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Update Eigen to 3.3.7

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This is in preparation for the QuadriFlow remesher lib.

Reviewed By: Brecht

Differential Revision: http://developer.blender.org/D5549
This commit is contained in:
Sebastian Parborg
2019-08-21 14:13:09 +02:00
parent 1658fd1f7e
commit b19c437eff
299 changed files with 61997 additions and 16088 deletions
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22
extern/Eigen3/Eigen/Cholesky vendored
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@@ -1,7 +1,15 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLESKY_MODULE_H
#define EIGEN_CHOLESKY_MODULE_H
#include "Core"
#include "Jacobi"
#include "src/Core/util/DisableStupidWarnings.h"
@@ -10,20 +18,26 @@
*
*
* This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
* Those decompositions are accessible via the following MatrixBase methods:
* - MatrixBase::llt(),
* Those decompositions are also accessible via the following methods:
* - MatrixBase::llt()
* - MatrixBase::ldlt()
* - SelfAdjointView::llt()
* - SelfAdjointView::ldlt()
*
* \code
* #include <Eigen/Cholesky>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Cholesky/LLT_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Cholesky/LLT_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

13
extern/Eigen3/Eigen/CholmodSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
#define EIGEN_CHOLMODSUPPORT_MODULE_H
@@ -12,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup CholmodSupport_Module CholmodSupport module
*
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the two following main factorization classes:
* - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
@@ -33,12 +40,8 @@ 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

311
extern/Eigen3/Eigen/Core vendored
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@@ -14,6 +14,74 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDACC __CUDACC__
#endif
#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDA_ARCH __CUDA_ARCH__
#endif
#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
#define EIGEN_CUDACC_VER ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
#elif defined(__CUDACC_VER__)
#define EIGEN_CUDACC_VER __CUDACC_VER__
#else
#define EIGEN_CUDACC_VER 0
#endif
// Handle NVCC/CUDA/SYCL
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
// Do not try asserts on CUDA and SYCL!
#ifndef EIGEN_NO_DEBUG
#define EIGEN_NO_DEBUG
#endif
#ifdef EIGEN_INTERNAL_DEBUGGING
#undef EIGEN_INTERNAL_DEBUGGING
#endif
#ifdef EIGEN_EXCEPTIONS
#undef EIGEN_EXCEPTIONS
#endif
// All functions callable from CUDA code must be qualified with __device__
#ifdef __CUDACC__
// Do not try to vectorize on CUDA and SYCL!
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#endif
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need cuda_runtime.h to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <cuda_runtime.h>
#else
#define EIGEN_DEVICE_FUNC
#endif
#else
#define EIGEN_DEVICE_FUNC
#endif
// When compiling CUDA device code with NVCC, pull in math functions from the
// global namespace. In host mode, and when device doee with clang, use the
// std versions.
#if defined(__CUDA_ARCH__) && defined(__NVCC__)
#define EIGEN_USING_STD_MATH(FUNC) using ::FUNC;
#else
#define EIGEN_USING_STD_MATH(FUNC) using std::FUNC;
#endif
#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(__CUDA_ARCH__) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
// then include this file where all our macros are defined. It's really important to do it first because
// it's where we do all the alignment settings (platform detection and honoring the user's will if he
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
@@ -21,7 +89,7 @@
// 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
@@ -31,26 +99,26 @@
// and inclusion of their respective header files
#include "src/Core/util/MKL_support.h"
// if alignment is disabled, then disable vectorization. Note: EIGEN_ALIGN is the proper check, it takes into
// account both the user's will (EIGEN_DONT_ALIGN) and our own platform checks
#if !EIGEN_ALIGN
// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
#if EIGEN_MAX_ALIGN_BYTES==0
#ifndef EIGEN_DONT_VECTORIZE
#define EIGEN_DONT_VECTORIZE
#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
@@ -82,6 +150,31 @@
#ifdef __SSE4_2__
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX__
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_SSE3
#define EIGEN_VECTORIZE_SSSE3
#define EIGEN_VECTORIZE_SSE4_1
#define EIGEN_VECTORIZE_SSE4_2
#endif
#ifdef __AVX2__
#define EIGEN_VECTORIZE_AVX2
#endif
#ifdef __FMA__
#define EIGEN_VECTORIZE_FMA
#endif
#if defined(__AVX512F__) && defined(EIGEN_ENABLE_AVX512)
#define EIGEN_VECTORIZE_AVX512
#define EIGEN_VECTORIZE_AVX2
#define EIGEN_VECTORIZE_AVX
#define EIGEN_VECTORIZE_FMA
#ifdef __AVX512DQ__
#define EIGEN_VECTORIZE_AVX512DQ
#endif
#ifdef __AVX512ER__
#define EIGEN_VECTORIZE_AVX512ER
#endif
#endif
// include files
@@ -95,9 +188,10 @@
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 <mmintrin.h>
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
@@ -112,8 +206,20 @@
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
#if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
#include <immintrin.h>
#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
@@ -123,13 +229,35 @@
#undef bool
#undef vector
#undef pixel
#elif defined __ARM_NEON
#elif (defined __ARM_NEON) || (defined __ARM_NEON__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_NEON
#include <arm_neon.h>
#elif (defined __s390x__ && defined __VEC__)
#define EIGEN_VECTORIZE
#define EIGEN_VECTORIZE_ZVECTOR
#include <vecintrin.h>
#endif
#endif
#if defined(__F16C__) && !defined(EIGEN_COMP_CLANG)
// We can use the optimized fp16 to float and float to fp16 conversion routines
#define EIGEN_HAS_FP16_C
#endif
#if defined __CUDACC__
#define EIGEN_VECTORIZE_CUDA
#include <vector_types.h>
#if EIGEN_CUDACC_VER >= 70500
#define EIGEN_HAS_CUDA_FP16
#endif
#endif
#if defined EIGEN_HAS_CUDA_FP16
#include <host_defines.h>
#include <cuda_fp16.h>
#endif
#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
#define EIGEN_HAS_OPENMP
#endif
@@ -139,7 +267,7 @@
#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
@@ -159,29 +287,30 @@
// for min/max:
#include <algorithm>
// for std::is_nothrow_move_assignable
#ifdef EIGEN_INCLUDE_TYPE_TRAITS
#include <type_traits>
#endif
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
#include <iostream>
#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
#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
#define EIGEN_EXCEPTIONS
#endif
#ifdef EIGEN_EXCEPTIONS
#include <new>
#endif
/** \brief Namespace containing all symbols from the %Eigen library. */
namespace Eigen {
inline static const char *SimdInstructionSetsInUse(void) {
#if defined(EIGEN_VECTORIZE_SSE4_2)
#if defined(EIGEN_VECTORIZE_AVX512)
return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_AVX)
return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_2)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
#elif defined(EIGEN_VECTORIZE_SSE4_1)
return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
@@ -193,8 +322,12 @@ 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";
#elif defined(EIGEN_VECTORIZE_ZVECTOR)
return "S390X ZVECTOR";
#else
return "None";
#endif
@@ -202,42 +335,21 @@ inline static const char *SimdInstructionSetsInUse(void) {
} // end namespace Eigen
#define STAGE10_FULL_EIGEN2_API 10
#define STAGE20_RESOLVE_API_CONFLICTS 20
#define STAGE30_FULL_EIGEN3_API 30
#define STAGE40_FULL_EIGEN3_STRICTNESS 40
#define STAGE99_NO_EIGEN2_SUPPORT 99
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE40_FULL_EIGEN3_STRICTNESS
#elif defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#elif defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE20_RESOLVE_API_CONFLICTS
#elif defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API
#define EIGEN2_SUPPORT
#define EIGEN2_SUPPORT_STAGE STAGE10_FULL_EIGEN2_API
#elif defined EIGEN2_SUPPORT
// default to stage 3, that's what it's always meant
#define EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API
#define EIGEN2_SUPPORT_STAGE STAGE30_FULL_EIGEN3_API
#else
#define EIGEN2_SUPPORT_STAGE STAGE99_NO_EIGEN2_SUPPORT
#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
// This will generate an error message:
#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
#endif
#ifdef EIGEN2_SUPPORT
#undef minor
#endif
namespace Eigen {
// we use size_t frequently and we'll never remember to prepend it with std:: everytime just to
// ensure QNX/QCC support
using std::size_t;
// gcc 4.6.0 wants std:: for ptrdiff_t
// gcc 4.6.0 wants std:: for ptrdiff_t
using std::ptrdiff_t;
}
/** \defgroup Core_Module Core module
* This is the main module of Eigen providing dense matrix and vector support
* (both fixed and dynamic size) with all the features corresponding to a BLAS library
@@ -249,8 +361,8 @@ 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"
@@ -258,41 +370,94 @@ using std::ptrdiff_t;
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.h"
#include "src/Core/arch/Default/ConjHelper.h"
#if defined EIGEN_VECTORIZE_SSE
#if defined EIGEN_VECTORIZE_AVX512
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX512/PacketMath.h"
#include "src/Core/arch/AVX512/MathFunctions.h"
#elif defined EIGEN_VECTORIZE_AVX
// Use AVX for floats and doubles, SSE for integers
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/Complex.h"
#include "src/Core/arch/SSE/MathFunctions.h"
#include "src/Core/arch/AVX/PacketMath.h"
#include "src/Core/arch/AVX/MathFunctions.h"
#include "src/Core/arch/AVX/Complex.h"
#include "src/Core/arch/AVX/TypeCasting.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined EIGEN_VECTORIZE_SSE
#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
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
#include "src/Core/arch/AltiVec/PacketMath.h"
#include "src/Core/arch/AltiVec/MathFunctions.h"
#include "src/Core/arch/AltiVec/Complex.h"
#elif defined EIGEN_VECTORIZE_NEON
#include "src/Core/arch/NEON/PacketMath.h"
#include "src/Core/arch/NEON/MathFunctions.h"
#include "src/Core/arch/NEON/Complex.h"
#elif defined EIGEN_VECTORIZE_ZVECTOR
#include "src/Core/arch/ZVector/PacketMath.h"
#include "src/Core/arch/ZVector/MathFunctions.h"
#include "src/Core/arch/ZVector/Complex.h"
#endif
// Half float support
#include "src/Core/arch/CUDA/Half.h"
#include "src/Core/arch/CUDA/PacketMathHalf.h"
#include "src/Core/arch/CUDA/TypeCasting.h"
#if defined EIGEN_VECTORIZE_CUDA
#include "src/Core/arch/CUDA/PacketMath.h"
#include "src/Core/arch/CUDA/MathFunctions.h"
#endif
#include "src/Core/arch/Default/Settings.h"
#include "src/Core/Functors.h"
#include "src/Core/functors/TernaryFunctors.h"
#include "src/Core/functors/BinaryFunctors.h"
#include "src/Core/functors/UnaryFunctors.h"
#include "src/Core/functors/NullaryFunctors.h"
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
// Specialized functors to enable the processing of complex numbers
// on CUDA devices
#include "src/Core/arch/CUDA/Complex.h"
#include "src/Core/IO.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
#include "src/Core/EigenBase.h"
#include "src/Core/Product.h"
#include "src/Core/CoreEvaluators.h"
#include "src/Core/AssignEvaluator.h"
#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
#include "src/Core/Assign.h"
#endif
#include "src/Core/ArrayBase.h"
#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/ReturnByValue.h"
#include "src/Core/NoAlias.h"
#include "src/Core/PlainObjectBase.h"
#include "src/Core/Matrix.h"
#include "src/Core/Array.h"
#include "src/Core/CwiseTernaryOp.h"
#include "src/Core/CwiseBinaryOp.h"
#include "src/Core/CwiseUnaryOp.h"
#include "src/Core/CwiseNullaryOp.h"
@@ -300,32 +465,32 @@ 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"
#include "src/Core/DiagonalProduct.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.h"
#include "src/Core/Redux.h"
#include "src/Core/Visitor.h"
#include "src/Core/Fuzzy.h"
#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/SolverBase.h"
#include "src/Core/PermutationMatrix.h"
#include "src/Core/Transpositions.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"
@@ -340,6 +505,7 @@ using std::ptrdiff_t;
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/CoreIterators.h"
#include "src/Core/ConditionEstimator.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"
@@ -347,18 +513,17 @@ using std::ptrdiff_t;
#include "src/Core/Random.h"
#include "src/Core/Replicate.h"
#include "src/Core/Reverse.h"
#include "src/Core/ArrayBase.h"
#include "src/Core/ArrayWrapper.h"
#ifdef EIGEN_USE_BLAS
#include "src/Core/products/GeneralMatrixMatrix_MKL.h"
#include "src/Core/products/GeneralMatrixVector_MKL.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular_MKL.h"
#include "src/Core/products/SelfadjointMatrixMatrix_MKL.h"
#include "src/Core/products/SelfadjointMatrixVector_MKL.h"
#include "src/Core/products/TriangularMatrixMatrix_MKL.h"
#include "src/Core/products/TriangularMatrixVector_MKL.h"
#include "src/Core/products/TriangularSolverMatrix_MKL.h"
#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
#include "src/Core/products/GeneralMatrixVector_BLAS.h"
#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
#include "src/Core/products/TriangularMatrixVector_BLAS.h"
#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
#endif // EIGEN_USE_BLAS
#ifdef EIGEN_USE_MKL_VML
@@ -369,8 +534,4 @@ using std::ptrdiff_t;
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigen2Support"
#endif
#endif // EIGEN_CORE_H

2
extern/Eigen3/Eigen/Eigen vendored
View File

@@ -1,2 +1,2 @@
#include "Dense"
//#include "Sparse"
#include "Sparse"

19
extern/Eigen3/Eigen/Eigenvalues vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_EIGENVALUES_MODULE_H
#define EIGEN_EIGENVALUES_MODULE_H
@@ -25,6 +32,7 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"
@@ -37,9 +45,14 @@
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Eigenvalues/RealSchur_MKL.h"
#include "src/Eigenvalues/ComplexSchur_MKL.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Eigenvalues/RealSchur_LAPACKE.h"
#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

59
extern/Eigen3/Eigen/Geometry vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GEOMETRY_MODULE_H
#define EIGEN_GEOMETRY_MODULE_H
@@ -9,21 +16,17 @@
#include "LU"
#include <limits>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
/** \defgroup Geometry_Module Geometry module
*
*
*
* This module provides support for:
* - fixed-size homogeneous transformations
* - translation, scaling, 2D and 3D rotations
* - quaternions
* - \ref MatrixBase::cross() "cross product"
* - \ref MatrixBase::unitOrthogonal() "orthognal vector generation"
* - some linear components: parametrized-lines and hyperplanes
* - \link Quaternion quaternions \endlink
* - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
* - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
* - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
* - \link AlignedBox axis aligned bounding boxes \endlink
* - \link umeyama least-square transformation fitting \endlink
*
* \code
* #include <Eigen/Geometry>
@@ -33,27 +36,23 @@
#include "src/Geometry/OrthoMethods.h"
#include "src/Geometry/EulerAngles.h"
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#include "src/Geometry/Homogeneous.h"
#include "src/Geometry/RotationBase.h"
#include "src/Geometry/Rotation2D.h"
#include "src/Geometry/Quaternion.h"
#include "src/Geometry/AngleAxis.h"
#include "src/Geometry/Transform.h"
#include "src/Geometry/Translation.h"
#include "src/Geometry/Scaling.h"
#include "src/Geometry/Hyperplane.h"
#include "src/Geometry/ParametrizedLine.h"
#include "src/Geometry/AlignedBox.h"
#include "src/Geometry/Umeyama.h"
#if defined EIGEN_VECTORIZE_SSE
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/Geometry/All.h"
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/Geometry/arch/Geometry_SSE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

7
extern/Eigen3/Eigen/Householder vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_HOUSEHOLDER_MODULE_H
#define EIGEN_HOUSEHOLDER_MODULE_H

24
extern/Eigen3/Eigen/IterativeLinearSolvers vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
@@ -12,28 +19,29 @@
* This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
* Those solvers are accessible via the following classes:
* - ConjugateGradient for selfadjoint (hermitian) matrices,
* - LeastSquaresConjugateGradient for rectangular least-square problems,
* - BiCGSTAB for general square matrices.
*
* These iterative solvers are associated with some preconditioners:
* - IdentityPreconditioner - not really useful
* - DiagonalPreconditioner - also called JAcobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteILUT - incomplete LU factorization with dual thresholding
* - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
* - IncompleteLUT - incomplete LU factorization with dual thresholding
*
* Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
*
* \code
* #include <Eigen/IterativeLinearSolvers>
* \endcode
\code
#include <Eigen/IterativeLinearSolvers>
\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"
#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
#include "src/IterativeLinearSolvers/BiCGSTAB.h"
#include "src/IterativeLinearSolvers/IncompleteLUT.h"
#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
#include "src/Core/util/ReenableStupidWarnings.h"

7
extern/Eigen3/Eigen/Jacobi vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_JACOBI_MODULE_H
#define EIGEN_JACOBI_MODULE_H

25
extern/Eigen3/Eigen/LU vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_LU_MODULE_H
#define EIGEN_LU_MODULE_H
@@ -16,25 +23,27 @@
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/misc/Kernel.h"
#include "src/misc/Image.h"
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/LU/PartialPivLU_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/Inverse.h"
#include "src/LU/InverseImpl.h"
#if defined EIGEN_VECTORIZE_SSE
// Use the SSE optimized version whenever possible. At the moment the
// SSE version doesn't compile when AVX is enabled
#if defined EIGEN_VECTORIZE_SSE && !defined EIGEN_VECTORIZE_AVX
#include "src/LU/arch/Inverse_SSE.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/LU.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_LU_MODULE_H

7
extern/Eigen3/Eigen/MetisSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_MODULE_H

7
extern/Eigen3/Eigen/OrderingMethods vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
#define EIGEN_ORDERINGMETHODS_MODULE_H

12
extern/Eigen3/Eigen/PaStiXSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
#define EIGEN_PASTIXSUPPORT_MODULE_H
@@ -5,7 +12,6 @@
#include "src/Core/util/DisableStupidWarnings.h"
#include <complex.h>
extern "C" {
#include <pastix_nompi.h>
#include <pastix.h>
@@ -35,12 +41,8 @@ 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

9
extern/Eigen3/Eigen/PardisoSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
#define EIGEN_PARDISOSUPPORT_MODULE_H
@@ -7,8 +14,6 @@
#include <mkl_pardiso.h>
#include <unsupported/Eigen/SparseExtra>
/** \ingroup Support_modules
* \defgroup PardisoSupport_Module PardisoSupport module
*

28
extern/Eigen3/Eigen/QR vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QR_MODULE_H
#define EIGEN_QR_MODULE_H
@@ -15,31 +22,30 @@
*
* This module provides various QR decompositions
* This module also provides some MatrixBase methods, including:
* - MatrixBase::qr(),
* - MatrixBase::householderQr()
* - MatrixBase::colPivHouseholderQr()
* - MatrixBase::fullPivHouseholderQr()
*
* \code
* #include <Eigen/QR>
* \endcode
*/
#include "src/misc/Solve.h"
#include "src/QR/HouseholderQR.h"
#include "src/QR/FullPivHouseholderQR.h"
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/QR/HouseholderQR_MKL.h"
#include "src/QR/ColPivHouseholderQR_MKL.h"
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/QR.h"
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#ifdef EIGEN2_SUPPORT
#include "Eigenvalues"
#endif
#endif // EIGEN_QR_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

12
extern/Eigen3/Eigen/QtAlignedMalloc vendored
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@@ -1,3 +1,9 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_QTMALLOC_MODULE_H
#define EIGEN_QTMALLOC_MODULE_H
@@ -8,7 +14,7 @@
#include "src/Core/util/DisableStupidWarnings.h"
void *qMalloc(size_t size)
void *qMalloc(std::size_t size)
{
return Eigen::internal::aligned_malloc(size);
}
@@ -18,10 +24,10 @@ void qFree(void *ptr)
Eigen::internal::aligned_free(ptr);
}
void *qRealloc(void *ptr, size_t size)
void *qRealloc(void *ptr, std::size_t size)
{
void* newPtr = Eigen::internal::aligned_malloc(size);
memcpy(newPtr, ptr, size);
std::memcpy(newPtr, ptr, size);
Eigen::internal::aligned_free(ptr);
return newPtr;
}

11
extern/Eigen3/Eigen/SPQRSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
@@ -10,7 +17,7 @@
/** \ingroup Support_modules
* \defgroup SPQRSupport_Module SuiteSparseQR module
*
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
*
* \code
* #include <Eigen/SPQRSupport>
@@ -21,8 +28,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"

32
extern/Eigen3/Eigen/SVD vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
@@ -12,23 +19,30 @@
*
*
* 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/JacobiSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#include "src/SVD/JacobiSVD_MKL.h"
#endif
#include "src/misc/RealSvd2x2.h"
#include "src/SVD/UpperBidiagonalization.h"
#ifdef EIGEN2_SUPPORT
#include "src/Eigen2Support/SVD.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)
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

15
extern/Eigen3/Eigen/Sparse vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
@@ -11,14 +18,16 @@
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
* \endcode
\code
#include <Eigen/Sparse>
\endcode
*/
#include "SparseCore"
#include "OrderingMethods"
#ifndef EIGEN_MPL2_ONLY
#include "SparseCholesky"
#endif
#include "SparseLU"
#include "SparseQR"
#include "IterativeLinearSolvers"

2
extern/Eigen3/Eigen/SparseCholesky vendored
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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

33
extern/Eigen3/Eigen/SparseCore vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECORE_MODULE_H
#define EIGEN_SPARSECORE_MODULE_H
@@ -26,37 +33,35 @@
* 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/SparseCompressedBase.h"
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/SparseMap.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseRef.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
extern/Eigen3/Eigen/SparseLU vendored
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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"

10
extern/Eigen3/Eigen/SparseQR vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
@@ -21,9 +28,6 @@
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"

2
extern/Eigen3/Eigen/StdDeque vendored
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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 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)

2
extern/Eigen3/Eigen/StdList vendored
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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 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)

2
extern/Eigen3/Eigen/StdVector vendored
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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 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)

13
extern/Eigen3/Eigen/SuperLUSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
#define EIGEN_SUPERLUSUPPORT_MODULE_H
@@ -36,6 +43,8 @@ namespace Eigen { struct SluMatrix; }
* - class SuperLU: a supernodal sequential LU factorization.
* - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
*
* \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
*
* \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
*
* \code
@@ -48,12 +57,8 @@ 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

12
extern/Eigen3/Eigen/UmfPackSupport vendored
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@@ -1,3 +1,10 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
#define EIGEN_UMFPACKSUPPORT_MODULE_H
@@ -12,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup UmfPackSupport_Module UmfPackSupport module
*
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the following factorization class:
* - class UmfPackLU: a multifrontal sequential LU factorization.
*
@@ -26,9 +33,6 @@ extern "C" {
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/UmfPackSupport/UmfPackSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"

274
extern/Eigen3/Eigen/src/Cholesky/LDLT.h vendored
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@@ -13,7 +13,7 @@
#ifndef EIGEN_LDLT_H
#define EIGEN_LDLT_H
namespace Eigen {
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
@@ -28,8 +28,8 @@ namespace internal {
*
* \brief Robust Cholesky decomposition of a matrix with pivoting
*
* \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
@@ -43,7 +43,9 @@ namespace internal {
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
*
* \sa MatrixBase::ldlt(), class LLT
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
{
@@ -52,15 +54,15 @@ template<typename _MatrixType, int _UpLo> class LDLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
@@ -72,11 +74,11 @@ template<typename _MatrixType, int _UpLo> class LDLT
* The default constructor is useful in cases in which the user intends to
* perform decompositions via LDLT::compute(const MatrixType&).
*/
LDLT()
: m_matrix(),
m_transpositions(),
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
@@ -85,7 +87,7 @@ 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),
@@ -96,16 +98,35 @@ template<typename _MatrixType, int _UpLo> class LDLT
/** \brief Constructor with decomposition
*
* This calculates the decomposition for the input \a matrix.
*
* \sa LDLT(Index size)
*/
LDLT(const MatrixType& matrix)
template<typename InputType>
explicit LDLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix);
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
*
* \sa LDLT(const EigenBase&)
*/
template<typename InputType>
explicit LDLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** Clear any existing decomposition
@@ -151,13 +172,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
#ifdef EIGEN2_SUPPORT
inline bool isPositiveDefinite() const
{
return isPositive();
}
#endif
/** \returns true if the matrix is negative (semidefinite) */
inline bool isNegative(void) const
@@ -173,37 +187,38 @@ template<typename _MatrixType, int _UpLo> class LDLT
* \note_about_checking_solutions
*
* More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
* \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
* \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
* least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
* computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
*
* \sa MatrixBase::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());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
#endif
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
LDLT& compute(const MatrixType& matrix);
template<typename InputType>
LDLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the LDLT decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
template <typename Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
@@ -220,22 +235,35 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType reconstructedMatrix() const;
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LDLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the factorization failed because of a zero pivot.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Success;
return m_info;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
void _solve_impl(const RhsType &rhs, DstType &dst) const;
#endif
protected:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
@@ -248,10 +276,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
* is not stored), and the diagonal entries correspond to D.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
internal::SignMatrix m_sign;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
@@ -266,15 +296,18 @@ template<> struct ldlt_inplace<Lower>
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
bool found_zero_pivot = false;
bool ret = true;
if (size <= 1)
{
transpositions.setIdentity();
if (numext::real(mat.coeff(0,0)) > 0) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < 0) sign = NegativeSemiDef;
if(size==0) sign = ZeroSign;
else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;
}
@@ -286,7 +319,7 @@ template<> struct ldlt_inplace<Lower>
mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
index_of_biggest_in_corner += k;
transpositions.coeffRef(k) = index_of_biggest_in_corner;
transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
if(k != index_of_biggest_in_corner)
{
// apply the transposition while taking care to consider only
@@ -295,7 +328,7 @@ template<> struct ldlt_inplace<Lower>
mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
for(int i=k+1;i<index_of_biggest_in_corner;++i)
for(Index i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
@@ -321,26 +354,46 @@ template<> struct ldlt_inplace<Lower>
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
// In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
// was smaller than the cutoff value. However, soince LDLT is not rank-revealing
// we should only make sure we do not introduce INF or NaN values.
// LAPACK also uses 0 as the cutoff value.
// was smaller than the cutoff value. However, since LDLT is not rank-revealing
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
if((rs>0) && (abs(realAkk) > RealScalar(0)))
bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
if(k==0 && !pivot_is_valid)
{
// The entire diagonal is zero, there is nothing more to do
// except filling the transpositions, and checking whether the matrix is zero.
sign = ZeroSign;
for(Index j = 0; j<size; ++j)
{
transpositions.coeffRef(j) = IndexType(j);
ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
}
return ret;
}
if((rs>0) && pivot_is_valid)
A21 /= realAkk;
else if(rs>0)
ret = ret && (A21.array()==Scalar(0)).all();
if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
else if(!pivot_is_valid) found_zero_pivot = true;
if (sign == PositiveSemiDef) {
if (realAkk < 0) sign = Indefinite;
if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > 0) sign = Indefinite;
if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > 0) sign = PositiveSemiDef;
else if (realAkk < 0) sign = NegativeSemiDef;
if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
}
}
return true;
return ret;
}
// Reference for the algorithm: Davis and Hager, "Multiple Rank
@@ -356,7 +409,6 @@ template<> struct ldlt_inplace<Lower>
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
const Index size = mat.rows();
eigen_assert(mat.cols() == size && w.size()==size);
@@ -420,16 +472,16 @@ 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
@@ -437,21 +489,35 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename InputType>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix = a;
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
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);
m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
m_isInitialized = true;
return *this;
@@ -466,18 +532,19 @@ template<typename MatrixType, int _UpLo>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,_UpLo>::RealScalar& sigma)
{
typedef typename TranspositionType::StorageIndex IndexType;
const Index size = w.rows();
if (m_isInitialized)
{
eigen_assert(m_matrix.rows()==size);
}
else
{
{
m_matrix.resize(size,size);
m_matrix.setZero();
m_transpositions.resize(size);
for (Index i = 0; i < size; i++)
m_transpositions.coeffRef(i) = i;
m_transpositions.coeffRef(i) = IndexType(i);
m_temporary.resize(size);
m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
@@ -488,53 +555,46 @@ 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 (or rather numeric_limits::min())
// and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
// RealScalar tolerance = numext::maxi(vecD.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 leads to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
// Using numeric_limits::min() gives us more robustness to denormals.
RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
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;
using std::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);
*
@@ -588,6 +648,7 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa MatrixBase::ldlt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
@@ -598,6 +659,7 @@ SelfAdjointView<MatrixType, UpLo>::ldlt() const
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa SelfAdjointView::ldlt()
*/
template<typename Derived>
inline const LDLT<typename MatrixBase<Derived>::PlainObject>

