed because it is too large Load Diff

145
Eigen/src/Core/CoreIterators.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -15,47 +15,116 @@ namespace Eigen {
/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
*/
/** \ingroup SparseCore_Module
* \class InnerIterator
* \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
*
* todo
namespace internal {
template<typename XprType, typename EvaluatorKind>
class inner_iterator_selector;
}
/** \class InnerIterator
* \brief An InnerIterator allows to loop over the element of any matrix expression.
*
* \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
*
* TODO: add a usage example
*/
// generic version for dense matrix and expressions
template<typename Derived> class DenseBase<Derived>::InnerIterator
template<typename XprType>
class InnerIterator
{
protected:
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer)
: m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize())
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner)
: m_expression.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const Derived& m_expression;
Index m_inner;
const Index m_outer;
const Index m_end;
protected:
typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
typedef typename internal::evaluator<XprType>::type EvaluatorType;
typedef typename internal::traits<XprType>::Scalar Scalar;
typedef typename internal::traits<XprType>::Index Index;
public:
/** Construct an iterator over the \a outerId -th row or column of \a xpr */
InnerIterator(const XprType &xpr, const Index &outerId)
: m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
{}
/// \returns the value of the current coefficient.
EIGEN_STRONG_INLINE Scalar value() const { return m_iter.value(); }
/** Increment the iterator \c *this to the next non-zero coefficient.
* Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
*/
EIGEN_STRONG_INLINE InnerIterator& operator++() { m_iter.operator++(); return *this; }
/// \returns the column or row index of the current coefficient.
EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
/// \returns the row index of the current coefficient.
EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
/// \returns the column index of the current coefficient.
EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
/// \returns \c true if the iterator \c *this still references a valid coefficient.
EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
protected:
EvaluatorType m_eval;
IteratorType m_iter;
private:
// If you get here, then you're not using the right InnerIterator type, e.g.:
// SparseMatrix<double,RowMajor> A;
// SparseMatrix<double>::InnerIterator it(A,0);
template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
};
namespace internal {
// Generic inner iterator implementation for dense objects
template<typename XprType>
class inner_iterator_selector<XprType, IndexBased>
{
protected:
typedef typename evaluator<XprType>::type EvaluatorType;
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::Index Index;
enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
: m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
{}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
: m_eval.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
EIGEN_STRONG_INLINE Index index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const EvaluatorType& m_eval;
Index m_inner;
const Index m_outer;
const Index m_end;
};
// For iterator-based evaluator, inner-iterator is already implemented as
// evaluator<>::InnerIterator
template<typename XprType>
class inner_iterator_selector<XprType, IteratorBased>
: public evaluator<XprType>::InnerIterator
{
protected:
typedef typename evaluator<XprType>::InnerIterator Base;
typedef typename evaluator<XprType>::type EvaluatorType;
typedef typename traits<XprType>::Index Index;
public:
EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
: Base(eval, outerId)
{}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COREITERATORS_H

112
Eigen/src/Core/CwiseBinaryOp.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
@ -56,8 +56,9 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
typename Rhs::Scalar
)
>::type Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind,
BinaryOp>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
typedef typename Lhs::Nested LhsNested;
@ -65,60 +66,37 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
enum {
LhsCoeffReadCost = _LhsNested::CoeffReadCost,
RhsCoeffReadCost = _RhsNested::CoeffReadCost,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit),
Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
HereditaryBits
| (int(LhsFlags) & int(RhsFlags) &
( AlignedBit
| (StorageOrdersAgree ? LinearAccessBit : 0)
| (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
)
)
),
Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost
Flags = _LhsNested::Flags & RowMajorBit
};
};
} // end namespace internal
// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor
// that would take two operands of different types. If there were such an example, then this check should be
// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as
// currently they take only one typename Scalar template parameter.
// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
// add together a float matrix and a double matrix.
#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
EIGEN_STATIC_ASSERT((internal::functor_is_product_like<BINOP>::ret \
? int(internal::scalar_product_traits<LHS, RHS>::Defined) \
: int(internal::is_same<LHS, RHS>::value)), \
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl;
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOp : internal::no_assignment_operator,
template<typename BinaryOp, typename LhsType, typename RhsType>
class CwiseBinaryOp :
public CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<RhsType>::StorageKind,
BinaryOp>::ret>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<LhsType>::type Lhs;
typedef typename internal::remove_all<RhsType>::type Rhs;
typedef typename CwiseBinaryOpImpl<
BinaryOp, Lhs, Rhs,
typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::ret>::Base Base;
BinaryOp, LhsType, RhsType,
typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
typename internal::traits<Rhs>::StorageKind,
BinaryOp>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
typedef typename internal::nested<Lhs>::type LhsNested;
typedef typename internal::nested<Rhs>::type RhsNested;
typedef typename internal::nested<LhsType>::type LhsNested;
typedef typename internal::nested<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
@ -165,43 +143,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
const BinaryOp m_functor;
};
template<typename BinaryOp, typename Lhs, typename Rhs>
class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
: public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
// Generic API dispatcher
template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
class CwiseBinaryOpImpl
: public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().lhs().coeff(rowId, colId),
derived().rhs().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(rowId, colId),
derived().rhs().template packet<LoadMode>(rowId, colId));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().lhs().coeff(index),
derived().rhs().coeff(index));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index),
derived().rhs().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
};
/** replaces \c *this by \c *this - \a other.
@ -213,8 +161,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
{
SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
return derived();
}
@ -227,8 +174,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
{
SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived());
tmp = other.derived();
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
return derived();
}

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

@ -35,19 +35,13 @@ template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
{
enum {
Flags = (traits<PlainObjectType>::Flags
& ( HereditaryBits
| (functor_has_linear_access<NullaryOp>::ret ? LinearAccessBit : 0)
| (functor_traits<NullaryOp>::PacketAccess ? PacketAccessBit : 0)))
| (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
CoeffReadCost = functor_traits<NullaryOp>::Cost
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
}
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : internal::no_assignment_operator,
public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type
class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
{
public:

54
Eigen/src/Core/CwiseUnaryOp.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
@ -44,10 +44,7 @@ struct traits<CwiseUnaryOp<UnaryOp, XprType> >
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & (
HereditaryBits | LinearAccessBit | AlignedBit
| (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost
Flags = _XprTypeNested::Flags & RowMajorBit
};
};
}
@ -56,16 +53,16 @@ template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
template<typename UnaryOp, typename XprType>
class CwiseUnaryOp : internal::no_assignment_operator,
public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>
class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
{
public:
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
typedef typename internal::remove_all<XprType>::type NestedExpression;
EIGEN_DEVICE_FUNC
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
explicit inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC
@ -92,42 +89,13 @@ class CwiseUnaryOp : internal::no_assignment_operator,
const UnaryOp m_functor;
};
// This is the generic implementation for dense storage.
// It can be used for any expression types implementing the dense concept.
template<typename UnaryOp, typename XprType>
class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
: public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
// Generic API dispatcher
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl
: public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
{
public:
typedef CwiseUnaryOp<UnaryOp, XprType> Derived;
typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().nestedExpression().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(rowId, colId));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index));
}
public:
typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
};
} // end namespace Eigen

44
Eigen/src/Core/CwiseUnaryView.h
View File

@ -37,8 +37,8 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)),
CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost,
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
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
@ -62,8 +62,9 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp())
explicit inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
@ -88,6 +89,15 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
ViewOp m_functor;
};
// Generic API dispatcher
template<typename ViewOp, typename XprType, typename StorageKind>
class CwiseUnaryViewImpl
: public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType> >::type Base;
};
template<typename ViewOp, typename MatrixType>
class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
: public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type
@ -100,38 +110,18 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
inline Scalar* data() { return &coeffRef(0); }
inline const Scalar* data() const { return &coeff(0); }
EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col));
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index));
}
};
} // end namespace Eigen

54
Eigen/src/Core/DenseBase.h
View File

@ -50,7 +50,11 @@ template<typename Derived> class DenseBase
using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*;
class InnerIterator;
/** Inner iterator type to iterate over the coefficients of a row or column.
* \sa class InnerIterator
*/
typedef Eigen::InnerIterator<Derived> InnerIterator;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
@ -74,16 +78,6 @@ template<typename Derived> class DenseBase
using Base::colIndexByOuterInner;
using Base::coeff;
using Base::coeffByOuterInner;
using Base::packet;
using Base::packetByOuterInner;
using Base::writePacket;
using Base::writePacketByOuterInner;
using Base::coeffRef;
using Base::coeffRefByOuterInner;
using Base::copyCoeff;
using Base::copyCoeffByOuterInner;
using Base::copyPacket;
using Base::copyPacketByOuterInner;
using Base::operator();
using Base::operator[];
using Base::x;
@ -169,16 +163,11 @@ template<typename Derived> class DenseBase
InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
: int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
/**< This is a rough measure of how expensive it is to read one coefficient from
* this expression.
*/
InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
};
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 0 };
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
@ -278,7 +267,8 @@ template<typename Derived> class DenseBase
Derived& operator=(const ReturnByValue<OtherDerived>& func);
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** Copies \a other into *this without evaluating other. \returns a reference to *this. */
/** Copies \a other into *this without evaluating other. \returns a reference to *this.
* \deprecated */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& lazyAssign(const DenseBase<OtherDerived>& other);
@ -287,27 +277,24 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const Scalar& s);
// TODO flagged is temporarly disabled. It seems useless now
template<unsigned int Added,unsigned int Removed>
const Flagged<Derived, Added, Removed> flagged() const;
EIGEN_DEPRECATED
const Derived& flagged() const
{ return derived(); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
Eigen::Transpose<Derived> transpose();
TransposeReturnType transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC
void transposeInPlace();
#ifndef EIGEN_NO_DEBUG
protected:
template<typename OtherDerived>
void checkTransposeAliasing(const OtherDerived& other) const;
public:
#endif
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
@ -387,16 +374,17 @@ template<typename Derived> class DenseBase
// size types on MSVC.
return typename internal::eval<Derived>::type(derived());
}
/** swaps *this with the expression \a other.
*
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(const DenseBase<OtherDerived>& other,
int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
void swap(const DenseBase<OtherDerived>& other)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
}
/** swaps *this with the matrix or array \a other.
@ -406,10 +394,10 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC
void swap(PlainObjectBase<OtherDerived>& other)
{
SwapWrapper<Derived>(derived()).lazyAssign(other.derived());
eigen_assert(rows()==other.rows() && cols()==other.cols());
call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
}
EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();

169
Eigen/src/Core/DenseCoeffsBase.h
View File

@ -97,8 +97,8 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
&& col >= 0 && col < cols());
return typename internal::evaluator<Derived>::type(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC
@ -117,7 +117,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeff(row, col);
return coeff(row, col);
}
/** Short version: don't use this function, use
@ -140,7 +140,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
return typename internal::evaluator<Derived>::type(derived()).coeff(index);
}
@ -159,7 +159,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** \returns the coefficient at given index.
@ -177,7 +177,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** equivalent to operator[](0). */
@ -217,9 +217,8 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().template packet<LoadMode>(row,col);
eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return typename internal::evaluator<Derived>::type(derived()).template packet<LoadMode>(row,col);
}
@ -245,7 +244,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return derived().template packet<LoadMode>(index);
return typename internal::evaluator<Derived>::type(derived()).template packet<LoadMode>(index);
}
protected:
@ -325,8 +324,8 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
&& col >= 0 && col < cols());
return typename internal::evaluator<Derived>::type(derived()).coeffRef(row,col);
}
EIGEN_DEVICE_FUNC
@ -348,7 +347,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
return coeffRef(row, col);
}
@ -372,7 +371,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return typename internal::evaluator<Derived>::type(derived()).coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@ -389,7 +388,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@ -406,7 +405,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** equivalent to operator[](0). */
@ -432,144 +431,6 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
w() { return (*this)[3]; }
/** \internal
* Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit.
*
* The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& val)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
val);
}
/** \internal
* Stores the given packet of coefficients, at the given index in this expression. It is your responsibility
* to ensure that a packet really starts there. This method is only available on expressions having the
* PacketAccessBit and the LinearAccessBit.
*
* The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select
* the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
* starting at an address which is a multiple of the packet size.
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,val);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Copies the coefficient at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().coeffRef(row, col) = other.derived().coeff(row, col);
}
/** \internal Copies the coefficient at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().coeffRef(index) = other.derived().coeff(index);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().copyCoeff(row, col, other);
}
/** \internal Copies the packet at position (row,col) of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row, col,
other.derived().template packet<LoadMode>(row, col));
}
/** \internal Copies the packet at the given index of other into *this.
*
* This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code
* with usual assignments.
*
* Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox.
*/
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,
other.derived().template packet<LoadMode>(index));
}
/** \internal */
template<typename OtherDerived, int StoreMode, int LoadMode>
EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other)
{
const Index row = rowIndexByOuterInner(outer,inner);
const Index col = colIndexByOuterInner(outer,inner);
// derived() is important here: copyCoeff() may be reimplemented in Derived!
derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other);
}
#endif
};
/** \brief Base class providing direct read-only coefficient access to matrices and arrays.

16
Eigen/src/Core/DenseStorage.h
View File

@ -130,7 +130,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
EIGEN_DEVICE_FUNC
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {}
@ -155,7 +155,7 @@ template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0
{
public:
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC DenseStorage(internal::constructor_without_unaligned_array_assert) {}
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(DenseIndex,DenseIndex,DenseIndex) {}
@ -186,7 +186,7 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
DenseIndex m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
@ -217,7 +217,7 @@ template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Si
DenseIndex m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
DenseStorage& operator=(const DenseStorage& other)
@ -246,7 +246,7 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
DenseIndex m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
@ -276,7 +276,7 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
DenseIndex m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows), m_cols(nbCols)
@ -350,7 +350,7 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
DenseIndex m_cols;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
DenseStorage(DenseIndex size, DenseIndex, DenseIndex nbCols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
DenseStorage(const DenseStorage& other)
@ -416,7 +416,7 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
DenseIndex m_rows;
public:
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
DenseStorage(const DenseStorage& other)

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

@ -52,8 +52,7 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
MaxColsAtCompileTime = 1,
MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit,
CoeffReadCost = _MatrixTypeNested::CoeffReadCost,
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
@ -71,17 +70,15 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
EIGEN_DEVICE_FUNC
inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{
EIGEN_USING_STD_MATH(min);
return m_index.value()<0 ? (min)(Index(m_matrix.cols()),Index(m_matrix.rows()+m_index.value()))
: (min)(Index(m_matrix.rows()),Index(m_matrix.cols()-m_index.value()));
return m_index.value()<0 ? numext::mini(Index(m_matrix.cols()),Index(m_matrix.rows()+m_index.value()))
: numext::mini(Index(m_matrix.rows()),Index(m_matrix.cols()-m_index.value()));
}
EIGEN_DEVICE_FUNC
@ -149,14 +146,14 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename MatrixType::Nested>::type&
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
EIGEN_DEVICE_FUNC
int index() const
inline Index index() const
{
return m_index.value();
}
@ -173,7 +170,7 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; }
// triger a compile time error is someone try to call packet
// trigger a compile time error is someone try to call packet
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
};
@ -190,7 +187,7 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
@ -213,18 +210,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<DynamicIndex>::Type
inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
return DiagonalDynamicIndexReturnType(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<DynamicIndex>::Type
inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
return ConstDiagonalDynamicIndexReturnType(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@ -239,20 +236,20 @@ MatrixBase<Derived>::diagonal(Index index) const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal()
{
return derived();
return typename DiagonalIndexReturnType<Index_>::Type(derived());
}
/** This is the const version of diagonal<int>(). */
template<typename Derived>
template<int Index>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type
template<int Index_>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
MatrixBase<Derived>::diagonal() const
{
return derived();
return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
}
} // end namespace Eigen

88
Eigen/src/Core/DiagonalMatrix.h
View File

@ -30,7 +30,7 @@ class DiagonalBase : public EigenBase<Derived>
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
IsVectorAtCompileTime = 0,
Flags = 0
Flags = NoPreferredStorageOrderBit
};
typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
@ -44,18 +44,7 @@ class DiagonalBase : public EigenBase<Derived>
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
EIGEN_DEVICE_FUNC
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
@ -66,44 +55,37 @@ class DiagonalBase : public EigenBase<Derived>
EIGEN_DEVICE_FUNC
inline Index cols() const { return diagonal().size(); }
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename MatrixDerived>
EIGEN_DEVICE_FUNC
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
const Product<Derived,MatrixDerived,LazyProduct>
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), 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 DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
inline const InverseReturnType
inverse() const
{
return diagonal().cwiseInverse();
return InverseReturnType(diagonal().cwiseInverse());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > ScalarMultipleReturnType;
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
inline const ScalarMultipleReturnType
operator*(const Scalar& scalar) const
{
return diagonal() * scalar;
return ScalarMultipleReturnType(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
friend inline const ScalarMultipleReturnType
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return other.diagonal() * scalar;
return ScalarMultipleReturnType(other.diagonal() * scalar);
}
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@ -125,10 +107,10 @@ struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> >
: traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
typedef Dense StorageKind;
typedef DiagonalShape StorageKind;
typedef DenseIndex Index;
enum {
Flags = LvalueBit
Flags = LvalueBit | NoPreferredStorageOrderBit
};
};
}
@ -164,7 +146,7 @@ class DiagonalMatrix
/** Constructs a diagonal matrix with given dimension */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
EIGEN_DEVICE_FUNC
@ -249,13 +231,14 @@ struct traits<DiagonalWrapper<_DiagonalVectorType> >
typedef _DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::Index Index;
typedef typename DiagonalVectorType::StorageKind StorageKind;
typedef DiagonalShape StorageKind;
typedef typename traits<DiagonalVectorType>::XprKind XprKind;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
Flags = traits<DiagonalVectorType>::Flags & LvalueBit
MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
Flags = (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
};
};
}
@ -272,7 +255,7 @@ class DiagonalWrapper
/** Constructor from expression of diagonal coefficients to wrap. */
EIGEN_DEVICE_FUNC
inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
@ -295,7 +278,7 @@ template<typename Derived>
inline const DiagonalWrapper<const Derived>
MatrixBase<Derived>::asDiagonal() const
{
return derived();
return DiagonalWrapper<const Derived>(derived());
}
/** \returns true if *this is approximately equal to a diagonal matrix,
@ -326,6 +309,33 @@ bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
return true;
}
namespace internal {
template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense, Scalar>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
{
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

106
Eigen/src/Core/DiagonalProduct.h
View File

@ -13,116 +13,14 @@
namespace Eigen {
namespace internal {
template<typename MatrixType, typename DiagonalType, int ProductOrder>
struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
: traits<MatrixType>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & 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,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_LinearAccessMask = (RowsAtCompileTime==1 || ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0) | AlignedBit,//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
};
};
}
template<typename MatrixType, typename DiagonalType, int ProductOrder>
class DiagonalProduct : internal::no_assignment_operator,
public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
{
public:
typedef MatrixBase<DiagonalProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct)
inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal)
: m_matrix(matrix), m_diagonal(diagonal)
{
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
}
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return coeff(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
{
enum {
StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
};
const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return packet<LoadMode>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
protected:
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const
{
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id)));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && (((InnerSize%16) == 0) || (int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit)==AlignedBit) ? Aligned : Unaligned)
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));
}
typename MatrixType::Nested m_matrix;
typename DiagonalType::Nested m_diagonal;
};
/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
*/
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
inline const Product<Derived, DiagonalDerived, LazyProduct>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived());
return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
}
} // end namespace Eigen

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

@ -113,8 +113,7 @@ template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested<Derived>::type Nested;
typedef typename internal::remove_reference<Nested>::type _Nested;
typedef typename internal::nested_eval<Derived,2>::type _Nested;
_Nested n(derived());
return n / n.norm();
}
@ -206,8 +205,8 @@ template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
typename internal::nested_eval<Derived,2>::type nested(derived());
typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}

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

@ -121,7 +121,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
other.derived().evalTo(derived());
call_assignment(derived(), other.derived());
return derived();
}
@ -129,7 +129,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
other.derived().addTo(derived());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
return derived();
}
@ -137,7 +137,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
other.derived().subTo(derived());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
return derived();
}

30
Eigen/src/Core/Flagged.h
View File

@ -48,39 +48,39 @@ template<typename ExpressionType, unsigned int Added, unsigned int Removed> clas
ExpressionType, const ExpressionType&>::type ExpressionTypeNested;
typedef typename ExpressionType::InnerIterator InnerIterator;
inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {}
explicit inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index outerStride() const { return m_matrix.outerStride(); }
inline Index innerStride() const { return m_matrix.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_matrix.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_matrix.innerStride(); }
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index row, Index col) const
{
return m_matrix.coeff(row, col);
}
inline CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(index);
}
inline const Scalar& coeffRef(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline const Scalar& coeffRef(Index index) const
EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(row, col);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(index);
}
@ -109,13 +109,13 @@ template<typename ExpressionType, unsigned int Added, unsigned int Removed> clas
m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x);
}
const ExpressionType& _expression() const { return m_matrix; }
EIGEN_DEVICE_FUNC const ExpressionType& _expression() const { return m_matrix; }
template<typename OtherDerived>
typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const;
protected:
ExpressionTypeNested m_matrix;
@ -132,7 +132,7 @@ template<unsigned int Added,unsigned int Removed>
inline const Flagged<Derived, Added, Removed>
DenseBase<Derived>::flagged() const
{
return derived();
return Flagged<Derived, Added, Removed>(derived());
}
} // end namespace Eigen

24
Eigen/src/Core/ForceAlignedAccess.h
View File

@ -39,29 +39,29 @@ template<typename ExpressionType> class ForceAlignedAccess
typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_expression.innerStride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
@ -90,7 +90,7 @@ template<typename ExpressionType> class ForceAlignedAccess
m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType& m_expression;
@ -127,7 +127,7 @@ template<bool Enable>
inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type
MatrixBase<Derived>::forceAlignedAccessIf() const
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
/** \returns an expression of *this with forced aligned access if \a Enable is true.
@ -138,7 +138,7 @@ template<bool Enable>
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type
MatrixBase<Derived>::forceAlignedAccessIf()
{
return derived();
return derived(); // FIXME This should not work but apparently is never used
}
} // end namespace Eigen

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

@ -22,10 +22,9 @@ struct isApprox_selector
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
EIGEN_USING_STD_MATH(min);
typename internal::nested<Derived,2>::type nested(x);
typename internal::nested<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
typename internal::nested_eval<Derived,2>::type nested(x);
typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
}
};