190
extern/Eigen3/Eigen/src/Cholesky/LLT.h vendored
View File

@@ -10,7 +10,7 @@
#ifndef EIGEN_LLT_H
#define EIGEN_LLT_H
namespace Eigen {
namespace Eigen {
namespace internal{
template<typename MatrixType, int UpLo> struct LLT_Traits;
@@ -22,9 +22,9 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
*
* \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
* \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
* \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
* The other triangular part won't be read.
*
* This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
* matrix A such that A = LL^* = U^*U, where L is lower triangular.
@@ -40,12 +40,18 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
*
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \sa MatrixBase::llt(), class LDLT
*/
/* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*
* \b Performance: for best performance, it is recommended to use a column-major storage format
* with the Lower triangular part (the default), or, equivalently, a row-major storage format
* with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
* step, and rank-updates can be up to 3 times slower.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
* Therefore, the strict lower part does not have to store correct values.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
template<typename _MatrixType, int _UpLo> class LLT
{
@@ -54,12 +60,12 @@ template<typename _MatrixType, int _UpLo> class LLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
PacketSize = internal::packet_traits<Scalar>::size,
@@ -83,14 +89,30 @@ 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)
template<typename InputType>
explicit LLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
compute(matrix);
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
* \c MatrixType is a Eigen::Ref.
*
* \sa LLT(const EigenBase&)
*/
template<typename InputType>
explicit LLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \returns a view of the upper triangular matrix U */
@@ -115,33 +137,33 @@ template<typename _MatrixType, int _UpLo> class LLT
* Example: \include LLT_solve.cpp
* Output: \verbinclude LLT_solve.out
*
* \sa solveInPlace(), MatrixBase::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());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived, typename ResultType>
bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const
{
*result = this->solve(b);
return true;
}
bool isPositiveDefinite() const { return true; }
#endif
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;
void solveInPlace(const MatrixBase<Derived> &bAndX) const;
LLT& compute(const MatrixType& matrix);
template<typename InputType>
LLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which \c *this is the Cholesky decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
/** \returns the LLT decomposition matrix
*
@@ -159,7 +181,7 @@ template<typename _MatrixType, int _UpLo> class LLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the matrix.appears not to be positive definite.
*/
ComputationInfo info() const
{
@@ -167,24 +189,38 @@ template<typename _MatrixType, int _UpLo> class LLT
return m_info;
}
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
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:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
/** \internal
* Used to compute and store L
* The strict upper part is not used and even not initialized.
*/
MatrixType m_matrix;
RealScalar m_l1_norm;
bool m_isInitialized;
ComputationInfo m_info;
};
@@ -194,12 +230,11 @@ namespace internal {
template<typename Scalar, int UpLo> struct llt_inplace;
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::ColXpr ColXpr;
typedef typename internal::remove_all<ColXpr>::type ColXprCleaned;
typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
@@ -268,11 +303,10 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static typename MatrixType::Index unblocked(MatrixType& mat)
static Index unblocked(MatrixType& mat)
{
using std::sqrt;
typedef typename MatrixType::Index Index;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
for(Index k = 0; k < size; ++k)
@@ -295,9 +329,8 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
}
template<typename MatrixType>
static typename MatrixType::Index blocked(MatrixType& m)
static Index blocked(MatrixType& m)
{
typedef typename MatrixType::Index Index;
eigen_assert(m.rows()==m.cols());
Index size = m.rows();
if(size<32)
@@ -322,36 +355,36 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Index ret;
if((ret=unblocked(A11))>=0) return k+ret;
if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck
if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
}
return -1;
}
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
}
};
template<typename Scalar> struct llt_inplace<Scalar, Upper>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat)
static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::unblocked(matt);
}
template<typename MatrixType>
static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat)
static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::blocked(matt);
}
template<typename MatrixType, typename VectorType>
static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
{
Transpose<MatrixType> matt(mat);
return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
@@ -362,8 +395,8 @@ 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; }
};
@@ -372,8 +405,8 @@ 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; }
};
@@ -388,14 +421,29 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
* Output: \verbinclude TutorialLinAlgComputeTwice.out
*/
template<typename MatrixType, int _UpLo>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
template<typename InputType>
LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
m_matrix = a;
if (!internal::is_same_dense(m_matrix, a.derived()))
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_isInitialized = true;
bool ok = Traits::inplace_decomposition(m_matrix);
@@ -423,39 +471,33 @@ 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>
{
typedef LLT<_MatrixType,UpLo> LLTType;
EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
dst = rhs();
dec().solveInPlace(dst);
}
};
#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
{
dst = rhs;
solveInPlace(dst);
}
#endif
/** \internal use x = llt_object.solve(x);
*
*
* This is the \em in-place version of solve().
*
* \param bAndX represents both the right-hand side matrix b and result x.
*
* \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
* This version avoids a copy when the right hand side matrix b is not needed anymore.
*
* This version avoids a copy when the right hand side matrix b is not
* needed anymore.
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());
@@ -475,6 +517,7 @@ MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename Derived>
inline const LLT<typename MatrixBase<Derived>::PlainObject>
@@ -485,6 +528,7 @@ MatrixBase<Derived>::llt() const
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
*/
template<typename MatrixType, unsigned int UpLo>
inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>

99
extern/Eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h vendored Normal file
View File

@@ -0,0 +1,99 @@
/*
Copyright (c) 2011, Intel Corporation. All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of Intel Corporation nor the names of its contributors may
be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************
* Content : Eigen bindings to LAPACKe
* LLt decomposition based on LAPACKE_?potrf function.
********************************************************************************
*/
#ifndef EIGEN_LLT_LAPACKE_H
#define EIGEN_LLT_LAPACKE_H
namespace Eigen {
namespace internal {
template<typename Scalar> struct lapacke_llt;
#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
template<> struct lapacke_llt<EIGTYPE> \
{ \
template<typename MatrixType> \
static inline Index potrf(MatrixType& m, char uplo) \
{ \
lapack_int matrix_order; \
lapack_int size, lda, info, StorageOrder; \
EIGTYPE* a; \
eigen_assert(m.rows()==m.cols()); \
/* Set up parameters for ?potrf */ \
size = convert_index<lapack_int>(m.rows()); \
StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
a = &(m.coeffRef(0,0)); \
lda = convert_index<lapack_int>(m.outerStride()); \
\
info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
}; \
template<> struct llt_inplace<EIGTYPE, Lower> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
}; \
template<> struct llt_inplace<EIGTYPE, Upper> \
{ \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
{ \
Transpose<MatrixType> matt(mat); \
return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
} \
};
EIGEN_LAPACKE_LLT(double, double, d)
EIGEN_LAPACKE_LLT(float, float, s)
EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_LLT_LAPACKE_H

284
extern/Eigen3/Eigen/src/CholmodSupport/CholmodSupport.h vendored
View File

@@ -14,46 +14,52 @@ namespace Eigen {
namespace internal {
template<typename Scalar, typename CholmodType>
void cholmod_configure_matrix(CholmodType& mat)
{
if (internal::is_same<Scalar,float>::value)
{
mat.xtype = CHOLMOD_REAL;
mat.dtype = CHOLMOD_SINGLE;
}
else if (internal::is_same<Scalar,double>::value)
{
template<typename Scalar> struct cholmod_configure_matrix;
template<> struct cholmod_configure_matrix<double> {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_REAL;
mat.dtype = CHOLMOD_DOUBLE;
}
else if (internal::is_same<Scalar,std::complex<float> >::value)
{
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = CHOLMOD_SINGLE;
}
else if (internal::is_same<Scalar,std::complex<double> >::value)
{
};
template<> struct cholmod_configure_matrix<std::complex<double> > {
template<typename CholmodType>
static void run(CholmodType& mat) {
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = CHOLMOD_DOUBLE;
}
else
{
eigen_assert(false && "Scalar type not supported by CHOLMOD");
}
}
};
// Other scalar types are not yet suppotred by Cholmod
// template<> struct cholmod_configure_matrix<float> {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_REAL;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
//
// template<> struct cholmod_configure_matrix<std::complex<float> > {
// template<typename CholmodType>
// static void run(CholmodType& mat) {
// mat.xtype = CHOLMOD_COMPLEX;
// mat.dtype = CHOLMOD_SINGLE;
// }
// };
} // namespace internal
/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
* Note that the data are shared.
*/
template<typename _Scalar, int _Options, typename _Index>
cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
template<typename _Scalar, int _Options, typename _StorageIndex>
cholmod_sparse viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_StorageIndex> > mat)
{
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();;
res.nrow = mat.rows();
res.ncol = mat.cols();
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();
@@ -74,11 +80,11 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.dtype = 0;
res.stype = -1;
if (internal::is_same<_Index,int>::value)
if (internal::is_same<_StorageIndex,int>::value)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_Index,SuiteSparse_long>::value)
else if (internal::is_same<_StorageIndex,long>::value)
{
res.itype = CHOLMOD_LONG;
}
@@ -88,7 +94,7 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
}
// setup res.xtype
internal::cholmod_configure_matrix<_Scalar>(res);
internal::cholmod_configure_matrix<_Scalar>::run(res);
res.stype = 0;
@@ -98,16 +104,23 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(mat.const_cast_derived());
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
template<typename _Scalar, int _Options, typename _Index>
const cholmod_sparse viewAsCholmod(const SparseVector<_Scalar,_Options,_Index>& mat)
{
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.const_cast_derived()));
return res;
}
/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
* The data are not copied but shared. */
template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<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());
cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<_Scalar,_Options,_Index> >(mat.matrix().const_cast_derived()));
if(UpLo==Upper) res.stype = 1;
if(UpLo==Lower) res.stype = -1;
@@ -131,19 +144,19 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>(res);
internal::cholmod_configure_matrix<Scalar>::run(res);
return res;
}
/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
* The data are not copied but shared. */
template<typename Scalar, int Flags, typename Index>
MappedSparseMatrix<Scalar,Flags,Index> viewAsEigen(cholmod_sparse& cm)
template<typename Scalar, int Flags, typename StorageIndex>
MappedSparseMatrix<Scalar,Flags,StorageIndex> viewAsEigen(cholmod_sparse& cm)
{
return MappedSparseMatrix<Scalar,Flags,Index>
(cm.nrow, cm.ncol, static_cast<Index*>(cm.p)[cm.ncol],
static_cast<Index*>(cm.p), static_cast<Index*>(cm.i),static_cast<Scalar*>(cm.x) );
return MappedSparseMatrix<Scalar,Flags,StorageIndex>
(cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
}
enum CholmodMode {
@@ -157,29 +170,39 @@ 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 };
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef MatrixType CholMatrixType;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::StorageIndex StorageIndex;
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
public:
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
: m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 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_factorizationIsOk(false), m_analysisIsOk(false)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
cholmod_start(&m_cholmod);
compute(matrix);
}
@@ -191,11 +214,8 @@ class CholmodBase : internal::noncopyable
cholmod_finish(&m_cholmod);
}
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); }
inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
/** \brief Reports whether previous computation was successful.
*
@@ -216,34 +236,6 @@ 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.
@@ -277,7 +269,7 @@ class CholmodBase : internal::noncopyable
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
// If the factorization failed, minor is the column at which it did. On success minor == n.
this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
m_factorizationIsOk = true;
@@ -290,20 +282,22 @@ 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;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// Cholmod needs column-major stoarge without inner-stride, which corresponds to the default behavior of Ref.
Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
// note: cd stands for Cholmod Dense
Rhs& b_ref(b.const_cast_derived());
cholmod_dense b_cd = viewAsCholmod(b_ref);
cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
if(!x_cd)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
@@ -311,8 +305,8 @@ 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
template<typename RhsDerived, typename DestDerived>
void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &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;
@@ -320,14 +314,16 @@ class CholmodBase : internal::noncopyable
eigen_assert(size==b.rows());
// note: cs stands for Cholmod Sparse
cholmod_sparse b_cs = viewAsCholmod(b);
Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
cholmod_sparse b_cs = viewAsCholmod(b_ref);
cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod);
if(!x_cs)
{
this->m_info = NumericalIssue;
return;
}
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
@@ -344,10 +340,61 @@ class CholmodBase : internal::noncopyable
*/
Derived& setShift(const RealScalar& offset)
{
m_shiftOffset[0] = offset;
m_shiftOffset[0] = double(offset);
return derived();
}
/** \returns the determinant of the underlying matrix from the current factorization */
Scalar determinant() const
{
using std::exp;
return exp(logDeterminant());
}
/** \returns the log determinant of the underlying matrix from the current factorization */
Scalar logDeterminant() const
{
using std::log;
using numext::real;
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
RealScalar logDet = 0;
Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
if (m_cholmodFactor->is_super)
{
// Supernodal factorization stored as a packed list of dense column-major blocs,
// as described by the following structure:
// super[k] == index of the first column of the j-th super node
StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
// pi[k] == offset to the description of row indices
StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
// px[k] == offset to the respective dense block
StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
Index nb_super_nodes = m_cholmodFactor->nsuper;
for (Index k=0; k < nb_super_nodes; ++k)
{
StorageIndex ncols = super[k + 1] - super[k];
StorageIndex nrows = pi[k + 1] - pi[k];
Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
logDet += sk.real().log().sum();
}
}
else
{
// Simplicial factorization stored as standard CSC matrix.
StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
Index size = m_cholmodFactor->n;
for (Index k=0; k<size; ++k)
logDet += log(real( x[p[k]] ));
}
if (m_cholmodFactor->is_ll)
logDet *= 2.0;
return logDet;
};
template<typename Stream>
void dumpMemory(Stream& /*s*/)
{}
@@ -355,9 +402,8 @@ class CholmodBase : internal::noncopyable
protected:
mutable cholmod_common m_cholmod;
cholmod_factor* m_cholmodFactor;
RealScalar m_shiftOffset[2];
double m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
int m_analysisIsOk;
};
@@ -376,9 +422,13 @@ class CholmodBase : internal::noncopyable
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
@@ -395,7 +445,7 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
CholmodSimplicialLLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLLT() {}
@@ -423,9 +473,13 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
@@ -442,7 +496,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSimplicialLDLT() {}
@@ -468,9 +522,13 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
@@ -487,7 +545,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
CholmodSupernodalLLT(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodSupernodalLLT() {}
@@ -515,9 +573,13 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
* or Upper. Default is Lower.
*
* \implsparsesolverconcept
*
* This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
*
* \sa \ref TutorialSparseDirectSolvers
* \warning Only double precision real and complex scalar types are supported by Cholmod.
*
* \sa \ref TutorialSparseSolverConcept
*/
template<typename _MatrixType, int _UpLo = Lower>
class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
@@ -534,7 +596,7 @@ class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecom
CholmodDecomposition(const MatrixType& matrix) : Base()
{
init();
Base::compute(matrix);
this->compute(matrix);
}
~CholmodDecomposition() {}
@@ -572,36 +634,6 @@ 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

156
extern/Eigen3/Eigen/src/Core/Array.h vendored
View File

@@ -12,7 +12,16 @@
namespace Eigen {
/** \class Array
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
/** \class Array
* \ingroup Core_Module
*
* \brief General-purpose arrays with easy API for coefficient-wise operations
@@ -24,20 +33,14 @@ namespace Eigen {
* API for the %Matrix class provides easy access to linear-algebra
* operations.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
* See documentation of class Matrix for detailed information on the template parameters
* storage layout.
*
* \sa \ref TutorialArrayClass, \ref TopicClassHierarchy
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
*
* \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef ArrayXpr XprKind;
typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase;
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Array
: public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
@@ -69,11 +72,27 @@ class Array
* the usage of 'using'. This should be done only for operator=.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
{
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.
*
* *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
@@ -84,7 +103,8 @@ class Array
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -92,11 +112,12 @@ class Array
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array& operator=(const Array& other)
{
return Base::_set(other);
}
/** Default constructor.
*
* For fixed-size matrices, does nothing.
@@ -107,6 +128,7 @@ class Array
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
@@ -116,6 +138,7 @@ class Array
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ??
/** \internal */
EIGEN_DEVICE_FUNC
Array(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{
@@ -124,56 +147,62 @@ class Array
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
Array(Array&& other)
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
Array& operator=(Array&& other)
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Array(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(const T& x)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
Base::template _init1<T>(x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(val0, val1);
}
#else
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
/** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
/** Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Array() instead.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array(Index dim);
/** constructs an initialized 1x1 Array with the given coefficient */
Array(const Scalar& value);
/** constructs an uninitialized array with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size arrays. For fixed-size arrays,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Array() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
@@ -183,6 +212,7 @@ class Array
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
@@ -193,51 +223,27 @@ class Array
m_storage.data()[3] = val3;
}
explicit Array(const Scalar *data);
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const Array& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
: Base(other)
{ }
private:
struct PrivateType {};
public:
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::_resize_to_match(other);
*this = other;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType>::type = PrivateType())
: Base(other.derived())
{ }
/** 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()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
#ifdef EIGEN_ARRAY_PLUGIN
#include EIGEN_ARRAY_PLUGIN

76
extern/Eigen3/Eigen/src/Core/ArrayBase.h vendored
View File

@@ -32,7 +32,7 @@ template<typename ExpressionType> class MatrixWrapper;
* \tparam Derived is the derived type, e.g., an array or an expression type.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
*
* \sa class MatrixBase, \ref TopicClassHierarchy
*/
@@ -47,13 +47,11 @@ template<typename Derived> class ArrayBase
typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseBase<Derived> Base;
using Base::operator*;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
@@ -62,8 +60,7 @@ 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;
@@ -83,25 +80,14 @@ template<typename Derived> class ArrayBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal the plain matrix type corresponding to this expression. Note that is not necessarily
* exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const
* reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either
* PlainObject or const PlainObject&.
*/
typedef Array<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainObject;
typedef typename Base::PlainObject PlainObject;
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/ArrayCwiseUnaryOps.h"
@@ -112,44 +98,62 @@ template<typename Derived> class ArrayBase
# include EIGEN_ARRAYBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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 EIGEN_STRONG_INLINE
Derived& operator=(const Scalar &value)
{ Base::setConstant(value); return derived(); }
Derived& operator+=(const Scalar& scalar)
{ return *this = derived() + scalar; }
Derived& operator-=(const Scalar& scalar)
{ return *this = derived() - scalar; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const Scalar& scalar);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const Scalar& scalar);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:
EIGEN_DEVICE_FUNC
ArrayBase<Derived>& array() { return *this; }
EIGEN_DEVICE_FUNC
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
MatrixWrapper<Derived> matrix() { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return derived(); }
EIGEN_DEVICE_FUNC
MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
EIGEN_DEVICE_FUNC
const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
// template<typename Dest>
// inline void evalTo(Dest& dst) const { dst = matrix(); }
protected:
EIGEN_DEVICE_FUNC
ArrayBase() : Base() {}
private:
@@ -171,11 +175,10 @@ template<typename Derived> class ArrayBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC 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,typename OtherDerived::Scalar>());
return derived();
}
@@ -185,11 +188,10 @@ ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC 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,typename OtherDerived::Scalar>());
return derived();
}
@@ -199,11 +201,10 @@ ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC 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();
}
@@ -213,11 +214,10 @@ ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC 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,typename OtherDerived::Scalar>());
return derived();
}

151
extern/Eigen3/Eigen/src/Core/ArrayWrapper.h vendored
View File

@@ -32,7 +32,8 @@ struct traits<ArrayWrapper<ExpressionType> >
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
@@ -44,6 +45,7 @@ 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,
@@ -51,76 +53,45 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
inline ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
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 ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
return m_expression.coeffRef(index);
}
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const { dst = m_expression; }
const typename internal::remove_all<NestedExpressionType>::type&
EIGEN_DEVICE_FUNC
nestedExpression() const
{
return m_expression;
@@ -128,10 +99,12 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
@@ -157,7 +130,8 @@ struct traits<MatrixWrapper<ExpressionType> >
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
@@ -169,6 +143,7 @@ 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,
@@ -176,72 +151,40 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_matrix) {}
using Base::coeffRef;
EIGEN_DEVICE_FUNC
explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
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 ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.derived().coeffRef(rowId, colId);
}
inline CoeffReturnType coeff(Index index) const
{
return m_expression.coeff(index);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return m_expression.template packet<LoadMode>(index);
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<NestedExpressionType>::type&
nestedExpression() const
{
@@ -250,10 +193,12 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;

540
extern/Eigen3/Eigen/src/Core/Assign.h vendored
View File

@@ -14,478 +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(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
};
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>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {}
};
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
{
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>
{
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
{
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>
{
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>
{
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;
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>
{
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;
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;
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>
{
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 typename Derived1::Scalar Scalar;
typedef packet_traits<Scalar> PacketTraits;
enum {
packetSize = PacketTraits::size,
alignable = PacketTraits::AlignedOnScalar,
dstIsAligned = assign_traits<Derived1,Derived2>::DstIsAligned,
dstAlignment = alignable ? Aligned : int(dstIsAligned),
srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment
};
const Scalar *dst_ptr = &dst.coeffRef(0,0);
if((!bool(dstIsAligned)) && (size_t(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return assign_impl<Derived1,Derived2,DefaultTraversal,NoUnrolling>::run(dst, src);
}
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) || bool(dstIsAligned)) ? 0 : internal::first_aligned(dst_ptr, 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>
@@ -499,90 +27,62 @@ 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_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());
#ifndef EIGEN_NO_DEBUG
checkTransposeAliasing(other.derived());
#endif
internal::call_assignment_no_alias(derived(),other.derived());
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> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
template<typename ActualDerived, typename ActualOtherDerived>
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> {
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> {
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
template<typename ActualDerived, typename ActualOtherDerived>
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> {
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>
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>
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>
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

935
extern/Eigen3/Eigen/src/Core/AssignEvaluator.h vendored Normal file
View File

@@ -0,0 +1,935 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// 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
// 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_ASSIGN_EVALUATOR_H
#define EIGEN_ASSIGN_EVALUATOR_H
namespace Eigen {
// This implementation is based on Assign.h
namespace internal {
/***************************************************************************
* Part 1 : the logic deciding a strategy for traversal and unrolling *
***************************************************************************/
// copy_using_evaluator_traits is based on assign_traits
template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc>
struct copy_using_evaluator_traits
{
typedef typename DstEvaluator::XprType Dst;
typedef typename Dst::Scalar DstScalar;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags
};
public:
enum {
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
};
private:
enum {
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),
OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime
};
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type LinearPacketType;
typedef typename find_best_packet<DstScalar,InnerSize>::type InnerPacketType;
enum {
LinearPacketSize = unpacket_traits<LinearPacketType>::size,
InnerPacketSize = unpacket_traits<InnerPacketType>::size
};
public:
enum {
LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
};
private:
enum {
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = bool(StorageOrdersAgree)
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& bool(functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)),
MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
&& (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || 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 = bool(MightVectorize) && bool(DstHasDirectAccess)
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
/* 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
However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
};
public:
enum {
Traversal = int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize) ? int(LinearVectorizedTraversal)
: 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
};
typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
private:
enum {
ActualPacketSize = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
: Vectorized ? InnerPacketSize
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::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) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
#if EIGEN_UNALIGNED_VECTORIZE
: int(Traversal) == int(SliceVectorizedTraversal)
? ( bool(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling) )
#endif
: int(NoUnrolling)
};
#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);
std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstAlignment)
EIGEN_DEBUG_VAR(SrcAlignment)
EIGEN_DEBUG_VAR(LinearRequiredAlignment)
EIGEN_DEBUG_VAR(InnerRequiredAlignment)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(LinearPacketSize)
EIGEN_DEBUG_VAR(InnerPacketSize)
EIGEN_DEBUG_VAR(ActualPacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
EIGEN_DEBUG_VAR(MayInnerVectorize)
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
std::cerr << std::endl;
}
#endif
};
/***************************************************************************
* Part 2 : meta-unrollers
***************************************************************************/
/************************
*** Default traversal ***
************************/
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;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime
};
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);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.assignCoeffByOuterInner(outer, Index_);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel, int Index, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
{
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);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
/**************************
*** Inner vectorization ***
**************************/
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;
typedef typename Kernel::PacketType PacketType;
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime,
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
enum { NextIndex = Index + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling
{
typedef typename Kernel::PacketType PacketType;
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
}
};
template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
};
/***************************************************************************
* Part 3 : implementation of all cases
***************************************************************************/
// dense_assignment_loop is based on assign_impl
template<typename Kernel,
int Traversal = Kernel::AssignmentTraits::Traversal,
int Unrolling = Kernel::AssignmentTraits::Unrolling>
struct dense_assignment_loop;
/************************
*** Default traversal ***
************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
{
for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
kernel.assignCoeffByOuterInner(outer, inner);
}
}
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
{
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);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
}
};
/***************************
*** Linear vectorization ***
***************************/
// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
// of the non vectorizable beginning and ending parts
template <bool IsAligned = false>
struct unaligned_dense_assignment_loop
{
// if IsAligned = true, then do nothing
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index, Index) {}
};
template <>
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
#if EIGEN_COMP_MSVC
template <typename Kernel>
static EIGEN_DONT_INLINE void run(Kernel &kernel,
Index start,
Index end)
#else
template <typename Kernel>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel,
Index start,
Index end)
#endif
{
for (Index index = start; index < end; ++index)
kernel.assignCoeff(index);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
packetSize = unpacket_traits<PacketType>::size,
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment),
srcAlignment = Kernel::AssignmentTraits::JointAlignment
};
const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
for(Index index = alignedStart; index < alignedEnd; index += packetSize)
kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::SizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
alignedSize = (size/packetSize)*packetSize };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
}
};
/**************************
*** Inner vectorization ***
**************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
typedef typename Kernel::PacketType PacketType;
enum {
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index packetSize = unpacket_traits<PacketType>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
{
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);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::AssignmentTraits Traits;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
}
};
/***********************
*** Linear traversal ***
***********************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
const Index size = kernel.size();
for(Index i = 0; i < size; ++i)
kernel.assignCoeff(i);
}
};
template<typename Kernel>
struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
{
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);
}
};
/**************************
*** Slice vectorization ***
***************************/
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = unpacket_traits<PacketType>::size,
requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = alignable ? int(requestedAlignment)
: int(Kernel::AssignmentTraits::DstAlignment)
};
const Scalar *dst_ptr = kernel.dstDataPtr();
if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
}
const Index packetAlignedMask = packetSize - 1;
const Index innerSize = kernel.innerSize();
const Index outerSize = kernel.outerSize();
const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, 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)
kernel.assignCoeffByOuterInner(outer, inner);
// do the vectorizable part of the assignment
for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
// do the non-vectorizable part of the assignment
for(Index inner = alignedEnd; inner<innerSize ; ++inner)
kernel.assignCoeffByOuterInner(outer, inner);
alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
}
}
};
#if EIGEN_UNALIGNED_VECTORIZE
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::InnerSizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
vectorizableSize = (size/packetSize)*packetSize };
for(Index outer = 0; outer < kernel.outerSize(); ++outer)
{
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, size>::run(kernel, outer);
}
}
};
#endif
/***************************************************************************
* Part 4 : Generic dense assignment kernel
***************************************************************************/
// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
// to another dense writable evaluator.
// It is parametrized by the two evaluators, and the actual assignment functor.
// 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, int Version = Specialized>
class generic_dense_assignment_kernel
{
protected:
typedef typename DstEvaluatorTypeT::XprType DstXprType;
typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
public:
typedef DstEvaluatorTypeT DstEvaluatorType;
typedef SrcEvaluatorTypeT SrcEvaluatorType;
typedef typename DstEvaluatorType::Scalar Scalar;
typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
typedef typename AssignmentTraits::PacketType PacketType;
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
}
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(); }
EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() { return m_dst; }
EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const { return m_src; }
/// Assign src(row,col) to dst(row,col) through the assignment functor.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
{
m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
{
m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
}
/// \sa assignCoeff(Index,Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignCoeff(row, col);
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,PacketType>(row,col));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
{
m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,PacketType>(index));
}
template<int StoreMode, int LoadMode, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = rowIndexByOuterInner(outer, inner);
Index col = colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::RowsAtCompileTime) == 1 ? 0
: int(Traits::ColsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? outer
: inner;
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
{
typedef typename DstEvaluatorType::ExpressionTraits Traits;
return int(Traits::ColsAtCompileTime) == 1 ? 0
: int(Traits::RowsAtCompileTime) == 1 ? inner
: int(DstEvaluatorType::Flags)&RowMajorBit ? inner
: outer;
}
EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
{
return m_dstExpr.data();
}
protected:
DstEvaluatorType& m_dst;
const SrcEvaluatorType& m_src;
const Functor &m_functor;
// TODO find a way to avoid the needs of the original expression
DstXprType& m_dstExpr;
};
/***************************************************************************
* Part 5 : Entry point for dense rectangular assignment
***************************************************************************/
template<typename DstXprType,typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
{
EIGEN_ONLY_USED_FOR_DEBUG(dst);
EIGEN_ONLY_USED_FOR_DEBUG(src);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
}
template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
}
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
{
typedef evaluator<DstXprType> DstEvaluatorType;
typedef evaluator<SrcXprType> SrcEvaluatorType;
SrcEvaluatorType srcEvaluator(src);
// NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
// we need to resize the destination after the source evaluator has been created.
resize_if_allowed(dst, src, func);
DstEvaluatorType dstEvaluator(dst);
typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
dense_assignment_loop<Kernel>::run(kernel);
}
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
/***************************************************************************
* Part 6 : Generic assignment
***************************************************************************/
// 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;
// Assignement kind defined in this file:
struct Dense2Dense {};
struct EigenBase2EigenBase {};
template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
// 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 EnableIf = void>
struct Assignment;
// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// Deal with "assume-aliasing"
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
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 EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
// by-pass "assume-aliasing"
// When there is no aliasing, we require that 'dst' has been properly resized
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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 EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
|| (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
) && int(Dst::SizeAtCompileTime) != 1
};
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)
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 EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
// TODO check whether this is the right place to perform these checks:
EIGEN_STATIC_ASSERT_LVALUE(Dst)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
#ifndef EIGEN_NO_DEBUG
internal::check_for_aliasing(dst, src);
#endif
call_dense_assignment_loop(dst, src, func);
}
};
// Generic assignment through evalTo.
// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);
}
// NOTE The following two functions are templated to avoid their instanciation if not needed
// This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.addTo(dst);
}
template<typename SrcScalarType>
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.subTo(dst);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_ASSIGN_EVALUATOR_H