418
Eigen/src/Core/GeneralProduct.h
View File

@ -13,28 +13,6 @@
namespace Eigen {
/** \class GeneralProduct
* \ingroup Core_Module
*
* \brief Expression of the product of two general matrices or vectors
*
* \param LhsNested the type used to store the left-hand side
* \param RhsNested the type used to store the right-hand side
* \param ProductMode the type of the product
*
* This class represents an expression of the product of two general matrices.
* We call a general matrix, a dense matrix with full storage. For instance,
* This excludes triangular, selfadjoint, and sparse matrices.
* It is the return type of the operator* between general matrices. Its template
* arguments are determined automatically by ProductReturnType. Therefore,
* GeneralProduct should never be used direclty. To determine the result type of a
* function which involves a matrix product, use ProductReturnType::Type.
*
* \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
*/
template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value>
class GeneralProduct;
enum {
Large = 2,
Small = 3
@ -59,14 +37,14 @@ template<typename Lhs, typename Rhs> struct product_type
typedef typename remove_all<Lhs>::type _Lhs;
typedef typename remove_all<Rhs>::type _Rhs;
enum {
MaxRows = _Lhs::MaxRowsAtCompileTime,
Rows = _Lhs::RowsAtCompileTime,
MaxCols = _Rhs::MaxColsAtCompileTime,
Cols = _Rhs::ColsAtCompileTime,
MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime,
_Rhs::MaxRowsAtCompileTime),
Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime,
_Rhs::RowsAtCompileTime)
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)
};
// the splitting into different lines of code here, introducing the _select enums and the typedef below,
@ -81,7 +59,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@ -97,6 +76,31 @@ public:
#endif
};
// template<typename Lhs, typename Rhs> struct product_tag
// {
// private:
//
// typedef typename remove_all<Lhs>::type _Lhs;
// typedef typename remove_all<Rhs>::type _Rhs;
// enum {
// Rows = _Lhs::RowsAtCompileTime,
// Cols = _Rhs::ColsAtCompileTime,
// Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime, _Rhs::RowsAtCompileTime)
// };
//
// enum {
// rows_select = Rows==1 ? int(Rows) : int(Large),
// cols_select = Cols==1 ? int(Cols) : int(Large),
// depth_select = Depth==1 ? int(Depth) : int(Large)
// };
// typedef product_type_selector<rows_select, cols_select, depth_select> selector;
//
// public:
// enum {
// ret = selector::ret
// };
//
// };
/* The following allows to select the kind of product at compile time
* based on the three dimensions of the product.
@ -127,54 +131,6 @@ template<> struct product_type_selector<Large,Large,Small> { enum
} // end namespace internal
/** \class ProductReturnType
* \ingroup Core_Module
*
* \brief Helper class to get the correct and optimized returned type of operator*
*
* \param Lhs the type of the left-hand side
* \param Rhs the type of the right-hand side
* \param ProductMode the type of the product (determined automatically by internal::product_mode)
*
* This class defines the typename Type representing the optimized product expression
* between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type
* is the recommended way to define the result type of a function returning an expression
* which involve a matrix product. The class Product should never be
* used directly.
*
* \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
*/
template<typename Lhs, typename Rhs, int ProductType>
struct ProductReturnType
{
// TODO use the nested type to reduce instanciations ????
// typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
// typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type;
};
template<typename Lhs, typename Rhs>
struct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode>
{
typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type;
};
template<typename Lhs, typename Rhs>
struct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
{
typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type;
};
// this is a workaround for sun CC
template<typename Lhs, typename Rhs>
struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
{};
/***********************************************************************
* Implementation of Inner Vector Vector Product
***********************************************************************/
@ -186,119 +142,10 @@ struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedPr
// product ends up to a row-vector times col-vector product... To tackle this use
// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> >
: traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, InnerProduct>
: internal::no_assignment_operator,
public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1>
{
typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base;
public:
GeneralProduct(const Lhs& lhs, const Rhs& rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
}
/** Convertion to scalar */
operator const typename Base::Scalar() const {
return Base::coeff(0,0);
}
};
/***********************************************************************
* Implementation of Outer Vector Vector Product
***********************************************************************/
namespace internal {
// Column major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&)
{
typedef typename Dest::Index Index;
// FIXME make sure lhs is sequentially stored
// FIXME not very good if rhs is real and lhs complex while alpha is real too
const Index cols = dest.cols();
for (Index j=0; j<cols; ++j)
func(dest.col(j), prod.rhs().coeff(0,j) * prod.lhs());
}
// Row major
template<typename ProductType, typename Dest, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) {
typedef typename Dest::Index Index;
// FIXME make sure rhs is sequentially stored
// FIXME not very good if lhs is real and rhs complex while alpha is real too
const Index rows = dest.rows();
for (Index i=0; i<rows; ++i)
func(dest.row(i), prod.lhs().coeff(i,0) * prod.rhs());
}
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> >
{};
}
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, OuterProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
{
template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } };
struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
struct adds {
Scalar m_scale;
adds(const Scalar& s) : m_scale(s) {}
template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const {
dst.const_cast_derived() += m_scale * src;
}
};
template<typename Dest>
inline void evalTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>());
}
template<typename Dest>
inline void addTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>());
}
template<typename Dest>
inline void subTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>());
}
};
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@ -312,60 +159,13 @@ class GeneralProduct<Lhs, Rhs, OuterProduct>
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> >
: traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> >
{};
template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_selector;
struct gemv_dense_sense_selector;
} // end namespace internal
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, GemvProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs>
{
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
GeneralProduct(const Lhs& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_rhs)
{
// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value),
// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols());
internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha);
}
};
namespace internal {
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct>
(prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha);
}
};
template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
template<typename Scalar,int Size,int MaxSize>
@ -402,27 +202,43 @@ struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
#endif
};
template<> struct gemv_selector<OnTheRight,ColMajor,true>
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_dense_sense_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static inline void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::Index Index;
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::RealScalar RealScalar;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_dense_sense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(rhs.transpose(), lhs.transpose(), destT, alpha);
}
};
template<> struct gemv_dense_sense_selector<OnTheRight,ColMajor,true>
{
template<typename Lhs, typename Rhs, typename Dest>
static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
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>, Aligned> MappedDest;
ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs());
ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs());
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
@ -477,34 +293,35 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,true>
template<> struct gemv_dense_sense_selector<OnTheRight,RowMajor,true>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
typedef typename ProductType::Scalar ResScalar;
typedef typename ProductType::Index Index;
typedef typename ProductType::ActualLhsType ActualLhsType;
typedef typename ProductType::ActualRhsType ActualRhsType;
typedef typename ProductType::_ActualRhsType _ActualRhsType;
typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
typedef typename Dest::Index Index;
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
// on, the other hand it is good for the cache to pack the vector anyways...
DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1
DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
};
gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
@ -515,7 +332,7 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
Index size = actualRhs.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
}
typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
@ -530,29 +347,29 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
}
};
template<> struct gemv_selector<OnTheRight,ColMajor,false>
template<> struct gemv_dense_sense_selector<OnTheRight,ColMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure dest is sequentially stored in memory, otherwise use a temp
const Index size = prod.rhs().rows();
const Index size = rhs.rows();
for(Index k=0; k<size; ++k)
dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k);
dest += (alpha*rhs.coeff(k)) * lhs.col(k);
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,false>
template<> struct gemv_dense_sense_selector<OnTheRight,RowMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
template<typename Lhs, typename Rhs, typename Dest>
static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure rhs is sequentially stored in memory, otherwise use a temp
const Index rows = prod.rows();
const Index rows = dest.rows();
for(Index i=0; i<rows; ++i)
dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum();
dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(rhs.transpose())).sum();
}
};
@ -570,7 +387,6 @@ template<> struct gemv_selector<OnTheRight,RowMajor,false>
*/
#ifndef __CUDACC__
#ifdef EIGEN_TEST_EVALUATORS
template<typename Derived>
template<typename OtherDerived>
inline const Product<Derived, OtherDerived>
@ -601,39 +417,9 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
return Product<Derived, OtherDerived>(derived(), other.derived());
}
#else
template<typename Derived>
template<typename OtherDerived>
inline const typename ProductReturnType<Derived, OtherDerived>::Type
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
{
// A note regarding the function declaration: In MSVC, this function will sometimes
// not be inlined since DenseStorage is an unwindable object for dynamic
// matrices and product types are holding a member to store the result.
// Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
enum {
ProductIsValid = Derived::ColsAtCompileTime==Dynamic
|| OtherDerived::RowsAtCompileTime==Dynamic
|| int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
};
// note to the lost user:
// * for a dot product use: v1.dot(v2)
// * for a coeff-wise product use: v1.cwiseProduct(v2)
EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
#ifdef EIGEN_DEBUG_PRODUCT
internal::product_type<Derived,OtherDerived>::debug();
#endif
return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
}
#endif
#endif
#endif // __CUDACC__
/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
*
* The returned product will behave like any other expressions: the coefficients of the product will be
@ -647,7 +433,7 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
*/
template<typename Derived>
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
{
enum {
@ -666,7 +452,7 @@ MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
}
} // end namespace Eigen

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

@ -129,12 +129,12 @@ pdiv(const Packet& a,
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmin(const Packet& a,
const Packet& b) { EIGEN_USING_STD_MATH(min); return (min)(a, b); }
const Packet& b) { return numext::mini(a, b); }
/** \internal \returns the max of \a a and \a b (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmax(const Packet& a,
const Packet& b) { EIGEN_USING_STD_MATH(max); return (max)(a, b); }
const Packet& b) { return numext::maxi(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
@ -245,8 +245,8 @@ template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu
/** \internal tries to do cache prefetching of \a addr */
template<typename Scalar> inline void prefetch(const Scalar* addr)
{
#if !defined(_MSC_VER)
__builtin_prefetch(addr);
#if !EIGEN_COMP_MSVC
__builtin_prefetch(addr);
#endif
}

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

@ -15,7 +15,7 @@
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
NAME(const Eigen::ArrayBase<Derived>& x) { \
return x.derived(); \
return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
}
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
@ -30,7 +30,7 @@
{ \
static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
{ \
return x.derived(); \
return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
} \
};

129
Eigen/src/Core/Inverse.h Normal file
View File

@ -0,0 +1,129 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_INVERSE_H
#define EIGEN_INVERSE_H
namespace Eigen {
// TODO move the general declaration in Core, and rename this file DenseInverseImpl.h, or something like this...
template<typename XprType,typename StorageKind> class InverseImpl;
namespace internal {
template<typename XprType>
struct traits<Inverse<XprType> >
: traits<typename XprType::PlainObject>
{
typedef typename XprType::PlainObject PlainObject;
typedef traits<PlainObject> BaseTraits;
enum {
Flags = BaseTraits::Flags & RowMajorBit
};
};
} // end namespace internal
/** \class Inverse
*
* \brief Expression of the inverse of another expression
*
* \tparam XprType the type of the expression we are taking the inverse
*
* This class represents an abstract expression of A.inverse()
* and most of the time this is the only way it is used.
*
*/
template<typename XprType>
class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::StorageKind>
{
public:
typedef typename XprType::Index Index;
typedef typename XprType::PlainObject PlainObject;
typedef typename internal::nested<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprTypeNested>::type XprTypeNestedCleaned;
explicit Inverse(const XprType &xpr)
: m_xpr(xpr)
{}
EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
protected:
XprTypeNested m_xpr;
};
/** \internal
* Specialization of the Inverse expression for dense expressions.
* Direct access to the coefficients are discared.
* FIXME this intermediate class is probably not needed anymore.
*/
template<typename XprType>
class InverseImpl<XprType,Dense>
: public MatrixBase<Inverse<XprType> >
{
typedef Inverse<XprType> Derived;
public:
typedef MatrixBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
typedef typename internal::remove_all<XprType>::type NestedExpression;
private:
Scalar coeff(Index row, Index col) const;
Scalar coeff(Index i) const;
};
namespace internal {
/** \internal
* \brief Default evaluator for Inverse expression.
*
* This default evaluator for Inverse expression simply evaluate the inverse into a temporary
* by a call to internal::call_assignment_no_alias.
* Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
* there own nested expression.
*
* \sa class Inverse
*/
template<typename ArgType>
struct unary_evaluator<Inverse<ArgType> >
: public evaluator<typename Inverse<ArgType>::PlainObject>::type
{
typedef Inverse<ArgType> InverseType;
typedef typename InverseType::PlainObject PlainObject;
typedef typename evaluator<PlainObject>::type Base;
typedef evaluator<InverseType> type;
typedef evaluator<InverseType> nestedType;
enum { Flags = Base::Flags | EvalBeforeNestingBit };
unary_evaluator(const InverseType& inv_xpr)
: m_result(inv_xpr.rows(), inv_xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
internal::call_assignment_no_alias(m_result, inv_xpr);
}
protected:
PlainObject m_result;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_INVERSE_H

17
Eigen/src/Core/Map.h
View File

@ -79,22 +79,9 @@ struct traits<Map<PlainObjectType, MapOptions, StrideType> >
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? int(PlainObjectType::OuterStrideAtCompileTime)
: int(StrideType::OuterStrideAtCompileTime),
HasNoInnerStride = InnerStrideAtCompileTime == 1,
HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
HasNoStride = HasNoInnerStride && HasNoOuterStride,
IsAligned = bool(EIGEN_ALIGN) && ((int(MapOptions)&Aligned)==Aligned),
IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
KeepsPacketAccess = bool(HasNoInnerStride)
&& ( bool(IsDynamicSize)
|| HasNoOuterStride
|| ( OuterStrideAtCompileTime!=Dynamic
&& ((static_cast<int>(sizeof(Scalar))*OuterStrideAtCompileTime)%EIGEN_ALIGN_BYTES)==0 ) ),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit),
Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime))
? int(Flags1) : int(Flags1 & ~LinearAccessBit),
Flags3 = is_lvalue<PlainObjectType>::value ? int(Flags2) : (int(Flags2) & ~LvalueBit),
Flags = KeepsPacketAccess ? int(Flags3) : (int(Flags3) & ~PacketAccessBit)
Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
};
private:
enum { Options }; // Expressions don't have Options
@ -135,7 +122,7 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
* \param a_stride optional Stride object, passing the strides.
*/
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
explicit inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride)
{
PlainObjectType::Base::_check_template_params();

19
Eigen/src/Core/MapBase.h
View File

@ -12,7 +12,7 @@
#define EIGEN_MAPBASE_H
#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
EIGEN_STATIC_ASSERT((int(internal::traits<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
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)
namespace Eigen {
@ -85,7 +85,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
*
* \sa innerStride(), outerStride()
*/
inline const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
@ -128,7 +128,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
}
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
@ -161,11 +161,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
EIGEN_DEVICE_FUNC
void checkSanity() const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit,
internal::inner_stride_at_compile_time<Derived>::ret==1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % EIGEN_ALIGN_BYTES) == 0)
&& "data is not aligned");
eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::IsAligned, (size_t(m_data) % EIGEN_ALIGN_BYTES) == 0) && "data is not aligned");
}
PointerType m_data;
@ -176,6 +172,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{
typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
public:
typedef MapBase<Derived, ReadOnlyAccessors> Base;
@ -243,11 +240,13 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
EIGEN_DEVICE_FUNC
Derived& operator=(const MapBase& other)
{
Base::Base::operator=(other);
ReadOnlyMapBase::Base::operator=(other);
return derived();
}
using Base::Base::operator=;
// In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
// see bugs 821 and 920.
using ReadOnlyMapBase::Base::operator=;
};
#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS

78
Eigen/src/Core/MathFunctions.h
View File

@ -14,7 +14,7 @@ namespace Eigen {
// 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 defined(_WIN32_WCE) && defined(_MSC_VER) && _MSC_VER<=1500
#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)); }
@ -360,50 +360,31 @@ inline NewType cast(const OldType& x)
}
/****************************************************************************
* Implementation of atanh2 *
* Implementation of logp1 *
****************************************************************************/
template<typename Scalar>
struct atanh2_impl
struct log1p_impl
{
static inline Scalar run(const Scalar& x, const Scalar& r)
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#if (__cplusplus >= 201103L) && !defined(__CYGWIN__)
// Let's be conservative and enable the default C++11 implementation only if we are sure it exists
#if (__cplusplus >= 201103L) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC) \
&& (EIGEN_ARCH_i386_OR_x86_64) && (EIGEN_OS_GNULINUX || EIGEN_OS_WIN_STRICT || EIGEN_OS_MAC)
using std::log1p;
return log1p(2 * x / (r - x)) / 2;
return log1p(x);
#else
using std::abs;
typedef typename NumTraits<Scalar>::Real RealScalar;
using std::log;
using std::sqrt;
Scalar z = x / r;
if (r == 0 || abs(z) > sqrt(NumTraits<Scalar>::epsilon()))
return log((r + x) / (r - x)) / 2;
else
return z + z*z*z / 3;
Scalar x1p = RealScalar(1) + x;
return ( x1p == Scalar(1) ) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) );
#endif
}
};
template<typename RealScalar>
struct atanh2_impl<std::complex<RealScalar> >
{
typedef std::complex<RealScalar> Scalar;
static inline Scalar run(const Scalar& x, const Scalar& r)
{
using std::log;
using std::norm;
using std::sqrt;
Scalar z = x / r;
if (r == Scalar(0) || norm(z) > NumTraits<RealScalar>::epsilon())
return RealScalar(0.5) * log((r + x) / (r - x));
else
return z + z*z*z / RealScalar(3);
}
};
template<typename Scalar>
struct atanh2_retval
struct log1p_retval
{
typedef Scalar type;
};
@ -591,6 +572,22 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
****************************************************************************/
namespace numext {
template<typename T>
EIGEN_DEVICE_FUNC
inline T mini(const T& x, const T& y)
{
EIGEN_USING_STD_MATH(min);
return min EIGEN_NOT_A_MACRO (x,y);
}
template<typename T>
EIGEN_DEVICE_FUNC
inline T maxi(const T& x, const T& y)
{
EIGEN_USING_STD_MATH(max);
return max EIGEN_NOT_A_MACRO (x,y);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
@ -664,9 +661,9 @@ inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar&
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
}
template<typename Scalar>
@ -694,6 +691,21 @@ bool (isfinite)(const std::complex<T>& x)
return isfinite(real(x)) && isfinite(imag(x));
}
// Log base 2 for 32 bits positive integers.
// Conveniently returns 0 for x==0.
inline int log2(int x)
{
eigen_assert(x>=0);
unsigned int v(x);
static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 };
v |= v >> 1;
v |= v >> 2;
v |= v >> 4;
v |= v >> 8;
v |= v >> 16;
return table[(v * 0x07C4ACDDU) >> 27];
}
} // end namespace numext
namespace internal {

14
Eigen/src/Core/Matrix.h
View File

@ -115,7 +115,8 @@ struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
// FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
EvaluatorFlags = compute_matrix_evaluator_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime
@ -213,7 +214,7 @@ class Matrix
// FIXME is it still needed
EIGEN_DEVICE_FUNC
Matrix(internal::constructor_without_unaligned_array_assert)
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 }
@ -359,15 +360,6 @@ class Matrix
*this = other;
}
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }

50
Eigen/src/Core/MatrixBase.h
View File

@ -66,8 +66,7 @@ template<typename Derived> class MatrixBase
using Base::MaxSizeAtCompileTime;
using Base::IsVectorAtCompileTime;
using Base::Flags;
using Base::CoeffReadCost;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@ -181,26 +180,20 @@ template<typename Derived> class MatrixBase
#ifdef __CUDACC__
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
operator*(const MatrixBase<OtherDerived> &other) const
{ return this->lazyProduct(other); }
#else
#ifdef EIGEN_TEST_EVALUATORS
template<typename OtherDerived>
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
#else
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
operator*(const MatrixBase<OtherDerived> &other) const;
#endif
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const typename LazyProductReturnType<Derived,OtherDerived>::Type
const Product<Derived,OtherDerived,LazyProduct>
lazyProduct(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
@ -214,7 +207,7 @@ template<typename Derived> class MatrixBase
template<typename DiagonalDerived>
EIGEN_DEVICE_FUNC
const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
const Product<Derived, DiagonalDerived, LazyProduct>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename OtherDerived>
@ -251,19 +244,14 @@ template<typename Derived> class MatrixBase
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
// Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
// On the other hand they confuse MSVC8...
#if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later
typename MatrixBase::template DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#else
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#endif
DiagonalDynamicIndexReturnType diagonal(Index index);
EIGEN_DEVICE_FUNC
ConstDiagonalDynamicIndexReturnType 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; };
@ -334,10 +322,12 @@ template<typename Derived> class MatrixBase
NoAlias<Derived,Eigen::MatrixBase > noalias();
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
// TODO forceAlignedAccess is temporarily disabled
// Need to find a nicer workaround.
inline const Derived& forceAlignedAccess() const { return derived(); }
inline Derived& forceAlignedAccess() { return derived(); }
template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
Scalar trace() const;
@ -348,10 +338,10 @@ template<typename Derived> class MatrixBase
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
/** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
/////////// LU module ///////////
@ -361,7 +351,8 @@ template<typename Derived> class MatrixBase
const PartialPivLU<PlainObject> lu() const;
EIGEN_DEVICE_FUNC
const internal::inverse_impl<Derived> inverse() const;
const Inverse<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
ResultType& inverse,
@ -394,6 +385,7 @@ template<typename Derived> class MatrixBase
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
/////////// Geometry module ///////////

20
Eigen/src/Core/NestByValue.h
View File

@ -40,29 +40,29 @@ template<typename ExpressionType> class NestByValue
typedef typename internal::dense_xpr_base<NestByValue>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
inline Index rows() const { return m_expression.rows(); }
inline Index cols() const { return m_expression.cols(); }
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_expression.innerStride(); }
inline const CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
{
return m_expression.coeff(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_expression.const_cast_derived().coeffRef(row, col);
}
inline const CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
@ -91,7 +91,7 @@ template<typename ExpressionType> class NestByValue
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
}
operator const ExpressionType&() const { return m_expression; }
EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
protected:
const ExpressionType m_expression;

59
Eigen/src/Core/NoAlias.h
View File

@ -30,68 +30,35 @@ namespace Eigen {
template<typename ExpressionType, template <typename> class StorageBase>
class NoAlias
{
typedef typename ExpressionType::Scalar Scalar;
public:
NoAlias(ExpressionType& expression) : m_expression(expression) {}
/** Behaves like MatrixBase::lazyAssign(other)
* \sa MatrixBase::lazyAssign() */
typedef typename ExpressionType::Scalar Scalar;
explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar>());
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
typedef SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ExpressionType, OtherDerived> SelfAdder;
SelfAdder tmp(m_expression);
typedef typename internal::nested<OtherDerived>::type OtherDerivedNested;
typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested;
internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived()));
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar>());
return m_expression;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().addTo(m_expression); return m_expression; }
template<typename ProductDerived, typename Lhs, typename Rhs>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
{ other.derived().subTo(m_expression); return m_expression; }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() += CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename Lhs, typename Rhs, int NestingFlags>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
@ -133,7 +100,7 @@ class NoAlias
template<typename Derived>
NoAlias<Derived,MatrixBase> MatrixBase<Derived>::noalias()
{
return derived();
return NoAlias<Derived, Eigen::MatrixBase >(derived());
}
} // end namespace Eigen

133
Eigen/src/Core/PermutationMatrix.h
View File

@ -13,7 +13,8 @@
namespace Eigen {
template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
// TODO: this does not seems to be needed at all:
// template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
/** \class PermutationBase
* \ingroup Core_Module
@ -60,7 +61,6 @@ class PermutationBase : public EigenBase<Derived>
typedef typename Traits::IndicesType IndicesType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
@ -73,6 +73,7 @@ class PermutationBase : public EigenBase<Derived>
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndexType>
PlainPermutationType;
using Base::derived;
typedef Transpose<PermutationBase> TransposeReturnType;
#endif
/** Copies the other permutation into *this */
@ -118,7 +119,7 @@ class PermutationBase : public EigenBase<Derived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
for (Index i=0; i<rows(); ++i)
other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
}
#endif
@ -163,10 +164,10 @@ class PermutationBase : public EigenBase<Derived>
*
* \returns a reference to *this.
*
* \warning This is much slower than applyTranspositionOnTheRight(int,int):
* \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
* this has linear complexity and requires a lot of branching.
*
* \sa applyTranspositionOnTheRight(int,int)
* \sa applyTranspositionOnTheRight(Index,Index)
*/
Derived& applyTranspositionOnTheLeft(Index i, Index j)
{
@ -185,7 +186,7 @@ class PermutationBase : public EigenBase<Derived>
*
* This is a fast operation, it only consists in swapping two indices.
*
* \sa applyTranspositionOnTheLeft(int,int)
* \sa applyTranspositionOnTheLeft(Index,Index)
*/
Derived& applyTranspositionOnTheRight(Index i, Index j)
{
@ -198,14 +199,14 @@ class PermutationBase : public EigenBase<Derived>
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> inverse() const
{ return derived(); }
inline TransposeReturnType inverse() const
{ return TransposeReturnType(derived()); }
/** \returns the tranpose permutation matrix.
*
* \note \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> transpose() const
{ return derived(); }
inline TransposeReturnType transpose() const
{ return TransposeReturnType(derived()); }
/**** multiplication helpers to hopefully get RVO ****/
@ -215,13 +216,13 @@ class PermutationBase : public EigenBase<Derived>
template<typename OtherDerived>
void assignTranspose(const PermutationBase<OtherDerived>& other)
{
for (int i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
for (Index i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
void assignProduct(const Lhs& lhs, const Rhs& rhs)
{
eigen_assert(lhs.cols() == rhs.rows());
for (int i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
for (Index i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
}
#endif
@ -274,6 +275,7 @@ template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType> >
: traits<Matrix<_StorageIndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
@ -287,6 +289,8 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
typedef internal::traits<PermutationMatrix> Traits;
public:
typedef const PermutationMatrix& Nested;
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename Traits::StorageIndexType StorageIndexType;
@ -298,8 +302,10 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
/** Constructs an uninitialized permutation matrix of given size.
*/
inline PermutationMatrix(Index size) : m_indices(size)
{}
explicit inline PermutationMatrix(Index size) : m_indices(size)
{
eigen_internal_assert(size <= NumTraits<StorageIndexType>::highest());
}
/** Copy constructor. */
template<typename OtherDerived>
@ -370,7 +376,10 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
PermutationMatrix(const Transpose<PermutationBase<Other> >& other)
: m_indices(other.nestedPermutation().size())
{
for (int i=0; i<m_indices.size();++i) m_indices.coeffRef(other.nestedPermutation().indices().coeff(i)) = i;
eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndexType>::highest());
StorageIndexType end = StorageIndexType(m_indices.size());
for (StorageIndexType i=0; i<end;++i)
m_indices.coeffRef(other.nestedPermutation().indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
@ -391,6 +400,7 @@ template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndexType>,_PacketAccess> >
: traits<Matrix<_StorageIndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Map<const Matrix<_StorageIndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef typename IndicesType::Index Index;
typedef _StorageIndexType StorageIndexType;
@ -462,8 +472,6 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageInd
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
@ -477,10 +485,9 @@ struct traits<PermutationWrapper<_IndicesType> >
enum {
RowsAtCompileTime = _IndicesType::SizeAtCompileTime,
ColsAtCompileTime = _IndicesType::SizeAtCompileTime,
MaxRowsAtCompileTime = IndicesType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxColsAtCompileTime,
Flags = 0,
CoeffReadCost = _IndicesType::CoeffReadCost
MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
Flags = 0
};
};
}
@ -509,35 +516,39 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typename IndicesType::Nested m_indices;
};
// TODO: Do we need to define these operator* functions? Would it be better to have them inherited
// from MatrixBase?
/** \returns the matrix with the permutation applied to the columns.
*/
template<typename Derived, typename PermutationDerived>
inline const internal::permut_matrix_product_retval<PermutationDerived, Derived, OnTheRight>
operator*(const MatrixBase<Derived>& matrix,
const PermutationBase<PermutationDerived> &permutation)
template<typename MatrixDerived, typename PermutationDerived>
EIGEN_DEVICE_FUNC
const Product<MatrixDerived, PermutationDerived, DefaultProduct>
operator*(const MatrixBase<MatrixDerived> &matrix,
const PermutationBase<PermutationDerived>& permutation)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheRight>
(permutation.derived(), matrix.derived());
return Product<MatrixDerived, PermutationDerived, DefaultProduct>
(matrix.derived(), permutation.derived());
}
/** \returns the matrix with the permutation applied to the rows.
*/
template<typename Derived, typename PermutationDerived>
inline const internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheLeft>
template<typename PermutationDerived, typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<PermutationDerived, MatrixDerived, DefaultProduct>
operator*(const PermutationBase<PermutationDerived> &permutation,
const MatrixBase<Derived>& matrix)
const MatrixBase<MatrixDerived>& matrix)
{
return internal::permut_matrix_product_retval
<PermutationDerived, Derived, OnTheLeft>
(permutation.derived(), matrix.derived());
return Product<PermutationDerived, MatrixDerived, DefaultProduct>
(permutation.derived(), matrix.derived());
}
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct traits<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
: traits<typename MatrixType::PlainObject>
{
typedef typename MatrixType::PlainObject ReturnType;
};
@ -590,7 +601,7 @@ struct permut_matrix_product_retval
}
else
{
for(int i = 0; i < n; ++i)
for(Index i = 0; i < n; ++i)
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i)
@ -617,6 +628,8 @@ struct traits<Transpose<PermutationBase<Derived> > >
} // end namespace internal
// TODO: the specificties should be handled by the evaluator,
// at the very least we should only specialize TransposeImpl
template<typename Derived>
class Transpose<PermutationBase<Derived> >
: public EigenBase<Transpose<PermutationBase<Derived> > >
@ -631,26 +644,26 @@ class Transpose<PermutationBase<Derived> >
typedef typename Derived::DenseMatrixType DenseMatrixType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
typedef typename Traits::Index Index;
#endif
Transpose(const PermutationType& p) : m_permutation(p) {}
inline int rows() const { return m_permutation.rows(); }
inline int cols() const { return m_permutation.cols(); }
inline Index rows() const { return m_permutation.rows(); }
inline Index cols() const { return m_permutation.cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& other) const
{
other.setZero();
for (int i=0; i<rows();++i)
for (Index i=0; i<rows();++i)
other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1);
}
#endif
@ -663,19 +676,19 @@ class Transpose<PermutationBase<Derived> >
/** \returns the matrix with the inverse permutation applied to the columns.
*/
template<typename OtherDerived> friend
inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>
const Product<OtherDerived, Transpose, DefaultProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm)
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.derived());
return Product<OtherDerived, Transpose, DefaultProduct>(matrix.derived(), trPerm.derived());
}
/** \returns the matrix with the inverse permutation applied to the rows.
*/
template<typename OtherDerived>
inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>
const Product<Transpose, OtherDerived, DefaultProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived());
return Product<Transpose, OtherDerived, DefaultProduct>(*this, matrix.derived());
}
const PermutationType& nestedPermutation() const { return m_permutation; }
@ -690,6 +703,38 @@ const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() con
return derived();
}
namespace internal {
// TODO currently a permutation matrix expression has the form PermutationMatrix or PermutationWrapper
// or their transpose; in the future shape should be defined by the expression traits
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct evaluator_traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
{
typedef typename storage_kind_to_evaluator_kind<Dense>::Kind Kind;
typedef PermutationShape Shape;
static const int AssumeAliasing = 0;
};
template<typename IndicesType>
struct evaluator_traits<PermutationWrapper<IndicesType> >
{
typedef typename storage_kind_to_evaluator_kind<Dense>::Kind Kind;
typedef PermutationShape Shape;
static const int AssumeAliasing = 0;
};
template<typename Derived>
struct evaluator_traits<Transpose<PermutationBase<Derived> > >
{
typedef typename storage_kind_to_evaluator_kind<Dense>::Kind Kind;
typedef PermutationShape Shape;
static const int AssumeAliasing = 0;
};
template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_PERMUTATIONMATRIX_H

90
Eigen/src/Core/PlainObjectBase.h
View File

@ -128,7 +128,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
public:
enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 };
enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::EvaluatorFlags & AlignedBit) != 0 };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
EIGEN_DEVICE_FUNC
@ -221,11 +221,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
/** \returns a const pointer to the data array of this matrix */
EIGEN_STRONG_INLINE const Scalar *data() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
{ return m_storage.data(); }
/** \returns a pointer to the data array of this matrix */
EIGEN_STRONG_INLINE Scalar *data()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
{ return m_storage.data(); }
/** Resizes \c *this to a \a rows x \a cols matrix.
@ -457,7 +457,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
// FIXME is it still needed ?
/** \internal */
EIGEN_DEVICE_FUNC
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
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
@ -639,22 +639,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \internal
*/
// aliasing is dealt once in internall::call_assignment
// so at this stage we have to assume aliasing... and resising has to be done later.
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
{
_set_selector(other.derived(), typename internal::conditional<static_cast<bool>(int(OtherDerived::Flags) & EvalBeforeAssigningBit), internal::true_type, internal::false_type>::type());
internal::call_assignment(this->derived(), other.derived());
return this->derived();
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::false_type&) { _set_noalias(other); }
/** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
* is the case when creating a new matrix) so one can enforce lazy evaluation.
*
@ -669,7 +663,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//_resize_to_match(other);
// the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
// it wouldn't allow to copy a row-vector into a column-vector.
return internal::assign_selector<Derived,OtherDerived,false>::run(this->derived(), other.derived());
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar>());
return this->derived();
}
template<typename T0, typename T1>
@ -704,9 +699,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
m_storage.data()[1] = Scalar(val1);
}
// The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
// 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,T>::type* = 0)
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)
{
// NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
const bool is_integer = NumTraits<T>::IsInteger;
@ -714,6 +712,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(size);
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitely 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)
@ -722,6 +722,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
m_storage.data()[0] = val0;
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Index& val0,
@ -734,18 +735,21 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
m_storage.data()[0] = Scalar(val0);
}
// Initialize a fixed size matrix from a pointer to raw data
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Scalar* data){
this->_set_noalias(ConstMapType(data));
}
// Initialize an arbitrary matrix from a dense expression
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){
this->_set_noalias(other);
}
// Initialize an arbitrary matrix from a generic Eigen expression
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){
@ -766,23 +770,58 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
this->derived() = r;
}
// For fixed -size arrays:
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)
{
Base::setConstant(val0);
}
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)
{
Base::setConstant(val0);
}
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
friend struct internal::matrix_swap_impl;
/** \internal generic implementation of swap for dense storage since for dynamic-sized matrices of same type it is enough to swap the
* data pointers.
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal
* \brief Override DenseBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void _swap(DenseBase<OtherDerived> const & other)
void swap(DenseBase<OtherDerived> & other)
{
enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.const_cast_derived());
internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
}
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal
* \brief const version forwarded to DenseBase::swap
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void swap(DenseBase<OtherDerived> const & other)
{ Base::swap(other.derived()); }
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void _check_template_params()
{
@ -797,10 +836,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
&& (Options & (DontAlign|RowMajor)) == Options),
INVALID_MATRIX_TEMPLATE_PARAMETERS)
}
#endif
private:
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 1 };
#endif
};
namespace internal {

184
Eigen/src/Core/Product.h
View File

@ -12,8 +12,7 @@
namespace Eigen {
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
/** \class Product
* \ingroup Core_Module
@ -24,53 +23,108 @@ template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
* \param Rhs the type of the right-hand side expression
*
* This class represents an expression of the product of two arbitrary matrices.
*
* The other template parameters are:
* \tparam Option can be DefaultProduct or LazyProduct
*
*/
// Use ProductReturnType to get correct traits, in particular vectorization flags
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<Product<Lhs, Rhs> >
: traits<typename ProductReturnType<Lhs, Rhs>::Type>
{
// We want A+B*C to be of type Product<Matrix, Sum> and not Product<Matrix, Matrix>
// TODO: This flag should eventually go in a separate evaluator traits class
// Determine the scalar of Product<Lhs, Rhs>. This is normally the same as Lhs::Scalar times
// Rhs::Scalar, but product with permutation matrices inherit the scalar of the other factor.
template<typename Lhs, typename Rhs, typename LhsShape = typename evaluator_traits<Lhs>::Shape,
typename RhsShape = typename evaluator_traits<Rhs>::Shape >
struct product_result_scalar
{
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, PermutationShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, PermutationShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
typedef MatrixXpr XprKind;
typedef typename product_result_scalar<LhsCleaned,RhsCleaned>::Scalar Scalar;
typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
typename RhsTraits::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
typedef typename promote_index_type<typename LhsTraits::Index,
typename RhsTraits::Index>::type Index;
enum {
Flags = traits<typename ProductReturnType<Lhs, Rhs>::Type>::Flags & ~(EvalBeforeNestingBit | DirectAccessBit)
RowsAtCompileTime = LhsTraits::RowsAtCompileTime,
ColsAtCompileTime = RhsTraits::ColsAtCompileTime,
MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
// FIXME: only needed by GeneralMatrixMatrixTriangular
InnerSize = EIGEN_SIZE_MIN_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)
|| ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
|| ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) )
? RowMajorBit : (MaxColsAtCompileTime==1 ? 0 : NoPreferredStorageOrderBit)
};
};
} // end namespace internal
template<typename Lhs, typename Rhs>
class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind,
typename internal::traits<Rhs>::StorageKind>::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 typename ProductImpl<
Lhs, Rhs,
typename internal::promote_storage_type<typename Lhs::StorageKind,
typename Rhs::StorageKind>::ret>::Base Base;
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>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::nested<Lhs>::type LhsNested;
typedef typename internal::nested<Rhs>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
EIGEN_DEVICE_FUNC Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_rhs.cols(); }
const LhsNestedCleaned& lhs() const { return m_lhs; }
const RhsNestedCleaned& rhs() const { return m_rhs; }
EIGEN_DEVICE_FUNC const LhsNestedCleaned& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const RhsNestedCleaned& rhs() const { return m_rhs; }
protected:
@ -78,14 +132,77 @@ class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_ty
RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
{
typedef Product<Lhs, Rhs> Derived;
public:
namespace internal {
template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
class dense_product_base
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{};
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
/** Convertion to scalar for inner-products */
template<typename Lhs, typename Rhs, int Option>
class dense_product_base<Lhs, Rhs, Option, InnerProduct>
: public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
{
typedef Product<Lhs,Rhs,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
operator const Scalar() const
{
return typename internal::evaluator<ProductXpr>::type(derived()).coeff(0,0);
}
};
} // namespace internal
// Generic API dispatcher
template<typename Lhs, typename Rhs, int Option, typename StorageKind>
class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
{
public:
typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
};
template<typename Lhs, typename Rhs, int Option>
class ProductImpl<Lhs,Rhs,Option,Dense>
: public internal::dense_product_base<Lhs,Rhs,Option>
{
typedef Product<Lhs, Rhs, Option> Derived;
public:
typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
protected:
enum {
IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
(ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
EnableCoeff = IsOneByOne || Option==LazyProduct
};
public:
EIGEN_DEVICE_FUNC Scalar coeff(Index row, Index col) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return typename internal::evaluator<Derived>::type(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC Scalar coeff(Index i) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
return typename internal::evaluator<Derived>::type(derived()).coeff(i);
}
};
/***************************************************************************
@ -102,6 +219,15 @@ prod(const Lhs& lhs, const Rhs& rhs)
return Product<Lhs,Rhs>(lhs,rhs);
}
/** \internal used to test the evaluator only
*/
template<typename Lhs,typename Rhs>
const Product<Lhs,Rhs,LazyProduct>
lazyprod(const Lhs& lhs, const Rhs& rhs)
{
return Product<Lhs,Rhs,LazyProduct>(lhs,rhs);
}
} // end namespace Eigen
#endif // EIGEN_PRODUCT_H

247
Eigen/src/Core/ProductBase.h
View File

@ -12,253 +12,6 @@
namespace Eigen {
/** \class ProductBase
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived, typename _Lhs, typename _Rhs>
struct traits<ProductBase<Derived,_Lhs,_Rhs> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<_Lhs>::type Lhs;
typedef typename remove_all<_Rhs>::type Rhs;
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<Lhs>::StorageKind,
typename traits<Rhs>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<Lhs>::Index,
typename traits<Rhs>::Index>::type Index;
enum {
RowsAtCompileTime = traits<Lhs>::RowsAtCompileTime,
ColsAtCompileTime = traits<Rhs>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Lhs>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Rhs>::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0)
| EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit,
// Note that EvalBeforeNestingBit and NestByRefBit
// are not used in practice because nested is overloaded for products
CoeffReadCost = 0 // FIXME why is it needed ?
};
};
}
#define EIGEN_PRODUCT_PUBLIC_INTERFACE(Derived) \
typedef ProductBase<Derived, Lhs, Rhs > Base; \
EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
typedef typename Base::LhsNested LhsNested; \
typedef typename Base::_LhsNested _LhsNested; \
typedef typename Base::LhsBlasTraits LhsBlasTraits; \
typedef typename Base::ActualLhsType ActualLhsType; \
typedef typename Base::_ActualLhsType _ActualLhsType; \
typedef typename Base::RhsNested RhsNested; \
typedef typename Base::_RhsNested _RhsNested; \
typedef typename Base::RhsBlasTraits RhsBlasTraits; \
typedef typename Base::ActualRhsType ActualRhsType; \
typedef typename Base::_ActualRhsType _ActualRhsType; \
using Base::m_lhs; \
using Base::m_rhs;
template<typename Derived, typename Lhs, typename Rhs>
class ProductBase : public MatrixBase<Derived>
{
public:
typedef MatrixBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(ProductBase)
typedef typename Lhs::Nested LhsNested;
typedef typename internal::remove_all<LhsNested>::type _LhsNested;
typedef internal::blas_traits<_LhsNested> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type _ActualLhsType;
typedef typename internal::traits<Lhs>::Scalar LhsScalar;
typedef typename Rhs::Nested RhsNested;
typedef typename internal::remove_all<RhsNested>::type _RhsNested;
typedef internal::blas_traits<_RhsNested> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type _ActualRhsType;
typedef typename internal::traits<Rhs>::Scalar RhsScalar;
// Diagonal of a product: no need to evaluate the arguments because they are going to be evaluated only once
typedef CoeffBasedProduct<LhsNested, RhsNested, 0> FullyLazyCoeffBaseProductType;
public:
typedef typename Base::PlainObject PlainObject;
ProductBase(const Lhs& a_lhs, const Rhs& a_rhs)
: m_lhs(a_lhs), m_rhs(a_rhs)
{
eigen_assert(a_lhs.cols() == a_rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
inline Index rows() const { return m_lhs.rows(); }
inline Index cols() const { return m_rhs.cols(); }
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst, const Scalar& alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
// Implicit conversion to the nested type (trigger the evaluation of the product)
operator const PlainObject& () const
{
m_result.resize(m_lhs.rows(), m_rhs.cols());
derived().evalTo(m_result);
return m_result;
}
const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
template<int Index>
const Diagonal<FullyLazyCoeffBaseProductType,Index> diagonal() const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); }
const Diagonal<FullyLazyCoeffBaseProductType,Dynamic> diagonal(Index index) const
{ return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); }
// restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isn't an Lvalue expression
typename Base::CoeffReturnType coeff(Index row, Index col) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(row,col);
}
typename Base::CoeffReturnType coeff(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
Matrix<Scalar,1,1> result = *this;
return result.coeff(i);
}
const Scalar& coeffRef(Index row, Index col) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(row,col);
}
const Scalar& coeffRef(Index i) const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
eigen_assert(this->rows() == 1 && this->cols() == 1);
return derived().coeffRef(i);
}
protected:
LhsNested m_lhs;
RhsNested m_rhs;
mutable PlainObject m_result;
};
// here we need to overload the nested rule for products
// such that the nested type is a const reference to a plain matrix
namespace internal {
template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
{
typedef PlainObject const& type;
};
}
template<typename NestedProduct>
class ScaledProduct;
// Note that these two operator* functions are not defined as member
// functions of ProductBase, because, otherwise we would have to
// define all overloads defined in MatrixBase. Furthermore, Using
// "using Base::operator*" would not work with MSVC.
//
// Also note that here we accept any compatible scalar types
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::Scalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::RealScalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const typename Derived::Scalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const typename Derived::RealScalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
template<typename NestedProduct>
struct traits<ScaledProduct<NestedProduct> >
: traits<ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> >
{
typedef typename traits<NestedProduct>::StorageKind StorageKind;
};
}
template<typename NestedProduct>
class ScaledProduct
: public ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested>
{
public:
typedef ProductBase<ScaledProduct<NestedProduct>,
typename NestedProduct::_LhsNested,
typename NestedProduct::_RhsNested> Base;
typedef typename Base::Scalar Scalar;
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, const Scalar& x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
template<typename Dest>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst, const Scalar& a_alpha) const { m_prod.derived().scaleAndAddTo(dst,a_alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }
protected:
const NestedProduct& m_prod;
Scalar m_alpha;
};
/** \internal
* Overloaded to perform an efficient C = (A*B).lazy() */
template<typename Derived>