258
extern/Eigen3/Eigen/src/Core/Assign_MKL.h vendored
View File

@@ -1,6 +1,7 @@
/*
Copyright (c) 2011, Intel Corporation. All rights reserved.
Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
@@ -37,17 +38,13 @@ namespace Eigen {
namespace internal {
template<typename Op> struct vml_call
{ enum { IsSupported = 0 }; };
template<typename Dst, typename Src, typename UnaryOp>
template<typename Dst, typename Src>
class vml_assign_traits
{
private:
enum {
DstHasDirectAccess = Dst::Flags & DirectAccessBit,
SrcHasDirectAccess = Src::Flags & DirectAccessBit,
StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
: int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
@@ -57,165 +54,122 @@ class vml_assign_traits
: int(Dst::MaxRowsAtCompileTime),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
MightEnableVml = vml_call<UnaryOp>::IsSupported && StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess
&& Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD,
MayEnableVml = MightEnableVml && LargeEnough,
MayLinearize = MayEnableVml && MightLinearize
LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
};
public:
enum {
Traversal = MayLinearize ? LinearVectorizedTraversal
: MayEnableVml ? InnerVectorizedTraversal
: DefaultTraversal
EnableVml = MightEnableVml && LargeEnough,
Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
};
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling,
int VmlTraversal = vml_assign_traits<Derived1, Derived2, UnaryOp>::Traversal >
struct vml_assign_impl
: assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>
{
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, InnerVectorizedTraversal>
{
typedef typename Derived1::Scalar Scalar;
typedef typename Derived1::Index Index;
static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
{
// in case we want to (or have to) skip VML at runtime we can call:
// assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
const Index innerSize = dst.innerSize();
const Index outerSize = dst.outerSize();
for(Index outer = 0; outer < outerSize; ++outer) {
const Scalar *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) :
&(src.nestedExpression().coeffRef(0, outer));
Scalar *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));
vml_call<UnaryOp>::run(src.functor(), innerSize, src_ptr, dst_ptr );
}
}
};
template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, LinearVectorizedTraversal>
{
static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
{
// in case we want to (or have to) skip VML at runtime we can call:
// assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
vml_call<UnaryOp>::run(src.functor(), dst.size(), src.nestedExpression().data(), dst.data() );
}
};
// Macroses
#define EIGEN_MKL_VML_SPECIALIZE_ASSIGN(TRAVERSAL,UNROLLING) \
template<typename Derived1, typename Derived2, typename UnaryOp> \
struct assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>, TRAVERSAL, UNROLLING, Specialized> { \
static inline void run(Derived1 &dst, const Eigen::CwiseUnaryOp<UnaryOp, Derived2> &src) { \
vml_assign_impl<Derived1,Derived2,UnaryOp,TRAVERSAL,UNROLLING>::run(dst, src); \
} \
};
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,InnerUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,InnerUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,CompleteUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,NoUnrolling)
EIGEN_MKL_VML_SPECIALIZE_ASSIGN(SliceVectorizedTraversal,NoUnrolling)
#define EIGEN_PP_EXPAND(ARG) ARG
#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
#define EIGEN_MKL_VML_MODE VML_HA
#define EIGEN_VMLMODE_EXPAND_LA , VML_HA
#else
#define EIGEN_MKL_VML_MODE VML_LA
#define EIGEN_VMLMODE_EXPAND_LA , VML_LA
#endif
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst); \
} \
#define EIGEN_VMLMODE_EXPAND__
#define EIGEN_VMLMODE_PREFIX_LA vm
#define EIGEN_VMLMODE_PREFIX__ v
#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_,VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) { \
VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : \
&(src.nestedExpression().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
}; \
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested, typename Plain> \
struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \
{ \
VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent, \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) : \
&(src.lhs().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \
} \
} \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \
template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \
enum { IsSupported = 1 }; \
static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& func, \
int size, const EIGENTYPE* src, EIGENTYPE* dst) { \
EIGENTYPE exponent = func.m_exponent; \
MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \
VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent, \
(VMLTYPE*)dst, &vmlMode); \
} \
};
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8) \
EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16)
#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin, Sin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos, Cos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan, Tan)
//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
// The vm*powx functions are not avaibale in the windows version of MKL.
#ifndef _WIN32
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16)
#endif
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
} // end namespace internal

61
extern/Eigen3/Eigen/src/Core/BandMatrix.h vendored
View File

@@ -32,7 +32,7 @@ class BandMatrixBase : public EigenBase<Derived>
};
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
typedef typename DenseMatrixType::Index Index;
typedef typename DenseMatrixType::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
typedef EigenBase<Derived> Base;
@@ -161,15 +161,15 @@ class BandMatrixBase : public EigenBase<Derived>
*
* \brief Represents a rectangular matrix with a banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Rows Number of rows, or \b Dynamic
* \param Cols Number of columns, or \b Dynamic
* \param Supers Number of super diagonal
* \param Subs Number of sub diagonal
* \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
* \tparam _Scalar Numeric type, i.e. float, double, int
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
* \tparam _Supers Number of super diagonal
* \tparam _Subs Number of sub diagonal
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
* The former controls \ref TopicStorageOrders "storage order", and defaults to
* column-major. The latter controls whether the matrix represents a selfadjoint
* matrix in which case either Supers of Subs have to be null.
*
* \sa class TridiagonalMatrix
*/
@@ -179,7 +179,7 @@ struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef Eigen::Index StorageIndex;
enum {
CoeffReadCost = NumTraits<Scalar>::ReadCost,
RowsAtCompileTime = _Rows,
@@ -201,10 +201,10 @@ class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Sub
public:
typedef typename internal::traits<BandMatrix>::Scalar Scalar;
typedef typename internal::traits<BandMatrix>::Index Index;
typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
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)
{
@@ -241,7 +241,7 @@ struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Opt
{
typedef typename _CoefficientsType::Scalar Scalar;
typedef typename _CoefficientsType::StorageKind StorageKind;
typedef typename _CoefficientsType::Index Index;
typedef typename _CoefficientsType::StorageIndex StorageIndex;
enum {
CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost,
RowsAtCompileTime = _Rows,
@@ -264,9 +264,9 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
typedef typename internal::traits<BandMatrixWrapper>::Index Index;
typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
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)
{
@@ -302,9 +302,9 @@ class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsT
*
* \brief Represents a tridiagonal matrix with a compact banded storage
*
* \param _Scalar Numeric type, i.e. float, double, int
* \param Size Number of rows and cols, or \b Dynamic
* \param _Options Can be 0 or \b SelfAdjoint
* \tparam Scalar Numeric type, i.e. float, double, int
* \tparam Size Number of rows and cols, or \b Dynamic
* \tparam Options Can be 0 or \b SelfAdjoint
*
* \sa class BandMatrix
*/
@@ -312,9 +312,9 @@ template<typename Scalar, int Size, int Options>
class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
{
typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
typedef typename Base::Index Index;
typedef typename Base::StorageIndex StorageIndex;
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

280
extern/Eigen3/Eigen/src/Core/Block.h vendored
View File

@@ -13,14 +13,70 @@
namespace Eigen {
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename ref_selector<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
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
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(outer_stride_at_compile_time<XprType>::ret)
: int(inner_stride_at_compile_time<XprType>::ret),
// 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,
Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
// FIXME DirectAccessBit should not be handled by expressions
//
// Alignment 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
Alignment = 0
};
};
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
} // end namespace internal
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
/** \class Block
* \ingroup Core_Module
*
* \brief Expression of a fixed-size or dynamic-size block
*
* \param XprType the type of the expression in which we are taking a block
* \param BlockRows the number of rows of the block we are taking at compile time (optional)
* \param BlockCols the number of columns of the block we are taking at compile time (optional)
* \tparam XprType the type of the expression in which we are taking a block
* \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
* \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
* \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
* to set of columns of a column major matrix (optional). The parameter allows to determine
* at compile time whether aligned access is possible on the block expression.
*
* This class represents an expression of either a fixed-size or dynamic-size block. It is the return
* type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
@@ -44,62 +100,6 @@ namespace Eigen {
*
* \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
*/
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::XprKind XprKind;
typedef typename nested<XprType>::type XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum{
MatrixRows = traits<XprType>::RowsAtCompileTime,
MatrixCols = traits<XprType>::ColsAtCompileTime,
RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
MaxRowsAtCompileTime = BlockRows==0 ? 0
: RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime),
MaxColsAtCompileTime = BlockCols==0 ? 0
: ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime),
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
IsDense = is_same<StorageKind,Dense>::value,
IsRowMajor = (IsDense&&MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (IsDense&&MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: XprTypeIsRowMajor,
HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
? int(inner_stride_at_compile_time<XprType>::ret)
: int(outer_stride_at_compile_time<XprType>::ret),
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))) % 16) == 0)) ? AlignedBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (traits<XprType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
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
};
};
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
} // end namespace internal
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
: public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
{
@@ -109,9 +109,12 @@ 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
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index i) : Impl(xpr,i)
{
eigen_assert( (i>=0) && (
@@ -121,25 +124,27 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index a_startRow, Index a_startCol)
: Impl(xpr, a_startRow, a_startCol)
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr, Index startRow, Index startCol)
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(a_startRow >= 0 && BlockRows >= 1 && a_startRow + BlockRows <= xpr.rows()
&& a_startCol >= 0 && BlockCols >= 1 && a_startCol + BlockCols <= xpr.cols());
eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline Block(XprType& xpr,
Index a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(a_startRow >= 0 && blockRows >= 0 && a_startRow <= xpr.rows() - blockRows
&& a_startCol >= 0 && blockCols >= 0 && a_startCol <= xpr.cols() - blockCols);
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows
&& startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
}
};
@@ -150,14 +155,15 @@ class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
: public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
{
typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
typedef typename XprType::Index Index;
typedef typename XprType::StorageIndex StorageIndex;
public:
typedef Impl Base;
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol) : Impl(xpr, a_startRow, a_startCol) {}
inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol, Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_startCol, blockRows, blockCols) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
EIGEN_DEVICE_FUNC
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: Impl(xpr, startRow, startCol, blockRows, blockCols) {}
};
namespace internal {
@@ -167,16 +173,18 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
public:
typedef typename internal::dense_xpr_base<BlockType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
// class InnerIterator; // FIXME apparently never used
/** Column or Row constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
@@ -191,75 +199,76 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
/** Fixed-size constructor
*/
inline BlockImpl_dense(XprType& xpr, Index a_startRow, Index a_startCol)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index a_startRow, Index a_startCol,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{}
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr
.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value(), val);
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@@ -273,40 +282,46 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
m_xpr.template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
/** \sa MapBase::data() */
inline const Scalar* data() const;
inline Index innerStride() const;
inline Index outerStride() const;
EIGEN_DEVICE_FUNC inline const Scalar* data() const;
EIGEN_DEVICE_FUNC inline Index innerStride() const;
EIGEN_DEVICE_FUNC inline Index outerStride() const;
#endif
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
Index startRow() const
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
Index startCol() const
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
protected:
const typename XprType::Nested m_xpr;
const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
};
/** \internal Internal implementation of dense Blocks in the direct access case.*/
@@ -315,6 +330,10 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
enum {
XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
};
public:
typedef MapBase<BlockType> Base;
@@ -323,42 +342,52 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
/** Column or Row constructor
*/
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)
m_xpr(xpr),
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
{
init();
}
/** Fixed-size constructor
*/
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), m_startRow(startRow), m_startCol(startCol)
{
init();
}
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr,
Index startRow, Index startCol,
Index blockRows, Index blockCols)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols),
m_xpr(xpr)
: Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
{
init();
}
const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const
EIGEN_DEVICE_FUNC
const typename internal::remove_all<XprTypeNested>::type& nestedExpression() const
{
return m_xpr;
}
EIGEN_DEVICE_FUNC
XprType& nestedExpression() { return m_xpr; }
/** \sa MapBase::innerStride() */
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return internal::traits<BlockType>::HasSameStorageOrderAsXprType
@@ -367,11 +396,24 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
}
/** \sa MapBase::outerStride() */
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return m_outerStride;
}
EIGEN_DEVICE_FUNC
StorageIndex startRow() const
{
return m_startRow.value();
}
EIGEN_DEVICE_FUNC
StorageIndex startCol() const
{
return m_startCol.value();
}
#ifndef __SUNPRO_CC
// FIXME sunstudio is not friendly with the above friend...
// META-FIXME there is no 'friend' keyword around here. Is this obsolete?
@@ -380,6 +422,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
EIGEN_DEVICE_FUNC
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
@@ -388,6 +431,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
#endif
protected:
EIGEN_DEVICE_FUNC
void init()
{
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
@@ -395,7 +439,9 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: m_xpr.innerStride();
}
typename XprType::Nested m_xpr;
XprTypeNested m_xpr;
const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
Index m_outerStride;
};

42
extern/Eigen3/Eigen/src/Core/BooleanRedux.h vendored
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 +80,19 @@ struct any_unroller<Derived, Dynamic>
template<typename Derived>
inline bool DenseBase<Derived>::all() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& 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 +104,19 @@ inline bool DenseBase<Derived>::all() const
template<typename Derived>
inline bool DenseBase<Derived>::any() const
{
typedef internal::evaluator<Derived> Evaluator;
enum {
unroll = SizeAtCompileTime != Dynamic
&& 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;
}
}
@@ -124,7 +126,7 @@ inline bool DenseBase<Derived>::any() const
* \sa all(), any()
*/
template<typename Derived>
inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
inline Eigen::Index DenseBase<Derived>::count() const
{
return derived().template cast<bool>().template cast<Index>().sum();
}
@@ -136,7 +138,11 @@ inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isNaN().any();
#else
return !((derived().array()==derived().array()).all());
#endif
}
/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
@@ -146,7 +152,11 @@ inline bool DenseBase<Derived>::hasNaN() const
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
{
#if EIGEN_COMP_MSVC || (defined __FAST_MATH__)
return derived().array().isFinite().all();
#else
return !((derived()-derived()).hasNaN());
#endif
}
} // end namespace Eigen

40
extern/Eigen3/Eigen/src/Core/CommaInitializer.h vendored
View File

@@ -22,14 +22,14 @@ namespace Eigen {
* the return type of MatrixBase::operator<<, and most of the time this is the only
* way it is used.
*
* \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
* \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
*/
template<typename XprType>
struct CommaInitializer
{
typedef typename XprType::Scalar Scalar;
typedef typename XprType::Index Index;
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const Scalar& s)
: m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
{
@@ -37,6 +37,7 @@ struct CommaInitializer
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
: m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
{
@@ -46,6 +47,7 @@ struct CommaInitializer
/* Copy/Move constructor which transfers ownership. This is crucial in
* absence of return value optimization to avoid assertions during destruction. */
// FIXME in C++11 mode this could be replaced by a proper RValue constructor
EIGEN_DEVICE_FUNC
inline CommaInitializer(const CommaInitializer& o)
: m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
// Mark original object as finished. In absence of R-value references we need to const_cast:
@@ -55,6 +57,7 @@ struct CommaInitializer
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
{
if (m_col==m_xpr.cols())
@@ -74,11 +77,10 @@ struct CommaInitializer
/* inserts a matrix expression in the target matrix */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if(other.cols()==0 || other.rows()==0)
return *this;
if (m_col==m_xpr.cols())
if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
{
m_row+=m_currentBlockRows;
m_col = 0;
@@ -86,24 +88,22 @@ struct CommaInitializer
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
eigen_assert((m_col + other.cols() <= m_xpr.cols())
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
if (OtherDerived::SizeAtCompileTime != Dynamic)
m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
(m_row, m_col) = other;
else
m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
(m_row, m_col, other.rows(), other.cols()) = other;
m_col += other.cols();
return *this;
}
EIGEN_DEVICE_FUNC
inline ~CommaInitializer()
#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
#endif
{
eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
finished();
}
/** \returns the built matrix once all its coefficients have been set.
@@ -113,9 +113,15 @@ struct CommaInitializer
* quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
* \endcode
*/
inline XprType& finished() { return m_xpr; }
EIGEN_DEVICE_FUNC
inline XprType& finished() {
eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
return m_xpr;
}
XprType& m_xpr; // target expression
XprType& m_xpr; // target expression
Index m_row; // current row id
Index m_col; // current col id
Index m_currentBlockRows; // current block height

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extern/Eigen3/Eigen/src/Core/ConditionEstimator.h vendored Normal file
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@@ -0,0 +1,175 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CONDITIONESTIMATOR_H
#define EIGEN_CONDITIONESTIMATOR_H
namespace Eigen {
namespace internal {
template <typename Vector, typename RealVector, bool IsComplex>
struct rcond_compute_sign {
static inline Vector run(const Vector& v) {
const RealVector v_abs = v.cwiseAbs();
return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
.select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
}
};
// Partial specialization to avoid elementwise division for real vectors.
template <typename Vector>
struct rcond_compute_sign<Vector, Vector, false> {
static inline Vector run(const Vector& v) {
return (v.array() < static_cast<typename Vector::RealScalar>(0))
.select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
}
};
/**
* \returns an estimate of ||inv(matrix)||_1 given a decomposition of
* \a matrix that implements .solve() and .adjoint().solve() methods.
*
* This function implements Algorithms 4.1 and 5.1 from
* http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
* which also forms the basis for the condition number estimators in
* LAPACK. Since at most 10 calls to the solve method of dec are
* performed, the total cost is O(dims^2), as opposed to O(dims^3)
* needed to compute the inverse matrix explicitly.
*
* The most common usage is in estimating the condition number
* ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
* computed directly in O(n^2) operations.
*
* Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
* LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
{
typedef typename Decomposition::MatrixType MatrixType;
typedef typename Decomposition::Scalar Scalar;
typedef typename Decomposition::RealScalar RealScalar;
typedef typename internal::plain_col_type<MatrixType>::type Vector;
typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
eigen_assert(dec.rows() == dec.cols());
const Index n = dec.rows();
if (n == 0)
return 0;
// Disable Index to float conversion warning
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
// lower_bound is a lower bound on
// ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1
// and is the objective maximized by the ("super-") gradient ascent
// algorithm below.
RealScalar lower_bound = v.template lpNorm<1>();
if (n == 1)
return lower_bound;
// Gradient ascent algorithm follows: We know that the optimum is achieved at
// one of the simplices v = e_i, so in each iteration we follow a
// super-gradient to move towards the optimal one.
RealScalar old_lower_bound = lower_bound;
Vector sign_vector(n);
Vector old_sign_vector;
Index v_max_abs_index = -1;
Index old_v_max_abs_index = v_max_abs_index;
for (int k = 0; k < 4; ++k)
{
sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
// Break if the solution stagnated.
break;
}
// v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
v = dec.adjoint().solve(sign_vector);
v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {
// Break if the solution stagnated.
break;
}
// Move to the new simplex e_j, where j = v_max_abs_index.
v = dec.solve(Vector::Unit(n, v_max_abs_index)); // v = inv(matrix) * e_j.
lower_bound = v.template lpNorm<1>();
if (lower_bound <= old_lower_bound) {
// Break if the gradient step did not increase the lower_bound.
break;
}
if (!is_complex) {
old_sign_vector = sign_vector;
}
old_v_max_abs_index = v_max_abs_index;
old_lower_bound = lower_bound;
}
// The following calculates an independent estimate of ||matrix||_1 by
// multiplying matrix by a vector with entries of slowly increasing
// magnitude and alternating sign:
// v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
// This improvement to Hager's algorithm above is due to Higham. It was
// added to make the algorithm more robust in certain corner cases where
// large elements in the matrix might otherwise escape detection due to
// exact cancellation (especially when op and op_adjoint correspond to a
// sequence of backsubstitutions and permutations), which could cause
// Hager's algorithm to vastly underestimate ||matrix||_1.
Scalar alternating_sign(RealScalar(1));
for (Index i = 0; i < n; ++i) {
// The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
alternating_sign = -alternating_sign;
}
v = dec.solve(v);
const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
return numext::maxi(lower_bound, alternate_lower_bound);
}
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
} // namespace internal
} // namespace Eigen
#endif

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142
extern/Eigen3/Eigen/src/Core/CoreIterators.h vendored
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@@ -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 +15,113 @@ 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 internal::evaluator<XprType> EvaluatorType;
typedef typename internal::traits<XprType>::Scalar Scalar;
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 evaluator<XprType> EvaluatorType;
typedef typename traits<XprType>::Scalar Scalar;
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 evaluator<XprType> EvaluatorType;
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

166
extern/Eigen3/Eigen/src/Core/CwiseBinaryOp.h vendored
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@@ -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
@@ -13,26 +13,6 @@
namespace Eigen {
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
*
* \param BinaryOp template functor implementing the operator
* \param Lhs the type of the left-hand side
* \param Rhs the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs>
struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
@@ -52,77 +32,75 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
// we still want to handle the case when the result type is different.
typedef typename result_of<
BinaryOp(
typename Lhs::Scalar,
typename Rhs::Scalar
const typename Lhs::Scalar&,
const typename Rhs::Scalar&
)
>::type 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;
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>::StorageIndex,
typename traits<Rhs>::StorageIndex>::type StorageIndex;
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
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),
Cost0 = EIGEN_ADD_COST(LhsCoeffReadCost,RhsCoeffReadCost),
CoeffReadCost = EIGEN_ADD_COST(Cost0,functor_traits<BinaryOp>::Cost)
Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,_LhsNested::Flags & RowMajorBit,_RhsNested::Flags & RowMajorBit>::value
};
};
} // 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,
/** \class CwiseBinaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
*
* \tparam BinaryOp template functor implementing the operator
* \tparam LhsType the type of the left-hand side
* \tparam RhsType the type of the right-hand side
*
* This class represents an expression where a coefficient-wise binary operator is applied to two expressions.
* It is the return type of binary operators, by which we mean only those binary operators where
* both the left-hand side and the right-hand side are Eigen expressions.
* For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
*
* Most of the time, this is the only way that it is used, so you typically don't have to name
* CwiseBinaryOp types explicitly.
*
* \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
*/
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<BinaryOp>::type Functor;
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::ref_selector<LhsType>::type LhsNested;
typedef typename internal::ref_selector<RhsType>::type RhsNested;
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
@@ -132,6 +110,7 @@ class CwiseBinaryOp : internal::no_assignment_operator,
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic)
@@ -139,6 +118,7 @@ class CwiseBinaryOp : internal::no_assignment_operator,
else
return m_lhs.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time optimizations
if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic)
@@ -148,10 +128,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
}
/** \returns the left hand side nested expression */
EIGEN_DEVICE_FUNC
const _LhsNested& lhs() const { return m_lhs; }
/** \returns the right hand side nested expression */
EIGEN_DEVICE_FUNC
const _RhsNested& rhs() const { return m_rhs; }
/** \returns the functor representing the binary operation */
EIGEN_DEVICE_FUNC
const BinaryOp& functor() const { return m_functor; }
protected:
@@ -160,41 +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_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_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.
@@ -206,8 +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,typename OtherDerived::Scalar>());
return derived();
}
@@ -220,11 +174,11 @@ 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,typename OtherDerived::Scalar>());
return derived();
}
} // end namespace Eigen
#endif // EIGEN_CWISE_BINARY_OP_H