927
Eigen/src/Core/ProductEvaluators.h
View File

File diff suppressed because it is too large Load Diff

120
Eigen/src/Core/Redux.h
View File

@ -65,6 +65,25 @@ public:
? CompleteUnrolling
: NoUnrolling
};
#ifdef EIGEN_DEBUG_ASSIGN
static void debug()
{
std::cerr << "Xpr: " << typeid(typename Derived::XprType).name() << std::endl;
std::cerr.setf(std::ios::hex, std::ios::basefield);
EIGEN_DEBUG_VAR(Derived::Flags)
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
EIGEN_DEBUG_VAR(Traversal)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(Unrolling)
std::cerr << std::endl;
}
#endif
};
/***************************************************************************
@ -174,7 +193,7 @@ struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
Scalar res;
@ -200,10 +219,10 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
static Scalar run(const Derived& mat, const Func& func)
static Scalar run(const Derived &mat, const Func& func)
{
const Index size = mat.size();
eigen_assert(size && "you are using an empty matrix");
const Index packetSize = packet_traits<Scalar>::size;
const Index alignedStart = internal::first_aligned(mat);
enum {
@ -258,7 +277,7 @@ struct redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
static Scalar run(const Derived& mat, const Func& func)
EIGEN_DEVICE_FUNC static Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
const Index innerSize = mat.innerSize();
@ -300,7 +319,7 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
Size = Derived::SizeAtCompileTime,
VectorizedSize = (Size / PacketSize) * PacketSize
};
static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
if (VectorizedSize > 0) {
@ -315,6 +334,66 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
}
};
// evaluator adaptor
template<typename _XprType>
class redux_evaluator
{
public:
typedef _XprType XprType;
EIGEN_DEVICE_FUNC explicit redux_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketScalar PacketScalar;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
// TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
Flags = evaluator<XprType>::Flags & ~DirectAccessBit,
IsRowMajor = XprType::IsRowMajor,
SizeAtCompileTime = XprType::SizeAtCompileTime,
InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime,
CoeffReadCost = evaluator<XprType>::CoeffReadCost
};
EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC Index size() const { return m_xpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_xpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_xpr.outerSize(); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeff(Index index) const
{ return m_evaluator.coeff(index); }
template<int LoadMode>
PacketReturnType packet(Index row, Index col) const
{ return m_evaluator.template packet<LoadMode>(row, col); }
template<int LoadMode>
PacketReturnType packet(Index index) const
{ return m_evaluator.template packet<LoadMode>(index); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{ return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
template<int LoadMode>
PacketReturnType packetByOuterInner(Index outer, Index inner) const
{ return m_evaluator.template packet<LoadMode>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
protected:
typename internal::evaluator<XprType>::nestedType m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/***************************************************************************
@ -325,7 +404,7 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
/** \returns the result of a full redux operation on the whole matrix or vector using \a func
*
* The template parameter \a BinaryOp is the type of the functor \a func which must be
* an associative operator. Both current STL and TR1 functor styles are handled.
* an associative operator. Both current C++98 and C++11 functor styles are handled.
*
* \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
*/
@ -334,9 +413,22 @@ template<typename Func>
EIGEN_STRONG_INLINE typename internal::result_of<Func(typename internal::traits<Derived>::Scalar)>::type
DenseBase<Derived>::redux(const Func& func) const
{
typedef typename internal::remove_all<typename Derived::Nested>::type ThisNested;
return internal::redux_impl<Func, ThisNested>
::run(derived(), func);
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
// FIXME, eval_nest should be handled by redux_evaluator, however:
// - it is currently difficult to provide the right Flags since they are still handled by the expressions
// - handling it here might reduce the number of template instantiations
// typedef typename internal::nested_eval<Derived,1>::type ThisNested;
// typedef typename internal::remove_all<ThisNested>::type ThisNestedCleaned;
// typedef typename internal::redux_evaluator<ThisNestedCleaned> ThisEvaluator;
//
// ThisNested thisNested(derived());
// ThisEvaluator thisEval(thisNested);
typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func);
}
/** \returns the minimum of all coefficients of \c *this.
@ -346,7 +438,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
{
return this->redux(Eigen::internal::scalar_min_op<Scalar>());
return derived().redux(Eigen::internal::scalar_min_op<Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@ -356,7 +448,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff() const
{
return this->redux(Eigen::internal::scalar_max_op<Scalar>());
return derived().redux(Eigen::internal::scalar_max_op<Scalar>());
}
/** \returns the sum of all coefficients of *this
@ -369,7 +461,7 @@ DenseBase<Derived>::sum() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(0);
return this->redux(Eigen::internal::scalar_sum_op<Scalar>());
return derived().redux(Eigen::internal::scalar_sum_op<Scalar>());
}
/** \returns the mean of all coefficients of *this
@ -380,7 +472,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::mean() const
{
return Scalar(this->redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
}
/** \returns the product of all coefficients of *this
@ -396,7 +488,7 @@ DenseBase<Derived>::prod() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(1);
return this->redux(Eigen::internal::scalar_product_op<Scalar>());
return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
}
/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.

44
Eigen/src/Core/Ref.h
View File

@ -12,10 +12,6 @@
namespace Eigen {
template<typename Derived> class RefBase;
template<typename PlainObjectType, int Options = 0,
typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref;
/** \class Ref
* \ingroup Core_Module
*
@ -131,12 +127,12 @@ public:
typedef MapBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
inline Index outerStride() const
EIGEN_DEVICE_FUNC inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
@ -144,7 +140,7 @@ public:
: this->rows();
}
RefBase()
EIGEN_DEVICE_FUNC RefBase()
: Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
// Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
@ -158,7 +154,7 @@ protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
void construct(Expression& expr)
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
if(PlainObjectType::RowsAtCompileTime==1)
{
@ -188,6 +184,8 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
: public RefBase<Ref<PlainObjectType, Options, StrideType> >
{
typedef internal::traits<Ref> Traits;
template<typename Derived>
EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr);
public:
typedef RefBase<Ref> Base;
@ -196,20 +194,21 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived>
inline Ref(PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr)
{
Base::construct(expr);
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
Base::construct(expr.derived());
}
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(internal::is_lvalue<Derived>::value&&bool(Traits::template match<Derived>::MatchAtCompileTime)),Derived>::type* = 0,
int = Derived::ThisConstantIsPrivateInPlainObjectBase)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr)
#else
template<typename Derived>
inline Ref(DenseBase<Derived>& expr)
#endif
{
EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
Base::construct(expr.const_cast_derived());
}
@ -228,7 +227,7 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr)
{
// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
@ -236,18 +235,27 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
construct(expr.derived(), typename Traits::template match<Derived>::type());
}
EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) {
// copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
}
template<typename OtherRef>
EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
construct(other.derived(), typename Traits::template match<OtherRef>::type());
}
protected:
template<typename Expression>
void construct(const Expression& expr,internal::true_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
{
Base::construct(expr);
}
template<typename Expression>
void construct(const Expression& expr, internal::false_type)
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
m_object.lazyAssign(expr);
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar>());
Base::construct(m_object);
}

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

@ -53,8 +53,9 @@ struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1
: MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0
: (MatrixType::Flags & RowMajorBit) ? 1 : 0,
Flags = (_MatrixTypeNested::Flags & HereditaryBits & ~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
// FIXME enable DirectAccess with negative strides?
Flags = IsRowMajor ? RowMajorBit : 0
};
};
}
@ -68,6 +69,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
typedef typename internal::dense_xpr_base<Replicate>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
template<typename OriginalMatrixType>
inline explicit Replicate(const OriginalMatrixType& a_matrix)

36
Eigen/src/Core/ReturnByValue.h
View File

@ -38,9 +38,10 @@ struct traits<ReturnByValue<Derived> >
* So internal::nested always gives the plain return matrix type.
*
* FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ??
* Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
*/
template<typename Derived,int n,typename PlainObject>
struct nested<ReturnByValue<Derived>, n, PlainObject>
struct nested_eval<ReturnByValue<Derived>, n, PlainObject>
{
typedef typename traits<Derived>::ReturnType type;
};
@ -48,7 +49,7 @@ struct nested<ReturnByValue<Derived>, n, PlainObject>
} // end namespace internal
template<typename Derived> class ReturnByValue
: internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;
@ -73,6 +74,7 @@ template<typename Derived> class ReturnByValue
const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); }
Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); }
#undef Unusable
#endif
};
@ -84,6 +86,36 @@ Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
return derived();
}
namespace internal {
// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
// when a ReturnByValue expression is assigned, the evaluator is not constructed.
// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
template<typename Derived>
struct evaluator<ReturnByValue<Derived> >
: public evaluator<typename internal::traits<Derived>::ReturnType>::type
{
typedef ReturnByValue<Derived> XprType;
typedef typename internal::traits<Derived>::ReturnType PlainObject;
typedef typename evaluator<PlainObject>::type Base;
typedef evaluator type;
typedef evaluator nestedType;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: m_result(xpr.rows(), xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
xpr.evalTo(m_result);
}
protected:
PlainObject m_result;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_RETURNBYVALUE_H

36
Eigen/src/Core/Reverse.h
View File

@ -44,14 +44,7 @@ struct traits<Reverse<MatrixType, Direction> >
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
// let's enable LinearAccess only with vectorization because of the product overhead
LinearAccess = ( (Direction==BothDirections) && (int(_MatrixTypeNested::Flags)&PacketAccessBit) )
? LinearAccessBit : 0,
Flags = int(_MatrixTypeNested::Flags) & (HereditaryBits | LvalueBit | PacketAccessBit | LinearAccess),
CoeffReadCost = _MatrixTypeNested::CoeffReadCost
Flags = _MatrixTypeNested::Flags & (RowMajorBit | LvalueBit)
};
};
@ -74,6 +67,7 @@ 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;
using Base::IsRowMajor;
// next line is necessary because otherwise const version of operator()
@ -95,47 +89,47 @@ template<typename MatrixType, int Direction> class Reverse
typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
public:
inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); }
inline Index innerStride() const
EIGEN_DEVICE_FUNC inline Index innerStride() const
{
return -m_matrix.innerStride();
}
inline Scalar& operator()(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return coeffRef(row, col);
}
inline Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(ReverseRow ? m_matrix.rows() - row - 1 : row,
ReverseCol ? m_matrix.cols() - col - 1 : col);
}
inline CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index row, Index col) const
{
return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row,
ReverseCol ? m_matrix.cols() - col - 1 : col);
}
inline CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(m_matrix.size() - index - 1);
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1);
}
inline Scalar& operator()(Index index)
EIGEN_DEVICE_FUNC inline Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < m_matrix.size());
return coeffRef(index);
@ -170,7 +164,7 @@ template<typename MatrixType, int Direction> class Reverse
m_matrix.const_cast_derived().template writePacket<LoadMode>(m_matrix.size() - index - PacketSize, internal::preverse(x));
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
@ -190,7 +184,7 @@ template<typename Derived>
inline typename DenseBase<Derived>::ReverseReturnType
DenseBase<Derived>::reverse()
{
return derived();
return ReverseReturnType(derived());
}
/** This is the const version of reverse(). */
@ -198,7 +192,7 @@ template<typename Derived>
inline const typename DenseBase<Derived>::ConstReverseReturnType
DenseBase<Derived>::reverse() const
{
return derived();
return ConstReverseReturnType(derived());
}
/** This is the "in place" version of reverse: it reverses \c *this.

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

@ -43,23 +43,21 @@ struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime,
Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & HereditaryBits,
CoeffReadCost = traits<typename remove_all<ConditionMatrixNested>::type>::CoeffReadCost
+ EIGEN_SIZE_MAX(traits<typename remove_all<ThenMatrixNested>::type>::CoeffReadCost,
traits<typename remove_all<ElseMatrixNested>::type>::CoeffReadCost)
Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit
};
};
}
template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
class Select : internal::no_assignment_operator,
public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type
class Select : public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type,
internal::no_assignment_operator
{
public:
typedef typename internal::dense_xpr_base<Select>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Select)
inline EIGEN_DEVICE_FUNC
Select(const ConditionMatrixType& a_conditionMatrix,
const ThenMatrixType& a_thenMatrix,
const ElseMatrixType& a_elseMatrix)
@ -69,9 +67,10 @@ class Select : internal::no_assignment_operator,
eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
}
Index rows() const { return m_condition.rows(); }
Index cols() const { return m_condition.cols(); }
inline EIGEN_DEVICE_FUNC Index rows() const { return m_condition.rows(); }
inline EIGEN_DEVICE_FUNC Index cols() const { return m_condition.cols(); }
inline EIGEN_DEVICE_FUNC
const Scalar coeff(Index i, Index j) const
{
if (m_condition.coeff(i,j))
@ -80,6 +79,7 @@ class Select : internal::no_assignment_operator,
return m_else.coeff(i,j);
}
inline EIGEN_DEVICE_FUNC
const Scalar coeff(Index i) const
{
if (m_condition.coeff(i))
@ -88,17 +88,17 @@ class Select : internal::no_assignment_operator,
return m_else.coeff(i);
}
const ConditionMatrixType& conditionMatrix() const
inline EIGEN_DEVICE_FUNC const ConditionMatrixType& conditionMatrix() const
{
return m_condition;
}
const ThenMatrixType& thenMatrix() const
inline EIGEN_DEVICE_FUNC const ThenMatrixType& thenMatrix() const
{
return m_then;
}
const ElseMatrixType& elseMatrix() const
inline EIGEN_DEVICE_FUNC const ElseMatrixType& elseMatrix() const
{
return m_else;
}

179
Eigen/src/Core/SelfAdjointView.h
View File

@ -35,26 +35,23 @@ struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef MatrixType ExpressionType;
typedef typename MatrixType::PlainObject DenseMatrixType;
typedef typename MatrixType::PlainObject FullMatrixType;
enum {
Mode = UpLo | SelfAdjoint,
Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits)
& (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)), // FIXME these flags should be preserved
CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits|FlagsLvalueBit)
& (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)) // FIXME these flags should be preserved
};
};
}
template <typename Lhs, int LhsMode, bool LhsIsVector,
typename Rhs, int RhsMode, bool RhsIsVector>
struct SelfadjointProductMatrix;
// FIXME could also be called SelfAdjointWrapper to be consistent with DiagonalWrapper ??
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:
typedef _MatrixType MatrixType;
typedef TriangularBase<SelfAdjointView> Base;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
@ -65,12 +62,13 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
typedef typename MatrixType::Index Index;
enum {
Mode = internal::traits<SelfAdjointView>::Mode
Mode = internal::traits<SelfAdjointView>::Mode,
Flags = internal::traits<SelfAdjointView>::Flags
};
typedef typename MatrixType::PlainObject PlainObject;
EIGEN_DEVICE_FUNC
inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{}
EIGEN_DEVICE_FUNC
@ -98,6 +96,7 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
Base::check_coordinates_internal(row, col);
return m_matrix.const_cast_derived().coeffRef(row, col);
}
@ -111,26 +110,29 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
EIGEN_DEVICE_FUNC
MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); }
/** Efficient self-adjoint matrix times vector/matrix product */
/** Efficient triangular matrix times vector/matrix product */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
const Product<SelfAdjointView,OtherDerived>
operator*(const MatrixBase<OtherDerived>& rhs) const
{
return SelfadjointProductMatrix
<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
(m_matrix, rhs.derived());
return Product<SelfAdjointView,OtherDerived>(*this, rhs.derived());
}
/** Efficient vector/matrix times self-adjoint matrix product */
/** Efficient vector/matrix times triangular matrix product */
template<typename OtherDerived> friend
EIGEN_DEVICE_FUNC
SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
const Product<OtherDerived,SelfAdjointView>
operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
{
return SelfadjointProductMatrix
<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
(lhs.derived(),rhs.m_matrix);
return Product<OtherDerived,SelfAdjointView>(lhs.derived(),rhs);
}
friend EIGEN_DEVICE_FUNC
const SelfAdjointView<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,MatrixType>,UpLo>
operator*(const Scalar& s, const SelfAdjointView& mat)
{
return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
}
/** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
@ -194,96 +196,57 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
namespace internal {
template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount, ClearOpposite>
// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
// in the future selfadjoint-ness should be defined by the expression traits
// such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
template<typename MatrixType, unsigned int Mode>
struct evaluator_traits<SelfAdjointView<MatrixType,Mode> >
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
typedef SelfAdjointShape Shape;
static const int AssumeAliasing = 0;
};
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
template<int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version>
class triangular_dense_assignment_kernel<UpLo,SelfAdjoint,SetOpposite,DstEvaluatorTypeT,SrcEvaluatorTypeT,Functor,Version>
: public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
{
protected:
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
typedef typename Base::DstXprType DstXprType;
typedef typename Base::SrcXprType SrcXprType;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::DstEvaluatorType DstEvaluatorType;
typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::AssignmentTraits AssignmentTraits;
EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
: Base(dst, src, func, dstExpr)
{}
EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
eigen_internal_assert(row!=col);
Scalar tmp = m_src.coeff(row,col);
m_functor.assignCoeff(m_dst.coeffRef(row,col), tmp);
m_functor.assignCoeff(m_dst.coeffRef(col,row), numext::conj(tmp));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, 0, ClearOpposite>
{
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount, ClearOpposite>
{
enum {
col = (UnrollCount-1) / Derived1::RowsAtCompileTime,
row = (UnrollCount-1) % Derived1::RowsAtCompileTime
};
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
{
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, 0, ClearOpposite>
{
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, Dynamic, ClearOpposite>
{
typedef typename Derived1::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
{
for(Index j = 0; j < dst.cols(); ++j)
{
for(Index i = 0; i < j; ++i)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(j, j, src);
}
}
};
template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dynamic, ClearOpposite>
{
EIGEN_DEVICE_FUNC
static inline void run(Derived1 &dst, const Derived2 &src)
{
typedef typename Derived1::Index Index;
for(Index i = 0; i < dst.rows(); ++i)
{
for(Index j = 0; j < i; ++j)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(i, i, src);
}
Base::assignCoeff(id,id);
}
EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index)
{ eigen_internal_assert(false && "should never be called"); }
};
} // end namespace internal
@ -297,7 +260,7 @@ template<unsigned int UpLo>
typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView() const
{
return derived();
return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
}
template<typename Derived>
@ -305,7 +268,7 @@ template<unsigned int UpLo>
typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView()
{
return derived();
return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
}
} // end namespace Eigen

179
Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@ -12,179 +12,11 @@
namespace Eigen {
/** \class SelfCwiseBinaryOp
* \ingroup Core_Module
*
* \internal
*
* \brief Internal helper class for optimizing operators like +=, -=
*
* This is a pseudo expression class re-implementing the copyCoeff/copyPacket
* method to directly performs a +=/-= operations in an optimal way. In particular,
* this allows to make sure that the input/output data are loaded only once using
* aligned packet loads.
*
* \sa class SwapWrapper for a similar trick.
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<SelfCwiseBinaryOp<BinaryOp,Lhs,Rhs> >
: traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >
{
enum {
// Note that it is still a good idea to preserve the DirectAccessBit
// so that assign can correctly align the data.
Flags = traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >::Flags | (Lhs::Flags&AlignedBit) | (Lhs::Flags&DirectAccessBit) | (Lhs::Flags&LvalueBit),
OuterStrideAtCompileTime = Lhs::OuterStrideAtCompileTime,
InnerStrideAtCompileTime = Lhs::InnerStrideAtCompileTime
};
};
}
template<typename BinaryOp, typename Lhs, typename Rhs> class SelfCwiseBinaryOp
: public internal::dense_xpr_base< SelfCwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
{
public:
typedef typename internal::dense_xpr_base<SelfCwiseBinaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SelfCwiseBinaryOp)
typedef typename internal::packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC
inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_matrix.outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_matrix.innerStride(); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_matrix.data(); }
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(row, col);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index col) const
{
return m_matrix.coeffRef(row, col);
}
// note that this function is needed by assign to correctly align loads/stores
// TODO make Assign use .data()
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar& tmp = m_matrix.coeffRef(row,col);
tmp = m_functor(tmp, _other.coeff(row,col));
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
Scalar& tmp = m_matrix.coeffRef(index);
tmp = m_functor(tmp, _other.coeff(index));
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
m_matrix.template writePacket<StoreMode>(row, col,
m_functor.packetOp(m_matrix.template packet<StoreMode>(row, col),_other.template packet<LoadMode>(row, col)) );
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_matrix.size());
m_matrix.template writePacket<StoreMode>(index,
m_functor.packetOp(m_matrix.template packet<StoreMode>(index),_other.template packet<LoadMode>(index)) );
}
// reimplement lazyAssign to handle complex *= real
// see CwiseBinaryOp ctor for details
template<typename RhsDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE SelfCwiseBinaryOp& lazyAssign(const DenseBase<RhsDerived>& rhs)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs,RhsDerived)
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename RhsDerived::Scalar);
#ifdef EIGEN_DEBUG_ASSIGN
internal::assign_traits<SelfCwiseBinaryOp, RhsDerived>::debug();
#endif
eigen_assert(rows() == rhs.rows() && cols() == rhs.cols());
internal::assign_impl<SelfCwiseBinaryOp, RhsDerived>::run(*this,rhs.derived());
#ifndef EIGEN_NO_DEBUG
this->checkTransposeAliasing(rhs.derived());
#endif
return *this;
}
// overloaded to honor evaluation of special matrices
// maybe another solution would be to not use SelfCwiseBinaryOp
// at first...
EIGEN_DEVICE_FUNC
SelfCwiseBinaryOp& operator=(const Rhs& _rhs)
{
typename internal::nested<Rhs>::type rhs(_rhs);
return Base::operator=(rhs);
}
EIGEN_DEVICE_FUNC
Lhs& expression() const
{
return m_matrix;
}
EIGEN_DEVICE_FUNC
const BinaryOp& functor() const
{
return m_functor;
}
protected:
Lhs& m_matrix;
const BinaryOp& m_functor;
private:
SelfCwiseBinaryOp& operator=(const SelfCwiseBinaryOp&);
};
template<typename Derived>
inline Derived& DenseBase<Derived>::operator*=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(),other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar>());
return derived();
}
@ -192,8 +24,7 @@ template<typename Derived>
inline Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(),other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar>());
return derived();
}
@ -201,8 +32,7 @@ template<typename Derived>
inline Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(),other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar>());
return derived();
}
@ -210,8 +40,7 @@ template<typename Derived>
inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(), other);
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar>());
return derived();
}

152
Eigen/src/Core/Solve.h Normal file
View File

@ -0,0 +1,152 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SOLVE_H
#define EIGEN_SOLVE_H
namespace Eigen {
template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
/** \class Solve
* \ingroup Core_Module
*
* \brief Pseudo expression representing a solving operation
*
* \tparam Decomposition the type of the matrix or decomposion object
* \tparam Rhstype the type of the right-hand side
*
* This class represents an expression of A.solve(B)
* and most of the time this is the only way it is used.
*
*/
namespace internal {
// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
template<typename Decomposition, typename RhsType,typename StorageKind> struct solve_traits;
template<typename Decomposition, typename RhsType>
struct solve_traits<Decomposition,RhsType,Dense>
{
typedef typename Decomposition::MatrixType MatrixType;
typedef Matrix<typename RhsType::Scalar,
MatrixType::ColsAtCompileTime,
RhsType::ColsAtCompileTime,
RhsType::PlainObject::Options,
MatrixType::MaxColsAtCompileTime,
RhsType::MaxColsAtCompileTime> PlainObject;
};
template<typename Decomposition, typename RhsType>
struct traits<Solve<Decomposition, RhsType> >
: traits<typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject>
{
typedef typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject PlainObject;
typedef traits<PlainObject> BaseTraits;
enum {
Flags = BaseTraits::Flags & RowMajorBit,
CoeffReadCost = Dynamic
};
};
}
template<typename Decomposition, typename RhsType>
class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
{
public:
typedef typename RhsType::Index Index;
typedef typename internal::traits<Solve>::PlainObject PlainObject;
Solve(const Decomposition &dec, const RhsType &rhs)
: m_dec(dec), m_rhs(rhs)
{}
EIGEN_DEVICE_FUNC Index rows() const { return m_dec.cols(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC const Decomposition& dec() const { return m_dec; }
EIGEN_DEVICE_FUNC const RhsType& rhs() const { return m_rhs; }
protected:
const Decomposition &m_dec;
const RhsType &m_rhs;
};
// Specialization of the Solve expression for dense results
template<typename Decomposition, typename RhsType>
class SolveImpl<Decomposition,RhsType,Dense>
: public MatrixBase<Solve<Decomposition,RhsType> >
{
typedef Solve<Decomposition,RhsType> Derived;
public:
typedef MatrixBase<Solve<Decomposition,RhsType> > Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
private:
Scalar coeff(Index row, Index col) const;
Scalar coeff(Index i) const;
};
// Generic API dispatcher
template<typename Decomposition, typename RhsType, typename StorageKind>
class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type
{
public:
typedef typename internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type Base;
};
namespace internal {
// Evaluator of Solve -> eval into a temporary
template<typename Decomposition, typename RhsType>
struct evaluator<Solve<Decomposition,RhsType> >
: public evaluator<typename Solve<Decomposition,RhsType>::PlainObject>::type
{
typedef Solve<Decomposition,RhsType> SolveType;
typedef typename SolveType::PlainObject PlainObject;
typedef typename evaluator<PlainObject>::type Base;
typedef evaluator type;
typedef evaluator nestedType;
EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
: m_result(solve.rows(), solve.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
solve.dec()._solve_impl(solve.rhs(), m_result);
}
protected:
PlainObject m_result;
};
// Specialization for "dst = dec.solve(rhs)"
// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
{
// FIXME shall we resize dst here?
src.dec()._solve_impl(src.rhs(), dst);
}
};
} // end namepsace internal
} // end namespace Eigen
#endif // EIGEN_SOLVE_H

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

@ -171,10 +171,10 @@ struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
*/
template<typename MatrixType, unsigned int Mode>
template<int Side, typename OtherDerived>
void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
{
OtherDerived& other = _other.const_cast_derived();
eigen_assert( cols() == rows() && ((Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols())) );
eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
@ -183,7 +183,7 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
OtherCopy otherCopy(other);
internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type,
Side, Mode>::run(nestedExpression(), otherCopy);
Side, Mode>::run(derived().nestedExpression(), otherCopy);
if (copy)
other = otherCopy;
@ -213,9 +213,9 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
template<typename Derived, unsigned int Mode>
template<int Side, typename Other>
const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other>
TriangularView<Derived,Mode>::solve(const MatrixBase<Other>& other) const
TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) const
{
return internal::triangular_solve_retval<Side,TriangularView,Other>(*this, other.derived());
return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
}
namespace internal {

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

@ -17,7 +17,6 @@ namespace internal {
template<typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
{
using std::max;
Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
if(maxCoeff>scale)
@ -58,8 +57,6 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
typedef typename Derived::RealScalar RealScalar;
typedef typename Derived::Index Index;
using std::pow;
EIGEN_USING_STD_MATH(min);
EIGEN_USING_STD_MATH(max);
using std::sqrt;
using std::abs;
const Derived& vec(_vec.derived());
@ -136,8 +133,8 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
}
else
return sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
asml = numext::mini(abig, amed);
abig = numext::maxi(abig, amed);
if(asml <= abig*relerr)
return abig;
else
@ -160,7 +157,6 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
EIGEN_USING_STD_MATH(min);
using std::sqrt;
const Index blockSize = 4096;
RealScalar scale(0);
@ -174,7 +170,7 @@ MatrixBase<Derived>::stableNorm() const
if (bi>0)
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
for (; bi<n; bi+=blockSize)
internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
internal::stable_norm_kernel(this->segment(bi,numext::mini(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
return scale * sqrt(ssq);
}

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

@ -86,26 +86,26 @@ class Stride
/** \brief Convenience specialization of Stride to specify only an inner stride
* See class Map for some examples */
template<int Value = Dynamic>
template<int Value>
class InnerStride : public Stride<0, Value>
{
typedef Stride<0, Value> Base;
public:
typedef DenseIndex Index;
EIGEN_DEVICE_FUNC InnerStride() : Base() {}
EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {}
EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} // FIXME making this explicit could break valid code
};
/** \brief Convenience specialization of Stride to specify only an outer stride
* See class Map for some examples */
template<int Value = Dynamic>
template<int Value>
class OuterStride : public Stride<Value, 0>
{
typedef Stride<Value, 0> Base;
public:
typedef DenseIndex Index;
EIGEN_DEVICE_FUNC OuterStride() : Base() {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
};
} // end namespace Eigen

161
Eigen/src/Core/Swap.h
View File

@ -12,129 +12,54 @@
namespace Eigen {
/** \class SwapWrapper
* \ingroup Core_Module
*
* \internal
*
* \brief Internal helper class for swapping two expressions
*/
namespace internal {
template<typename ExpressionType>
struct traits<SwapWrapper<ExpressionType> > : traits<ExpressionType> {};
}
template<typename ExpressionType> class SwapWrapper
: public internal::dense_xpr_base<SwapWrapper<ExpressionType> >::type
// Overload default assignPacket behavior for swapping them
template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
: public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
{
public:
typedef typename internal::dense_xpr_base<SwapWrapper>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SwapWrapper)
typedef typename internal::packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC
inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_expression.cols(); }
EIGEN_DEVICE_FUNC
inline Index outerStride() const { return m_expression.outerStride(); }
EIGEN_DEVICE_FUNC
inline Index innerStride() const { return m_expression.innerStride(); }
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void copyCoeff(Index rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(rowId >= 0 && rowId < rows()
&& colId >= 0 && colId < cols());
Scalar tmp = m_expression.coeff(rowId, colId);
m_expression.coeffRef(rowId, colId) = _other.coeff(rowId, colId);
_other.coeffRef(rowId, colId) = tmp;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
void copyCoeff(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
Scalar tmp = m_expression.coeff(index);
m_expression.coeffRef(index) = _other.coeff(index);
_other.coeffRef(index) = tmp;
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(rowId >= 0 && rowId < rows()
&& colId >= 0 && colId < cols());
Packet tmp = m_expression.template packet<StoreMode>(rowId, colId);
m_expression.template writePacket<StoreMode>(rowId, colId,
_other.template packet<LoadMode>(rowId, colId)
);
_other.template writePacket<LoadMode>(rowId, colId, tmp);
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index index, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(index >= 0 && index < m_expression.size());
Packet tmp = m_expression.template packet<StoreMode>(index);
m_expression.template writePacket<StoreMode>(index,
_other.template packet<LoadMode>(index)
);
_other.template writePacket<LoadMode>(index, tmp);
}
EIGEN_DEVICE_FUNC
ExpressionType& expression() const { return m_expression; }
protected:
ExpressionType& m_expression;
protected:
typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> Base;
typedef typename DstEvaluatorTypeT::PacketScalar PacketScalar;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::Scalar Scalar;
typedef typename Base::Index Index;
typedef typename Base::DstXprType DstXprType;
typedef swap_assign_op<Scalar> Functor;
EIGEN_DEVICE_FUNC generic_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
: Base(dst, src, func, dstExpr)
{}
template<int StoreMode, int LoadMode>
void assignPacket(Index row, Index col)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(row,col), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(row,col));
}
template<int StoreMode, int LoadMode>
void assignPacket(Index index)
{
m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(index), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(index));
}
// TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
template<int StoreMode, int LoadMode>
void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = Base::rowIndexByOuterInner(outer, inner);
Index col = Base::colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode>(row, col);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_SWAP_H

122
Eigen/src/Core/Transpose.h
View File

@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -29,9 +29,10 @@ namespace Eigen {
namespace internal {
template<typename MatrixType>
struct traits<Transpose<MatrixType> > : traits<MatrixType>
struct traits<Transpose<MatrixType> >
{
typedef typename MatrixType::Scalar Scalar;
typedef typename traits<MatrixType>::Scalar Scalar;
typedef typename traits<MatrixType>::Index Index;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
typedef typename traits<MatrixType>::StorageKind StorageKind;
@ -45,7 +46,6 @@ struct traits<Transpose<MatrixType> > : traits<MatrixType>
Flags0 = MatrixTypeNestedPlain::Flags & ~(LvalueBit | NestByRefBit),
Flags1 = Flags0 | FlagsLvalueBit,
Flags = Flags1 ^ RowMajorBit,
CoeffReadCost = MatrixTypeNestedPlain::CoeffReadCost,
InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
};
@ -61,9 +61,10 @@ 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;
EIGEN_DEVICE_FUNC
inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {}
explicit inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
@ -100,12 +101,22 @@ struct TransposeImpl_base<MatrixType, false>
} // end namespace internal
// Generic API dispatcher
template<typename XprType, typename StorageKind>
class TransposeImpl
: public internal::generic_xpr_base<Transpose<XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
};
template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
: public internal::TransposeImpl_base<MatrixType>::type
{
public:
typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
using Base::coeffRef;
EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
@ -118,23 +129,10 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
inline const Scalar* data() const { return derived().nestedExpression().data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(colId, rowId);
}
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return derived().nestedExpression().data(); }
// FIXME: shall we keep the const version of coeffRef?
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
@ -146,42 +144,6 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
{
return derived().nestedExpression().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return derived().nestedExpression().coeff(colId, rowId);
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index index) const
{
return derived().nestedExpression().coeff(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index rowId, Index colId) const
{
return derived().nestedExpression().template packet<LoadMode>(colId, rowId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& x)
{
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(colId, rowId, x);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return derived().nestedExpression().template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(index, x);
}
};
/** \returns an expression of the transpose of *this.
@ -207,7 +169,7 @@ template<typename Derived>
inline Transpose<Derived>
DenseBase<Derived>::transpose()
{
return derived();
return TransposeReturnType(derived());
}
/** This is the const version of transpose().
@ -245,8 +207,7 @@ template<typename Derived>
inline const typename MatrixBase<Derived>::AdjointReturnType
MatrixBase<Derived>::adjoint() const
{
return this->transpose(); // in the complex case, the .conjugate() is be implicit here
// due to implicit conversion to return type
return AdjointReturnType(this->transpose());
}
/***************************************************************************
@ -256,18 +217,39 @@ MatrixBase<Derived>::adjoint() const
namespace internal {
template<typename MatrixType,
bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic>
bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic,
bool MatchPacketSize =
(int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size))
&& (internal::evaluator<MatrixType>::Flags&PacketAccessBit) >
struct inplace_transpose_selector;
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,true> { // square matrix
struct inplace_transpose_selector<MatrixType,true,false> { // square matrix
static void run(MatrixType& m) {
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
}
};
// TODO: vectorized path is currently limited to LargestPacketSize x LargestPacketSize cases only.
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,false> { // non square matrix
struct inplace_transpose_selector<MatrixType,true,true> { // PacketSize x PacketSize
static void run(MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
typedef typename MatrixType::Index Index;
const Index PacketSize = internal::packet_traits<Scalar>::size;
const Index Alignment = internal::evaluator<MatrixType>::Flags&AlignedBit ? Aligned : Unaligned;
PacketBlock<Packet> A;
for (Index i=0; i<PacketSize; ++i)
A.packet[i] = m.template packetByOuterInner<Alignment>(i,0);
internal::ptranspose(A);
for (Index i=0; i<PacketSize; ++i)
m.template writePacket<Alignment>(m.rowIndexByOuterInner(i,0), m.colIndexByOuterInner(i,0), A.packet[i]);
}
};
template<typename MatrixType,bool MatchPacketSize>
struct inplace_transpose_selector<MatrixType,false,MatchPacketSize> { // non square matrix
static void run(MatrixType& m) {
if (m.rows()==m.cols())
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
@ -413,15 +395,15 @@ struct checkTransposeAliasing_impl<Derived, OtherDerived, false>
}
};
template<typename Dst, typename Src>
void check_for_aliasing(const Dst &dst, const Src &src)
{
internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
}
} // end namespace internal
template<typename Derived>
template<typename OtherDerived>
void DenseBase<Derived>::checkTransposeAliasing(const OtherDerived& other) const
{
internal::checkTransposeAliasing_impl<Derived, OtherDerived>::run(derived(), other);
}
#endif
#endif // EIGEN_NO_DEBUG
} // end namespace Eigen

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

@ -240,7 +240,7 @@ class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndexTyp
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
inline Map(const StorageIndexType* indicesPtr)
explicit inline Map(const StorageIndexType* indicesPtr)
: m_indices(indicesPtr)
{}
@ -299,7 +299,7 @@ class TranspositionsWrapper
typedef typename IndicesType::Scalar StorageIndexType;
typedef typename IndicesType::Index Index;
inline TranspositionsWrapper(IndicesType& a_indices)
explicit inline TranspositionsWrapper(IndicesType& a_indices)
: m_indices(a_indices)
{}
@ -414,7 +414,7 @@ class Transpose<TranspositionsBase<TranspositionsDerived> >
typedef typename TranspositionType::IndicesType IndicesType;
public:
Transpose(const TranspositionType& t) : m_transpositions(t) {}
explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
inline int size() const { return m_transpositions.size(); }

981
Eigen/src/Core/TriangularMatrix.h
View File

File diff suppressed because it is too large Load Diff

112
Eigen/src/Core/VectorwiseOp.h
View File

@ -48,25 +48,15 @@ struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits,
Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0),
Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime
};
#if EIGEN_GNUC_AT_LEAST(3,4)
typedef typename MemberOp::template Cost<InputScalar,int(TraversalSize)> CostOpType;
#else
typedef typename MemberOp::template Cost<InputScalar,TraversalSize> CostOpType;
#endif
enum {
CoeffReadCost = TraversalSize==Dynamic ? Dynamic
: TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value)
};
};
}
template< typename MatrixType, typename MemberOp, int Direction>
class PartialReduxExpr : internal::no_assignment_operator,
public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type
class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
internal::no_assignment_operator
{
public:
@ -75,7 +65,7 @@ class PartialReduxExpr : internal::no_assignment_operator,
typedef typename internal::traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested;
PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
: m_matrix(mat), m_functor(func) {}
Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); }
@ -138,7 +128,7 @@ struct member_redux {
>::type result_type;
template<typename _Scalar, int Size> struct Cost
{ enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
member_redux(const BinaryOp func) : m_functor(func) {}
explicit member_redux(const BinaryOp func) : m_functor(func) {}
template<typename Derived>
inline result_type operator()(const DenseBase<Derived>& mat) const
{ return mat.redux(m_functor); }
@ -175,10 +165,10 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
template<template<typename _Scalar> class Functor,
typename Scalar=typename internal::traits<ExpressionType>::Scalar> struct ReturnType
typename Scalar_=Scalar> struct ReturnType
{
typedef PartialReduxExpr<ExpressionType,
Functor<Scalar>,
Functor<Scalar_>,
Direction
> Type;
};
@ -186,7 +176,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
template<typename BinaryOp> struct ReduxReturnType
{
typedef PartialReduxExpr<ExpressionType,
internal::member_redux<BinaryOp,typename internal::traits<ExpressionType>::Scalar>,
internal::member_redux<BinaryOp,Scalar>,
Direction
> Type;
};
@ -259,7 +249,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
public:
inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
inline const ExpressionType& _expression() const { return m_matrix; }
@ -274,7 +264,22 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
template<typename BinaryOp>
const typename ReduxReturnType<BinaryOp>::Type
redux(const BinaryOp& func = BinaryOp()) const
{ return typename ReduxReturnType<BinaryOp>::Type(_expression(), func); }
{ return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func)); }
typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
typedef typename ReturnType<internal::member_squaredNorm,RealScalar>::Type SquaredNormReturnType;
typedef typename ReturnType<internal::member_norm,RealScalar>::Type NormReturnType;
typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
typedef typename ReturnType<internal::member_mean>::Type MeanReturnType;
typedef typename ReturnType<internal::member_all>::Type AllReturnType;
typedef typename ReturnType<internal::member_any>::Type AnyReturnType;
typedef PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> CountReturnType;
typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType;
/** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression.
@ -285,8 +290,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_minCoeff.out
*
* \sa DenseBase::minCoeff() */
const typename ReturnType<internal::member_minCoeff>::Type minCoeff() const
{ return _expression(); }
const MinCoeffReturnType minCoeff() const
{ return MinCoeffReturnType(_expression()); }
/** \returns a row (or column) vector expression of the largest coefficient
* of each column (or row) of the referenced expression.
@ -297,8 +302,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_maxCoeff.out
*
* \sa DenseBase::maxCoeff() */
const typename ReturnType<internal::member_maxCoeff>::Type maxCoeff() const
{ return _expression(); }
const MaxCoeffReturnType maxCoeff() const
{ return MaxCoeffReturnType(_expression()); }
/** \returns a row (or column) vector expression of the squared norm
* of each column (or row) of the referenced expression.
@ -308,8 +313,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_squaredNorm.out
*
* \sa DenseBase::squaredNorm() */
const typename ReturnType<internal::member_squaredNorm,RealScalar>::Type squaredNorm() const
{ return _expression(); }
const SquaredNormReturnType squaredNorm() const
{ return SquaredNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression.
@ -319,8 +324,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa DenseBase::norm() */
const typename ReturnType<internal::member_norm,RealScalar>::Type norm() const
{ return _expression(); }
const NormReturnType norm() const
{ return NormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@ -329,8 +334,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::blueNorm() */
const typename ReturnType<internal::member_blueNorm,RealScalar>::Type blueNorm() const
{ return _expression(); }
const BlueNormReturnType blueNorm() const
{ return BlueNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@ -339,8 +344,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::stableNorm() */
const typename ReturnType<internal::member_stableNorm,RealScalar>::Type stableNorm() const
{ return _expression(); }
const StableNormReturnType stableNorm() const
{ return StableNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
@ -349,8 +354,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* This is a vector with real entries, even if the original matrix has complex entries.
*
* \sa DenseBase::hypotNorm() */
const typename ReturnType<internal::member_hypotNorm,RealScalar>::Type hypotNorm() const
{ return _expression(); }
const HypotNormReturnType hypotNorm() const
{ return HypotNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the sum
* of each column (or row) of the referenced expression.
@ -359,31 +364,31 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_sum.out
*
* \sa DenseBase::sum() */
const typename ReturnType<internal::member_sum>::Type sum() const
{ return _expression(); }
const SumReturnType sum() const
{ return SumReturnType(_expression()); }
/** \returns a row (or column) vector expression of the mean
* of each column (or row) of the referenced expression.
*
* \sa DenseBase::mean() */
const typename ReturnType<internal::member_mean>::Type mean() const
{ return _expression(); }
const MeanReturnType mean() const
{ return MeanReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* whether \b all coefficients of each respective column (or row) are \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::all() */
const typename ReturnType<internal::member_all>::Type all() const
{ return _expression(); }
const AllReturnType all() const
{ return AllReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* whether \b at \b least one coefficient of each respective column (or row) is \c true.
* This expression can be assigned to a vector with entries of type \c bool.
*
* \sa DenseBase::any() */
const typename ReturnType<internal::member_any>::Type any() const
{ return _expression(); }
const AnyReturnType any() const
{ return Any(_expression()); }
/** \returns a row (or column) vector expression representing
* the number of \c true coefficients of each respective column (or row).
@ -394,8 +399,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_count.out
*
* \sa DenseBase::count() */
const PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> count() const
{ return _expression(); }
const CountReturnType count() const
{ return CountReturnType(_expression()); }
/** \returns a row (or column) vector expression of the product
* of each column (or row) of the referenced expression.
@ -404,8 +409,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude PartialRedux_prod.out
*
* \sa DenseBase::prod() */
const typename ReturnType<internal::member_prod>::Type prod() const
{ return _expression(); }
const ProdReturnType prod() const
{ return ProdReturnType(_expression()); }
/** \returns a matrix expression
@ -415,8 +420,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* Output: \verbinclude Vectorwise_reverse.out
*
* \sa DenseBase::reverse() */
const Reverse<ExpressionType, Direction> reverse() const
{ return Reverse<ExpressionType, Direction>( _expression() ); }
const ReverseReturnType reverse() const
{ return ReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,Direction==Vertical?Dynamic:1,Direction==Horizontal?Dynamic:1> ReplicateReturnType;
const ReplicateReturnType replicate(Index factor) const;
@ -560,7 +565,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/////////// Geometry module ///////////
Homogeneous<ExpressionType,Direction> homogeneous() const;
typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
typedef typename ExpressionType::PlainObject CrossReturnType;
template<typename OtherDerived>
@ -605,7 +611,7 @@ template<typename Derived>
inline const typename DenseBase<Derived>::ConstColwiseReturnType
DenseBase<Derived>::colwise() const
{
return derived();
return ConstColwiseReturnType(derived());
}
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
@ -616,7 +622,7 @@ template<typename Derived>
inline typename DenseBase<Derived>::ColwiseReturnType
DenseBase<Derived>::colwise()
{
return derived();
return ColwiseReturnType(derived());
}
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
@ -630,7 +636,7 @@ template<typename Derived>
inline const typename DenseBase<Derived>::ConstRowwiseReturnType
DenseBase<Derived>::rowwise() const
{
return derived();
return ConstRowwiseReturnType(derived());
}
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
@ -641,7 +647,7 @@ template<typename Derived>
inline typename DenseBase<Derived>::RowwiseReturnType
DenseBase<Derived>::rowwise()
{
return derived();
return RowwiseReturnType(derived());
}
} // end namespace Eigen

44
Eigen/src/Core/Visitor.h
View File

@ -53,6 +53,33 @@ struct visitor_impl<Visitor, Derived, Dynamic>
}
};
// evaluator adaptor
template<typename XprType>
class visitor_evaluator
{
public:
explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = XprType::RowsAtCompileTime,
CoeffReadCost = internal::evaluator<XprType>::CoeffReadCost
};
Index rows() const { return m_xpr.rows(); }
Index cols() const { return m_xpr.cols(); }
Index size() const { return m_xpr.size(); }
CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
protected:
typename internal::evaluator<XprType>::nestedType m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
@ -76,14 +103,17 @@ template<typename Derived>
template<typename Visitor>
void DenseBase<Derived>::visit(Visitor& visitor) const
{
enum { unroll = SizeAtCompileTime != Dynamic
&& CoeffReadCost != Dynamic
&& (SizeAtCompileTime == 1 || internal::functor_traits<Visitor>::Cost != Dynamic)
&& SizeAtCompileTime * CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits<Visitor>::Cost
<= EIGEN_UNROLLING_LIMIT };
return internal::visitor_impl<Visitor, Derived,
typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
enum { unroll = SizeAtCompileTime != Dynamic
&& ThisEvaluator::CoeffReadCost != Dynamic
&& (SizeAtCompileTime == 1 || internal::functor_traits<Visitor>::Cost != Dynamic)
&& SizeAtCompileTime * ThisEvaluator::CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits<Visitor>::Cost
<= EIGEN_UNROLLING_LIMIT };
return internal::visitor_impl<Visitor, ThisEvaluator,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived(), visitor);
>::run(thisEval, visitor);
}
namespace internal {

10
Eigen/src/Core/arch/AVX/PacketMath.h
View File

@ -22,9 +22,9 @@ namespace internal {
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
#endif
#ifdef EIGEN_VECTORIZE_FMA
#ifndef EIGEN_HAS_FUSED_MADD
#define EIGEN_HAS_FUSED_MADD 1
#ifdef __FMA__
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#endif
#endif
@ -137,7 +137,7 @@ template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, co
#ifdef EIGEN_VECTORIZE_FMA
template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
#if defined(__clang__) || defined(__GNUC__)
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
// clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and 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
@ -150,7 +150,7 @@ template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f&
#endif
}
template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
#if defined(__clang__) || defined(__GNUC__)
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
// see above
Packet4d res = c;
__asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));