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extern/Eigen3/Eigen/src/Core/CwiseNullaryOp.h vendored
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@@ -12,13 +12,24 @@
namespace Eigen {
namespace internal {
template<typename NullaryOp, typename PlainObjectType>
struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
{
enum {
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
} // namespace internal
/** \class CwiseNullaryOp
* \ingroup Core_Module
*
* \brief Generic expression of a matrix where all coefficients are defined by a functor
*
* \param NullaryOp template functor implementing the operator
* \param PlainObjectType the underlying plain matrix/array type
* \tparam NullaryOp template functor implementing the operator
* \tparam PlainObjectType the underlying plain matrix/array type
*
* This class represents an expression of a generic nullary operator.
* It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
@@ -27,68 +38,49 @@ namespace Eigen {
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr()
* The functor NullaryOp must expose one of the following method:
<table class="manual">
<tr ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
<tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
<tr ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
</table>
* It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
*
* See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
* C++11 random number generators.
*
* A nullary expression can also be used to implement custom sophisticated matrix manipulations
* that cannot be covered by the existing set of natively supported matrix manipulations.
* See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
* on the behavior of CwiseNullaryOp.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
*/
namespace internal {
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
};
};
}
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:
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
CwiseNullaryOp(Index nbRows, Index nbCols, const NullaryOp& func = NullaryOp())
: m_rows(nbRows), m_cols(nbCols), m_functor(func)
EIGEN_DEVICE_FUNC
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
{
eigen_assert(nbRows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols));
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return m_functor(rowId, colId);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return m_functor.packetOp(rowId, colId);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return m_functor(index);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return m_functor.packetOp(index);
}
/** \returns the functor representing the nullary operation */
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
protected:
@@ -113,10 +105,10 @@ class CwiseNullaryOp : internal::no_assignment_operator,
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(rows, cols, func);
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
@@ -132,16 +124,19 @@ DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& f
*
* The template parameter \a CustomNullaryOp is the type of the functor.
*
* Here is an example with C++11 random generators: \include random_cpp11.cpp
* Output: \verbinclude random_cpp11.out
*
* \sa class CwiseNullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, Derived>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, Derived>(size, 1, func);
if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
}
/** \returns an expression of a matrix defined by a custom functor \a func
@@ -155,19 +150,19 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func);
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
}
/** \returns an expression of a constant matrix of value \a value
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this DenseBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@@ -176,9 +171,9 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value));
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
@@ -197,7 +192,7 @@ DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index size, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
@@ -213,53 +208,40 @@ DenseBase<Derived>::Constant(Index size, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(const Scalar& value)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
}
/**
* \brief Sets a linearly space vector.
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* This particular version of LinSpaced() uses sequential access, i.e. vector access is
* assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization
* and yields faster code than the random access version.
*
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* Example: \include DenseBase_LinSpaced_seq.cpp
* Output: \verbinclude DenseBase_LinSpaced_seq.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Index,Scalar,Scalar), CwiseNullaryOp
* \sa LinSpaced(Index,Scalar,Scalar), setLinSpaced(Index,const Scalar&,const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size));
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/**
* \copydoc DenseBase::LinSpaced(Sequential_t, Index, const Scalar&, const Scalar&)
* Special version for fixed size types which does not require the size parameter.
/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
*
* \sa LinSpaced(Scalar,Scalar)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,false>(low,high,Derived::SizeAtCompileTime));
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
@@ -269,14 +251,24 @@ DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& hig
* Example: \include DenseBase_LinSpaced.cpp
* Output: \verbinclude DenseBase_LinSpaced.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Sequential_t,Index,const Scalar&,const Scalar&,Index), CwiseNullaryOp
* For integer scalar types, an even spacing is possible if and only if the length of the range,
* i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
* number of values \c high-low+1 (meaning each value can be repeated the same number of time).
* If one of these two considions is not satisfied, then \c high is lowered to the largest value
* satisfying one of this constraint.
* Here are some examples:
*
* Example: \include DenseBase_LinSpacedInt.cpp
* Output: \verbinclude DenseBase_LinSpacedInt.out
*
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,true>(low,high,size));
return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,PacketScalar>(low,high,size));
}
/**
@@ -284,22 +276,23 @@ DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,PacketScalar>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), val, prec))
if(!internal::isApprox(self.coeff(i, j), val, prec))
return false;
return true;
}
@@ -308,7 +301,7 @@ bool DenseBase<Derived>::isApproxToConstant
*
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isConstant
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(val, prec);
@@ -319,22 +312,22 @@ bool DenseBase<Derived>::isConstant
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(val);
}
/** Sets all coefficients in this expression to \a value.
/** Sets all coefficients in this expression to value \a val.
*
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), val);
}
/** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value.
/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
*
* \only_for_vectors
*
@@ -344,17 +337,17 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
return setConstant(val);
}
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
@@ -363,15 +356,15 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar& val)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(val);
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
@@ -381,27 +374,33 @@ PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar&
* Example: \include DenseBase_setLinSpaced.cpp
* Output: \verbinclude DenseBase_setLinSpaced.out
*
* \sa CwiseNullaryOp
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,false>(low,high,newSize));
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar>(low,high,newSize));
}
/**
* \brief Sets a linearly space vector.
* \brief Sets a linearly spaced vector.
*
* The function fill *this with equally spaced values in the closed interval [low,high].
* The function fills \c *this with equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors
*
* \sa setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
* For integer scalar types, do not miss the explanations on the definition
* of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
*
* \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return setLinSpaced(size(), low, high);
@@ -424,10 +423,10 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
* \sa Zero(), Zero(Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(0));
return Constant(rows, cols, Scalar(0));
}
/** \returns an expression of a zero vector.
@@ -447,7 +446,7 @@ DenseBase<Derived>::Zero(Index nbRows, Index nbCols)
* \sa Zero(), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index size)
{
return Constant(size, Scalar(0));
@@ -464,7 +463,7 @@ DenseBase<Derived>::Zero(Index size)
* \sa Zero(Index), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero()
{
return Constant(Scalar(0));
@@ -479,11 +478,12 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(const RealScalar& prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
return true;
}
@@ -496,7 +496,7 @@ bool DenseBase<Derived>::isZero(const RealScalar& prec) const
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
{
return setConstant(Scalar(0));
}
@@ -511,7 +511,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
* \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(newSize);
@@ -520,8 +520,8 @@ PlainObjectBase<Derived>::setZero(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
@@ -529,10 +529,10 @@ PlainObjectBase<Derived>::setZero(Index newSize)
* \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(0));
}
@@ -540,7 +540,7 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -553,10 +553,10 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
* \sa Ones(), Ones(Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(nbRows, nbCols, Scalar(1));
return Constant(rows, cols, Scalar(1));
}
/** \returns an expression of a vector where all coefficients equal one.
@@ -576,7 +576,7 @@ DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
* \sa Ones(), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(newSize, Scalar(1));
@@ -593,7 +593,7 @@ DenseBase<Derived>::Ones(Index newSize)
* \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones()
{
return Constant(Scalar(1));
@@ -608,7 +608,7 @@ DenseBase<Derived>::Ones()
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
@@ -622,7 +622,7 @@ bool DenseBase<Derived>::isOnes
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
{
return setConstant(Scalar(1));
}
@@ -637,7 +637,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
* \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(newSize);
@@ -646,8 +646,8 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
@@ -655,10 +655,10 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
* \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setConstant(Scalar(1));
}
@@ -666,7 +666,7 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@@ -679,10 +679,10 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
* \sa Identity(), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_identity_op<Scalar>());
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
@@ -696,7 +696,7 @@ MatrixBase<Derived>::Identity(Index nbRows, Index nbCols)
* \sa Identity(Index,Index), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity()
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@@ -716,18 +716,19 @@ template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
{
for(Index i = 0; i < rows(); ++i)
{
if(i == j)
{
if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec))
if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
return false;
}
else
{
if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec))
if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
return false;
}
}
@@ -740,6 +741,7 @@ namespace internal {
template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
struct setIdentity_impl
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
return m = Derived::Identity(m.rows(), m.cols());
@@ -749,11 +751,11 @@ struct setIdentity_impl
template<typename Derived>
struct setIdentity_impl<Derived, true>
{
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
m.setZero();
const Index size = (std::min)(m.rows(), m.cols());
const Index size = numext::mini(m.rows(), m.cols());
for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
return m;
}
@@ -769,15 +771,15 @@ struct setIdentity_impl<Derived, true>
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
{
return internal::setIdentity_impl<Derived>::run(derived());
}
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out
@@ -785,9 +787,9 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(nbRows, nbCols);
derived().resize(rows, cols);
return setIdentity();
}
@@ -798,7 +800,7 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index nbRows, Inde
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
@@ -813,7 +815,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(),i);
@@ -826,7 +828,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
{ return Derived::Unit(0); }
/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
@@ -836,7 +838,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
{ return Derived::Unit(1); }
/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
@@ -846,7 +848,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
{ return Derived::Unit(2); }
/** \returns an expression of the W axis unit vector (0,0,0,1)
@@ -856,7 +858,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
{ return Derived::Unit(3); }
} // end namespace Eigen

197
extern/Eigen3/Eigen/src/Core/CwiseTernaryOp.h vendored Normal file
View File

@@ -0,0 +1,197 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_TERNARY_OP_H
#define EIGEN_CWISE_TERNARY_OP_H
namespace Eigen {
namespace internal {
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
// we must not inherit from traits<Arg1> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Arg1>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Arg1, Arg2, and Arg3 to have the same scalar type
// (see CwiseTernaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<TernaryOp(
const typename Arg1::Scalar&, const typename Arg2::Scalar&,
const typename Arg3::Scalar&)>::type Scalar;
typedef typename internal::traits<Arg1>::StorageKind StorageKind;
typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
typedef typename Arg1::Nested Arg1Nested;
typedef typename Arg2::Nested Arg2Nested;
typedef typename Arg3::Nested Arg3Nested;
typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
enum { Flags = _Arg1Nested::Flags & RowMajorBit };
};
} // end namespace internal
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl;
/** \class CwiseTernaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise ternary operator is
* applied to two expressions
*
* \tparam TernaryOp template functor implementing the operator
* \tparam Arg1Type the type of the first argument
* \tparam Arg2Type the type of the second argument
* \tparam Arg3Type the type of the third argument
*
* This class represents an expression where a coefficient-wise ternary
* operator is applied to three expressions.
* It is the return type of ternary operators, by which we mean only those
* ternary operators where
* all three arguments are Eigen expressions.
* For example, the return type of betainc(matrix1, matrix2, matrix3) is a
* CwiseTernaryOp.
*
* Most of the time, this is the only way that it is used, so you typically
* don't have to name
* CwiseTernaryOp types explicitly.
*
* \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
* MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
* class CwiseUnaryOp, class CwiseNullaryOp
*/
template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
typename Arg3Type>
class CwiseTernaryOp : public CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
typedef typename internal::remove_all<Arg3Type>::type Arg3;
typedef typename CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
const Arg3& a3,
const TernaryOp& func = TernaryOp())
: m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
// The index types should match
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
a1.rows() == a3.rows() && a1.cols() == a3.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg3.rows();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg2.rows();
else
return m_arg1.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg3.cols();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg2.cols();
else
return m_arg1.cols();
}
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg1Nested& arg1() const { return m_arg1; }
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg2Nested& arg2() const { return m_arg2; }
/** \returns the third argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg3Nested& arg3() const { return m_arg3; }
/** \returns the functor representing the ternary operation */
EIGEN_DEVICE_FUNC
const TernaryOp& functor() const { return m_functor; }
protected:
Arg1Nested m_arg1;
Arg2Nested m_arg2;
Arg3Nested m_arg3;
const TernaryOp m_functor;
};
// Generic API dispatcher
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl
: public internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
public:
typedef typename internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
};
} // end namespace Eigen
#endif // EIGEN_CWISE_TERNARY_OP_H

111
extern/Eigen3/Eigen/src/Core/CwiseUnaryOp.h vendored
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
@@ -13,13 +13,32 @@
namespace Eigen {
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(const typename XprType::Scalar&)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
enum {
Flags = _XprTypeNested::Flags & RowMajorBit
};
};
}
template<typename UnaryOp, typename XprType, typename StorageKind>
class CwiseUnaryOpImpl;
/** \class CwiseUnaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise unary operator is applied to an expression
*
* \param UnaryOp template functor implementing the operator
* \param XprType the type of the expression to which we are applying the unary operator
* \tparam UnaryOp template functor implementing the operator
* \tparam XprType the type of the expression to which we are applying the unary operator
*
* This class represents an expression where a unary operator is applied to an expression.
* It is the return type of all operations taking exactly 1 input expression, regardless of the
@@ -32,93 +51,51 @@ namespace Eigen {
*
* \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
*/
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(typename XprType::Scalar)
>::type Scalar;
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 = EIGEN_ADD_COST(_XprTypeNested::CoeffReadCost, functor_traits<UnaryOp>::Cost)
};
};
}
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::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index cols() const { return m_xpr.cols(); }
/** \returns the functor representing the unary operation */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename XprType::Nested>::type&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_all<XprTypeNested>::type&
nestedExpression() { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
XprTypeNested m_xpr;
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_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_STRONG_INLINE const Scalar coeff(Index index) const
{
return derived().functor()(derived().nestedExpression().coeff(index));
}
template<int LoadMode>
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

81
extern/Eigen3/Eigen/src/Core/CwiseUnaryView.h vendored
View File

@@ -12,33 +12,19 @@
namespace Eigen {
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \param ViewOp template functor implementing the view
* \param MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
namespace internal {
template<typename ViewOp, typename MatrixType>
struct traits<CwiseUnaryView<ViewOp, MatrixType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(typename traits<MatrixType>::Scalar)
ViewOp(const typename traits<MatrixType>::Scalar&)
>::type Scalar;
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
@@ -55,6 +41,19 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
/** \class CwiseUnaryView
* \ingroup Core_Module
*
* \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
*
* \tparam ViewOp template functor implementing the view
* \tparam MatrixType the type of the matrix we are applying the unary operator
*
* This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
* It is the return type of real() and imag(), and most of the time this is the only way it is used.
*
* \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
*/
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
@@ -62,8 +61,10 @@ 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::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
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)
@@ -75,19 +76,27 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename MatrixType::Nested>::type&
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_all<typename MatrixType::Nested>::type&
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
protected:
// FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC
typename internal::nested<MatrixType>::type m_matrix;
MatrixTypeNested m_matrix;
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 +109,18 @@ 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

384
extern/Eigen3/Eigen/src/Core/DenseBase.h vendored
View File

@@ -34,37 +34,45 @@ static inline void check_DenseIndex_is_signed() {
* \tparam Derived is the derived type, e.g., a matrix type or an expression.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class DenseBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
: public internal::special_scalar_op_base<Derived, typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real,
DenseCoeffsBase<Derived> >
#else
: public DenseCoeffsBase<Derived>
#else
: public DenseCoeffsBase<Derived,DirectWriteAccessors>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
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;
/** \brief The type of indices
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \sa \ref TopicPreprocessorDirectives.
*/
typedef typename internal::traits<Derived>::Index Index;
/**
* \brief The type used to store indices
* \details This typedef is relevant for types that store multiple indices such as
* PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
* \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
*/
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
*
* It is an alias for the Scalar type */
typedef Scalar value_type;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef internal::special_scalar_op_base<Derived,Scalar,RealScalar, DenseCoeffsBase<Derived> > Base;
typedef DenseCoeffsBase<Derived> Base;
using Base::operator*;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@@ -74,16 +82,6 @@ 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;
@@ -169,19 +167,46 @@ 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
};
typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
enum { ThisConstantIsPrivateInPlainObjectBase };
enum { IsPlainObjectBase = 0 };
/** The plain matrix type corresponding to this expression.
* \sa PlainObject */
typedef Matrix<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainMatrix;
/** The plain array type corresponding to this expression.
* \sa PlainObject */
typedef Array<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainArray;
/** \brief The plain matrix or array type corresponding to this expression.
*
* This is not necessarily exactly the return type of eval(). In the case of plain matrices,
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef typename internal::conditional<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
PlainMatrix, PlainArray>::type PlainObject;
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
EIGEN_DEVICE_FUNC
inline Index nonZeros() const { return size(); }
/** \returns the outer size.
@@ -189,6 +214,7 @@ template<typename Derived> class DenseBase
* \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
* column-major matrix, and the number of rows for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index outerSize() const
{
return IsVectorAtCompileTime ? 1
@@ -200,6 +226,7 @@ template<typename Derived> class DenseBase
* \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
* with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
* column-major matrix, and the number of columns for a row-major matrix. */
EIGEN_DEVICE_FUNC
Index innerSize() const
{
return IsVectorAtCompileTime ? this->size()
@@ -210,6 +237,7 @@ template<typename Derived> class DenseBase
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
EIGEN_DEVICE_FUNC
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
@@ -220,22 +248,22 @@ template<typename Derived> class DenseBase
* Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
* nothing else.
*/
void resize(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols)
{
EIGEN_ONLY_USED_FOR_DEBUG(nbRows);
EIGEN_ONLY_USED_FOR_DEBUG(nbCols);
eigen_assert(nbRows == this->rows() && nbCols == this->cols()
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,false>,Derived> SequentialLinSpacedReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> SequentialLinSpacedReturnType;
/** \internal Represents a vector with linearly spaced coefficients that allows random access. */
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,true>,Derived> RandomAccessLinSpacedReturnType;
typedef CwiseNullaryOp<internal::linspaced_op<Scalar,PacketScalar>,PlainObject> RandomAccessLinSpacedReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
@@ -243,120 +271,133 @@ template<typename Derived> class DenseBase
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator+=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator-=(const EigenBase<OtherDerived> &other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& func);
/** \internal Copies \a other into *this without evaluating other. \returns a reference to *this. */
/** \internal
* 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);
/** \internal Evaluates \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
Derived& lazyAssign(const ReturnByValue<OtherDerived>& other);
EIGEN_DEVICE_FUNC
CommaInitializer<Derived> operator<< (const Scalar& s);
/** \deprecated it now returns \c *this */
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);
Eigen::Transpose<Derived> transpose();
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
typedef Transpose<Derived> TransposeReturnType;
EIGEN_DEVICE_FUNC
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
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(Index size, const Scalar& value);
static const ConstantReturnType
EIGEN_DEVICE_FUNC static const ConstantReturnType
Constant(const Scalar& value);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(Index size, const Scalar& low, const Scalar& high);
static const SequentialLinSpacedReturnType
EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType
LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
static const RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
LinSpaced(const Scalar& low, const Scalar& high);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(Index size, const CustomNullaryOp& func);
template<typename CustomNullaryOp>
static const CwiseNullaryOp<CustomNullaryOp, Derived>
template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
NullaryExpr(const CustomNullaryOp& func);
static const ConstantReturnType Zero(Index rows, Index cols);
static const ConstantReturnType Zero(Index size);
static const ConstantReturnType Zero();
static const ConstantReturnType Ones(Index rows, Index cols);
static const ConstantReturnType Ones(Index size);
static const ConstantReturnType Ones();
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
void fill(const Scalar& value);
Derived& setConstant(const Scalar& value);
Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
Derived& setLinSpaced(const Scalar& low, const Scalar& high);
Derived& setZero();
Derived& setOnes();
Derived& setRandom();
EIGEN_DEVICE_FUNC void fill(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
EIGEN_DEVICE_FUNC Derived& setZero();
EIGEN_DEVICE_FUNC Derived& setOnes();
EIGEN_DEVICE_FUNC Derived& setRandom();
template<typename OtherDerived>
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isApprox(const DenseBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const RealScalar& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
template<typename OtherDerived> EIGEN_DEVICE_FUNC
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
inline bool hasNaN() const;
inline bool allFinite() const;
inline Derived& operator*=(const Scalar& other);
inline Derived& operator/=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*
* \warning Be carefull with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE EvalReturnType eval() const
{
// Even though MSVC does not honor strong inlining when the return type
@@ -364,61 +405,78 @@ 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>
void swap(const DenseBase<OtherDerived>& other,
int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase)
EIGEN_DEVICE_FUNC
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.
*
*/
template<typename OtherDerived>
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();
template<bool Enable> EIGEN_DEVICE_FUNC
inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> EIGEN_DEVICE_FUNC
inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
inline const NestByValue<Derived> nestByValue() const;
inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
inline ForceAlignedAccess<Derived> forceAlignedAccess();
template<bool Enable> inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const;
template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf();
EIGEN_DEVICE_FUNC Scalar sum() const;
EIGEN_DEVICE_FUNC Scalar mean() const;
EIGEN_DEVICE_FUNC Scalar trace() const;
Scalar sum() const;
Scalar mean() const;
Scalar trace() const;
EIGEN_DEVICE_FUNC Scalar prod() const;
Scalar prod() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
typename internal::traits<Derived>::Scalar minCoeff() const;
typename internal::traits<Derived>::Scalar maxCoeff() const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
template<typename IndexType>
template<typename IndexType> EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
template<typename BinaryOp>
typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
redux(const BinaryOp& func) const;
EIGEN_DEVICE_FUNC
Scalar redux(const BinaryOp& func) const;
template<typename Visitor>
EIGEN_DEVICE_FUNC
void visit(Visitor& func) const;
inline const WithFormat<Derived> format(const IOFormat& fmt) const;
/** \returns a WithFormat proxy object allowing to print a matrix the with given
* format \a fmt.
*
* See class IOFormat for some examples.
*
* \sa class IOFormat, class WithFormat
*/
inline const WithFormat<Derived> format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
/** \returns the unique coefficient of a 1x1 expression */
EIGEN_DEVICE_FUNC
CoeffReturnType value() const
{
EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
@@ -426,23 +484,44 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
bool all(void) const;
bool any(void) const;
Index count() const;
EIGEN_DEVICE_FUNC bool all() const;
EIGEN_DEVICE_FUNC bool any() const;
EIGEN_DEVICE_FUNC Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
ConstRowwiseReturnType rowwise() const;
RowwiseReturnType rowwise();
ConstColwiseReturnType colwise() const;
ColwiseReturnType colwise();
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_rowwise.cpp
* Output: \verbinclude MatrixBase_rowwise.out
*
* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
return ConstRowwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index rows, Index cols);
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index size);
static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random();
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
return ConstColwiseReturnType(derived());
}
EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
static const RandomReturnType Random(Index rows, Index cols);
static const RandomReturnType Random(Index size);
static const RandomReturnType Random();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
@@ -460,45 +539,56 @@ template<typename Derived> class DenseBase
template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor>
inline const Replicate<Derived,RowFactor,ColFactor> replicate() const;
typedef Replicate<Derived,Dynamic,Dynamic> ReplicateReturnType;
inline const ReplicateReturnType replicate(Index rowFacor,Index colFactor) const;
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() const;
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate_int_int.cpp
* Output: \verbinclude MatrixBase_replicate_int_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
*/
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC
const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
{
return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
}
typedef Reverse<Derived, BothDirections> ReverseReturnType;
typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
ReverseReturnType reverse();
ConstReverseReturnType reverse() const;
void reverseInPlace();
EIGEN_DEVICE_FUNC ReverseReturnType reverse();
/** This is the const version of reverse(). */
//Code moved here due to a CUDA compiler bug
EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
{
return ConstReverseReturnType(derived());
}
EIGEN_DEVICE_FUNC void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
# include "../plugins/BlockMethods.h"
# ifdef EIGEN_DENSEBASE_PLUGIN
# include EIGEN_DENSEBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#ifdef EIGEN2_SUPPORT
Block<Derived> corner(CornerType type, Index cRows, Index cCols);
const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const;
template<int CRows, int CCols>
Block<Derived, CRows, CCols> corner(CornerType type);
template<int CRows, int CCols>
const Block<Derived, CRows, CCols> corner(CornerType type) const;
#endif // EIGEN2_SUPPORT
#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest> inline void evalTo(Dest& ) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& ) const
{
EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
}
protected:
/** Default constructor. Do nothing. */
DenseBase()
EIGEN_DEVICE_FUNC DenseBase()
{
/* Just checks for self-consistency of the flags.
* Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down
@@ -511,9 +601,9 @@ template<typename Derived> class DenseBase
}
private:
explicit DenseBase(int);
DenseBase(int,int);
template<typename OtherDerived> explicit DenseBase(const DenseBase<OtherDerived>&);
EIGEN_DEVICE_FUNC explicit DenseBase(int);
EIGEN_DEVICE_FUNC DenseBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
};
} // end namespace Eigen

281
extern/Eigen3/Eigen/src/Core/DenseCoeffsBase.h vendored
View File

@@ -35,7 +35,6 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
{
public:
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
@@ -61,6 +60,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
using Base::size;
using Base::derived;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::RowsAtCompileTime) == 1 ? 0
@@ -69,6 +69,7 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
: inner;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
{
return int(Derived::ColsAtCompileTime) == 1 ? 0
@@ -91,13 +92,15 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
*
* \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
*/
EIGEN_DEVICE_FUNC
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 internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{
return coeff(rowIndexByOuterInner(outer, inner),
@@ -108,11 +111,12 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
*
* \sa operator()(Index,Index), operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
{
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
@@ -130,11 +134,14 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
coeff(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return derived().coeff(index);
return internal::evaluator<Derived>(derived()).coeff(index);
}
@@ -146,15 +153,14 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator[](Index index) const
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** \returns the coefficient at given index.
@@ -167,32 +173,49 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
* z() const, w() const
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
operator()(Index index) const
{
eigen_assert(index >= 0 && index < size());
return derived().coeff(index);
return coeff(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
x() const { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
y() const { return (*this)[1]; }
y() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
z() const { return (*this)[2]; }
z() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CoeffReturnType
w() const { return (*this)[3]; }
w() const
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
/** \internal
* \returns the packet of coefficients starting at the given row and column. It is your responsibility
@@ -207,9 +230,9 @@ 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);
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
}
@@ -234,8 +257,11 @@ class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
eigen_internal_assert(index >= 0 && index < size());
return derived().template packet<LoadMode>(index);
return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
}
protected:
@@ -278,7 +304,6 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -311,13 +336,15 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
*
* \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
*/
EIGEN_DEVICE_FUNC
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 internal::evaluator<Derived>(derived()).coeffRef(row,col);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRefByOuterInner(Index outer, Index inner)
{
@@ -330,12 +357,13 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index row, Index col)
{
eigen_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
return derived().coeffRef(row, col);
return coeffRef(row, col);
}
@@ -354,11 +382,14 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
coeffRef(Index index)
{
EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
eigen_internal_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return internal::evaluator<Derived>(derived()).coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@@ -368,15 +399,14 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator[](Index index)
{
#ifndef EIGEN2_SUPPORT
EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
#endif
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** \returns a reference to the coefficient at given index.
@@ -388,167 +418,49 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return derived().coeffRef(index);
return coeffRef(index);
}
/** equivalent to operator[](0). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
x() { return (*this)[0]; }
/** equivalent to operator[](1). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
y() { return (*this)[1]; }
y()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
return (*this)[1];
}
/** equivalent to operator[](2). */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar&
z() { return (*this)[2]; }
z()
{
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
return (*this)[2];
}
/** equivalent to operator[](3). */
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)
w()
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,val);
EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
return (*this)[3];
}
/** \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_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_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_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.
@@ -560,7 +472,7 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
* inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
* \c operator() .
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
@@ -568,7 +480,6 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
public:
typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -581,6 +492,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -591,6 +503,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -606,6 +519,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -615,6 +529,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -630,7 +545,7 @@ class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived
* inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
* \c operator().
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived>
class DenseCoeffsBase<Derived, DirectWriteAccessors>
@@ -639,7 +554,6 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
public:
typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -652,6 +566,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa outerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return derived().innerStride();
@@ -662,6 +577,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), rowStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return derived().outerStride();
@@ -677,6 +593,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), colStride()
*/
EIGEN_DEVICE_FUNC
inline Index rowStride() const
{
return Derived::IsRowMajor ? outerStride() : innerStride();
@@ -686,6 +603,7 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
*
* \sa innerStride(), outerStride(), rowStride()
*/
EIGEN_DEVICE_FUNC
inline Index colStride() const
{
return Derived::IsRowMajor ? innerStride() : outerStride();
@@ -694,33 +612,42 @@ class DenseCoeffsBase<Derived, DirectWriteAccessors>
namespace internal {
template<typename Derived, bool JustReturnZero>
template<int Alignment, typename Derived, bool JustReturnZero>
struct first_aligned_impl
{
static inline typename Derived::Index run(const Derived&)
static inline Index run(const Derived&)
{ return 0; }
};
template<typename Derived>
struct first_aligned_impl<Derived, false>
template<int Alignment, typename Derived>
struct first_aligned_impl<Alignment, Derived, false>
{
static inline typename Derived::Index run(const Derived& m)
static inline Index run(const Derived& m)
{
return internal::first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size());
return internal::first_aligned<Alignment>(m.data(), m.size());
}
};
/** \internal \returns the index of the first element of the array that is well aligned for vectorization.
/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
*
* \tparam Alignment requested alignment in Bytes.
*
* There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
* documentation.
*/
template<typename Derived>
static inline typename Derived::Index first_aligned(const Derived& m)
template<int Alignment, typename Derived>
static inline Index first_aligned(const DenseBase<Derived>& m)
{
return first_aligned_impl
<Derived, (Derived::Flags & AlignedBit) || !(Derived::Flags & DirectAccessBit)>
::run(m);
enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
}
template<typename Derived>
static inline Index first_default_aligned(const DenseBase<Derived>& m)
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type DefaultPacketType;
return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
}
template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>