230
Eigen/src/Core/arch/AltiVec/Complex.h
View File

@ -7,23 +7,21 @@
// 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_COMPLEX_ALTIVEC_H
#define EIGEN_COMPLEX_ALTIVEC_H
#ifndef EIGEN_COMPLEX32_ALTIVEC_H
#define EIGEN_COMPLEX32_ALTIVEC_H
namespace Eigen {
namespace internal {
static Packet4ui p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_ZERO_);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
static Packet16uc p16uc_COMPLEX_RE = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_COMPLEX_IM = vec_sld(p16uc_DUPLICATE, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_COMPLEX_REV = vec_sld(p16uc_REVERSE, p16uc_REVERSE, 8);//{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
static Packet16uc p16uc_COMPLEX_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);//{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_COMPLEX_RE, (Packet4ui)p16uc_COMPLEX_IM);//{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_COMPLEX_RE, (Packet4ui)p16uc_COMPLEX_IM);//{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
static Packet16uc p16uc_COMPLEX_MASK16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);//{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
static Packet16uc p16uc_COMPLEX_TRANSPOSE_0 = vec_add(p16uc_PSET_HI, p16uc_COMPLEX_MASK16);//{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_COMPLEX_TRANSPOSE_1 = vec_add(p16uc_PSET_LO, p16uc_COMPLEX_MASK16);//{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
#ifdef _BIG_ENDIAN
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#else
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#endif
//---------- float ----------
struct Packet2cf
@ -65,7 +63,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<flo
res.v = pload<Packet4f>((const float *)&from);
else
res.v = ploadu<Packet4f>((const float *)&from);
res.v = vec_perm(res.v, res.v, p16uc_PSET_HI);
res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
return res;
}
@ -95,16 +93,16 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
Packet4f v1, v2;
// Permute and multiply the real parts of a and b
v1 = vec_perm(a.v, a.v, p16uc_COMPLEX_RE);
v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
// Get the imaginary parts of a
v2 = vec_perm(a.v, a.v, p16uc_COMPLEX_IM);
v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
// multiply a_re * b
v1 = vec_madd(v1, b.v, p4f_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(v2, b.v, p4f_ZERO);
v2 = (Packet4f) vec_xor((Packet4ui)v2, p4ui_CONJ_XOR);
// permute back to a proper order
v2 = vec_perm(v2, v2, p16uc_COMPLEX_REV);
v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
return Packet2cf(vec_add(v1, v2));
}
@ -138,7 +136,7 @@ template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Pack
template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
{
Packet4f rev_a;
rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX_REV2);
rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
return Packet2cf(rev_a);
}
@ -153,9 +151,13 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packe
template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs)
{
Packet4f b1, b2;
#ifdef _BIG_ENDIAN
b1 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
b2 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
#else
b1 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
b2 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
#endif
b2 = (Packet4f) vec_sld(b2, b2, 8);
b2 = padd(b1, b2);
@ -179,7 +181,11 @@ struct palign_impl<Offset,Packet2cf>
{
if (Offset==1)
{
#ifdef _BIG_ENDIAN
first.v = vec_sld(first.v, second.v, 8);
#else
first.v = vec_sld(second.v, first.v, 8);
#endif
}
}
};
@ -222,23 +228,203 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
// TODO optimize it for AltiVec
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s = vec_madd(b.v, b.v, p4f_ZERO);
return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX_REV))));
return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX32_REV))));
}
template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
{
return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX_REV));
return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
}
EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
{
Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_COMPLEX_TRANSPOSE_0);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_COMPLEX_TRANSPOSE_1);
Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
kernel.packet[0].v = tmp;
}
//---------- double ----------
#ifdef __VSX__
struct Packet1cd
{
EIGEN_STRONG_INLINE Packet1cd() {}
EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
Packet2d v;
};
template<> struct packet_traits<std::complex<double> > : default_packet_traits
{
typedef Packet1cd type;
typedef Packet1cd half;
enum {
Vectorizable = 1,
AlignedOnScalar = 0,
size = 1,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasNegate = 1,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasSetLinear = 0
};
};
template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, DenseIndex stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return pload<Packet1cd>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, DenseIndex stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
pstore<std::complex<double> >(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_add(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_sub(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
Packet2d a_re, a_im, v1, v2;
// Permute and multiply the real parts of a and b
a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
// Get the imaginary parts of a
a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
// multiply a_re * b
v1 = vec_madd(a_re, b.v, p2d_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(a_im, b.v, p2d_ZERO);
v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
return Packet1cd(vec_add(v1, v2));
}
template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
{
return pset1<Packet1cd>(*from);
}
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { vec_dstt((long *)addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
{
std::complex<double> EIGEN_ALIGN16 res[2];
pstore<std::complex<double> >(res, a);
return res[0];
}
template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs)
{
return vecs[0];
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<int Offset>
struct palign_impl<Offset,Packet1cd>
{
static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/)
{
// FIXME is it sure we never have to align a Packet1cd?
// Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary...
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, false,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return pconj(internal::pmul(a, b));
}
};
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for AltiVec
Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b);
Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
return Packet1cd(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX32_REV))));
}
EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
{
return Packet1cd(preverse(Packet2d(x.v)));
}
EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
{
Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
kernel.packet[0].v = tmp;
}
#endif // __VSX__
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COMPLEX_ALTIVEC_H
#endif // EIGEN_COMPLEX32_ALTIVEC_H

433
Eigen/src/Core/arch/AltiVec/PacketMath.h
View File

@ -18,17 +18,17 @@ namespace internal {
#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
#endif
#ifndef EIGEN_HAS_FUSED_MADD
#define EIGEN_HAS_FUSED_MADD 1
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#endif
#ifndef EIGEN_HAS_FUSE_CJMADD
#define EIGEN_HAS_FUSE_CJMADD 1
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#endif
// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
#endif
typedef __vector float Packet4f;
@ -50,22 +50,20 @@ typedef __vector unsigned char Packet16uc;
#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
Packet4f p4f_##NAME = pset1<Packet4f>(X)
#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int>(X))
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
Packet4i p4i_##NAME = pset1<Packet4i>(X)
#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
Packet2d p2d_##NAME = pset1<Packet2d>(X)
#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
Packet2l p2l_##NAME = pset1<Packet2l>(X)
#define DST_CHAN 1
#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
// Define global static constants:
static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
static Packet16uc p16uc_REVERSE = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3};
static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0); //{ 0,1,2,3, 4,5,6,7, 8,9,10,11, 12,13,14,15}
static Packet16uc p16uc_DUPLICATE = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7};
// These constants are endian-agnostic
static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
@ -74,6 +72,50 @@ static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
static Packet4f p4f_ZERO_ = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
// Mask alignment
#ifdef __PPC64__
#define _EIGEN_MASK_ALIGNMENT 0xfffffffffffffff0
#else
#define _EIGEN_MASK_ALIGNMENT 0xfffffff0
#endif
#define _EIGEN_ALIGNED_PTR(x) ((ptrdiff_t)(x) & _EIGEN_MASK_ALIGNMENT)
// Handle endianness properly while loading constants
// Define global static constants:
#ifdef _BIG_ENDIAN
static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
#else
static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
#endif // _BIG_ENDIAN
static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16); //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16); //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8); //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
#ifdef _BIG_ENDIAN
static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#else
static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#endif // _BIG_ENDIAN
template<> struct packet_traits<float> : default_packet_traits
{
typedef Packet4f type;
@ -105,9 +147,22 @@ template<> struct packet_traits<int> : default_packet_traits
};
};
template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}; typedef Packet4f half; };
template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; };
/*
inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v)
{
union {
Packet16uc v;
unsigned char n[16];
} vt;
vt.v = v;
for (int i=0; i< 16; i++)
s << (int)vt.n[i] << ", ";
return s;
}
inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
{
union {
@ -140,7 +195,7 @@ inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}
/*
inline std::ostream & operator <<(std::ostream & s, const Packetbi & v)
{
union {
@ -150,14 +205,21 @@ inline std::ostream & operator <<(std::ostream & s, const Packetbi & v)
vt.v = v;
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}
*/
}*/
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
// Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
float EIGEN_ALIGN16 af[4];
af[0] = from;
Packet4f vc = vec_ld(0, af);
Packet4f vc = pload<Packet4f>(af);
vc = vec_splat(vc, 0);
return vc;
}
@ -165,17 +227,15 @@ template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
int EIGEN_ALIGN16 ai[4];
ai[0] = from;
Packet4i vc = vec_ld(0, ai);
Packet4i vc = pload<Packet4i>(ai);
vc = vec_splat(vc, 0);
return vc;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet4f>(const float *a,
Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
{
a3 = vec_ld(0,a);
a3 = pload<Packet4f>(a);
a0 = vec_splat(a3, 0);
a1 = vec_splat(a3, 1);
a2 = vec_splat(a3, 2);
@ -185,7 +245,7 @@ template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet4i>(const int *a,
Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
{
a3 = vec_ld(0,a);
a3 = pload<Packet4i>(a);
a0 = vec_splat(a3, 0);
a1 = vec_splat(a3, 1);
a2 = vec_splat(a3, 2);
@ -199,7 +259,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const floa
af[1] = from[1*stride];
af[2] = from[2*stride];
af[3] = from[3*stride];
return vec_ld(0, af);
return pload<Packet4f>(af);
}
template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, DenseIndex stride)
{
@ -208,12 +268,12 @@ template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* f
ai[1] = from[1*stride];
ai[2] = from[2*stride];
ai[3] = from[3*stride];
return vec_ld(0, ai);
return pload<Packet4i>(ai);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, DenseIndex stride)
{
float EIGEN_ALIGN16 af[4];
vec_st(from, 0, af);
pstore<float>(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
to[2*stride] = af[2];
@ -222,7 +282,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, co
template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, DenseIndex stride)
{
int EIGEN_ALIGN16 ai[4];
vec_st(from, 0, ai);
pstore<int>((int *)ai, from);
to[0*stride] = ai[0];
to[1*stride] = ai[1];
to[2*stride] = ai[2];
@ -283,7 +343,8 @@ template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const
*/
template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
{
Packet4f t, y_0, y_1, res;
#ifndef __VSX__ // VSX actually provides a div instruction
Packet4f t, y_0, y_1;
// Altivec does not offer a divide instruction, we have to do a reciprocal approximation
y_0 = vec_re(b);
@ -292,8 +353,10 @@ template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const
t = vec_nmsub(y_0, b, p4f_ONE);
y_1 = vec_madd(y_0, t, y_0);
res = vec_madd(a, y_1, p4f_ZERO);
return res;
return vec_madd(a, y_1, p4f_ZERO);
#else
return vec_div(a, b);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
@ -311,7 +374,6 @@ template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
@ -324,13 +386,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
#ifdef _BIG_ENDIAN
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
// Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
Packet16uc MSQ, LSQ;
Packet16uc mask;
MSQ = vec_ld(0, (unsigned char *)from); // most significant quadword
@ -350,25 +409,36 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
mask = vec_lvsl(0, from); // create the permute mask
return (Packet4i) vec_perm(MSQ, LSQ, mask); // align the data
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet4i) vec_vsx_ld((long)from & 15, (const int*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet4f) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
}
#endif
template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
{
Packet4f p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4f>(from);
else p = ploadu<Packet4f>(from);
return vec_perm(p, p, p16uc_DUPLICATE);
return vec_perm(p, p, p16uc_DUPLICATE32_HI);
}
template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from)
{
Packet4i p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4i>(from);
else p = ploadu<Packet4i>(from);
return vec_perm(p, p, p16uc_DUPLICATE);
return vec_perm(p, p, p16uc_DUPLICATE32_HI);
}
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
#ifdef _BIG_ENDIAN
template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
{
EIGEN_DEBUG_UNALIGNED_STORE
@ -405,15 +475,30 @@ template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& f
vec_st( LSQ, 15, (unsigned char *)to ); // Store the LSQ part first
vec_st( MSQ, 0, (unsigned char *)to ); // Store the MSQ part
}
#else
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st(from, (long)to & 15, (int*) _EIGEN_ALIGNED_PTR(to));
}
template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st(from, (long)to & 15, (float*) _EIGEN_ALIGNED_PTR(to));
}
#endif
#ifndef __VSX__
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
#endif
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE); }
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE); }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
@ -460,7 +545,11 @@ template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
{
Packet4i sum;
sum = vec_sums(a, p4i_ZERO);
#ifdef _BIG_ENDIAN
sum = vec_sld(sum, p4i_ZERO, 12);
#else
sum = vec_sld(p4i_ZERO, sum, 4);
#endif
return pfirst(sum);
}
@ -547,8 +636,25 @@ struct palign_impl<Offset,Packet4f>
{
static EIGEN_STRONG_INLINE void run(Packet4f& first, const Packet4f& second)
{
if (Offset!=0)
first = vec_sld(first, second, Offset*4);
#ifdef _BIG_ENDIAN
switch (Offset % 4) {
case 1:
first = vec_sld(first, second, 4); break;
case 2:
first = vec_sld(first, second, 8); break;
case 3:
first = vec_sld(first, second, 12); break;
}
#else
switch (Offset % 4) {
case 1:
first = vec_sld(second, first, 12); break;
case 2:
first = vec_sld(second, first, 8); break;
case 3:
first = vec_sld(second, first, 4); break;
}
#endif
}
};
@ -557,8 +663,25 @@ struct palign_impl<Offset,Packet4i>
{
static EIGEN_STRONG_INLINE void run(Packet4i& first, const Packet4i& second)
{
if (Offset!=0)
first = vec_sld(first, second, Offset*4);
#ifdef _BIG_ENDIAN
switch (Offset % 4) {
case 1:
first = vec_sld(first, second, 4); break;
case 2:
first = vec_sld(first, second, 8); break;
case 3:
first = vec_sld(first, second, 12); break;
}
#else
switch (Offset % 4) {
case 1:
first = vec_sld(second, first, 12); break;
case 2:
first = vec_sld(second, first, 8); break;
case 3:
first = vec_sld(second, first, 4); break;
}
#endif
}
};
@ -588,6 +711,222 @@ ptranspose(PacketBlock<Packet4i,4>& kernel) {
kernel.packet[3] = vec_mergel(t1, t3);
}
//---------- double ----------
#ifdef __VSX__
typedef __vector double Packet2d;
typedef __vector unsigned long long Packet2ul;
typedef __vector long long Packet2l;
static Packet2l p2l_ZERO = (Packet2l) p4i_ZERO;
static Packet2d p2d_ONE = { 1.0, 1.0 };
static Packet2d p2d_ZERO = (Packet2d) p4f_ZERO;
static Packet2d p2d_ZERO_ = { -0.0, -0.0 };
#ifdef _BIG_ENDIAN
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ZERO, (Packet16uc) p2d_ONE, 8);
#else
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ONE, (Packet16uc) p2d_ZERO, 8);
#endif
static EIGEN_STRONG_INLINE Packet2d vec_splat_dbl(Packet2d& a, int index)
{
switch (index) {
case 0:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_HI);
case 1:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_LO);
}
return a;
}
template<> struct packet_traits<double> : default_packet_traits
{
typedef Packet2d type;
typedef Packet2d half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size=2,
HasHalfPacket = 0,
HasDiv = 1,
HasExp = 0,
HasSqrt = 0
};
};
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; };
inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
{
union {
Packet2d v;
double n[2];
} vt;
vt.v = v;
s << vt.n[0] << ", " << vt.n[1];
return s;
}
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return (Packet2d) vec_ld(0, (const float *) from); } //FIXME
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st((Packet4f)from, 0, (float *)to); }
template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
double EIGEN_ALIGN16 af[2];
af[0] = from;
Packet2d vc = pload<Packet2d>(af);
vc = vec_splat_dbl(vc, 0);
return vc;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet2d>(const double *a,
Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
{
a1 = pload<Packet2d>(a);
a0 = vec_splat_dbl(a1, 0);
a1 = vec_splat_dbl(a1, 1);
a3 = pload<Packet2d>(a+2);
a2 = vec_splat_dbl(a3, 0);
a3 = vec_splat_dbl(a3, 1);
}
template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, DenseIndex stride)
{
double EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return pload<Packet2d>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, DenseIndex stride)
{
double EIGEN_ALIGN16 af[2];
pstore<double>(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet2d plset<double>(const double& a) { return vec_add(pset1<Packet2d>(a), p2d_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_sub(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return psub<Packet2d>(p2d_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_ZERO); }
template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
// for some weird raisons, it has to be overloaded for packet of integers
template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet2d) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from)
{
Packet2d p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet2d>(from);
else p = ploadu<Packet2d>(from);
return vec_perm(p, p, p16uc_PSET64_HI);
}
template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
{
EIGEN_DEBUG_ALIGNED_STORE
vec_vsx_st((Packet4f)from, (long)to & 15, (float*) _EIGEN_ALIGNED_PTR(to));
}
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { vec_dstt((const float *) addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return (Packet2d)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE64); }
template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
{
Packet2d b, sum;
b = (Packet2d) vec_sld((Packet4ui) a, (Packet4ui)a, 8);
sum = vec_add(a, b);
return pfirst(sum);
}
template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
{
Packet2d v[2], sum;
v[0] = vec_add(vecs[0], (Packet2d) vec_sld((Packet4ui) vecs[0], (Packet4ui) vecs[0], 8));
v[1] = vec_add(vecs[1], (Packet2d) vec_sld((Packet4ui) vecs[1], (Packet4ui) vecs[1], 8));
#ifdef _BIG_ENDIAN
sum = (Packet2d) vec_sld((Packet4ui) v[0], (Packet4ui) v[1], 8);
#else
sum = (Packet2d) vec_sld((Packet4ui) v[1], (Packet4ui) v[0], 8);
#endif
return sum;
}
// Other reduction functions:
// mul
template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
{
return pfirst(pmul(a, (Packet2d)vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
// min
template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
{
return pfirst(vec_min(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
// max
template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
{
return pfirst(vec_max(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
}
template<int Offset>
struct palign_impl<Offset,Packet2d>
{
static EIGEN_STRONG_INLINE void run(Packet2d& first, const Packet2d& second)
{
if (Offset == 1)
#ifdef _BIG_ENDIAN
first = (Packet2d) vec_sld((Packet4ui) first, (Packet4ui) second, 8);
#else
first = (Packet2d) vec_sld((Packet4ui) second, (Packet4ui) first, 8);
#endif
}
};
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet2d,2>& kernel) {
Packet2d t0, t1;
t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
kernel.packet[0] = t0;
kernel.packet[1] = t1;
}
#endif // __VSX__
} // end namespace internal
} // end namespace Eigen

194
Eigen/src/Core/arch/NEON/Complex.h
View File

@ -33,6 +33,7 @@ template<> struct packet_traits<std::complex<float> > : default_packet_traits
Vectorizable = 1,
AlignedOnScalar = 1,
size = 2,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
@ -88,7 +89,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v))));
return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v))));
}
template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
@ -252,7 +253,7 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
// TODO optimize it for AltiVec
// TODO optimize it for NEON
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s, rev_s;
@ -265,11 +266,198 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet2cf,2>& kernel) {
float32x4_t tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v));
kernel.packet[1].v = tmp;
}
//---------- double ----------
#if EIGEN_ARCH_ARM64
static uint64x2_t p2ul_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x0, 0x8000000000000000);
struct Packet1cd
{
EIGEN_STRONG_INLINE Packet1cd() {}
EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
Packet2d v;
};
template<> struct packet_traits<std::complex<double> > : default_packet_traits
{
typedef Packet1cd type;
typedef Packet1cd half;
enum {
Vectorizable = 1,
AlignedOnScalar = 0,
size = 1,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasNegate = 1,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasSetLinear = 0
};
};
template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(padd<Packet2d>(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(psub<Packet2d>(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate<Packet2d>(a.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); }
template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
Packet2d v1, v2;
// Get the real values of a
v1 = vdupq_lane_f64(vget_low_f64(a.v), 0);
// Get the real values of a
v2 = vdupq_lane_f64(vget_high_f64(a.v), 1);
// Multiply the real a with b
v1 = vmulq_f64(v1, b.v);
// Multiply the imag a with b
v2 = vmulq_f64(v2, b.v);
// Conjugate v2
v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR));
// Swap real/imag elements in v2.
v2 = preverse<Packet2d>(v2);
// Add and return the result
return Packet1cd(vaddq_f64(v1, v2));
}
template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v))));
}
template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v))));
}
template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v))));
}
template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v))));
}
template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { EIGEN_ARM_PREFETCH((double *)addr); }
template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, DenseIndex stride)
{
Packet2d res;
res = vsetq_lane_f64(std::real(from[0*stride]), res, 0);
res = vsetq_lane_f64(std::imag(from[0*stride]), res, 1);
return Packet1cd(res);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, DenseIndex stride)
{
to[stride*0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1));
}
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
{
std::complex<double> EIGEN_ALIGN16 res;
pstore<std::complex<double> >(&res, a);
return res;
}
template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs) { return vecs[0]; }
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
template<int Offset>
struct palign_impl<Offset,Packet1cd>
{
static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/)
{
// FIXME is it sure we never have to align a Packet1cd?
// Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary...
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, false,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const
{
return pconj(internal::pmul(a, b));
}
};
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for NEON
Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b);
Packet2d s = pmul<Packet2d>(b.v, b.v);
Packet2d rev_s = preverse<Packet2d>(s);
return Packet1cd(pdiv(res.v, padd<Packet2d>(s,rev_s)));
}
EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
{
return Packet1cd(preverse(Packet2d(x.v)));
}
EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
{
Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v));
kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v));
kernel.packet[1].v = tmp;
}
#endif // EIGEN_ARCH_ARM64
} // end namespace internal