452
extern/Eigen3/Eigen/src/Core/DenseStorage.h vendored
View File

@@ -3,7 +3,7 @@
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@@ -13,9 +13,9 @@
#define EIGEN_MATRIXSTORAGE_H
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
#else
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
#define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
#endif
namespace Eigen {
@@ -24,7 +24,9 @@ namespace internal {
struct constructor_without_unaligned_array_assert {};
template<typename T, int Size> void check_static_allocation_size()
template<typename T, int Size>
EIGEN_DEVICE_FUNC
void check_static_allocation_size()
{
// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
#if EIGEN_STACK_ALLOCATION_LIMIT
@@ -38,18 +40,19 @@ template<typename T, int Size> void check_static_allocation_size()
*/
template <typename T, int Size, int MatrixOrArrayOptions,
int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
: (((Size*sizeof(T))%16)==0) ? 16
: 0 >
: compute_default_alignment<T,Size>::value >
struct plain_array
{
T array[Size];
plain_array()
EIGEN_DEVICE_FUNC
plain_array()
{
check_static_allocation_size<T,Size>();
}
plain_array(constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
@@ -64,29 +67,88 @@ struct plain_array
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & sizemask) == 0 \
eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(array) & sizemask) == 0 \
eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
struct plain_array<T, Size, MatrixOrArrayOptions, 8>
{
EIGEN_USER_ALIGN16 T array[Size];
EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf);
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 32>
{
EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
}
};
template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 64>
{
EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
EIGEN_DEVICE_FUNC
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
check_static_allocation_size<T,Size>();
}
EIGEN_DEVICE_FUNC
plain_array(constructor_without_unaligned_array_assert)
{
check_static_allocation_size<T,Size>();
@@ -96,9 +158,9 @@ struct plain_array<T, Size, MatrixOrArrayOptions, 16>
template <typename T, int MatrixOrArrayOptions, int Alignment>
struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
{
EIGEN_USER_ALIGN16 T array[1];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
T array[1];
EIGEN_DEVICE_FUNC plain_array() {}
EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {}
};
} // end namespace internal
@@ -122,41 +184,54 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
DenseStorage() {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data) {}
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
EIGEN_DEVICE_FUNC DenseStorage() {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
static DenseIndex rows(void) {return _Rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
void resize(DenseIndex,DenseIndex,DenseIndex) {}
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC
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) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other) m_data = other.m_data;
return *this;
}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) {
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols==_Cols);
EIGEN_UNUSED_VARIABLE(size);
EIGEN_UNUSED_VARIABLE(rows);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// null matrix
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
DenseStorage() {}
DenseStorage(internal::constructor_without_unaligned_array_assert) {}
DenseStorage(const DenseStorage&) {}
DenseStorage& operator=(const DenseStorage&) { return *this; }
DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
void swap(DenseStorage& ) {}
static DenseIndex rows(void) {return _Rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {}
void resize(DenseIndex,DenseIndex,DenseIndex) {}
const T *data() const { return 0; }
T *data() { return 0; }
EIGEN_DEVICE_FUNC DenseStorage() {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) {}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; }
EIGEN_DEVICE_FUNC DenseStorage(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {}
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC void resize(Index,Index,Index) {}
EIGEN_DEVICE_FUNC const T *data() const { return 0; }
EIGEN_DEVICE_FUNC T *data() { return 0; }
};
// more specializations for null matrices; these are necessary to resolve ambiguities
@@ -173,74 +248,74 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic,
template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
DenseStorage() : m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
{
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
m_cols = other.m_cols;
}
return *this;
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {}
void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
DenseIndex rows() const {return m_rows;}
DenseIndex cols() const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC Index rows() const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols() const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) { m_rows = rows; m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed width
template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
Index m_rows;
public:
DenseStorage() : m_rows(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
m_data = other.m_data;
m_rows = other.m_rows;
}
return *this;
return *this;
}
DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
DenseIndex rows(void) const {return m_rows;}
DenseIndex cols(void) const {return _Cols;}
void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
void resize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return _Cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) { m_rows = rows; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// dynamic-size matrix with fixed-size storage and fixed height
template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options>
{
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
Index m_cols;
public:
DenseStorage() : m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
DenseStorage& operator=(const DenseStorage& other)
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
@@ -249,38 +324,62 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
}
return *this;
}
DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {}
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
DenseIndex rows(void) const {return _Rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
void resize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
const T *data() const { return m_data.array; }
T *data() { return m_data.array; }
EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC Index rows(void) const {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
void resize(Index, Index, Index cols) { m_cols = cols; }
EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; }
EIGEN_DEVICE_FUNC T *data() { return m_data.array; }
};
// purely dynamic matrix.
template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options>
{
T *m_data;
DenseIndex m_rows;
DenseIndex m_cols;
Index m_rows;
Index m_cols;
public:
DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
DenseStorage(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
EIGEN_DEVICE_FUNC 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)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(DenseStorage&& other)
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols))
, m_rows(other.m_rows)
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_rows = 0;
other.m_cols = 0;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -289,18 +388,18 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
void swap(DenseStorage& other)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
{ std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); }
DenseIndex rows(void) const {return m_rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
void conservativeResize(Index size, Index rows, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
void resize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
{
if(size != m_rows*m_cols)
{
@@ -309,36 +408,56 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = nbRows;
m_cols = nbCols;
m_rows = rows;
m_cols = cols;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic width and fixed height (so that matrix has dynamic size).
template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options>
{
T *m_data;
DenseIndex m_cols;
Index m_cols;
public:
DenseStorage() : m_data(0), m_cols(0) {}
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 }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(DenseStorage&& other)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows==_Rows && cols >=0);
EIGEN_UNUSED_VARIABLE(rows);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols))
, m_cols(other.m_cols)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*_Rows)
internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_cols(std::move(other.m_cols))
{
other.m_data = nullptr;
other.m_cols = 0;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -346,16 +465,16 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
static DenseIndex rows(void) {return _Rows;}
DenseIndex cols(void) const {return m_cols;}
void conservativeResize(DenseIndex size, DenseIndex, DenseIndex nbCols)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
EIGEN_DEVICE_FUNC static Index rows(void) {return _Rows;}
EIGEN_DEVICE_FUNC Index cols(void) const {return m_cols;}
EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = nbCols;
m_cols = cols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex nbCols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
{
if(size != _Rows*m_cols)
{
@@ -364,35 +483,55 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_cols = nbCols;
m_cols = cols;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
// matrix with dynamic height and fixed width (so that matrix has dynamic size).
template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options>
{
T *m_data;
DenseIndex m_rows;
Index m_rows;
public:
DenseStorage() : m_data(0), m_rows(0) {}
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 }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
DenseStorage(DenseStorage&& other)
EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {}
explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
eigen_internal_assert(size==rows*cols && rows>=0 && cols == _Cols);
EIGEN_UNUSED_VARIABLE(cols);
}
EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols))
, m_rows(other.m_rows)
{
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*_Cols)
internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data);
}
EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
{
if (this != &other)
{
DenseStorage tmp(other);
this->swap(tmp);
}
return *this;
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
{
other.m_data = nullptr;
other.m_rows = 0;
}
DenseStorage& operator=(DenseStorage&& other)
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@@ -400,16 +539,16 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
return *this;
}
#endif
~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
DenseIndex rows(void) const {return m_rows;}
static DenseIndex cols(void) {return _Cols;}
void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex)
EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
EIGEN_DEVICE_FUNC Index rows(void) const {return m_rows;}
EIGEN_DEVICE_FUNC static Index cols(void) {return _Cols;}
void conservativeResize(Index size, Index rows, Index)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = nbRows;
m_rows = rows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex nbRows, DenseIndex)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
{
if(size != m_rows*_Cols)
{
@@ -418,15 +557,12 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size);
else
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
}
m_rows = nbRows;
m_rows = rows;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
private:
DenseStorage(const DenseStorage&);
DenseStorage& operator=(const DenseStorage&);
EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC T *data() { return m_data; }
};
} // end namespace Eigen

69
extern/Eigen3/Eigen/src/Core/Diagonal.h vendored
View File

@@ -21,7 +21,7 @@ namespace Eigen {
* \param MatrixType the type of the object in which we are taking a sub/main/super diagonal
* \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
* A positive value means a superdiagonal, a negative value means a subdiagonal.
* You can also use Dynamic so the index can be set at runtime.
* You can also use DynamicIndex so the index can be set at runtime.
*
* The matrix is not required to be square.
*
@@ -37,7 +37,7 @@ template<typename MatrixType, int DiagIndex>
struct traits<Diagonal<MatrixType,DiagIndex> >
: traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
@@ -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
@@ -70,20 +69,31 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
{
eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
EIGEN_DEVICE_FUNC
inline Index rows() const
{ return m_index.value()<0 ? (std::min<Index>)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min<Index>)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{
return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
: numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
}
EIGEN_DEVICE_FUNC
inline Index cols() const { return 1; }
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return m_matrix.outerStride() + 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return 0;
@@ -95,62 +105,75 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
inline const Scalar* data() const { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index row, Index) const
{
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline CoeffReturnType coeff(Index idx) const
{
return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC
inline const typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() const
{
return m_matrix;
}
int index() const
EIGEN_DEVICE_FUNC
inline Index index() const
{
return m_index.value();
}
protected:
typename MatrixType::Nested m_matrix;
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
private:
// some compilers may fail to optimize std::max etc in case of compile-time constants...
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
EIGEN_DEVICE_FUNC
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 if someone try to call packet
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
};
@@ -167,7 +190,7 @@ template<typename Derived>
inline typename MatrixBase<Derived>::DiagonalReturnType
MatrixBase<Derived>::diagonal()
{
return derived();
return DiagonalReturnType(derived());
}
/** This is the const version of diagonal(). */
@@ -216,20 +239,20 @@ 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

140
extern/Eigen3/Eigen/src/Core/DiagonalMatrix.h vendored
View File

@@ -22,7 +22,7 @@ class DiagonalBase : public EigenBase<Derived>
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
enum {
RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
@@ -30,79 +30,61 @@ 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;
typedef DenseMatrixType DenseType;
typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
EIGEN_DEVICE_FUNC
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& derived() { return *static_cast<Derived*>(this); }
EIGEN_DEVICE_FUNC
DenseMatrixType toDenseMatrix() const { return derived(); }
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived> &other) const;
template<typename DenseDerived>
void addTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() += diagonal(); }
template<typename DenseDerived>
void subTo(MatrixBase<DenseDerived> &other) const
{ other.diagonal() -= diagonal(); }
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
EIGEN_DEVICE_FUNC
inline Index rows() const { return diagonal().size(); }
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>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
EIGEN_DEVICE_FUNC
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());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > InverseReturnType;
EIGEN_DEVICE_FUNC
inline const InverseReturnType
inverse() const
{
return diagonal().cwiseInverse();
return InverseReturnType(diagonal().cwiseInverse());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
EIGEN_DEVICE_FUNC
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return diagonal() * scalar;
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
EIGEN_DEVICE_FUNC
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return other.diagonal() * scalar;
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return diagonal().isApprox(other.diagonal(), precision);
}
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const
{
return toDenseMatrix().isApprox(other, precision);
}
#endif
};
template<typename Derived>
template<typename DenseDerived>
void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
{
other.setZero();
other.diagonal() = diagonal();
}
#endif
/** \class DiagonalMatrix
@@ -124,10 +106,9 @@ 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 DenseIndex Index;
typedef DiagonalShape StorageKind;
enum {
Flags = LvalueBit
Flags = LvalueBit | NoPreferredStorageOrderBit
};
};
}
@@ -141,7 +122,7 @@ class DiagonalMatrix
typedef const DiagonalMatrix& Nested;
typedef _Scalar Scalar;
typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
typedef typename internal::traits<DiagonalMatrix>::Index Index;
typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
#endif
protected:
@@ -151,24 +132,31 @@ class DiagonalMatrix
public:
/** const version of diagonal(). */
EIGEN_DEVICE_FUNC
inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
/** \returns a reference to the stored vector of diagonal coefficients. */
EIGEN_DEVICE_FUNC
inline DiagonalVectorType& diagonal() { return m_diagonal; }
/** Default constructor without initialization */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix() {}
/** Constructs a diagonal matrix with given dimension */
inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
/** 2D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
/** 3D constructor. */
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
/** Copy constructor. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@@ -178,11 +166,13 @@ class DiagonalMatrix
/** generic constructor from expression of the diagonal coefficients */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
{}
/** Copy operator. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
{
m_diagonal = other.diagonal();
@@ -193,6 +183,7 @@ class DiagonalMatrix
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
DiagonalMatrix& operator=(const DiagonalMatrix& other)
{
m_diagonal = other.diagonal();
@@ -201,14 +192,19 @@ class DiagonalMatrix
#endif
/** Resizes to given size. */
EIGEN_DEVICE_FUNC
inline void resize(Index size) { m_diagonal.resize(size); }
/** Sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero() { m_diagonal.setZero(); }
/** Resizes and sets all coefficients to zero. */
EIGEN_DEVICE_FUNC
inline void setZero(Index size) { m_diagonal.setZero(size); }
/** Sets this matrix to be the identity matrix of the current size. */
EIGEN_DEVICE_FUNC
inline void setIdentity() { m_diagonal.setOnes(); }
/** Sets this matrix to be the identity matrix of the given size. */
EIGEN_DEVICE_FUNC
inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
};
@@ -232,14 +228,15 @@ struct traits<DiagonalWrapper<_DiagonalVectorType> >
{
typedef _DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::Index Index;
typedef typename DiagonalVectorType::StorageKind StorageKind;
typedef typename DiagonalVectorType::StorageIndex StorageIndex;
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
};
};
}
@@ -255,9 +252,11 @@ class DiagonalWrapper
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
EIGEN_DEVICE_FUNC
explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
EIGEN_DEVICE_FUNC
const DiagonalVectorType& diagonal() const { return m_diagonal; }
protected:
@@ -277,7 +276,7 @@ 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,
@@ -291,12 +290,11 @@ MatrixBase<Derived>::asDiagonal() const
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
{
using std::abs;
if(cols() != rows()) return false;
RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
RealScalar absOnDiagonal = abs(coeff(j,j));
RealScalar absOnDiagonal = numext::abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)
@@ -308,6 +306,38 @@ 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>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_DIAGONALMATRIX_H

107
extern/Eigen3/Eigen/src/Core/DiagonalProduct.h vendored
View File

@@ -13,117 +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|AlignedBit) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
Cost0 = EIGEN_ADD_COST(NumTraits<Scalar>::MulCost, MatrixType::CoeffReadCost),
CoeffReadCost = EIGEN_ADD_COST(Cost0,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

173
extern/Eigen3/Eigen/src/Core/Dot.h vendored
View File

@@ -28,26 +28,33 @@ template<typename T, typename U,
>
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.template binaryExpr<conj_prod>(b).sum();
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
};
} // end namespace internal
/** \returns the dot product of *this with other.
/** \fn MatrixBase::dot
* \returns the dot product of *this with other.
*
* \only_for_vectors
*
@@ -59,55 +66,31 @@ struct dot_nocheck<T, U, true>
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func;
EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar);
#endif
eigen_assert(size() == other.size());
return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
}
#ifdef EIGEN2_SUPPORT
/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable
* (conjugating the second variable). Of course this only makes a difference in the complex case.
*
* This method is only available in EIGEN2_SUPPORT mode.
*
* \only_for_vectors
*
* \sa dot()
*/
template<typename Derived>
template<typename OtherDerived>
typename internal::traits<Derived>::Scalar
MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(size() == other.size());
return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this);
}
#endif
//---------- implementation of L2 norm and related functions ----------
/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm.
* In both cases, it consists in the sum of the square of all the matrix entries.
* For vectors, this is also equals to the dot product of \c *this with itself.
*
* \sa dot(), norm()
* \sa dot(), norm(), lpNorm()
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
@@ -119,41 +102,98 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
* In both cases, it consists in the square root of the sum of the square of all the matrix entries.
* For vectors, this is also equals to the square root of the dot product of \c *this with itself.
*
* \sa dot(), squaredNorm()
* \sa lpNorm(), dot(), squaredNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
using std::sqrt;
return sqrt(squaredNorm());
return numext::sqrt(squaredNorm());
}
/** \returns an expression of the quotient of *this by its own norm.
/** \returns an expression of the quotient of \c *this by its own norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \only_for_vectors
*
* \sa norm(), normalize()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
EIGEN_STRONG_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();
RealScalar z = n.squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
return n / numext::sqrt(z);
else
return n;
}
/** Normalizes the vector, i.e. divides it by its own norm.
*
* \only_for_vectors
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa norm(), normalized()
*/
template<typename Derived>
inline void MatrixBase<Derived>::normalize()
EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
{
*this /= norm();
RealScalar z = squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
if(z>RealScalar(0))
derived() /= numext::sqrt(z);
}
/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
*
* \only_for_vectors
*
* This method is analogue to the normalized() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \sa stableNorm(), stableNormalize(), normalized()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
_Nested n(derived());
RealScalar w = n.cwiseAbs().maxCoeff();
RealScalar z = (n/w).squaredNorm();
if(z>RealScalar(0))
return n / (numext::sqrt(z)*w);
else
return n;
}
/** Normalizes the vector while avoid underflow and overflow
*
* \only_for_vectors
*
* This method is analogue to the normalize() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa stableNorm(), stableNormalized(), normalize()
*/
template<typename Derived>
EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
{
RealScalar w = cwiseAbs().maxCoeff();
RealScalar z = (derived()/w).squaredNorm();
if(z>RealScalar(0))
derived() /= numext::sqrt(z)*w;
}
//---------- implementation of other norms ----------
@@ -164,9 +204,10 @@ template<typename Derived, int p>
struct lpNorm_selector
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
using std::pow;
EIGEN_USING_STD_MATH(pow)
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@@ -174,6 +215,7 @@ struct lpNorm_selector
template<typename Derived>
struct lpNorm_selector<Derived, 1>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwiseAbs().sum();
@@ -183,6 +225,7 @@ struct lpNorm_selector<Derived, 1>
template<typename Derived>
struct lpNorm_selector<Derived, 2>
{
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
@@ -192,23 +235,35 @@ struct lpNorm_selector<Derived, 2>
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
return RealScalar(0);
return m.cwiseAbs().maxCoeff();
}
};
} // end namespace internal
/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of *this.
/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of \c *this.
*
* In all cases, if \c *this is empty, then the value 0 is returned.
*
* \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
#else
MatrixBase<Derived>::RealScalar
#endif
MatrixBase<Derived>::lpNorm() const
{
return internal::lpNorm_selector<Derived, p>::run(*this);
@@ -227,8 +282,8 @@ 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();
}
@@ -246,13 +301,13 @@ bool MatrixBase<Derived>::isOrthogonal
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename Derived::Nested nested(derived());
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index i = 0; i < cols(); ++i)
{
if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
return false;
for(Index j = 0; j < i; ++j)
if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))
if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
return false;
}
return true;

50
extern/Eigen3/Eigen/src/Core/EigenBase.h vendored
View File

@@ -13,7 +13,10 @@
namespace Eigen {
/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
/** \class EigenBase
* \ingroup Core_Module
*
* Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
*
* In other words, an EigenBase object is an object that can be copied into a MatrixBase.
*
@@ -21,39 +24,57 @@ namespace Eigen {
*
* Notice that this class is trivial, it is only used to disambiguate overloaded functions.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> struct EigenBase
{
// typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
/** \brief The interface type of indices
* \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
* \deprecated Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
* \sa StorageIndex, \ref TopicPreprocessorDirectives.
*/
typedef Eigen::Index Index;
// FIXME is it needed?
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
/** \returns a reference to the derived object */
EIGEN_DEVICE_FUNC
Derived& derived() { return *static_cast<Derived*>(this); }
/** \returns a const reference to the derived object */
EIGEN_DEVICE_FUNC
const Derived& derived() const { return *static_cast<const Derived*>(this); }
EIGEN_DEVICE_FUNC
inline Derived& const_cast_derived() const
{ return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
EIGEN_DEVICE_FUNC
inline const Derived& const_derived() const
{ return *static_cast<const Derived*>(this); }
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
EIGEN_DEVICE_FUNC
inline Index rows() const { return derived().rows(); }
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
EIGEN_DEVICE_FUNC
inline Index cols() const { return derived().cols(); }
/** \returns the number of coefficients, which is rows()*cols().
* \sa rows(), cols(), SizeAtCompileTime. */
EIGEN_DEVICE_FUNC
inline Index size() const { return rows() * cols(); }
/** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
template<typename Dest> inline void evalTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const
{ derived().evalTo(dst); }
/** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
template<typename Dest> inline void addTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void addTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -63,7 +84,9 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
template<typename Dest> inline void subTo(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void subTo(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -73,7 +96,8 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -81,7 +105,8 @@ template<typename Derived> struct EigenBase
}
/** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const
template<typename Dest>
EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const
{
// This is the default implementation,
// derived class can reimplement it in a more optimized way.
@@ -104,25 +129,28 @@ template<typename Derived> struct EigenBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
other.derived().evalTo(derived());
call_assignment(derived(), other.derived());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
other.derived().addTo(derived());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
other.derived().subTo(derived());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

24
extern/Eigen3/Eigen/src/Core/ForceAlignedAccess.h vendored
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

13
extern/Eigen3/Eigen/src/Core/Fuzzy.h vendored
View File

@@ -19,18 +19,19 @@ namespace internal
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
using std::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());
}
};
template<typename Derived, typename OtherDerived>
struct isApprox_selector<Derived, OtherDerived, true>
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
{
return x.matrix() == y.matrix();
@@ -40,6 +41,7 @@ struct isApprox_selector<Derived, OtherDerived, true>
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
@@ -49,6 +51,7 @@ struct isMuchSmallerThan_object_selector
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
@@ -58,6 +61,7 @@ struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
@@ -67,6 +71,7 @@ struct isMuchSmallerThan_scalar_selector
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();

518
extern/Eigen3/Eigen/src/Core/GeneralProduct.h vendored
View File

@@ -11,29 +11,7 @@
#ifndef EIGEN_GENERAL_PRODUCT_H
#define EIGEN_GENERAL_PRODUCT_H
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;
namespace Eigen {
enum {
Large = 2,
@@ -46,11 +24,17 @@ template<int Rows, int Cols, int Depth> struct product_type_selector;
template<int Size, int MaxSize> struct product_size_category
{
enum { is_large = MaxSize == Dynamic ||
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD,
value = is_large ? Large
: Size == 1 ? 1
: Small
enum {
#ifndef EIGEN_CUDA_ARCH
is_large = MaxSize == Dynamic ||
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
(Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
#else
is_large = 0,
#endif
value = is_large ? Large
: Size == 1 ? 1
: Small
};
};
@@ -59,15 +43,14 @@ 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),
LargeThreshold = EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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,
@@ -82,7 +65,8 @@ private:
public:
enum {
value = selector::ret
value = selector::ret,
ret = selector::ret
};
#ifdef EIGEN_DEBUG_PRODUCT
static void debug()
@@ -98,12 +82,13 @@ public:
#endif
};
/* The following allows to select the kind of product at compile time
* based on the three dimensions of the product.
* This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
// FIXME I'm not sure the current mapping is the ideal one.
template<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; };
template<int M> struct product_type_selector<M, 1, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int N> struct product_type_selector<1, N, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; };
@@ -122,60 +107,12 @@ template<> struct product_type_selector<Small,Small,Large> { enum
template<> struct product_type_selector<Large,Small,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Small,Large,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Large,Large> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Small,Small> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Small,Large,Small> { enum { ret = GemmProduct }; };
template<> struct product_type_selector<Large,Small,Small> { enum { ret = CoeffBasedProductMode }; };
template<> struct product_type_selector<Small,Large,Small> { enum { ret = CoeffBasedProductMode }; };
template<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; };
} // 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
***********************************************************************/
@@ -187,119 +124,10 @@ 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 is_row_major : 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(), is_row_major<Dest>());
}
template<typename Dest>
inline void addTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, add(), is_row_major<Dest>());
}
template<typename Dest>
inline void subTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, sub(), is_row_major<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
internal::outer_product_selector_run(*this, dest, adds(alpha), is_row_major<Dest>());
}
};
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@@ -313,60 +141,13 @@ 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_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>
@@ -384,126 +165,161 @@ struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
template<typename Scalar,int Size,int MaxSize>
struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
{
#if EIGEN_ALIGN_STATICALLY
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
enum {
ForceAlignment = internal::packet_traits<Scalar>::Vectorizable,
PacketSize = internal::packet_traits<Scalar>::size
};
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data;
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() {
return ForceAlignment
? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(15))) + 16)
? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
: m_data.array;
}
#endif
};
template<> struct gemv_selector<OnTheRight,ColMajor,true>
// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_dense_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;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
::run(rhs.transpose(), lhs.transpose(), destT, alpha);
}
};
ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs());
ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs());
template<> struct gemv_dense_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 Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar ResScalar;
typedef typename Dest::RealScalar RealScalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
* RhsBlasTraits::extractScalarFactor(prod.rhs());
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
// make sure Dest is a compile-time vector type (bug 1166)
typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;
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...
EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1,
EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal
MightCannotUseDest = (!EvalToDestAtCompileTime) || ComplexByReal
};
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
evalToDest ? dest.data() : static_dest.data());
if(!evalToDest)
if(!MightCannotUseDest)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
{
MappedDest(actualDestPtr, dest.size()).setZero();
compatibleAlpha = RhsScalar(1);
}
else
MappedDest(actualDestPtr, dest.size()) = dest;
// shortcut if we are sure to be able to use dest directly,
// this ease the compiler to generate cleaner and more optimzized code for most common cases
general_matrix_vector_product
<Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhs.data(), actualRhs.innerStride()),
dest.data(), 1,
compatibleAlpha);
}
general_matrix_vector_product
<Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
actualLhs.data(), actualLhs.outerStride(),
actualRhs.data(), actualRhs.innerStride(),
actualDestPtr, 1,
compatibleAlpha);
if (!evalToDest)
else
{
if(!alphaIsCompatible)
dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
else
dest = MappedDest(actualDestPtr, dest.size());
gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
evalToDest ? dest.data() : static_dest.data());
if(!evalToDest)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
Index size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
{
MappedDest(actualDestPtr, dest.size()).setZero();
compatibleAlpha = RhsScalar(1);
}
else
MappedDest(actualDestPtr, dest.size()) = dest;
}
general_matrix_vector_product
<Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhs.data(), actualRhs.innerStride()),
actualDestPtr, 1,
compatibleAlpha);
if (!evalToDest)
{
if(!alphaIsCompatible)
dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
else
dest = MappedDest(actualDestPtr, dest.size());
}
}
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,true>
template<> struct gemv_dense_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 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());
@@ -511,45 +327,48 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
if(!DirectlyUseRhs)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = actualRhs.size();
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;
typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
general_matrix_vector_product
<Index,LhsScalar,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
<Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
actualLhs.rows(), actualLhs.cols(),
actualLhs.data(), actualLhs.outerStride(),
actualRhsPtr, 1,
dest.data(), dest.innerStride(),
LhsMapper(actualLhs.data(), actualLhs.outerStride()),
RhsMapper(actualRhsPtr, 1),
dest.data(), dest.col(0).innerStride(), //NOTE if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
actualAlpha);
}
};
template<> struct gemv_selector<OnTheRight,ColMajor,false>
template<> struct gemv_dense_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();
EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
// TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
typename nested_eval<Rhs,1>::type actual_rhs(rhs);
const Index size = rhs.rows();
for(Index k=0; k<size; ++k)
dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k);
dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k);
}
};
template<> struct gemv_selector<OnTheRight,RowMajor,false>
template<> struct gemv_dense_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();
EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
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(actual_rhs.transpose())).sum();
}
};
@@ -567,7 +386,7 @@ template<> struct gemv_selector<OnTheRight,RowMajor,false>
*/
template<typename Derived>
template<typename OtherDerived>
inline const typename ProductReturnType<Derived, OtherDerived>::Type
inline const Product<Derived, OtherDerived>
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
{
// A note regarding the function declaration: In MSVC, this function will sometimes
@@ -592,7 +411,8 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
#ifdef EIGEN_DEBUG_PRODUCT
internal::product_type<Derived,OtherDerived>::debug();
#endif
return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
return Product<Derived, OtherDerived>(derived(), other.derived());
}
/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
@@ -608,7 +428,7 @@ 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 {
@@ -627,7 +447,7 @@ 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