233
Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -20,14 +20,24 @@ namespace internal {
#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
#endif
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#endif
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#endif
// FIXME NEON has 16 quad registers, but since the current register allocator
// is so bad, it is much better to reduce it to 8
#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
#endif
typedef float32x2_t Packet2f;
typedef float32x4_t Packet4f;
typedef int32x4_t Packet4i;
typedef int32x2_t Packet2i;
typedef uint32x4_t Packet4ui;
#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
@ -39,7 +49,7 @@ typedef uint32x4_t Packet4ui;
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
const Packet4i p4i_##NAME = pset1<Packet4i>(X)
#if defined(__llvm__) && !defined(__clang__)
#if EIGEN_COMP_LLVM && !EIGEN_COMP_CLANG
//Special treatment for Apple's llvm-gcc, its NEON packet types are unions
#define EIGEN_INIT_NEON_PACKET2(X, Y) {{X, Y}}
#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {{X, Y, Z, W}}
@ -52,11 +62,11 @@ typedef uint32x4_t Packet4ui;
// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
// which available on LLVM and GCC (at least)
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || defined(__GNUC__)
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
#define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
#elif defined __pld
#define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
#elif !defined(__aarch64__)
#elif !EIGEN_ARCH_ARM64
#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ( " pld [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
#else
// by default no explicit prefetching
@ -66,11 +76,12 @@ typedef uint32x4_t Packet4ui;
template<> struct packet_traits<float> : default_packet_traits
{
typedef Packet4f type;
typedef Packet4f half;
typedef Packet2f half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size = 4,
HasHalfPacket=1,
HasDiv = 1,
// FIXME check the Has*
@ -84,16 +95,17 @@ template<> struct packet_traits<float> : default_packet_traits
template<> struct packet_traits<int> : default_packet_traits
{
typedef Packet4i type;
typedef Packet4i half;
typedef Packet2i half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size=4
size=4,
HasHalfPacket=1
// FIXME check the Has*
};
};
#if EIGEN_GNUC_AT_MOST(4,4) && !defined(__llvm__)
#if EIGEN_GNUC_AT_MOST(4,4) && !EIGEN_COMP_LLVM
// workaround gcc 4.2, 4.3 and 4.4 compilatin issue
EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); }
EIGEN_STRONG_INLINE float32x2_t vld1_f32 (const float* x) { return ::vld1_f32 ((const float32_t*)x); }
@ -136,6 +148,9 @@ template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
{
#if EIGEN_ARCH_ARM64
return vdivq_f32(a,b);
#else
Packet4f inv, restep, div;
// NEON does not offer a divide instruction, we have to do a reciprocal approximation
@ -154,14 +169,27 @@ template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const
div = vmulq_f32(a, inv);
return div;
#endif
}
template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
{ eigen_assert(false && "packet integer division are not supported by NEON");
return pset1<Packet4i>(0);
}
// for some weird raisons, it has to be overloaded for packet of integers
#ifdef __ARM_FEATURE_FMA
// See bug 936.
// FMA is available on VFPv4 i.e. when compiling with -mfpu=neon-vfpv4.
// FMA is a true fused multiply-add i.e. only 1 rounding at the end, no intermediate rounding.
// MLA is not fused i.e. does 2 roundings.
// In addition to giving better accuracy, FMA also gives better performance here on a Krait (Nexus 4):
// MLA: 10 GFlop/s ; FMA: 12 GFlops/s.
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vfmaq_f32(c,a,b); }
#else
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vmlaq_f32(c,a,b); }
#endif
// No FMA instruction for int, so use MLA unconditionally.
template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return vmlaq_s32(c,a,b); }
template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); }
@ -472,6 +500,193 @@ ptranspose(PacketBlock<Packet4i,4>& kernel) {
kernel.packet[3] = vcombine_s32(vget_high_s32(tmp1.val[1]), vget_high_s32(tmp2.val[1]));
}
//---------- double ----------
#if EIGEN_ARCH_ARM64
#if (EIGEN_COMP_GNUC_STRICT && defined(__ANDROID__)) || defined(__apple_build_version__)
// Bug 907: workaround missing declarations of the following two functions in the ADK
__extension__ static __inline uint64x2_t __attribute__ ((__always_inline__))
vreinterpretq_u64_f64 (float64x2_t __a)
{
return (uint64x2_t) __a;
}
__extension__ static __inline float64x2_t __attribute__ ((__always_inline__))
vreinterpretq_f64_u64 (uint64x2_t __a)
{
return (float64x2_t) __a;
}
#endif
typedef float64x2_t Packet2d;
typedef float64x1_t Packet1d;
template<> struct packet_traits<double> : default_packet_traits
{
typedef Packet2d type;
typedef Packet1d half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size = 2,
HasHalfPacket=1,
HasDiv = 1,
// FIXME check the Has*
HasSin = 0,
HasCos = 0,
HasLog = 0,
HasExp = 0,
HasSqrt = 0
};
};
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; };
template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return vdupq_n_f64(from); }
template<> EIGEN_STRONG_INLINE Packet2d plset<double>(const double& a)
{
Packet2d countdown = EIGEN_INIT_NEON_PACKET2(0, 1);
return vaddq_f64(pset1<Packet2d>(a), countdown);
}
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vsubq_f64(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return vnegq_f64(a); }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmulq_f64(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); }
#ifdef __ARM_FEATURE_FMA
// See bug 936. See above comment about FMA for float.
template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vfmaq_f64(c,a,b); }
#else
template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vmlaq_f64(c,a,b); }
#endif
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vminq_f64(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxq_f64(a,b); }
// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b)
{
return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b)));
}
template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b)
{
return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b)));
}
template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b)
{
return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b)));
}
template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b)
{
return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b)));
}
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(from); }
template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from); }
template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from)
{
return vld1q_dup_f64(from);
}
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(to, from); }
template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to, from); }
template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, DenseIndex stride)
{
Packet2d res;
res = vsetq_lane_f64(from[0*stride], res, 0);
res = vsetq_lane_f64(from[1*stride], res, 1);
return res;
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, DenseIndex stride)
{
to[stride*0] = vgetq_lane_f64(from, 0);
to[stride*1] = vgetq_lane_f64(from, 1);
}
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ARM_PREFETCH(addr); }
// FIXME only store the 2 first elements ?
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(a, 0); }
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); }
template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); }
#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
// workaround ICE, see bug 907
template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return (vget_low_f64(a) + vget_high_f64(a))[0]; }
#else
template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return vget_lane_f64(vget_low_f64(a) + vget_high_f64(a), 0); }
#endif
template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
{
float64x2_t trn1, trn2;
// NEON zip performs interleaving of the supplied vectors.
// We perform two interleaves in a row to acquire the transposed vector
trn1 = vzip1q_f64(vecs[0], vecs[1]);
trn2 = vzip2q_f64(vecs[0], vecs[1]);
// Do the addition of the resulting vectors
return vaddq_f64(trn1, trn2);
}
// Other reduction functions:
// mul
#if EIGEN_COMP_CLANG && defined(__apple_build_version__)
template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) { return (vget_low_f64(a) * vget_high_f64(a))[0]; }
#else
template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) { return vget_lane_f64(vget_low_f64(a) * vget_high_f64(a), 0); }
#endif
// min
template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(vpminq_f64(a, a), 0); }
// max
template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(vpmaxq_f64(a, a), 0); }
// this PALIGN_NEON business is to work around a bug in LLVM Clang 3.0 causing incorrect compilation errors,
// see bug 347 and this LLVM bug: http://llvm.org/bugs/show_bug.cgi?id=11074
#define PALIGN_NEON(Offset,Type,Command) \
template<>\
struct palign_impl<Offset,Type>\
{\
EIGEN_STRONG_INLINE static void run(Type& first, const Type& second)\
{\
if (Offset!=0)\
first = Command(first, second, Offset);\
}\
};\
PALIGN_NEON(0,Packet2d,vextq_f64)
PALIGN_NEON(1,Packet2d,vextq_f64)
#undef PALIGN_NEON
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet2d,2>& kernel) {
float64x2_t trn1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]);
float64x2_t trn2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]);
kernel.packet[0] = trn1;
kernel.packet[1] = trn2;
}
#endif // EIGEN_ARCH_ARM64
} // end namespace internal
} // end namespace Eigen

6
Eigen/src/Core/arch/SSE/MathFunctions.h
View File

@ -52,7 +52,7 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
Packet4i emm0;
Packet4f invalid_mask = _mm_cmplt_ps(x, _mm_setzero_ps());
Packet4f invalid_mask = _mm_cmpnge_ps(x, _mm_setzero_ps()); // not greater equal is true if x is NaN
Packet4f iszero_mask = _mm_cmpeq_ps(x, _mm_setzero_ps());
x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */
@ -167,7 +167,7 @@ Packet4f pexp<Packet4f>(const Packet4f& _x)
emm0 = _mm_cvttps_epi32(fx);
emm0 = _mm_add_epi32(emm0, p4i_0x7f);
emm0 = _mm_slli_epi32(emm0, 23);
return pmul(y, Packet4f(_mm_castsi128_ps(emm0)));
return pmax(pmul(y, Packet4f(_mm_castsi128_ps(emm0))), _x);
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d pexp<Packet2d>(const Packet2d& _x)
@ -241,7 +241,7 @@ Packet2d pexp<Packet2d>(const Packet2d& _x)
emm0 = _mm_add_epi32(emm0, p4i_1023_0);
emm0 = _mm_slli_epi32(emm0, 20);
emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(1,2,0,3));
return pmul(x, Packet2d(_mm_castsi128_pd(emm0)));
return pmax(pmul(x, Packet2d(_mm_castsi128_pd(emm0))), _x);
}
/* evaluation of 4 sines at onces, using SSE2 intrinsics.

26
Eigen/src/Core/arch/SSE/PacketMath.h
View File

@ -22,13 +22,13 @@ namespace internal {
#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
#endif
#ifdef EIGEN_VECTORIZE_FMA
#ifndef EIGEN_HAS_FUSED_MADD
#define EIGEN_HAS_FUSED_MADD 1
#ifdef __FMA__
#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD 1
#endif
#endif
#if defined EIGEN_VECTORIZE_AVX && defined __GNUC__ && !(defined __clang__ || defined __INTEL_COMPILER)
#if defined EIGEN_VECTORIZE_AVX && EIGEN_COMP_GNUC_STRICT
// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
// have overloads for both types without linking error.
// One solution is to increase ABI version using -fabi-version=4 (or greater).
@ -147,7 +147,7 @@ template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; };
template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; };
#if defined(_MSC_VER) && (_MSC_VER==1500)
#if EIGEN_COMP_MSVC==1500
// Workaround MSVC 9 internal compiler error.
// TODO: It has been detected with win64 builds (amd64), so let's check whether it also happens in 32bits+SSE mode
// TODO: let's check whether there does not exist a better fix, like adding a pset0() function. (it crashed on pset1(0)).
@ -165,7 +165,7 @@ template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) { re
// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions.
// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply.
// Also note that with AVX, we want it to generate a vbroadcastss.
#if (defined __GNUC__) && (!defined __INTEL_COMPILER) && (!defined __clang__) && (!defined __AVX__)
#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__)
template<> EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float *from) {
return vec4f_swizzle1(_mm_load_ss(from),0,0,0,0);
}
@ -282,10 +282,10 @@ template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { E
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
#if defined(_MSC_VER)
#if EIGEN_COMP_MSVC
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
EIGEN_DEBUG_UNALIGNED_LOAD
#if (_MSC_VER==1600)
#if (EIGEN_COMP_MSVC==1600)
// NOTE Some version of MSVC10 generates bad code when using _mm_loadu_ps
// (i.e., it does not generate an unaligned load!!
// TODO On most architectures this version should also be faster than a single _mm_loadu_ps
@ -307,11 +307,11 @@ template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { E
// TODO: do the same for MSVC (ICC is compatible)
// NOTE: with the code below, MSVC's compiler crashes!
#if defined(__GNUC__) && (defined(__i386__) || (defined(__x86_64) && EIGEN_GNUC_AT_LEAST(4, 8)))
#if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386 || (EIGEN_ARCH_x86_64 && EIGEN_GNUC_AT_LEAST(4, 8)))
// bug 195: gcc/i386 emits weird x87 fldl/fstpl instructions for _mm_load_sd
#define EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS 1
#define EIGEN_AVOID_CUSTOM_UNALIGNED_STORES 1
#elif defined(__clang__)
#elif EIGEN_COMP_CLANG
// bug 201: Segfaults in __mm_loadh_pd with clang 2.8
#define EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS 1
#define EIGEN_AVOID_CUSTOM_UNALIGNED_STORES 0
@ -439,13 +439,13 @@ template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_p
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
#endif
#if defined(_MSC_VER) && defined(_WIN64) && !defined(__INTEL_COMPILER)
#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
// Direct of the struct members fixed bug #62.
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return a.m128_f32[0]; }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return a.m128d_f64[0]; }
template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
#elif defined(_MSC_VER) && !defined(__INTEL_COMPILER)
#elif EIGEN_COMP_MSVC_STRICT
// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float x = _mm_cvtss_f32(a); return x; }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double x = _mm_cvtsd_f64(a); return x; }
@ -680,7 +680,7 @@ template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
#endif // EIGEN_VECTORIZE_SSE4_1
}
#if (defined __GNUC__)
#if EIGEN_COMP_GNUC
// template <> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
// {
// Packet4f res = b;

33
Eigen/src/Core/functors/AssignmentFunctors.h
View File

@ -31,7 +31,7 @@ template<typename Scalar>
struct functor_traits<assign_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost,
PacketAccess = packet_traits<Scalar>::IsVectorized
PacketAccess = packet_traits<Scalar>::Vectorizable
};
};
@ -73,7 +73,7 @@ template<typename Scalar>
struct functor_traits<sub_assign_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
PacketAccess = packet_traits<Scalar>::HasSub
};
};
@ -81,22 +81,24 @@ struct functor_traits<sub_assign_op<Scalar> > {
* \brief Template functor for scalar/packet assignment with multiplication
*
*/
template<typename Scalar> struct mul_assign_op {
template<typename DstScalar, typename SrcScalar=DstScalar>
struct mul_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(mul_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a *= b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a *= b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<mul_assign_op<Scalar> > {
template<typename DstScalar, typename SrcScalar>
struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasMul
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
};
};
template<typename DstScalar,typename SrcScalar> struct functor_is_product_like<mul_assign_op<DstScalar,SrcScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor for scalar/packet assignment with diviving
@ -115,13 +117,13 @@ template<typename Scalar>
struct functor_traits<div_assign_op<Scalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasMul
PacketAccess = packet_traits<Scalar>::HasDiv
};
};
/** \internal
* \brief Template functor for scalar/packet assignment with swaping
* \brief Template functor for scalar/packet assignment with swapping
*
* It works as follow. For a non-vectorized evaluation loop, we have:
* for(i) func(A.coeffRef(i), B.coeff(i));
@ -140,8 +142,13 @@ template<typename Scalar> struct swap_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(swap_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const
{
#ifdef __CUDACC__
// FIXME is there some kind of cuda::swap?
Scalar t=b; const_cast<Scalar&>(b)=a; a=t;
#else
using std::swap;
swap(a,const_cast<Scalar&>(b));
#endif
}
template<int LhsAlignment, int RhsAlignment, typename Packet>
@ -156,7 +163,7 @@ template<typename Scalar>
struct functor_traits<swap_assign_op<Scalar> > {
enum {
Cost = 3 * NumTraits<Scalar>::ReadCost,
PacketAccess = packet_traits<Scalar>::IsVectorized
PacketAccess = packet_traits<Scalar>::Vectorizable
};
};

6
Eigen/src/Core/functors/BinaryFunctors.h
View File

@ -115,7 +115,7 @@ struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
*/
template<typename Scalar> struct scalar_min_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { EIGEN_USING_STD_MATH(min); return (min)(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::mini(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
@ -138,7 +138,7 @@ struct functor_traits<scalar_min_op<Scalar> > {
*/
template<typename Scalar> struct scalar_max_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { EIGEN_USING_STD_MATH(max); return (max)(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::maxi(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
@ -164,8 +164,6 @@ template<typename Scalar> struct scalar_hypot_op {
// typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
{
EIGEN_USING_STD_MATH(max);
EIGEN_USING_STD_MATH(min);
using std::sqrt;
Scalar p, qp;
if(_x>_y)

452
Eigen/src/Core/products/CoeffBasedProduct.h
View File

@ -1,452 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COEFFBASED_PRODUCT_H
#define EIGEN_COEFFBASED_PRODUCT_H
namespace Eigen {
namespace internal {
/*********************************************************************************
* Coefficient based product implementation.
* It is designed for the following use cases:
* - small fixed sizes
* - lazy products
*********************************************************************************/
/* Since the all the dimensions of the product are small, here we can rely
* on the generic Assign mechanism to evaluate the product per coeff (or packet).
*
* Note that here the inner-loops should always be unrolled.
*/
template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl;
template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl;
template<typename LhsNested, typename RhsNested, int NestingFlags>
struct traits<CoeffBasedProduct<LhsNested,RhsNested,NestingFlags> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<LhsNested>::type _LhsNested;
typedef typename remove_all<RhsNested>::type _RhsNested;
typedef typename scalar_product_traits<typename _LhsNested::Scalar, typename _RhsNested::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<_LhsNested>::StorageKind,
typename traits<_RhsNested>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<_LhsNested>::Index,
typename traits<_RhsNested>::Index>::type Index;
enum {
LhsCoeffReadCost = _LhsNested::CoeffReadCost,
RhsCoeffReadCost = _RhsNested::CoeffReadCost,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
RowsAtCompileTime = _LhsNested::RowsAtCompileTime,
ColsAtCompileTime = _RhsNested::ColsAtCompileTime,
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
LhsRowMajor = LhsFlags & RowMajorBit,
RhsRowMajor = RhsFlags & RowMajorBit,
SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value,
CanVectorizeRhs = RhsRowMajor && (RhsFlags & PacketAccessBit)
&& (ColsAtCompileTime == Dynamic
|| ( (ColsAtCompileTime % packet_traits<Scalar>::size) == 0
&& (RhsFlags&AlignedBit)
)
),
CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit)
&& (RowsAtCompileTime == Dynamic
|| ( (RowsAtCompileTime % packet_traits<Scalar>::size) == 0
&& (LhsFlags&AlignedBit)
)
),
EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: (RhsRowMajor && !CanVectorizeLhs),
Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & ~RowMajorBit)
| (EvalToRowMajor ? RowMajorBit : 0)
| NestingFlags
| (CanVectorizeLhs ? (LhsFlags & AlignedBit) : 0)
| (CanVectorizeRhs ? (RhsFlags & AlignedBit) : 0)
// TODO enable vectorization for mixed types
| (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0),
CoeffReadCost = InnerSize == Dynamic ? Dynamic
: InnerSize * (NumTraits<Scalar>::MulCost + LhsCoeffReadCost + RhsCoeffReadCost)
+ (InnerSize - 1) * NumTraits<Scalar>::AddCost,
/* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
* of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
* loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
* the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
*/
CanVectorizeInner = SameType
&& LhsRowMajor
&& (!RhsRowMajor)
&& (LhsFlags & RhsFlags & ActualPacketAccessBit)
&& (LhsFlags & RhsFlags & AlignedBit)
&& (InnerSize % packet_traits<Scalar>::size == 0)
};
};
} // end namespace internal
template<typename LhsNested, typename RhsNested, int NestingFlags>
class CoeffBasedProduct
: internal::no_assignment_operator,
public MatrixBase<CoeffBasedProduct<LhsNested, RhsNested, NestingFlags> >
{
public:
typedef MatrixBase<CoeffBasedProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CoeffBasedProduct)
typedef typename Base::PlainObject PlainObject;
private:
typedef typename internal::traits<CoeffBasedProduct>::_LhsNested _LhsNested;
typedef typename internal::traits<CoeffBasedProduct>::_RhsNested _RhsNested;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
InnerSize = internal::traits<CoeffBasedProduct>::InnerSize,
Unroll = CoeffReadCost != Dynamic && CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
CanVectorizeInner = internal::traits<CoeffBasedProduct>::CanVectorizeInner
};
typedef internal::product_coeff_impl<CanVectorizeInner ? InnerVectorizedTraversal : DefaultTraversal,
Unroll ? InnerSize-1 : Dynamic,
_LhsNested, _RhsNested, Scalar> ScalarCoeffImpl;
typedef CoeffBasedProduct<LhsNested,RhsNested,NestByRefBit> LazyCoeffBasedProductType;
public:
EIGEN_DEVICE_FUNC
inline CoeffBasedProduct(const CoeffBasedProduct& other)
: Base(), m_lhs(other.m_lhs), m_rhs(other.m_rhs)
{}
template<typename Lhs, typename Rhs>
EIGEN_DEVICE_FUNC
inline CoeffBasedProduct(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
{
// we don't allow taking products of matrices of different real types, as that wouldn't be vectorizable.
// We still allow to mix T and complex<T>.
EIGEN_STATIC_ASSERT((internal::scalar_product_traits<typename Lhs::RealScalar, typename Rhs::RealScalar>::Defined),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
Scalar res;
ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res);
return res;
}
/* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
* which is why we don't set the LinearAccessBit.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
Scalar res;
const Index row = RowsAtCompileTime == 1 ? 0 : index;
const Index col = RowsAtCompileTime == 1 ? index : 0;
ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res);
return res;
}
template<int LoadMode>
EIGEN_STRONG_INLINE const PacketScalar packet(Index row, Index col) const
{
PacketScalar res;
internal::product_packet_impl<Flags&RowMajorBit ? RowMajor : ColMajor,
Unroll ? InnerSize-1 : Dynamic,
_LhsNested, _RhsNested, PacketScalar, LoadMode>
::run(row, col, m_lhs, m_rhs, res);
return res;
}
// Implicit conversion to the nested type (trigger the evaluation of the product)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE operator const PlainObject& () const
{
m_result.lazyAssign(*this);
return m_result;
}
EIGEN_DEVICE_FUNC const _LhsNested& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const _RhsNested& rhs() const { return m_rhs; }
EIGEN_DEVICE_FUNC
const Diagonal<const LazyCoeffBasedProductType,0> diagonal() const
{ return reinterpret_cast<const LazyCoeffBasedProductType&>(*this); }
template<int DiagonalIndex>
EIGEN_DEVICE_FUNC
const Diagonal<const LazyCoeffBasedProductType,DiagonalIndex> diagonal() const
{ return reinterpret_cast<const LazyCoeffBasedProductType&>(*this); }
EIGEN_DEVICE_FUNC
const Diagonal<const LazyCoeffBasedProductType,Dynamic> diagonal(Index index) const
{ return reinterpret_cast<const LazyCoeffBasedProductType&>(*this).diagonal(index); }
protected:
typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
mutable PlainObject m_result;
};
namespace internal {
// here we need to overload the nested rule for products
// such that the nested type is a const reference to a plain matrix
template<typename Lhs, typename Rhs, int N, typename PlainObject>
struct nested<CoeffBasedProduct<Lhs,Rhs,EvalBeforeNestingBit|EvalBeforeAssigningBit>, N, PlainObject>
{
typedef PlainObject const& type;
};
/***************************************************************************
* Normal product .coeff() implementation (with meta-unrolling)
***************************************************************************/
/**************************************
*** Scalar path - no vectorization ***
**************************************/
template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl<DefaultTraversal, UnrollingIndex, Lhs, Rhs, RetScalar>
{
typedef typename Lhs::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
{
product_coeff_impl<DefaultTraversal, UnrollingIndex-1, Lhs, Rhs, RetScalar>::run(row, col, lhs, rhs, res);
res += lhs.coeff(row, UnrollingIndex) * rhs.coeff(UnrollingIndex, col);
}
};
template<typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl<DefaultTraversal, 0, Lhs, Rhs, RetScalar>
{
typedef typename Lhs::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
{
res = lhs.coeff(row, 0) * rhs.coeff(0, col);
}
};
template<typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl<DefaultTraversal, Dynamic, Lhs, Rhs, RetScalar>
{
typedef typename Lhs::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar& res)
{
eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix");
res = lhs.coeff(row, 0) * rhs.coeff(0, col);
for(Index i = 1; i < lhs.cols(); ++i)
res += lhs.coeff(row, i) * rhs.coeff(i, col);
}
};
/*******************************************
*** Scalar path with inner vectorization ***
*******************************************/
template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet>
struct product_coeff_vectorized_unroller
{
typedef typename Lhs::Index Index;
enum { PacketSize = packet_traits<typename Lhs::Scalar>::size };
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
{
product_coeff_vectorized_unroller<UnrollingIndex-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, pres);
pres = padd(pres, pmul( lhs.template packet<Aligned>(row, UnrollingIndex) , rhs.template packet<Aligned>(UnrollingIndex, col) ));
}
};
template<typename Lhs, typename Rhs, typename Packet>
struct product_coeff_vectorized_unroller<0, Lhs, Rhs, Packet>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres)
{
pres = pmul(lhs.template packet<Aligned>(row, 0) , rhs.template packet<Aligned>(0, col));
}
};
template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl<InnerVectorizedTraversal, UnrollingIndex, Lhs, Rhs, RetScalar>
{
typedef typename Lhs::PacketScalar Packet;
typedef typename Lhs::Index Index;
enum { PacketSize = packet_traits<typename Lhs::Scalar>::size };
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res)
{
Packet pres;
product_coeff_vectorized_unroller<UnrollingIndex+1-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, pres);
res = predux(pres);
}
};
template<typename Lhs, typename Rhs, int LhsRows = Lhs::RowsAtCompileTime, int RhsCols = Rhs::ColsAtCompileTime>
struct product_coeff_vectorized_dyn_selector
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
{
res = lhs.row(row).transpose().cwiseProduct(rhs.col(col)).sum();
}
};
// NOTE the 3 following specializations are because taking .col(0) on a vector is a bit slower
// NOTE maybe they are now useless since we have a specialization for Block<Matrix>
template<typename Lhs, typename Rhs, int RhsCols>
struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index /*row*/, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
{
res = lhs.transpose().cwiseProduct(rhs.col(col)).sum();
}
};
template<typename Lhs, typename Rhs, int LhsRows>
struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
{
res = lhs.row(row).transpose().cwiseProduct(rhs).sum();
}
};
template<typename Lhs, typename Rhs>
struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,1>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
{
res = lhs.transpose().cwiseProduct(rhs).sum();
}
};
template<typename Lhs, typename Rhs, typename RetScalar>
struct product_coeff_impl<InnerVectorizedTraversal, Dynamic, Lhs, Rhs, RetScalar>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res)
{
product_coeff_vectorized_dyn_selector<Lhs,Rhs>::run(row, col, lhs, rhs, res);
}
};
/*******************
*** Packet path ***
*******************/
template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res)
{
product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, res);
res = pmadd(pset1<Packet>(lhs.coeff(row, UnrollingIndex)), rhs.template packet<LoadMode>(UnrollingIndex, col), res);
}
};
template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res)
{
product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, res);
res = pmadd(lhs.template packet<LoadMode>(row, UnrollingIndex), pset1<Packet>(rhs.coeff(UnrollingIndex, col)), res);
}
};
template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res)
{
res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col));
}
};
template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res)
{
res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col)));
}
};
template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res)
{
eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix");
res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col));
for(Index i = 1; i < lhs.cols(); ++i)
res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode>(i, col), res);
}
};
template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
struct product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
{
typedef typename Lhs::Index Index;
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res)
{
eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix");
res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col)));
for(Index i = 1; i < lhs.cols(); ++i)
res = pmadd(lhs.template packet<LoadMode>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COEFFBASED_PRODUCT_H