353
extern/Eigen3/Eigen/src/Core/GenericPacketMath.h vendored
View File

@@ -42,21 +42,28 @@ namespace internal {
struct default_packet_traits
{
enum {
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasNegate = 1,
HasAbs = 1,
HasArg = 0,
HasAbs2 = 1,
HasMin = 1,
HasMax = 1,
HasConj = 1,
HasSetLinear = 1,
HasBlend = 0,
HasDiv = 0,
HasSqrt = 0,
HasRsqrt = 0,
HasExp = 0,
HasLog = 0,
HasLog1p = 0,
HasLog10 = 0,
HasPow = 0,
HasSin = 0,
@@ -64,17 +71,37 @@ struct default_packet_traits
HasTan = 0,
HasASin = 0,
HasACos = 0,
HasATan = 0
HasATan = 0,
HasSinh = 0,
HasCosh = 0,
HasTanh = 0,
HasLGamma = 0,
HasDiGamma = 0,
HasZeta = 0,
HasPolygamma = 0,
HasErf = 0,
HasErfc = 0,
HasIGamma = 0,
HasIGammac = 0,
HasBetaInc = 0,
HasRound = 0,
HasFloor = 0,
HasCeil = 0,
HasSign = 0
};
};
template<typename T> struct packet_traits : default_packet_traits
{
typedef T type;
typedef T half;
enum {
Vectorizable = 0,
size = 1,
AlignedOnScalar = 0
AlignedOnScalar = 0,
HasHalfPacket = 0
};
enum {
HasAdd = 0,
@@ -90,135 +117,239 @@ template<typename T> struct packet_traits : default_packet_traits
};
};
template<typename T> struct packet_traits<const T> : packet_traits<T> { };
template <typename Src, typename Tgt> struct type_casting_traits {
enum {
VectorizedCast = 0,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a) {
return static_cast<TgtPacket>(a);
}
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a, const SrcPacket& /*b*/) {
return static_cast<TgtPacket>(a);
}
template <typename SrcPacket, typename TgtPacket>
EIGEN_DEVICE_FUNC inline TgtPacket
pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) {
return static_cast<TgtPacket>(a);
}
/** \internal \returns a + b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
padd(const Packet& a,
const Packet& b) { return a+b; }
/** \internal \returns a - b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
psub(const Packet& a,
const Packet& b) { return a-b; }
/** \internal \returns -a (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pconj(const Packet& a) { return numext::conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmul(const Packet& a,
const Packet& b) { return a*b; }
/** \internal \returns a / b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pdiv(const Packet& a,
const Packet& b) { return a/b; }
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmin(const Packet& a,
const Packet& b) { using std::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> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmax(const Packet& a,
const Packet& b) { using std::max; return (max)(a, b); }
const Packet& b) { return numext::maxi(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pabs(const Packet& a) { using std::abs; return abs(a); }
/** \internal \returns the phase angle of \a a */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
parg(const Packet& a) { using numext::arg; return arg(a); }
/** \internal \returns the bitwise and of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pand(const Packet& a, const Packet& b) { return a & b; }
/** \internal \returns the bitwise or of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
por(const Packet& a, const Packet& b) { return a | b; }
/** \internal \returns the bitwise xor of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pxor(const Packet& a, const Packet& b) { return a ^ b; }
/** \internal \returns the bitwise andnot of \a a and \a b */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pandnot(const Packet& a, const Packet& b) { return a & (!b); }
/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet version of \a *from, (un-aligned load) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from duplicated.
* For instance, for a packet of 8 elements, 4 scalar will be read from \a *from and
* duplicated to form: {from[0],from[0],from[1],from[1],,from[2],from[2],,from[3],from[3]}
* Currently, this function is only used for scalar * complex products.
*/
template<typename Packet> inline Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pload1(const typename unpacket_traits<Packet>::type *a) { return pset1<Packet>(*a); }
/** \internal \returns a packet with elements of \a *from duplicated.
* For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
* duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
* Currently, this function is only used for scalar * complex products.
*/
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from quadrupled.
* For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
* replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
* Currently, this function is only used in matrix products.
* For packet-size smaller or equal to 4, this function is equivalent to pload1
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
ploadquad(const typename unpacket_traits<Packet>::type* from)
{ return pload1<Packet>(from); }
/** \internal equivalent to
* \code
* a0 = pload1(a+0);
* a1 = pload1(a+1);
* a2 = pload1(a+2);
* a3 = pload1(a+3);
* \endcode
* \sa pset1, pload1, ploaddup, pbroadcast2
*/
template<typename Packet> EIGEN_DEVICE_FUNC
inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a,
Packet& a0, Packet& a1, Packet& a2, Packet& a3)
{
a0 = pload1<Packet>(a+0);
a1 = pload1<Packet>(a+1);
a2 = pload1<Packet>(a+2);
a3 = pload1<Packet>(a+3);
}
/** \internal equivalent to
* \code
* a0 = pload1(a+0);
* a1 = pload1(a+1);
* \endcode
* \sa pset1, pload1, ploaddup, pbroadcast4
*/
template<typename Packet> EIGEN_DEVICE_FUNC
inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
Packet& a0, Packet& a1)
{
a0 = pload1<Packet>(a+0);
a1 = pload1<Packet>(a+1);
}
/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
template<typename Scalar> inline typename packet_traits<Scalar>::type
plset(const Scalar& a) { return a; }
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
plset(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
template<typename Scalar, typename Packet> inline void pstore(Scalar* to, const Packet& from)
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
{ (*to) = from; }
/** \internal copy the packet \a from to \a *to, (un-aligned store) */
template<typename Scalar, typename Packet> inline void pstoreu(Scalar* to, const Packet& from)
{ (*to) = from; }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
{ (*to) = from; }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
{ return ploadu<Packet>(from); }
template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
{ pstore(to, from); }
/** \internal tries to do cache prefetching of \a addr */
template<typename Scalar> inline void prefetch(const Scalar* addr)
template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
{
#if !defined(_MSC_VER)
__builtin_prefetch(addr);
#ifdef __CUDA_ARCH__
#if defined(__LP64__)
// 64-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
#else
// 32-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
#endif
#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
__builtin_prefetch(addr);
#endif
}
/** \internal \returns the first element of a packet */
template<typename Packet> inline typename unpacket_traits<Packet>::type pfirst(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type pfirst(const Packet& a)
{ return a; }
/** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
preduxp(const Packet* vecs) { return vecs[0]; }
/** \internal \returns the sum of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux(const Packet& a)
{ return a; }
/** \internal \returns the sum of the elements of \a a by block of 4 elements.
* For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
* For packet-size smaller or equal to 4, this boils down to a noop.
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline
typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type
predux_downto4(const Packet& a)
{ return a; }
/** \internal \returns the product of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a)
{ return a; }
/** \internal \returns the min of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_min(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(const Packet& a)
{ return a; }
/** \internal \returns the max of the elements of \a a*/
template<typename Packet> inline typename unpacket_traits<Packet>::type predux_max(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(const Packet& a)
{ return a; }
/** \internal \returns the reversed elements of \a a*/
template<typename Packet> inline Packet preverse(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
{ return a; }
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> inline Packet pcplxflip(const Packet& a)
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
{
// FIXME: uncomment the following in case we drop the internal imag and real functions.
// using std::imag;
@@ -250,6 +381,22 @@ Packet pasin(const Packet& a) { using std::asin; return asin(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { using std::acos; return acos(a); }
/** \internal \returns the arc tangent of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet patan(const Packet& a) { using std::atan; return atan(a); }
/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psinh(const Packet& a) { using std::sinh; return sinh(a); }
/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcosh(const Packet& a) { using std::cosh; return cosh(a); }
/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptanh(const Packet& a) { using std::tanh; return tanh(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { using std::exp; return exp(a); }
@@ -258,10 +405,36 @@ Packet pexp(const Packet& a) { using std::exp; return exp(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the log1p of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog1p(const Packet& a) { return numext::log1p(a); }
/** \internal \returns the log10 of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog10(const Packet& a) { using std::log10; return log10(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); }
/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet prsqrt(const Packet& a) {
return pdiv(pset1<Packet>(1), psqrt(a));
}
/** \internal \returns the rounded value of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pround(const Packet& a) { using numext::round; return round(a); }
/** \internal \returns the floor of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
/** \internal \returns the ceil of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/
@@ -275,34 +448,45 @@ inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename u
}
/** \internal \returns a * b + c (coeff-wise) */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pmadd(const Packet& a,
const Packet& b,
const Packet& c)
{ return padd(pmul(a, b),c); }
/** \internal \returns a packet version of \a *from.
* If LoadMode equals #Aligned, \a from must be 16 bytes aligned */
template<typename Packet, int LoadMode>
inline Packet ploadt(const typename unpacket_traits<Packet>::type* from)
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from)
{
if(LoadMode == Aligned)
if(Alignment >= unpacket_traits<Packet>::alignment)
return pload<Packet>(from);
else
return ploadu<Packet>(from);
}
/** \internal copy the packet \a from to \a *to.
* If StoreMode equals #Aligned, \a to must be 16 bytes aligned */
template<typename Scalar, typename Packet, int LoadMode>
inline void pstoret(Scalar* to, const Packet& from)
* The pointer \a from must be aligned on a \a Alignment bytes boundary. */
template<typename Scalar, typename Packet, int Alignment>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from)
{
if(LoadMode == Aligned)
if(Alignment >= unpacket_traits<Packet>::alignment)
pstore(to, from);
else
pstoreu(to, from);
}
/** \internal \returns a packet version of \a *from.
* Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
* hardware if available to speedup the loading of data that won't be modified
* by the current computation.
*/
template<typename Packet, int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
{
return ploadt<Packet, LoadMode>(from);
}
/** \internal default implementation of palign() allowing partial specialization */
template<int Offset,typename PacketType>
struct palign_impl
@@ -336,15 +520,74 @@ inline void palign(PacketType& first, const PacketType& second)
* Fast complex products (GCC generates a function call which is very slow)
***************************************************************************/
// Eigen+CUDA does not support complexes.
#ifndef __CUDACC__
template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
{ return std::complex<float>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
{ return std::complex<double>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); }
#endif
/***************************************************************************
* PacketBlock, that is a collection of N packets where the number of words
* in the packet is a multiple of N.
***************************************************************************/
template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock {
Packet packet[N];
};
template<typename Packet> EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet,1>& /*kernel*/) {
// Nothing to do in the scalar case, i.e. a 1x1 matrix.
}
/***************************************************************************
* Selector, i.e. vector of N boolean values used to select (i.e. blend)
* words from 2 packets.
***************************************************************************/
template <size_t N> struct Selector {
bool select[N];
};
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
return ifPacket.select[0] ? thenPacket : elsePacket;
}
/** \internal \returns \a a with the first coefficient replaced by the scalar b */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pinsertfirst(const Packet& a, typename unpacket_traits<Packet>::type b)
{
// Default implementation based on pblend.
// It must be specialized for higher performance.
Selector<unpacket_traits<Packet>::size> mask;
mask.select[0] = true;
// This for loop should be optimized away by the compiler.
for(Index i=1; i<unpacket_traits<Packet>::size; ++i)
mask.select[i] = false;
return pblend(mask, pset1<Packet>(b), a);
}
/** \internal \returns \a a with the last coefficient replaced by the scalar b */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pinsertlast(const Packet& a, typename unpacket_traits<Packet>::type b)
{
// Default implementation based on pblend.
// It must be specialized for higher performance.
Selector<unpacket_traits<Packet>::size> mask;
// This for loop should be optimized away by the compiler.
for(Index i=0; i<unpacket_traits<Packet>::size-1; ++i)
mask.select[i] = false;
mask.select[unpacket_traits<Packet>::size-1] = true;
return pblend(mask, pset1<Packet>(b), a);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_GENERIC_PACKET_MATH_H

163
extern/Eigen3/Eigen/src/Core/GlobalFunctions.h vendored
View File

@@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -11,13 +11,30 @@
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR) \
#ifdef EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
/** \returns an expression of the coefficient-wise DOC_OP of \a x
DOC_DETAILS
\sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
*/ \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
NAME(const Eigen::ArrayBase<Derived>& x) { \
return x.derived(); \
NAME(const Eigen::ArrayBase<Derived>& x);
#else
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
(NAME)(const Eigen::ArrayBase<Derived>& x) { \
return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
}
#endif // EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
\
template<typename Derived> \
@@ -30,55 +47,133 @@
{ \
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()); \
} \
};
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
*
* \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived,typename ScalarExponent>
inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
#else
template<typename Derived,typename ScalarExponent>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,ScalarExponent>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent),
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,ScalarExponent,pow) >::type
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
return x.derived().pow(exponent);
}
template<typename Derived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<Derived>& exponents)
inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename Derived::Scalar,pow)
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
return x.derived().pow(exponent);
}
#endif
/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power.
*
* Example: \include Cwise_array_power_array.cpp
* Output: \verbinclude Cwise_array_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived>(
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
}
/**
* \brief Component-wise division of a scalar by array elements.
**/
template <typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>
operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a)
/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power between a scalar and an array of exponents.
*
* \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Scalar,typename Derived>
inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
#else
template<typename Scalar, typename Derived>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,Scalar>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar),
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow) >::type
pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>(s)
);
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
template<typename Derived>
inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,typename Derived::Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
#endif
namespace internal
{
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)

57
extern/Eigen3/Eigen/src/Core/IO.h vendored
View File

@@ -49,7 +49,7 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
*/
struct IOFormat
{
/** Default contructor, see class IOFormat for the meaning of the parameters */
/** Default constructor, see class IOFormat for the meaning of the parameters */
IOFormat(int _precision = StreamPrecision, int _flags = 0,
const std::string& _coeffSeparator = " ",
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
@@ -57,6 +57,10 @@ struct IOFormat
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
// TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
// don't add rowSpacer if columns are not to be aligned
if((flags & DontAlignCols))
return;
int i = int(matSuffix.length())-1;
while (i>=0 && matSuffix[i]!='\n')
{
@@ -76,7 +80,7 @@ struct IOFormat
*
* \brief Pseudo expression providing matrix output with given format
*
* \param ExpressionType the type of the object on which IO stream operations are performed
* \tparam ExpressionType the type of the object on which IO stream operations are performed
*
* This class represents an expression with stream operators controlled by a given IOFormat.
* It is the return type of DenseBase::format()
@@ -101,51 +105,23 @@ class WithFormat
}
protected:
const typename ExpressionType::Nested m_matrix;
typename ExpressionType::Nested m_matrix;
IOFormat m_format;
};
/** \returns a WithFormat proxy object allowing to print a matrix the with given
* format \a fmt.
*
* See class IOFormat for some examples.
*
* \sa class IOFormat, class WithFormat
*/
template<typename Derived>
inline const WithFormat<Derived>
DenseBase<Derived>::format(const IOFormat& fmt) const
{
return WithFormat<Derived>(derived(), fmt);
}
namespace internal {
template<typename Scalar, bool IsInteger>
struct significant_decimals_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline int run()
{
using std::ceil;
using std::log;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
template<typename Scalar>
struct significant_decimals_default_impl<Scalar, true>
{
static inline int run()
{
return 0;
}
};
// NOTE: This helper is kept for backward compatibility with previous code specializing
// this internal::significant_decimals_impl structure. In the future we should directly
// call digits10() which has been introduced in July 2016 in 3.3.
template<typename Scalar>
struct significant_decimals_impl
: significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger>
{};
{
static inline int run()
{
return NumTraits<Scalar>::digits10();
}
};
/** \internal
* print the matrix \a _m to the output stream \a s using the output format \a fmt */
@@ -160,7 +136,6 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
typename Derived::Nested m = _m;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
Index width = 0;

118
extern/Eigen3/Eigen/src/Core/Inverse.h vendored Normal file
View File

@@ -0,0 +1,118 @@
// 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 {
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::StorageIndex StorageIndex;
typedef typename XprType::PlainObject PlainObject;
typedef typename XprType::Scalar Scalar;
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprTypeNested>::type XprTypeNestedCleaned;
typedef typename internal::ref_selector<Inverse>::type Nested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
explicit EIGEN_DEVICE_FUNC 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;
};
// Generic API dispatcher
template<typename XprType, typename StorageKind>
class InverseImpl
: public internal::generic_xpr_base<Inverse<XprType> >::type
{
public:
typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
typedef typename XprType::Scalar Scalar;
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>
{
typedef Inverse<ArgType> InverseType;
typedef typename InverseType::PlainObject PlainObject;
typedef evaluator<PlainObject> Base;
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

125
extern/Eigen3/Eigen/src/Core/Map.h vendored
View File

@@ -13,13 +13,41 @@
namespace Eigen {
namespace internal {
template<typename PlainObjectType, int MapOptions, typename StrideType>
struct traits<Map<PlainObjectType, MapOptions, StrideType> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> TraitsBase;
enum {
PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
? PlainObjectType::ColsAtCompileTime
: PlainObjectType::RowsAtCompileTime,
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
? Dynamic
: int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
: int(StrideType::OuterStrideAtCompileTime),
Alignment = int(MapOptions)&int(AlignedMask),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
};
private:
enum { Options }; // Expressions don't have Options
};
}
/** \class Map
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing array of data.
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned.
* \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
* The default is \c #Unaligned.
* \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
* of an ordinary, contiguous array. This can be overridden by specifying strides.
@@ -63,44 +91,6 @@ namespace Eigen {
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
namespace internal {
template<typename PlainObjectType, int MapOptions, typename StrideType>
struct traits<Map<PlainObjectType, MapOptions, StrideType> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> TraitsBase;
typedef typename PlainObjectType::Index Index;
typedef typename PlainObjectType::Scalar Scalar;
enum {
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
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)%16)==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)
};
private:
enum { Options }; // Expressions don't have Options
};
}
template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
: public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
{
@@ -110,34 +100,34 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
EIGEN_DENSE_PUBLIC_INTERFACE(Map)
typedef typename Base::PointerType PointerType;
#if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API
typedef const Scalar* PointerArgType;
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast<PointerType>(ptr); }
#else
typedef PointerType PointerArgType;
EIGEN_DEVICE_FUNC
inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
#endif
EIGEN_DEVICE_FUNC
inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
return int(StrideType::OuterStrideAtCompileTime) != 0 ? m_stride.outer()
: int(internal::traits<Map>::OuterStrideAtCompileTime) != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
: IsVectorAtCompileTime ? (this->size() * innerStride())
: (int(Flags)&RowMajorBit) ? (this->cols() * innerStride())
: (this->rows() * innerStride());
}
/** Constructor in the fixed-size case.
*
* \param dataPtr pointer to the array to map
* \param a_stride optional Stride object, passing the strides.
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride)
EIGEN_DEVICE_FUNC
explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -145,11 +135,12 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size vector case.
*
* \param dataPtr pointer to the array to map
* \param a_size the size of the vector expression
* \param a_stride optional Stride object, passing the strides.
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -157,12 +148,13 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size matrix case.
*
* \param dataPtr pointer to the array to map
* \param nbRows the number of rows of the matrix expression
* \param nbCols the number of columns of the matrix expression
* \param a_stride optional Stride object, passing the strides.
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, Index nbRows, Index nbCols, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), nbRows, nbCols), m_stride(a_stride)
EIGEN_DEVICE_FUNC
inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
{
PlainObjectType::Base::_check_template_params();
}
@@ -173,19 +165,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
StrideType m_stride;
};
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
::Array(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Array>(data));
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
inline Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>
::Matrix(const Scalar *data)
{
this->_set_noalias(Eigen::Map<const Matrix>(data));
}
} // end namespace Eigen

106
extern/Eigen3/Eigen/src/Core/MapBase.h vendored
View File

@@ -12,15 +12,25 @@
#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 {
/** \class MapBase
* \ingroup Core_Module
/** \ingroup Core_Module
*
* \brief Base class for Map and Block expression with direct access
* \brief Base class for dense Map and Block expression with direct access
*
* This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
* Map and Block objects with direct access.
* Typical users do not have to directly deal with this class.
*
* This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
* See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
*
* The \c Derived class has to provide the following two methods describing the memory layout:
* \code Index innerStride() const; \endcode
* \code Index outerStride() const; \endcode
*
* \sa class Map, class Block
*/
@@ -33,11 +43,11 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
SizeAtCompileTime = Base::SizeAtCompileTime
};
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -76,8 +86,10 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
typedef typename Base::CoeffReturnType CoeffReturnType;
inline Index rows() const { return m_rows.value(); }
inline Index cols() const { return m_cols.value(); }
/** \copydoc DenseBase::rows() */
EIGEN_DEVICE_FUNC inline Index rows() const { return m_rows.value(); }
/** \copydoc DenseBase::cols() */
EIGEN_DEVICE_FUNC inline Index cols() const { return m_cols.value(); }
/** Returns a pointer to the first coefficient of the matrix or vector.
*
@@ -85,30 +97,39 @@ 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; }
/** \copydoc PlainObjectBase::coeff(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
{
return m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeff(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return m_data[index * innerStride()];
}
/** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return this->m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeffRef(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
return this->m_data[index * innerStride()];
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
@@ -116,6 +137,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
(m_data + (colId * colStride() + rowId * rowStride()));
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index index) const
{
@@ -123,12 +145,16 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
/** \internal Constructor for fixed size matrices or vectors */
EIGEN_DEVICE_FUNC
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
checkSanity<Derived>();
}
/** \internal Constructor for dynamically sized vectors */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index vecSize)
: m_data(dataPtr),
m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
@@ -137,34 +163,59 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(vecSize >= 0);
eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
checkSanity();
checkSanity<Derived>();
}
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols)
: m_data(dataPtr), m_rows(nbRows), m_cols(nbCols)
/** \internal Constructor for dynamically sized matrices */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index rows, Index cols)
: m_data(dataPtr), m_rows(rows), m_cols(cols)
{
eigen_assert( (dataPtr == 0)
|| ( nbRows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols)));
checkSanity();
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
checkSanity<Derived>();
}
#ifdef EIGEN_MAPBASE_PLUGIN
#include EIGEN_MAPBASE_PLUGIN
#endif
protected:
void checkSanity() const
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) 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) % 16) == 0)
&& "input pointer is not aligned on a 16 byte boundary");
#if EIGEN_MAX_ALIGN_BYTES>0
// innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
|| (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
#endif
}
template<typename T>
EIGEN_DEVICE_FUNC
void checkSanity(typename internal::enable_if<internal::traits<T>::Alignment==0,void*>::type = 0) const
{}
PointerType m_data;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
};
/** \ingroup Core_Module
*
* \brief Base class for non-const dense Map and Block expression with direct access
*
* This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
* dense Map and Block objects with direct access.
* It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
*
* \sa class Map, class Block
*/
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{
@@ -175,7 +226,7 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
typedef typename Base::Scalar Scalar;
typedef typename Base::PacketScalar PacketScalar;
typedef typename Base::Index Index;
typedef typename Base::StorageIndex StorageIndex;
typedef typename Base::PointerType PointerType;
using Base::derived;
@@ -196,14 +247,18 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
const Scalar
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return this->m_data; }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
{
return this->m_data[col * colStride() + row * rowStride()];
}
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
@@ -225,10 +280,11 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
(this->m_data + index * innerStride(), val);
}
explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols) : Base(dataPtr, nbRows, nbCols) {}
EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
EIGEN_DEVICE_FUNC
Derived& operator=(const MapBase& other)
{
ReadOnlyMapBase::Base::operator=(other);

883
extern/Eigen3/Eigen/src/Core/MathFunctions.h vendored
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File diff suppressed because it is too large Load Diff

101
extern/Eigen3/Eigen/src/Core/MathFunctionsImpl.h vendored Normal file
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@@ -0,0 +1,101 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATHFUNCTIONSIMPL_H
#define EIGEN_MATHFUNCTIONSIMPL_H
namespace Eigen {
namespace internal {
/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
Doesn't do anything fancy, just a 13/6-degree rational interpolant which
is accurate up to a couple of ulp in the range [-9, 9], outside of which
the tanh(x) = +/-1.
This implementation works on both scalars and packets.
*/
template<typename T>
T generic_fast_tanh_float(const T& a_x)
{
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
const T plus_9 = pset1<T>(9.f);
const T minus_9 = pset1<T>(-9.f);
// NOTE GCC prior to 6.3 might improperly optimize this max/min
// step such that if a_x is nan, x will be either 9 or -9,
// and tanh will return 1 or -1 instead of nan.
// This is supposed to be fixed in gcc6.3,
// see: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
const T x = pmax(minus_9,pmin(plus_9,a_x));
// The monomial coefficients of the numerator polynomial (odd).
const T alpha_1 = pset1<T>(4.89352455891786e-03f);
const T alpha_3 = pset1<T>(6.37261928875436e-04f);
const T alpha_5 = pset1<T>(1.48572235717979e-05f);
const T alpha_7 = pset1<T>(5.12229709037114e-08f);
const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
const T alpha_11 = pset1<T>(2.00018790482477e-13f);
const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
// The monomial coefficients of the denominator polynomial (even).
const T beta_0 = pset1<T>(4.89352518554385e-03f);
const T beta_2 = pset1<T>(2.26843463243900e-03f);
const T beta_4 = pset1<T>(1.18534705686654e-04f);
const T beta_6 = pset1<T>(1.19825839466702e-06f);
// Since the polynomials are odd/even, we need x^2.
const T x2 = pmul(x, x);
// Evaluate the numerator polynomial p.
T p = pmadd(x2, alpha_13, alpha_11);
p = pmadd(x2, p, alpha_9);
p = pmadd(x2, p, alpha_7);
p = pmadd(x2, p, alpha_5);
p = pmadd(x2, p, alpha_3);
p = pmadd(x2, p, alpha_1);
p = pmul(x, p);
// Evaluate the denominator polynomial p.
T q = pmadd(x2, beta_6, beta_4);
q = pmadd(x2, q, beta_2);
q = pmadd(x2, q, beta_0);
// Divide the numerator by the denominator.
return pdiv(p, q);
}
template<typename RealScalar>
EIGEN_STRONG_INLINE
RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
{
EIGEN_USING_STD_MATH(sqrt);
RealScalar p, qp;
p = numext::maxi(x,y);
if(p==RealScalar(0)) return RealScalar(0);
qp = numext::mini(y,x) / p;
return p * sqrt(RealScalar(1) + qp*qp);
}
template<typename Scalar>
struct hypot_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x, const Scalar& y)
{
EIGEN_USING_STD_MATH(abs);
return positive_real_hypot<RealScalar>(abs(x), abs(y));
}
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATHFUNCTIONSIMPL_H

243
extern/Eigen3/Eigen/src/Core/Matrix.h vendored
View File

@@ -13,6 +13,45 @@
namespace Eigen {
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
private:
enum { size = internal::size_at_compile_time<_Rows,_Cols>::ret };
typedef typename find_best_packet<_Scalar,size>::type PacketScalar;
enum {
row_major_bit = _Options&RowMajor ? RowMajorBit : 0,
is_dynamic_size_storage = _MaxRows==Dynamic || _MaxCols==Dynamic,
max_size = is_dynamic_size_storage ? Dynamic : _MaxRows*_MaxCols,
default_alignment = compute_default_alignment<_Scalar,max_size>::value,
actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
required_alignment = unpacket_traits<PacketScalar>::alignment,
packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
};
public:
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef Eigen::Index StorageIndex;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
// FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
Alignment = actual_alignment
};
};
}
/** \class Matrix
* \ingroup Core_Module
*
@@ -24,13 +63,13 @@ namespace Eigen {
* The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
*
* The first three template parameters are required:
* \tparam _Scalar \anchor matrix_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>.
* User defined sclar types are supported as well (see \ref user_defined_scalars "here").
* \tparam _Scalar Numeric type, e.g. float, double, int or std::complex<float>.
* User defined scalar types are supported as well (see \ref user_defined_scalars "here").
* \tparam _Rows Number of rows, or \b Dynamic
* \tparam _Cols Number of columns, or \b Dynamic
*
* The remaining template parameters are optional -- in most cases you don't have to worry about them.
* \tparam _Options \anchor matrix_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either
* \tparam _Options A combination of either \b #RowMajor or \b #ColMajor, and of either
* \b #AutoAlign or \b #DontAlign.
* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
* for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
@@ -67,7 +106,7 @@ namespace Eigen {
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
*
* <i><b>Some notes:</b></i>
*
@@ -97,32 +136,44 @@ namespace Eigen {
* are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd>
* </dl>
*
* \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
* \ref TopicStorageOrders
* <i><b>ABI and storage layout</b></i>
*
* The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
* <table class="manual">
* <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
* <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
* struct {
* T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
* Eigen::Index rows, cols;
* };
* \endcode</td></tr>
* <tr class="alt"><td>\code
* Matrix<T,Dynamic,1>
* Matrix<T,1,Dynamic> \endcode</td><td>\code
* struct {
* T *data; // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
* Eigen::Index size;
* };
* \endcode</td></tr>
* <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
* struct {
* T data[Rows*Cols]; // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
* };
* \endcode</td></tr>
* <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
* struct {
* T data[MaxRows*MaxCols]; // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
* Eigen::Index rows, cols;
* };
* \endcode</td></tr>
* </table>
* Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two
* smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
*
* \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
* \ref TopicStorageOrders
*/
namespace internal {
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
{
typedef _Scalar Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
typedef MatrixXpr XprKind;
enum {
RowsAtCompileTime = _Rows,
ColsAtCompileTime = _Cols,
MaxRowsAtCompileTime = _MaxRows,
MaxColsAtCompileTime = _MaxCols,
Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
Options = _Options,
InnerStrideAtCompileTime = 1,
OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime
};
};
}
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
class Matrix
: public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
@@ -151,6 +202,7 @@ class Matrix
*
* \callgraph
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
{
return Base::_set(other);
@@ -167,7 +219,8 @@ class Matrix
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
{
return Base::_set(other);
}
@@ -179,12 +232,14 @@ class Matrix
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
{
return Base::operator=(other);
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
{
return Base::operator=(func);
@@ -200,6 +255,7 @@ class Matrix
*
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
@@ -207,60 +263,85 @@ class Matrix
}
// FIXME is it still needed
Matrix(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
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 }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
Matrix(Matrix&& other)
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
Matrix& operator=(Matrix&& other)
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;
}
#endif
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass the dimension here, so it makes more sense to use the default
* constructor Matrix() instead.
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim)
: Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim)
#ifndef EIGEN_PARSED_BY_DOXYGEN
// This constructor is for both 1x1 matrices and dynamic vectors
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Matrix(const T& x)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
Base::template _init1<T>(x);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y)
{
Base::_check_template_params();
Base::template _init2<T0,T1>(x, y);
}
#else
/** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
EIGEN_DEVICE_FUNC
explicit Matrix(const Scalar *data);
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
* This is useful for dynamic-size vectors. For fixed-size vectors,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
* calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
* For fixed-size \c 1x1 matrices it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_STRONG_INLINE explicit Matrix(Index dim);
/** \brief Constructs an initialized 1x1 matrix with the given coefficient */
Matrix(const Scalar& x);
/** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
*
* This is useful for dynamic-size matrices. For fixed-size matrices,
* it is redundant to pass these parameters, so one should use the default constructor
* Matrix() instead. */
* Matrix() instead.
*
* \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
* calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
* For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
* constructor Matrix() instead, especially when using one of the non standard
* \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
*/
EIGEN_DEVICE_FUNC
Matrix(Index rows, Index cols);
/** \brief Constructs an initialized 2D vector with given coefficients */
Matrix(const Scalar& x, const Scalar& y);
#endif
/** \brief Constructs an initialized 3D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
{
Base::_check_template_params();
@@ -270,6 +351,7 @@ class Matrix
m_storage.data()[2] = z;
}
/** \brief Constructs an initialized 4D vector with given coefficients */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
{
Base::_check_template_params();
@@ -280,76 +362,33 @@ class Matrix
m_storage.data()[3] = w;
}
explicit Matrix(const Scalar *data);
/** \brief Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
// This test resides here, to bring the error messages closer to the user. Normally, these checks
// are performed deeply within the library, thus causing long and scary error traces.
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor */
EIGEN_STRONG_INLINE Matrix(const Matrix& other)
: Base(other.rows() * other.cols(), other.rows(), other.cols())
{
Base::_check_template_params();
Base::_set_noalias(other);
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other)
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
other.evalTo(*this);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
{ }
/** \brief Copy constructor for generic expressions.
* \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::_resize_to_match(other);
// FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to
// go for pure _set() implementations, right?
*this = other;
}
: Base(other.derived())
{ }
/** \internal
* \brief Override MatrixBase::swap() since for dynamic-sized matrices
* of same type it is enough to swap the data pointers.
*/
template<typename OtherDerived>
void swap(MatrixBase<OtherDerived> const & other)
{ this->_swap(other.derived()); }
inline Index innerStride() const { return 1; }
inline Index outerStride() const { return this->innerSize(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); }
/////////// Geometry module ///////////
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
template<typename OtherDerived>
Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r);
#endif
// allow to extend Matrix outside Eigen
#ifdef EIGEN_MATRIX_PLUGIN
#include EIGEN_MATRIX_PLUGIN

330
extern/Eigen3/Eigen/src/Core/MatrixBase.h vendored
View File

@@ -41,9 +41,9 @@ namespace Eigen {
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class MatrixBase
: public DenseBase<Derived>
@@ -52,7 +52,7 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef MatrixBase StorageBaseType;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -66,7 +66,6 @@ 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;
@@ -98,25 +97,14 @@ template<typename Derived> class MatrixBase
/** \returns the size of the main diagonal, which is min(rows(),cols()).
* \sa rows(), cols(), SizeAtCompileTime. */
inline Index diagonalSize() const { return (std::min)(rows(),cols()); }
EIGEN_DEVICE_FUNC
inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
/** \brief The plain matrix type corresponding to this expression.
*
* This is not necessarily exactly the return type of eval(). In the case of plain matrices,
* the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
* that the return type of eval() is either PlainObject or const PlainObject&.
*/
typedef Matrix<typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime,
AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime
> PlainObject;
typedef typename Base::PlainObject PlainObject;
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal Represents a matrix with all coefficients equal to one another*/
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType;
typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
@@ -125,7 +113,7 @@ template<typename Derived> class MatrixBase
/** \internal Return type of eigenvalues() */
typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
/** \internal the return type of identity */
typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,Derived> IdentityReturnType;
typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,PlainObject> IdentityReturnType;
/** \internal the return type of unit vectors */
typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
internal::traits<Derived>::RowsAtCompileTime,
@@ -133,6 +121,7 @@ template<typename Derived> class MatrixBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
@@ -141,41 +130,44 @@ template<typename Derived> class MatrixBase
# include EIGEN_MATRIXBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const MatrixBase& other);
// We cannot inherit here via Base::operator= since it is causing
// trouble with MSVC.
template <typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const EigenBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other);
template<typename MatrixPower, typename Lhs, typename Rhs>
Derived& lazyAssign(const MatrixPowerProduct<MatrixPower, Lhs,Rhs>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
const typename ProductReturnType<Derived,OtherDerived>::Type
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const typename LazyProductReturnType<Derived,OtherDerived>::Type
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
lazyProduct(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
@@ -188,84 +180,93 @@ template<typename Derived> class MatrixBase
void applyOnTheRight(const EigenBase<OtherDerived>& other);
template<typename DiagonalDerived>
const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
EIGEN_DEVICE_FUNC
const Product<Derived, DiagonalDerived, LazyProduct>
operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
template<typename OtherDerived>
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
EIGEN_DEVICE_FUNC
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
Scalar eigen2_dot(const MatrixBase<OtherDerived>& other) const;
#endif
RealScalar squaredNorm() const;
RealScalar norm() const;
EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
EIGEN_DEVICE_FUNC RealScalar norm() const;
RealScalar stableNorm() const;
RealScalar blueNorm() const;
RealScalar hypotNorm() const;
const PlainObject normalized() const;
void normalize();
EIGEN_DEVICE_FUNC const PlainObject normalized() const;
EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
EIGEN_DEVICE_FUNC void normalize();
EIGEN_DEVICE_FUNC void stableNormalize();
const AdjointReturnType adjoint() const;
void adjointInPlace();
EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
EIGEN_DEVICE_FUNC void adjointInPlace();
typedef Diagonal<Derived> DiagonalReturnType;
EIGEN_DEVICE_FUNC
DiagonalReturnType diagonal();
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
EIGEN_DEVICE_FUNC
ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
template<int Index> typename DiagonalIndexReturnType<Index>::Type diagonal();
template<int Index> typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
template<int Index>
EIGEN_DEVICE_FUNC
typename DiagonalIndexReturnType<Index>::Type diagonal();
template<int Index>
EIGEN_DEVICE_FUNC
typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
EIGEN_DEVICE_FUNC
DiagonalDynamicIndexReturnType diagonal(Index index);
EIGEN_DEVICE_FUNC
ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
#ifdef EIGEN2_SUPPORT
template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part();
template<unsigned int Mode> const typename internal::eigen2_part_return_type<Derived, Mode>::type part() const;
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
template<template<typename T, int N> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT
template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
template<unsigned int Mode> typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode> typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename TriangularViewReturnType<Mode>::Type triangularView();
template<unsigned int Mode>
EIGEN_DEVICE_FUNC
typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
template<unsigned int UpLo> typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo> typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
template<unsigned int UpLo>
EIGEN_DEVICE_FUNC
typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
static const IdentityReturnType Identity();
static const IdentityReturnType Identity(Index rows, Index cols);
static const BasisReturnType Unit(Index size, Index i);
static const BasisReturnType Unit(Index i);
static const BasisReturnType UnitX();
static const BasisReturnType UnitY();
static const BasisReturnType UnitZ();
static const BasisReturnType UnitW();
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
EIGEN_DEVICE_FUNC
const DiagonalWrapper<const Derived> asDiagonal() const;
const PermutationWrapper<const Derived> asPermutation() const;
EIGEN_DEVICE_FUNC
Derived& setIdentity();
EIGEN_DEVICE_FUNC
Derived& setIdentity(Index rows, Index cols);
bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
@@ -284,7 +285,7 @@ template<typename Derived> class MatrixBase
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator!= */
template<typename OtherDerived>
inline bool operator==(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
{ return cwiseEqual(other).all(); }
/** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
@@ -292,64 +293,50 @@ template<typename Derived> class MatrixBase
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator== */
template<typename OtherDerived>
inline bool operator!=(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
{ return cwiseNotEqual(other).any(); }
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;
EIGEN_DEVICE_FUNC Scalar trace() const;
/////////// Array module ///////////
template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
template<int p> RealScalar lpNorm() const;
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
ArrayWrapper<Derived> array() { return derived(); }
const ArrayWrapper<const Derived> array() const { 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 ArrayWrapper<const Derived>(derived()); }
/////////// LU module ///////////
const FullPivLU<PlainObject> fullPivLu() const;
const PartialPivLU<PlainObject> partialPivLu() const;
inline const FullPivLU<PlainObject> fullPivLu() const;
inline const PartialPivLU<PlainObject> partialPivLu() const;
#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS
const LU<PlainObject> lu() const;
#endif
inline const PartialPivLU<PlainObject> lu() const;
#ifdef EIGEN2_SUPPORT
const LU<PlainObject> eigen2_lu() const;
#endif
inline const Inverse<Derived> inverse() const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
const PartialPivLU<PlainObject> lu() const;
#endif
#ifdef EIGEN2_SUPPORT
template<typename ResultType>
void computeInverse(MatrixBase<ResultType> *result) const {
*result = this->inverse();
}
#endif
const internal::inverse_impl<Derived> inverse() const;
template<typename ResultType>
void computeInverseAndDetWithCheck(
inline void computeInverseAndDetWithCheck(
ResultType& inverse,
typename ResultType::Scalar& determinant,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
) const;
template<typename ResultType>
void computeInverseWithCheck(
inline void computeInverseWithCheck(
ResultType& inverse,
bool& invertible,
const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
@@ -358,65 +345,70 @@ template<typename Derived> class MatrixBase
/////////// Cholesky module ///////////
const LLT<PlainObject> llt() const;
const LDLT<PlainObject> ldlt() const;
inline const LLT<PlainObject> llt() const;
inline const LDLT<PlainObject> ldlt() const;
/////////// QR module ///////////
const HouseholderQR<PlainObject> householderQr() const;
const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
#ifdef EIGEN2_SUPPORT
const QR<PlainObject> qr() const;
#endif
inline const HouseholderQR<PlainObject> householderQr() const;
inline const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const;
inline const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const;
inline const CompleteOrthogonalDecomposition<PlainObject> completeOrthogonalDecomposition() const;
EigenvaluesReturnType eigenvalues() const;
RealScalar operatorNorm() const;
/////////// Eigenvalues module ///////////
inline EigenvaluesReturnType eigenvalues() const;
inline RealScalar operatorNorm() const;
/////////// SVD module ///////////
JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
#ifdef EIGEN2_SUPPORT
SVD<PlainObject> svd() const;
#endif
inline JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const;
inline BDCSVD<PlainObject> bdcSvd(unsigned int computationOptions = 0) const;
/////////// Geometry module ///////////
#ifndef EIGEN_PARSED_BY_DOXYGEN
/// \internal helper struct to form the return type of the cross product
template<typename OtherDerived> struct cross_product_return_type {
typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
};
#endif // EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
typename cross_product_return_type<OtherDerived>::type
EIGEN_DEVICE_FUNC
#ifndef EIGEN_PARSED_BY_DOXYGEN
inline typename cross_product_return_type<OtherDerived>::type
#else
inline PlainObject
#endif
cross(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
PlainObject unitOrthogonal(void) const;
Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
EIGEN_DEVICE_FUNC
inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC
inline PlainObject unitOrthogonal(void) const;
EIGEN_DEVICE_FUNC
inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal };
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
: ColsAtCompileTime==1 ? Vertical : Horizontal };
typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
HomogeneousReturnType homogeneous() const;
#endif
EIGEN_DEVICE_FUNC
inline HomogeneousReturnType homogeneous() const;
enum {
SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
};
typedef Block<const Derived,
internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>,
const ConstStartMinusOne > HNormalizedReturnType;
const HNormalizedReturnType hnormalized() const;
typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
EIGEN_DEVICE_FUNC
inline const HNormalizedReturnType hnormalized() const;
////////// Householder module ///////////
@@ -452,58 +444,32 @@ template<typename Derived> class MatrixBase
///////// MatrixFunctions module /////////
typedef typename internal::stem_function<Scalar>::type StemFunction;
const MatrixExponentialReturnValue<Derived> exp() const;
#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \
/** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
const ReturnType<Derived> Name() const;
#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \
/** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
const ReturnType<Derived> Name(Argument) const;
EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
/** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>.*/
const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
const MatrixFunctionReturnValue<Derived> cosh() const;
const MatrixFunctionReturnValue<Derived> sinh() const;
const MatrixFunctionReturnValue<Derived> cos() const;
const MatrixFunctionReturnValue<Derived> sin() const;
const MatrixSquareRootReturnValue<Derived> sqrt() const;
const MatrixLogarithmReturnValue<Derived> log() const;
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const;
#ifdef EIGEN2_SUPPORT
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
template<typename ProductDerived, typename Lhs, typename Rhs>
Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0,
EvalBeforeAssigningBit>& other);
/** \deprecated because .lazy() is deprecated
* Overloaded for cache friendly product evaluation */
template<typename OtherDerived>
Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other)
{ return lazyAssign(other._expression()); }
template<unsigned int Added>
const Flagged<Derived, Added, 0> marked() const;
const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const;
inline const Cwise<Derived> cwise() const;
inline Cwise<Derived> cwise();
VectorBlock<Derived> start(Index size);
const VectorBlock<const Derived> start(Index size) const;
VectorBlock<Derived> end(Index size);
const VectorBlock<const Derived> end(Index size) const;
template<int Size> VectorBlock<Derived,Size> start();
template<int Size> const VectorBlock<const Derived,Size> start() const;
template<int Size> VectorBlock<Derived,Size> end();
template<int Size> const VectorBlock<const Derived,Size> end() const;
Minor<Derived> minor(Index row, Index col);
const Minor<Derived> minor(Index row, Index col) const;
#endif
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue, pow, power to \c p, const RealScalar& p)
EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex<RealScalar>& p)
protected:
MatrixBase() : Base() {}
EIGEN_DEVICE_FUNC MatrixBase() : Base() {}
private:
explicit MatrixBase(int);
MatrixBase(int,int);
template<typename OtherDerived> explicit MatrixBase(const MatrixBase<OtherDerived>&);
EIGEN_DEVICE_FUNC explicit MatrixBase(int);
EIGEN_DEVICE_FUNC MatrixBase(int,int);
template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
protected:
// mixing arrays and matrices is not legal
template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )

45
extern/Eigen3/Eigen/src/Core/NestByValue.h vendored
View File

@@ -13,25 +13,24 @@
namespace Eigen {
/** \class NestByValue
* \ingroup Core_Module
*
* \brief Expression which must be nested by value
*
* \param ExpressionType the type of the object of which we are requiring nesting-by-value
*
* This class is the return type of MatrixBase::nestByValue()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::nestByValue()
*/
namespace internal {
template<typename ExpressionType>
struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
{};
}
/** \class NestByValue
* \ingroup Core_Module
*
* \brief Expression which must be nested by value
*
* \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
*
* This class is the return type of MatrixBase::nestByValue()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::nestByValue()
*/
template<typename ExpressionType> class NestByValue
: public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
{
@@ -40,29 +39,29 @@ 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 +90,7 @@ 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;

82
extern/Eigen3/Eigen/src/Core/NoAlias.h vendored
View File

@@ -17,7 +17,7 @@ namespace Eigen {
*
* \brief Pseudo expression providing an operator = assuming no aliasing
*
* \param ExpressionType the type of the object on which to do the lazy assignment
* \tparam ExpressionType the type of the object on which to do the lazy assignment
*
* This class represents an expression with special assignment operators
* assuming no aliasing between the target expression and the source expression.
@@ -30,62 +30,36 @@ 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() */
template<typename OtherDerived>
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{ return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); }
/** \sa MatrixBase::operator+= */
template<typename OtherDerived>
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()));
return m_expression;
}
/** \sa MatrixBase::operator-= */
template<typename OtherDerived>
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()));
return m_expression;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename ProductDerived, typename Lhs, typename Rhs>
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_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_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
typedef typename ExpressionType::Scalar Scalar;
explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
template<typename OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
EIGEN_DEVICE_FUNC
ExpressionType& expression() const
{
return m_expression;
@@ -126,7 +100,7 @@ class NoAlias
template<typename Derived>
NoAlias<Derived,MatrixBase> MatrixBase<Derived>::noalias()
{
return derived();
return NoAlias<Derived, Eigen::MatrixBase >(derived());
}
} // end namespace Eigen

138
extern/Eigen3/Eigen/src/Core/NumTraits.h vendored
View File

@@ -12,24 +12,57 @@
namespace Eigen {
namespace internal {
// default implementation of digits10(), based on numeric_limits if specialized,
// 0 for integer types, and log10(epsilon()) otherwise.
template< typename T,
bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
bool is_integer = NumTraits<T>::IsInteger>
struct default_digits10_impl
{
static int run() { return std::numeric_limits<T>::digits10; }
};
template<typename T>
struct default_digits10_impl<T,false,false> // Floating point
{
static int run() {
using std::log10;
using std::ceil;
typedef typename NumTraits<T>::Real Real;
return int(ceil(-log10(NumTraits<Real>::epsilon())));
}
};
template<typename T>
struct default_digits10_impl<T,false,true> // Integer
{
static int run() { return 0; }
};
} // end namespace internal
/** \class NumTraits
* \ingroup Core_Module
*
* \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
*
* \param T the numeric type at hand
* \tparam T the numeric type at hand
*
* This class stores enums, typedefs and static methods giving information about a numeric type.
*
* The provided data consists of:
* \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
* then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
* \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
* then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
* is a typedef to \a U.
* \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
* \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
* such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
* \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
* take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
* only intended as a helper for code that needs to explicitly promote types.
* \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
* Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
* \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
* this means, just use \a T here.
* \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
@@ -42,10 +75,14 @@ namespace Eigen {
* \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
* \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
* be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
* \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T.
* \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
* it returns a \a Real instead of a \a T.
* \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
* value by the fuzzy comparison operators.
* \li highest() and lowest() functions returning the highest and lowest possible values respectively.
* \li digits10() function returning the number of decimal digits that can be represented without change. This is
* the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
* which is used as the default implementation if specialized.
*/
template<typename T> struct GenericNumTraits
@@ -67,22 +104,47 @@ template<typename T> struct GenericNumTraits
T
>::type NonInteger;
typedef T Nested;
typedef T Literal;
static inline Real epsilon() { return std::numeric_limits<T>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real epsilon()
{
return numext::numeric_limits<T>::epsilon();
}
EIGEN_DEVICE_FUNC
static inline int digits10()
{
return internal::default_digits10_impl<T>::run();
}
EIGEN_DEVICE_FUNC
static inline Real dummy_precision()
{
// make sure to override this for floating-point types
return Real(0);
}
static inline T highest() { return (std::numeric_limits<T>::max)(); }
static inline T lowest() { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
#ifdef EIGEN2_SUPPORT
enum {
HasFloatingPoint = !IsInteger
};
typedef NonInteger FloatingPoint;
#endif
EIGEN_DEVICE_FUNC
static inline T highest() {
return (numext::numeric_limits<T>::max)();
}
EIGEN_DEVICE_FUNC
static inline T lowest() {
return IsInteger ? (numext::numeric_limits<T>::min)() : (-(numext::numeric_limits<T>::max)());
}
EIGEN_DEVICE_FUNC
static inline T infinity() {
return numext::numeric_limits<T>::infinity();
}
EIGEN_DEVICE_FUNC
static inline T quiet_NaN() {
return numext::numeric_limits<T>::quiet_NaN();
}
};
template<typename T> struct NumTraits : GenericNumTraits<T>
@@ -91,11 +153,13 @@ template<typename T> struct NumTraits : GenericNumTraits<T>
template<> struct NumTraits<float>
: GenericNumTraits<float>
{
EIGEN_DEVICE_FUNC
static inline float dummy_precision() { return 1e-5f; }
};
template<> struct NumTraits<double> : GenericNumTraits<double>
{
EIGEN_DEVICE_FUNC
static inline double dummy_precision() { return 1e-12; }
};
@@ -109,6 +173,7 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
{
typedef _Real Real;
typedef typename NumTraits<_Real>::Literal Literal;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
@@ -117,8 +182,12 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
};
EIGEN_DEVICE_FUNC
static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
EIGEN_DEVICE_FUNC
static inline int digits10() { return NumTraits<Real>::digits10(); }
};
template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
@@ -130,21 +199,50 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
typedef ArrayType & Nested;
typedef typename NumTraits<Scalar>::Literal Literal;
enum {
IsComplex = NumTraits<Scalar>::IsComplex,
IsInteger = NumTraits<Scalar>::IsInteger,
IsSigned = NumTraits<Scalar>::IsSigned,
RequireInitialization = 1,
ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
};
EIGEN_DEVICE_FUNC
static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
static inline int digits10() { return NumTraits<Scalar>::digits10(); }
};
template<> struct NumTraits<std::string>
: GenericNumTraits<std::string>
{
enum {
RequireInitialization = 1,
ReadCost = HugeCost,
AddCost = HugeCost,
MulCost = HugeCost
};
static inline int digits10() { return 0; }
private:
static inline std::string epsilon();
static inline std::string dummy_precision();
static inline std::string lowest();
static inline std::string highest();
static inline std::string infinity();
static inline std::string quiet_NaN();
};
// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
template<> struct NumTraits<void> {};
} // end namespace Eigen
#endif // EIGEN_NUMTRAITS_H