194
Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@ -158,8 +158,8 @@ inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n, i
computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n, num_threads);
}
#ifdef EIGEN_HAS_FUSE_CJMADD
#define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C);
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
#define CJMADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C);
#else
// FIXME (a bit overkill maybe ?)
@ -184,8 +184,8 @@ inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n, i
gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t);
}
#define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T);
// #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T);
#define CJMADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T);
// #define CJMADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T);
#endif
/* Vectorization logic
@ -220,7 +220,7 @@ public:
nr = 4,
// register block size along the M direction (currently, this one cannot be modified)
#if defined(EIGEN_HAS_FUSED_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC)
#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
// we assume 16 registers
mr = 3*LhsPacketSize,
#else
@ -286,7 +286,7 @@ public:
// let gcc allocate the register in which to store the result of the pmul
// (in the case where there is no FMA) gcc fails to figure out how to avoid
// spilling register.
#ifdef EIGEN_HAS_FUSED_MADD
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
EIGEN_UNUSED_VARIABLE(tmp);
c = pmadd(a,b,c);
#else
@ -328,7 +328,7 @@ public:
NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
nr = 4,
#if defined(EIGEN_HAS_FUSED_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC)
#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
// we assume 16 registers
mr = 3*LhsPacketSize,
#else
@ -391,7 +391,7 @@ public:
EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
{
#ifdef EIGEN_HAS_FUSED_MADD
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
EIGEN_UNUSED_VARIABLE(tmp);
c.v = pmadd(a.v,b,c.v);
#else
@ -675,7 +675,7 @@ public:
EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
{
#ifdef EIGEN_HAS_FUSED_MADD
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
EIGEN_UNUSED_VARIABLE(tmp);
c.v = pmadd(a,b.v,c.v);
#else
@ -801,31 +801,36 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 3p x 4");
EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4");
RhsPacket B_0, T0;
LhsPacket A2;
#define EIGEN_GEBGP_ONESTEP(K) \
internal::prefetch(blA+(3*K+16)*LhsProgress); \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(&blB[(0+4*K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, T0); \
traits.madd(A2, B_0, C8, B_0); \
traits.loadRhs(&blB[1+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C1, T0); \
traits.madd(A1, B_0, C5, T0); \
traits.madd(A2, B_0, C9, B_0); \
traits.loadRhs(&blB[2+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C2, T0); \
traits.madd(A1, B_0, C6, T0); \
traits.madd(A2, B_0, C10, B_0); \
traits.loadRhs(&blB[3+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C3 , T0); \
traits.madd(A1, B_0, C7, T0); \
traits.madd(A2, B_0, C11, B_0)
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
internal::prefetch(blA+(3*K+16)*LhsProgress); \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(&blB[(0+4*K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, T0); \
traits.madd(A2, B_0, C8, B_0); \
traits.loadRhs(&blB[1+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C1, T0); \
traits.madd(A1, B_0, C5, T0); \
traits.madd(A2, B_0, C9, B_0); \
traits.loadRhs(&blB[2+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C2, T0); \
traits.madd(A1, B_0, C6, T0); \
traits.madd(A2, B_0, C10, B_0); \
traits.loadRhs(&blB[3+4*K*RhsProgress], B_0); \
traits.madd(A0, B_0, C3 , T0); \
traits.madd(A1, B_0, C7, T0); \
traits.madd(A2, B_0, C11, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4"); \
} while(false)
internal::prefetch(blB+(48+0));
EIGEN_GEBGP_ONESTEP(0);
@ -840,6 +845,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*4*RhsProgress;
blA += pk*3*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4");
}
// process remaining peeled loop
for(Index k=peeled_kc; k<depth; k++)
@ -918,16 +925,21 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 3p x 1");
EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1");
RhsPacket B_0;
#define EIGEN_GEBGP_ONESTEP(K) \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B_0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A2, B_0, C8, B_0)
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B_0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A2, B_0, C8, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1"); \
} while(false)
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
@ -940,6 +952,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*RhsProgress;
blA += pk*3*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1");
}
// process remaining peeled loop
@ -1005,22 +1019,27 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 2pX4");
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
RhsPacket B_0, B1, B2, B3, T0;
#define EIGEN_GEBGP_ONESTEP(K) \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A0, B1, C1, T0); \
traits.madd(A1, B1, C5, B1); \
traits.madd(A0, B2, C2, T0); \
traits.madd(A1, B2, C6, B2); \
traits.madd(A0, B3, C3, T0); \
traits.madd(A1, B3, C7, B3)
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, B_0); \
traits.madd(A0, B1, C1, T0); \
traits.madd(A1, B1, C5, B1); \
traits.madd(A0, B2, C2, T0); \
traits.madd(A1, B2, C6, B2); \
traits.madd(A0, B3, C3, T0); \
traits.madd(A1, B3, C7, B3); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4"); \
} while(false)
internal::prefetch(blB+(48+0));
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
@ -1034,6 +1053,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*4*RhsProgress;
blA += pk*(2*Traits::LhsProgress);
EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4");
}
// process remaining peeled loop
for(Index k=peeled_kc; k<depth; k++)
@ -1096,15 +1117,20 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 2p x 1");
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
RhsPacket B_0, B1;
#define EIGEN_GEBGP_ONESTEP(K) \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B1); \
traits.madd(A1, B_0, C4, B_0)
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B1); \
traits.madd(A1, B_0, C4, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1"); \
} while(false)
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
@ -1117,6 +1143,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*RhsProgress;
blA += pk*2*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1");
}
// process remaining peeled loop
@ -1179,16 +1207,21 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 1pX4");
EIGEN_ASM_COMMENT("begin gebp micro kernel 1pX4");
RhsPacket B_0, B1, B2, B3;
#define EIGEN_GEBGP_ONESTEP(K) \
traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \
traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \
traits.madd(A0, B_0, C0, B_0); \
traits.madd(A0, B1, C1, B1); \
traits.madd(A0, B2, C2, B2); \
traits.madd(A0, B3, C3, B3);
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \
traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \
traits.madd(A0, B_0, C0, B_0); \
traits.madd(A0, B1, C1, B1); \
traits.madd(A0, B2, C2, B2); \
traits.madd(A0, B3, C3, B3); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 1pX4"); \
} while(false)
internal::prefetch(blB+(48+0));
EIGEN_GEBGP_ONESTEP(0);
@ -1203,6 +1236,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*4*RhsProgress;
blA += pk*1*LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 1pX4");
}
// process remaining peeled loop
for(Index k=peeled_kc; k<depth; k++)
@ -1251,14 +1286,19 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
for(Index k=0; k<peeled_kc; k+=pk)
{
EIGEN_ASM_COMMENT("begin gegp micro kernel 2p x 1");
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
RhsPacket B_0;
#define EIGEN_GEBGP_ONESTEP(K) \
traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B_0); \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \
traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \
traits.madd(A0, B_0, C0, B_0); \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1"); \
} while(false);
EIGEN_GEBGP_ONESTEP(0);
EIGEN_GEBGP_ONESTEP(1);
EIGEN_GEBGP_ONESTEP(2);
@ -1270,6 +1310,8 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
blB += pk*RhsProgress;
blA += pk*1*Traits::LhsProgress;
EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1");
}
// process remaining peeled loop
@ -1402,14 +1444,14 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
B_0 = blB[0];
B_1 = blB[1];
MADD(cj,A0,B_0,C0, B_0);
MADD(cj,A0,B_1,C1, B_1);
CJMADD(cj,A0,B_0,C0, B_0);
CJMADD(cj,A0,B_1,C1, B_1);
B_0 = blB[2];
B_1 = blB[3];
MADD(cj,A0,B_0,C2, B_0);
MADD(cj,A0,B_1,C3, B_1);
CJMADD(cj,A0,B_0,C2, B_0);
CJMADD(cj,A0,B_1,C3, B_1);
blB += 4;
}
res(i, j2 + 0) += alpha * C0;
@ -1434,7 +1476,7 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
{
LhsScalar A0 = blA[k];
RhsScalar B_0 = blB[k];
MADD(cj, A0, B_0, C0, B_0);
CJMADD(cj, A0, B_0, C0, B_0);
}
res(i, j2) += alpha * C0;
}

142
Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -228,8 +228,8 @@ struct gemm_functor
cols = m_rhs.cols();
Gemm::run(rows, cols, m_lhs.cols(),
/*(const Scalar*)*/&m_lhs.coeffRef(row,0), m_lhs.outerStride(),
/*(const Scalar*)*/&m_rhs.coeffRef(0,col), m_rhs.outerStride(),
&m_lhs.coeffRef(row,0), m_lhs.outerStride(),
&m_rhs.coeffRef(0,col), m_rhs.outerStride(),
(Scalar*)&(m_dest.coeffRef(row,col)), m_dest.outerStride(),
m_actualAlpha, m_blocking, info);
}
@ -379,84 +379,92 @@ class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, M
} // end namespace internal
namespace internal {
template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, GemmProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,GemmProduct>, Lhs, Rhs>
struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct> >
{
enum {
MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime)
};
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef Scalar ResScalar;
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
typedef typename Product<Lhs,Rhs>::Index Index;
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
enum {
MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime)
};
typedef generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> lazyproduct;
template<typename Dst>
static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
if((rhs.rows()+dst.rows()+dst.cols())<20 && rhs.rows()>0)
lazyproduct::evalTo(dst, lhs, rhs);
else
{
typedef internal::scalar_product_op<LhsScalar,RhsScalar> BinOp;
EIGEN_CHECK_BINARY_COMPATIBILIY(BinOp,LhsScalar,RhsScalar);
}
template<typename Dest>
inline void evalTo(Dest& dst) const
{
if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0)
dst.noalias() = m_lhs .lazyProduct( m_rhs );
else
{
dst.setZero();
scaleAndAddTo(dst,Scalar(1));
}
dst.setZero();
scaleAndAddTo(dst, lhs, rhs, Scalar(1));
}
}
template<typename Dest>
inline void addTo(Dest& dst) const
{
if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0)
dst.noalias() += m_lhs .lazyProduct( m_rhs );
else
scaleAndAddTo(dst,Scalar(1));
}
template<typename Dst>
static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
if((rhs.rows()+dst.rows()+dst.cols())<20 && rhs.rows()>0)
lazyproduct::addTo(dst, lhs, rhs);
else
scaleAndAddTo(dst,lhs, rhs, Scalar(1));
}
template<typename Dest>
inline void subTo(Dest& dst) const
{
if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0)
dst.noalias() -= m_lhs .lazyProduct( m_rhs );
else
scaleAndAddTo(dst,Scalar(-1));
}
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());
template<typename Dst>
static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
if((rhs.rows()+dst.rows()+dst.cols())<20 && rhs.rows()>0)
lazyproduct::subTo(dst, lhs, rhs);
else
scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
}
template<typename Dest>
static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha)
{
eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs)
* RhsBlasTraits::extractScalarFactor(m_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar,
Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType;
typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar,
Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType;
typedef internal::gemm_functor<
Scalar, Index,
internal::general_matrix_matrix_product<
Index,
LhsScalar, (_ActualLhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate),
RhsScalar, (_ActualRhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor>,
_ActualLhsType, _ActualRhsType, Dest, BlockingType> GemmFunctor;
typedef internal::gemm_functor<
Scalar, Index,
internal::general_matrix_matrix_product<
Index,
LhsScalar, (ActualLhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate),
RhsScalar, (ActualRhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor>,
ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType> GemmFunctor;
BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>(GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), this->rows(), this->cols(), Dest::Flags&RowMajorBit);
}
BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>
(GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), Dest::Flags&RowMajorBit);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_GENERAL_MATRIX_MATRIX_H

14
Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -20,7 +20,7 @@ namespace internal {
/**********************************************************************
* This file implements a general A * B product while
* evaluating only one triangular part of the product.
* This is more general version of self adjoint product (C += A A^T)
* This is a more general version of self adjoint product (C += A A^T)
* as the level 3 SYRK Blas routine.
**********************************************************************/
@ -270,14 +270,14 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
};
template<typename MatrixType, unsigned int UpLo>
template<typename ProductDerived, typename _Lhs, typename _Rhs>
TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha)
template<typename ProductType>
TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha)
{
eigen_assert(m_matrix.rows() == prod.rows() && m_matrix.cols() == prod.cols());
general_product_to_triangular_selector<MatrixType, ProductDerived, UpLo, (_Lhs::ColsAtCompileTime==1) || (_Rhs::RowsAtCompileTime==1)>::run(m_matrix.const_cast_derived(), prod.derived(), alpha);
eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
return *this;
general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha);
return derived();
}
} // end namespace Eigen

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

@ -129,7 +129,7 @@ void parallelize_gemm(const Functor& func, Index rows, Index cols, bool transpos
Index blockRows = (rows / threads);
blockRows = (blockRows/Functor::Traits::mr)*Functor::Traits::mr;
GemmParallelInfo<Index>* info = new GemmParallelInfo<Index>[threads];
ei_declare_aligned_stack_constructed_variable(GemmParallelInfo<Index>,info,threads,0);
#pragma omp parallel num_threads(threads)
{
@ -146,8 +146,6 @@ void parallelize_gemm(const Functor& func, Index rows, Index cols, bool transpos
if(transpose) func(c0, actualBlockCols, 0, rows, info);
else func(0, rows, c0, actualBlockCols, info);
}
delete[] info;
#endif
}

55
Eigen/src/Core/products/SelfadjointMatrixMatrix.h
View File

@ -469,55 +469,54 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,f
***************************************************************************/
namespace internal {
template<typename Lhs, int LhsMode, typename Rhs, int RhsMode>
struct traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false> >
: traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>, Lhs, Rhs> >
{};
}
template<typename Lhs, int LhsMode, typename Rhs, int RhsMode>
struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>
: public ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>, Lhs, Rhs >
struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
{
EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix)
SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
typedef typename Product<Lhs,Rhs>::Index Index;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
enum {
LhsIsUpper = (LhsMode&(Upper|Lower))==Upper,
LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint,
RhsIsUpper = (RhsMode&(Upper|Lower))==Upper,
RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint
};
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
template<typename Dest>
static void run(Dest &dst, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());
eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs)
* RhsBlasTraits::extractScalarFactor(m_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
internal::product_selfadjoint_matrix<Scalar, Index,
EIGEN_LOGICAL_XOR(LhsIsUpper,
internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
EIGEN_LOGICAL_XOR(LhsIsUpper,internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)),
EIGEN_LOGICAL_XOR(RhsIsUpper,
internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint,
EIGEN_LOGICAL_XOR(RhsIsUpper,internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint,
NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsIsUpper,bool(RhsBlasTraits::NeedToConjugate)),
internal::traits<Dest>::Flags&RowMajorBit ? RowMajor : ColMajor>
::run(
lhs.rows(), rhs.cols(), // sizes
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
&rhs.coeffRef(0,0), rhs.outerStride(), // rhs info
&dst.coeffRef(0,0), dst.outerStride(), // result info
actualAlpha // alpha
lhs.rows(), rhs.cols(), // sizes
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
&rhs.coeffRef(0,0), rhs.outerStride(), // rhs info
&dst.coeffRef(0,0), dst.outerStride(), // result info
actualAlpha // alpha
);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H

77
Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -169,45 +169,45 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
***************************************************************************/
namespace internal {
template<typename Lhs, int LhsMode, typename Rhs>
struct traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> >
: traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs> >
{};
}
template<typename Lhs, int LhsMode, typename Rhs>
struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>
: public ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs >
struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true>
{
EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix)
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
typedef typename Product<Lhs,Rhs>::Index Index;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
enum {
LhsUpLo = LhsMode&(Upper|Lower)
};
enum { LhsUpLo = LhsMode&(Upper|Lower) };
SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
template<typename Dest>
static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
{
typedef typename Dest::Scalar ResScalar;
typedef typename Base::RhsScalar RhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
eigen_assert(dest.rows()==m_lhs.rows() && dest.cols()==m_rhs.cols());
eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols());
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs)
* RhsBlasTraits::extractScalarFactor(m_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
enum {
EvalToDest = (Dest::InnerStrideAtCompileTime==1),
UseRhs = (_ActualRhsType::InnerStrideAtCompileTime==1)
UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime==1)
};
internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest;
internal::gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!UseRhs> static_rhs;
internal::gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!UseRhs> static_rhs;
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
EvalToDest ? dest.data() : static_dest.data());
@ -230,11 +230,12 @@ struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>
Index size = rhs.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
}
internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<_ActualLhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run
internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor,
int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run
(
lhs.rows(), // size
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
@ -248,34 +249,24 @@ struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>
}
};
namespace internal {
template<typename Lhs, typename Rhs, int RhsMode>
struct traits<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> >
: traits<ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs> >
{};
}
template<typename Lhs, typename Rhs, int RhsMode>
struct SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>
: public ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs >
struct selfadjoint_product_impl<Lhs,0,true,Rhs,RhsMode,false>
{
EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix)
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
enum { RhsUpLo = RhsMode&(Upper|Lower) };
enum {
RhsUpLo = RhsMode&(Upper|Lower)
};
SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
template<typename Dest>
static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
{
// let's simply transpose the product
Transpose<Dest> destT(dest);
SelfadjointProductMatrix<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false,
Transpose<const Lhs>, 0, true>(m_rhs.transpose(), m_lhs.transpose()).scaleAndAddTo(destT, alpha);
selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false,
Transpose<const Lhs>, 0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H

8
Eigen/src/Core/products/SelfadjointRank2Update.h
View File

@ -79,11 +79,11 @@ SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
if (IsRowMajor)
actualAlpha = numext::conj(actualAlpha);
internal::selfadjoint_rank2_update_selector<Scalar, Index,
typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ UBlasTraits::NeedToConjugate,_ActualUType>::type>::type,
typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ VBlasTraits::NeedToConjugate,_ActualVType>::type>::type,
typedef typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ UBlasTraits::NeedToConjugate,_ActualUType>::type>::type UType;
typedef typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ VBlasTraits::NeedToConjugate,_ActualVType>::type>::type VType;
internal::selfadjoint_rank2_update_selector<Scalar, Index, UType, VType,
(IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)>
::run(_expression().const_cast_derived().data(),_expression().outerStride(),actualU,actualV,actualAlpha);
::run(_expression().const_cast_derived().data(),_expression().outerStride(),UType(actualU),VType(actualV),actualAlpha);
return *this;
}

43
Eigen/src/Core/products/TriangularMatrixMatrix.h
View File

@ -380,28 +380,29 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
* Wrapper to product_triangular_matrix_matrix
***************************************************************************/
template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
struct traits<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false> >
: traits<ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>, Lhs, Rhs> >
{};
} // end namespace internal
namespace internal {
template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
struct TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
: public ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>, Lhs, Rhs >
struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
{
EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct)
TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
template<typename Dest> static void run(Dest& dst, const Lhs &a_lhs, const Rhs &a_rhs, const typename Dest::Scalar& alpha)
{
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);
typedef typename Dest::Index Index;
typedef typename Dest::Scalar Scalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs)
* RhsBlasTraits::extractScalarFactor(m_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType;
@ -416,19 +417,21 @@ struct TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
internal::product_triangular_matrix_matrix<Scalar, Index,
Mode, LhsIsTriangular,
(internal::traits<_ActualLhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
(internal::traits<_ActualRhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
(internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
(internal::traits<ActualRhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
(internal::traits<Dest >::Flags&RowMajorBit) ? RowMajor : ColMajor>
::run(
stripedRows, stripedCols, stripedDepth, // sizes
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
&rhs.coeffRef(0,0), rhs.outerStride(), // rhs info
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
&rhs.coeffRef(0,0), rhs.outerStride(), // rhs info
&dst.coeffRef(0,0), dst.outerStride(), // result info
actualAlpha, blocking
);
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H

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