364
extern/Eigen3/Eigen/src/Core/PermutationMatrix.h vendored
View File

@@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2015 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
@@ -13,14 +13,18 @@
namespace Eigen {
template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl;
namespace internal {
enum PermPermProduct_t {PermPermProduct};
} // end namespace internal
/** \class PermutationBase
* \ingroup Core_Module
*
* \brief Base class for permutations
*
* \param Derived the derived class
* \tparam Derived the derived class
*
* This class is the base class for all expressions representing a permutation matrix,
* internally stored as a vector of integers.
@@ -38,17 +42,6 @@ template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKi
*
* \sa class PermutationMatrix, class PermutationWrapper
*/
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false>
struct permut_matrix_product_retval;
template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false>
struct permut_sparsematrix_product_retval;
enum PermPermProduct_t {PermPermProduct};
} // end namespace internal
template<typename Derived>
class PermutationBase : public EigenBase<Derived>
{
@@ -60,19 +53,20 @@ 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,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
typedef typename Traits::Index Index;
typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
typedef typename Traits::StorageIndex StorageIndex;
typedef Matrix<StorageIndex,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
DenseMatrixType;
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,Index>
typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
PlainPermutationType;
typedef PlainPermutationType PlainObject;
using Base::derived;
typedef Inverse<Derived> InverseReturnType;
typedef void Scalar;
#endif
/** Copies the other permutation into *this */
@@ -118,7 +112,7 @@ 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
@@ -147,7 +141,8 @@ class PermutationBase : public EigenBase<Derived>
/** Sets *this to be the identity permutation matrix */
void setIdentity()
{
for(Index i = 0; i < size(); ++i)
StorageIndex n = StorageIndex(size());
for(StorageIndex i = 0; i < n; ++i)
indices().coeffRef(i) = i;
}
@@ -163,18 +158,18 @@ 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)
{
eigen_assert(i>=0 && j>=0 && i<size() && j<size());
for(Index k = 0; k < size(); ++k)
{
if(indices().coeff(k) == i) indices().coeffRef(k) = j;
else if(indices().coeff(k) == j) indices().coeffRef(k) = i;
if(indices().coeff(k) == i) indices().coeffRef(k) = StorageIndex(j);
else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndex(i);
}
return derived();
}
@@ -185,7 +180,7 @@ 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)
{
@@ -196,16 +191,16 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the inverse permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> inverse() const
{ return derived(); }
inline InverseReturnType inverse() const
{ return InverseReturnType(derived()); }
/** \returns the tranpose permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
inline Transpose<PermutationBase> transpose() const
{ return derived(); }
inline InverseReturnType transpose() const
{ return InverseReturnType(derived()); }
/**** multiplication helpers to hopefully get RVO ****/
@@ -215,13 +210,13 @@ 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
@@ -229,7 +224,7 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the product permutation matrix.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
@@ -237,18 +232,18 @@ class PermutationBase : public EigenBase<Derived>
/** \returns the product of a permutation with another inverse permutation.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other>
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other) const
inline PlainPermutationType operator*(const InverseImpl<Other,PermutationStorage>& other) const
{ return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
/** \returns the product of an inverse permutation with another permutation.
*
* \note \note_try_to_help_rvo
* \note \blank \note_try_to_help_rvo
*/
template<typename Other> friend
inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other, const PermutationBase& perm)
inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other, const PermutationBase& perm)
{ return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
/** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
@@ -284,39 +279,43 @@ class PermutationBase : public EigenBase<Derived>
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef PermutationStorage StorageKind;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef _StorageIndex StorageIndex;
typedef void Scalar;
};
}
/** \class PermutationMatrix
* \ingroup Core_Module
*
* \brief Permutation matrix
*
* \param SizeAtCompileTime the number of rows/cols, or Dynamic
* \param MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \param IndexType the interger type of the indices
* \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
* \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
* \tparam _StorageIndex the integer type of the indices
*
* This class represents a permutation matrix, internally stored as a vector of integers.
*
* \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
*/
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex> >
{
typedef PermutationBase<PermutationMatrix> Base;
typedef internal::traits<PermutationMatrix> Traits;
public:
typedef const PermutationMatrix& Nested;
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename Traits::StorageIndex StorageIndex;
#endif
inline PermutationMatrix()
@@ -324,8 +323,10 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
/** Constructs an uninitialized permutation matrix of given size.
*/
inline PermutationMatrix(int size) : m_indices(size)
{}
explicit inline PermutationMatrix(Index size) : m_indices(size)
{
eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
}
/** Copy constructor. */
template<typename OtherDerived>
@@ -346,7 +347,7 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
* array's size.
*/
template<typename Other>
explicit inline PermutationMatrix(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Convert the Transpositions \a tr to a permutation matrix */
@@ -393,10 +394,13 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename Other>
PermutationMatrix(const Transpose<PermutationBase<Other> >& other)
: m_indices(other.nestedPermutation().size())
PermutationMatrix(const InverseImpl<Other,PermutationStorage>& other)
: m_indices(other.derived().nestedExpression().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<StorageIndex>::highest());
StorageIndex end = StorageIndex(m_indices.size());
for (StorageIndex i=0; i<end;++i)
m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
}
template<typename Lhs,typename Rhs>
PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
@@ -413,18 +417,20 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
: traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
: traits<Matrix<_StorageIndex,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
{
typedef IndexType Index;
typedef Map<const Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef PermutationStorage StorageKind;
typedef Map<const Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef void Scalar;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess>
: public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex>,_PacketAccess> >
{
typedef PermutationBase<Map> Base;
typedef internal::traits<Map> Traits;
@@ -432,14 +438,14 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
#endif
inline Map(const Index* indicesPtr)
inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indicesPtr, Index size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
@@ -474,40 +480,36 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
IndicesType m_indices;
};
/** \class PermutationWrapper
* \ingroup Core_Module
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \param _IndicesType the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
struct PermutationStorage {};
template<typename _IndicesType> class TranspositionsWrapper;
namespace internal {
template<typename _IndicesType>
struct traits<PermutationWrapper<_IndicesType> >
{
typedef PermutationStorage StorageKind;
typedef typename _IndicesType::Scalar Scalar;
typedef typename _IndicesType::Scalar Index;
typedef void Scalar;
typedef typename _IndicesType::Scalar StorageIndex;
typedef _IndicesType 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
};
};
}
/** \class PermutationWrapper
* \ingroup Core_Module
*
* \brief Class to view a vector of integers as a permutation matrix
*
* \tparam _IndicesType the type of the vector of integer (can be any compatible expression)
*
* This class allows to view any vector expression of integers as a permutation matrix.
*
* \sa class PermutationBase, class PermutationMatrix
*/
template<typename _IndicesType>
class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> >
{
@@ -519,8 +521,8 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationWrapper(const IndicesType& a_indices)
: m_indices(a_indices)
inline PermutationWrapper(const IndicesType& indices)
: m_indices(indices)
{}
/** const version of indices(). */
@@ -532,182 +534,86 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typename IndicesType::Nested m_indices;
};
/** \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, AliasFreeProduct>
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, AliasFreeProduct>
(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, AliasFreeProduct>
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, AliasFreeProduct>
(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> >
template<typename PermutationType>
class InverseImpl<PermutationType, PermutationStorage>
: public EigenBase<Inverse<PermutationType> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct permut_matrix_product_retval
: public ReturnByValue<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
typedef typename MatrixType::Index Index;
permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const Index n = Side==OnTheLeft ? rows() : cols();
// FIXME we need an is_same for expression that is not sensitive to constness. For instance
// is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
if( is_same<MatrixTypeNestedCleaned,Dest>::value
&& blas_traits<MatrixTypeNestedCleaned>::HasUsableDirectAccess
&& blas_traits<Dest>::HasUsableDirectAccess
&& extract_data(dst) == extract_data(m_matrix))
{
// apply the permutation inplace
Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
mask.fill(false);
Index r = 0;
while(r < m_permutation.size())
{
// search for the next seed
while(r<m_permutation.size() && mask[r]) r++;
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
Index k0 = r++;
Index kPrev = k0;
mask.coeffRef(k0) = true;
for(Index k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
{
Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
.swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
(dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
mask.coeffRef(k) = true;
kPrev = k;
}
}
}
else
{
for(int 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)
=
Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime>
(m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i);
}
}
}
protected:
const PermutationType& m_permutation;
typename MatrixType::Nested m_matrix;
};
/* Template partial specialization for transposed/inverse permutations */
template<typename Derived>
struct traits<Transpose<PermutationBase<Derived> > >
: traits<Derived>
{};
} // end namespace internal
template<typename Derived>
class Transpose<PermutationBase<Derived> >
: public EigenBase<Transpose<PermutationBase<Derived> > >
{
typedef Derived PermutationType;
typedef typename PermutationType::IndicesType IndicesType;
typedef typename PermutationType::PlainPermutationType PlainPermutationType;
typedef internal::traits<PermutationType> PermTraits;
protected:
InverseImpl() {}
public:
typedef Inverse<PermutationType> InverseType;
using EigenBase<Inverse<PermutationType> >::derived;
#ifndef EIGEN_PARSED_BY_DOXYGEN
typedef internal::traits<PermutationType> Traits;
typedef typename Derived::DenseMatrixType DenseMatrixType;
typedef typename PermutationType::DenseMatrixType DenseMatrixType;
enum {
Flags = Traits::Flags,
CoeffReadCost = Traits::CoeffReadCost,
RowsAtCompileTime = Traits::RowsAtCompileTime,
ColsAtCompileTime = Traits::ColsAtCompileTime,
MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
RowsAtCompileTime = PermTraits::RowsAtCompileTime,
ColsAtCompileTime = PermTraits::ColsAtCompileTime,
MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
};
typedef typename Traits::Scalar Scalar;
#endif
Transpose(const PermutationType& p) : m_permutation(p) {}
inline int rows() const { return m_permutation.rows(); }
inline int 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)
other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1);
for (Index i=0; i<derived().rows();++i)
other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
}
#endif
/** \return the equivalent permutation matrix */
PlainPermutationType eval() const { return *this; }
PlainPermutationType eval() const { return derived(); }
DenseMatrixType toDenseMatrix() const { return *this; }
DenseMatrixType toDenseMatrix() const { return 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>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm)
const Product<OtherDerived, InverseType, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const InverseType& trPerm)
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.derived());
return Product<OtherDerived, InverseType, AliasFreeProduct>(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<InverseType, OtherDerived, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived());
return Product<InverseType, OtherDerived, AliasFreeProduct>(derived(), matrix.derived());
}
const PermutationType& nestedPermutation() const { return m_permutation; }
protected:
const PermutationType& m_permutation;
};
template<typename Derived>
@@ -716,6 +622,12 @@ const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() con
return derived();
}
namespace internal {
template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_PERMUTATIONMATRIX_H

451
extern/Eigen3/Eigen/src/Core/PlainObjectBase.h vendored
View File

@@ -28,6 +28,7 @@ namespace internal {
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
@@ -35,11 +36,12 @@ template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<> struct check_rows_cols_for_overflow<Dynamic> {
template<typename Index>
EIGEN_DEVICE_FUNC
static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols)
{
// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
// we assume Index is signed
Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
Index max_index = (std::size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
bool error = (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
@@ -56,33 +58,41 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
} // end namespace internal
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace doxygen {
// This is a workaround to doxygen not being able to understand the inheritance logic
// when it is hidden by the dense_xpr_base helper struct.
// Moreover, doxygen fails to include members that are not documented in the declaration body of
// MatrixBase if we inherits MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >,
// this is why we simply inherits MatrixBase, though this does not make sense.
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename Derived> struct dense_xpr_base_dispatcher;
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public ArrayBase {};
} // namespace doxygen
/** \class PlainObjectBase
* \ingroup Core_Module
* \brief %Dense storage base class for matrices and arrays.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
*
* \tparam Derived is the derived type, e.g., a Matrix or Array
*
* \sa \ref TopicClassHierarchy
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace internal {
// this is a warkaround to doxygen not being able to understand the inheritence logic
// when it is hidden by the dense_xpr_base helper struct.
template<typename Derived> struct dense_xpr_base_dispatcher_for_doxygen;// : public MatrixBase<Derived> {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
} // namespace internal
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base_dispatcher_for_doxygen<Derived>
class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
#else
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
@@ -93,8 +103,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef typename internal::dense_xpr_base<Derived>::type Base;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Derived DenseType;
@@ -113,28 +123,40 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef Eigen::Map<Derived, Unaligned> MapType;
friend class Eigen::Map<const Derived, Unaligned>;
typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
friend class Eigen::Map<Derived, Aligned>;
typedef Eigen::Map<Derived, Aligned> AlignedMapType;
friend class Eigen::Map<const Derived, Aligned>;
typedef const Eigen::Map<const Derived, Aligned> ConstAlignedMapType;
#if EIGEN_MAX_ALIGN_BYTES>0
// for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
friend class Eigen::Map<Derived, AlignedMax>;
friend class Eigen::Map<const Derived, AlignedMax>;
#endif
typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, Aligned, StrideType> type; };
template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
protected:
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>::Alignment>0) };
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
EIGEN_DEVICE_FUNC
Base& base() { return *static_cast<Base*>(this); }
EIGEN_DEVICE_FUNC
const Base& base() const { return *static_cast<const Base*>(this); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
@@ -143,11 +165,21 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
return m_storage.data()[index];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
if(Flags & RowMajorBit)
@@ -156,11 +188,19 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
/** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
@@ -169,6 +209,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
return m_storage.data()[index];
@@ -209,11 +252,11 @@ 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.
@@ -232,22 +275,22 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_STRONG_INLINE void resize(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
{
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,nbRows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,nbCols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,nbRows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,nbCols<=MaxColsAtCompileTime)
&& nbRows>=0 && nbCols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,rows==RowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,cols==ColsAtCompileTime)
&& EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,rows<=MaxRowsAtCompileTime)
&& EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,cols<=MaxColsAtCompileTime)
&& rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(rows, cols);
#ifdef EIGEN_INITIALIZE_COEFFS
Index size = nbRows*nbCols;
Index size = rows*cols;
bool size_changed = size != this->size();
m_storage.resize(size, nbRows, nbCols);
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
m_storage.resize(nbRows*nbCols, nbRows, nbCols);
m_storage.resize(rows*cols, rows, cols);
#endif
}
@@ -262,6 +305,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
*/
EIGEN_DEVICE_FUNC
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
@@ -286,9 +330,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(NoChange_t, Index nbCols)
EIGEN_DEVICE_FUNC
inline void resize(NoChange_t, Index cols)
{
resize(rows(), nbCols);
resize(rows(), cols);
}
/** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
@@ -299,9 +344,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(Index nbRows, NoChange_t)
EIGEN_DEVICE_FUNC
inline void resize(Index rows, NoChange_t)
{
resize(nbRows, cols());
resize(rows, cols());
}
/** Resizes \c *this to have the same dimensions as \a other.
@@ -312,6 +358,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
@@ -339,9 +386,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, nbRows, nbCols);
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -351,10 +399,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new rows will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, NoChange_t)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(nbRows, cols());
conservativeResize(rows, cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@@ -364,10 +413,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* In case the matrix is growing, new columns will be uninitialized.
*/
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index nbCols)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows(), nbCols);
conservativeResize(rows(), cols);
}
/** Resizes the vector to \a size while retaining old values.
@@ -378,6 +428,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* When values are appended, they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResize(Index size)
{
internal::conservative_resize_like_impl<Derived>::run(*this, size);
@@ -393,6 +444,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* appended to the matrix they will copied from \c other.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
{
internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
@@ -401,6 +453,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
{
return _set(other);
@@ -408,6 +461,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \sa MatrixBase::lazyAssign() */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
{
_resize_to_match(other);
@@ -415,12 +469,18 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
{
resize(func.rows(), func.cols());
return Base::operator=(func);
}
// Prevent user from trying to instantiate PlainObjectBase objects
// by making all its constructor protected. See bug 1074.
protected:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
@@ -430,20 +490,23 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
#ifndef EIGEN_PARSED_BY_DOXYGEN
// FIXME is it still needed ?
/** \internal */
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
EIGEN_DEVICE_FUNC
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
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
PlainObjectBase(PlainObjectBase&& other)
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
: m_storage( std::move(other.m_storage) )
{
}
PlainObjectBase& operator=(PlainObjectBase&& other)
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_storage, other.m_storage);
@@ -452,31 +515,56 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
#endif
/** Copy constructor */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
: m_storage()
{
_check_template_params();
lazyAssign(other);
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
lazyAssign(other);
}
EIGEN_STRONG_INLINE PlainObjectBase(Index a_size, Index nbRows, Index nbCols)
: m_storage(a_size, nbRows, nbCols)
: Base(), m_storage(other.m_storage) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
{
// _check_template_params();
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
/** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&)
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
_set_noalias(other);
}
/** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage()
{
_check_template_params();
resizeLike(other);
*this = other.derived();
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
{
_check_template_params();
// FIXME this does not automatically transpose vectors if necessary
resize(other.rows(), other.cols());
other.evalTo(this->derived());
}
public:
/** \brief Copies the generic expression \a other into *this.
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
{
_resize_to_match(other);
@@ -484,21 +572,15 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return this->derived();
}
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
_check_template_params();
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
}
/** \name Map
* These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
* \a data pointers.
*
* Here is an example using strides:
* \include Matrix_Map_stride.cpp
* Output: \verbinclude Matrix_Map_stride.out
*
* \see class Map
*/
//@{
@@ -568,16 +650,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//@}
using Base::setConstant;
Derived& setConstant(Index size, const Scalar& value);
Derived& setConstant(Index rows, Index cols, const Scalar& value);
EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
using Base::setZero;
Derived& setZero(Index size);
Derived& setZero(Index rows, Index cols);
EIGEN_DEVICE_FUNC Derived& setZero(Index size);
EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
using Base::setOnes;
Derived& setOnes(Index size);
Derived& setOnes(Index rows, Index cols);
EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
using Base::setRandom;
Derived& setRandom(Index size);
@@ -596,6 +678,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
@@ -603,8 +686,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
: (rows() == other.rows() && cols() == other.cols())))
&& "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
EIGEN_ONLY_USED_FOR_DEBUG(other);
if(this->size()==0)
resizeLike(other);
#else
resizeLike(other);
#endif
@@ -624,25 +705,23 @@ 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_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); }
template<typename OtherDerived>
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.
*
* \sa operator=(const MatrixBase<OtherDerived>&), _set()
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
{
// I don't think we need this resize call since the lazyAssign will anyways resize
@@ -650,40 +729,175 @@ 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,typename OtherDerived::Scalar>());
return this->derived();
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(Index nbRows, Index nbCols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT(bool(NumTraits<T0>::IsInteger) &&
bool(NumTraits<T1>::IsInteger),
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(nbRows,nbCols);
resize(rows,cols);
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[0] = Scalar(val0);
m_storage.data()[1] = Scalar(val1);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<T0,Index>::value)
&& (internal::is_same<T1,Index>::value)
&& Base::SizeAtCompileTime==2,T1>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = Scalar(val0);
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)
&& ((!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;
EIGEN_UNUSED_VARIABLE(is_integer);
EIGEN_STATIC_ASSERT(is_integer,
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)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
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,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<Index,T>::value)
&& Base::SizeAtCompileTime==1
&& internal::is_convertible<T, Scalar>::value,T*>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
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 an object convertible to the Derived type.
template<typename T>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const Derived& 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){
this->derived() = other;
}
template<typename T, typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other)
{
resize(other.rows(), other.cols());
other.evalTo(this->derived());
}
template<typename T, typename OtherDerived, int ColsAtCompileTime>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
{
this->derived() = r;
}
// For fixed-size Array<Scalar,...>
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);
}
// For fixed-size Array<Index,...>
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>
void _swap(DenseBase<OtherDerived> const & other)
EIGEN_DEVICE_FUNC
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()
{
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor)
@@ -697,10 +911,9 @@ 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 {
@@ -708,7 +921,6 @@ namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct conservative_resize_like_impl
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
{
if (_this.rows() == rows && _this.cols() == cols) return;
@@ -724,8 +936,8 @@ struct conservative_resize_like_impl
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(rows,cols);
const Index common_rows = (std::min)(rows, _this.rows());
const Index common_cols = (std::min)(cols, _this.cols());
const Index common_rows = numext::mini(rows, _this.rows());
const Index common_cols = numext::mini(cols, _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
@@ -758,8 +970,8 @@ struct conservative_resize_like_impl
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(other);
const Index common_rows = (std::min)(tmp.rows(), _this.rows());
const Index common_cols = (std::min)(tmp.cols(), _this.cols());
const Index common_rows = numext::mini(tmp.rows(), _this.rows());
const Index common_cols = numext::mini(tmp.cols(), _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
@@ -774,7 +986,6 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
{
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
@@ -800,6 +1011,7 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
struct matrix_swap_impl
{
EIGEN_DEVICE_FUNC
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
a.base().swap(b);
@@ -809,6 +1021,7 @@ struct matrix_swap_impl
template<typename MatrixTypeA, typename MatrixTypeB>
struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
{
EIGEN_DEVICE_FUNC
static inline void run(MatrixTypeA& a, MatrixTypeB& b)
{
static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);

196
extern/Eigen3/Eigen/src/Core/Product.h vendored
View File

@@ -3,96 +3,184 @@
//
// Copyright (C) 2008-2011 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/.
// 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_PRODUCT_H
#define EIGEN_PRODUCT_H
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
namespace Eigen {
template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
namespace internal {
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef traits<LhsCleaned> LhsTraits;
typedef traits<RhsCleaned> RhsTraits;
typedef MatrixXpr XprKind;
typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
typename RhsTraits::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
typedef typename promote_index_type<typename LhsTraits::StorageIndex,
typename RhsTraits::StorageIndex>::type StorageIndex;
enum {
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) ? RowMajorBit
: (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
: ( ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
|| ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit
: NoPreferredStorageOrderBit
};
};
} // end namespace internal
/** \class Product
* \ingroup Core_Module
*
* \brief Expression of the product of two arbitrary matrices or vectors
*
* \param Lhs the type of the left-hand side expression
* \param Rhs the type of the right-hand side expression
* \tparam _Lhs the type of the left-hand side expression
* \tparam _Rhs the type of the right-hand side expression
*
* This class represents an expression of the product of two arbitrary matrices.
*
* The other template parameters are:
* \tparam Option can be DefaultProduct, AliasFreeProduct, or LazyProduct
*
*/
namespace internal {
template<typename Lhs, typename Rhs>
struct traits<Product<Lhs, Rhs> >
{
typedef MatrixXpr XprKind;
typedef typename remove_all<Lhs>::type LhsCleaned;
typedef typename remove_all<Rhs>::type RhsCleaned;
typedef typename scalar_product_traits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename promote_storage_type<typename traits<LhsCleaned>::StorageKind,
typename traits<RhsCleaned>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsCleaned>::Index,
typename traits<RhsCleaned>::Index>::type Index;
enum {
RowsAtCompileTime = LhsCleaned::RowsAtCompileTime,
ColsAtCompileTime = RhsCleaned::ColsAtCompileTime,
MaxRowsAtCompileTime = LhsCleaned::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsCleaned::MaxColsAtCompileTime,
Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0), // TODO should be no storage order
CoeffReadCost = 0 // TODO CoeffReadCost should not be part of the expression traits
};
};
} // 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::ref_selector<Lhs>::type LhsNested;
typedef typename internal::ref_selector<Rhs>::type RhsNested;
typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
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 EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_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:
const LhsNested m_lhs;
const RhsNested m_rhs;
LhsNested m_lhs;
RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
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
{};
/** 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> Derived;
public:
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
typedef Product<Lhs,Rhs,Option> ProductXpr;
typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
public:
using Base::derived;
typedef typename Base::Scalar Scalar;
EIGEN_STRONG_INLINE operator const Scalar() const
{
return internal::evaluator<ProductXpr>(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 EIGEN_STRONG_INLINE 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 internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE 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 internal::evaluator<Derived>(derived()).coeff(i);
}
};
} // end namespace Eigen
#endif // EIGEN_PRODUCT_H

1112
extern/Eigen3/Eigen/src/Core/ProductEvaluators.h vendored Normal file
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File diff suppressed because it is too large Load Diff

58
extern/Eigen3/Eigen/src/Core/Random.h vendored
View File

@@ -16,8 +16,7 @@ namespace internal {
template<typename Scalar> struct scalar_random_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
template<typename Index>
inline const Scalar operator() (Index, Index = 0) const { return random<Scalar>(); }
inline const Scalar operator() () const { return random<Scalar>(); }
};
template<typename Scalar>
@@ -28,12 +27,18 @@ struct functor_traits<scalar_random_op<Scalar> >
/** \returns a random matrix expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* \not_reentrant
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
* instead.
*
*
* Example: \include MatrixBase_random_int_int.cpp
* Output: \verbinclude MatrixBase_random_int_int.out
@@ -41,22 +46,28 @@ struct functor_traits<scalar_random_op<Scalar> >
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index), MatrixBase::Random()
* \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random(Index rows, Index cols)
{
return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
}
/** \returns a random vector expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* The parameter \a size is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
* \not_reentrant
*
* This variant is meant to be used for dynamic-size vector types. For fixed-size types,
* it is redundant to pass \a size as argument, so Random() should be used
@@ -69,10 +80,10 @@ DenseBase<Derived>::Random(Index rows, Index cols)
* a temporary vector whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random()
* \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random(Index size)
{
return NullaryExpr(size, internal::scalar_random_op<Scalar>());
@@ -80,6 +91,9 @@ DenseBase<Derived>::Random(Index size)
/** \returns a fixed-size random matrix or vector expression
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
* need to use the variants taking size arguments.
*
@@ -89,11 +103,13 @@ DenseBase<Derived>::Random(Index size)
* This expression has the "evaluate before nesting" flag so that it will be evaluated into
* a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
* behavior with expressions involving random matrices.
*
* \not_reentrant
*
* \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random(Index)
* \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
*/
template<typename Derived>
inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived>
inline const typename DenseBase<Derived>::RandomReturnType
DenseBase<Derived>::Random()
{
return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
@@ -101,6 +117,11 @@ DenseBase<Derived>::Random()
/** Sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \not_reentrant
*
* Example: \include MatrixBase_setRandom.cpp
* Output: \verbinclude MatrixBase_setRandom.out
*
@@ -114,12 +135,16 @@ inline Derived& DenseBase<Derived>::setRandom()
/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
*
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \only_for_vectors
* \not_reentrant
*
* Example: \include Matrix_setRandom_int.cpp
* Output: \verbinclude Matrix_setRandom_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
@@ -131,19 +156,24 @@ PlainObjectBase<Derived>::setRandom(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* Numbers are uniformly spread through their whole definition range for integer types,
* and in the [-1:1] range for floating point scalar types.
*
* \not_reentrant
*
* \param rows the new number of rows
* \param cols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
*
* \sa MatrixBase::setRandom(), setRandom(Index), class CwiseNullaryOp, MatrixBase::Random()
* \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index nbRows, Index nbCols)
PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
{
resize(nbRows, nbCols);
resize(rows, cols);
return setRandom();
}

210
extern/Eigen3/Eigen/src/Core/Redux.h vendored
View File

@@ -27,8 +27,9 @@ template<typename Func, typename Derived>
struct redux_traits
{
public:
typedef typename find_best_packet<typename Derived::Scalar,Derived::SizeAtCompileTime>::type PacketType;
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = unpacket_traits<PacketType>::size,
InnerMaxSize = int(Derived::IsRowMajor)
? Derived::MaxColsAtCompileTime
: Derived::MaxRowsAtCompileTime
@@ -37,8 +38,8 @@ public:
enum {
MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit)
&& (functor_traits<Func>::PacketAccess),
MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize
MayLinearVectorize = bool(MightVectorize) && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = bool(MightVectorize) && int(InnerMaxSize)>=3*PacketSize
};
public:
@@ -50,21 +51,34 @@ public:
public:
enum {
Cost = ( Derived::SizeAtCompileTime == Dynamic
|| Derived::CoeffReadCost == Dynamic
|| (Derived::SizeAtCompileTime!=1 && functor_traits<Func>::Cost == Dynamic)
) ? Dynamic
: Derived::SizeAtCompileTime * Derived::CoeffReadCost
+ (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
Cost = Derived::SizeAtCompileTime == Dynamic ? HugeCost
: Derived::SizeAtCompileTime * Derived::CoeffReadCost + (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
};
public:
enum {
Unrolling = Cost != Dynamic && Cost <= UnrollingLimit
? CompleteUnrolling
: NoUnrolling
Unrolling = Cost <= UnrollingLimit ? 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
};
/***************************************************************************
@@ -82,6 +96,7 @@ struct redux_novec_unroller
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
{
return func(redux_novec_unroller<Func, Derived, Start, HalfLength>::run(mat,func),
@@ -99,6 +114,7 @@ struct redux_novec_unroller<Func, Derived, Start, 1>
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func&)
{
return mat.coeffByOuterInner(outer, inner);
@@ -112,6 +128,7 @@ template<typename Func, typename Derived, int Start>
struct redux_novec_unroller<Func, Derived, Start, 0>
{
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Derived&, const Func&) { return Scalar(); }
};
@@ -121,12 +138,12 @@ template<typename Func, typename Derived, int Start, int Length>
struct redux_vec_unroller
{
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func)
{
@@ -140,18 +157,18 @@ template<typename Func, typename Derived, int Start>
struct redux_vec_unroller<Func, Derived, Start, 1>
{
enum {
index = Start * packet_traits<typename Derived::Scalar>::size,
index = Start * redux_traits<Func, Derived>::PacketSize,
outer = index / int(Derived::InnerSizeAtCompileTime),
inner = index % int(Derived::InnerSizeAtCompileTime),
alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned
alignment = Derived::Alignment
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&)
{
return mat.template packetByOuterInner<alignment>(outer, inner);
return mat.template packetByOuterInner<alignment,PacketScalar>(outer, inner);
}
};
@@ -169,8 +186,8 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
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");
Scalar res;
@@ -193,19 +210,19 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
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);
const Index packetSize = redux_traits<Func, Derived>::PacketSize;
const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
enum {
alignment = bool(Derived::Flags & DirectAccessBit) || bool(Derived::Flags & AlignedBit)
? Aligned : Unaligned
alignment0 = (bool(Derived::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Derived::Alignment)
};
const Index alignedStart = internal::first_default_aligned(mat.nestedExpression());
const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
const Index alignedEnd2 = alignedStart + alignedSize2;
@@ -213,19 +230,19 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
Scalar res;
if(alignedSize)
{
PacketScalar packet_res0 = mat.template packet<alignment>(alignedStart);
PacketScalar packet_res0 = mat.template packet<alignment,PacketScalar>(alignedStart);
if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop
{
PacketScalar packet_res1 = mat.template packet<alignment>(alignedStart+packetSize);
PacketScalar packet_res1 = mat.template packet<alignment,PacketScalar>(alignedStart+packetSize);
for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize)
{
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(index));
packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment>(index+packetSize));
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(index));
packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment,PacketScalar>(index+packetSize));
}
packet_res0 = func.packetOp(packet_res0,packet_res1);
if(alignedEnd>alignedEnd2)
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(alignedEnd2));
packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(alignedEnd2));
}
res = func.predux(packet_res0);
@@ -252,25 +269,24 @@ template<typename Func, typename Derived, int Unrolling>
struct redux_impl<Func, Derived, SliceVectorizedTraversal, Unrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
typedef typename redux_traits<Func, Derived>::PacketType PacketType;
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();
const Index outerSize = mat.outerSize();
enum {
packetSize = packet_traits<Scalar>::size
packetSize = redux_traits<Func, Derived>::PacketSize
};
const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
Scalar res;
if(packetedInnerSize)
{
PacketScalar packet_res = mat.template packet<Unaligned>(0,0);
PacketType packet_res = mat.template packet<Unaligned,PacketType>(0,0);
for(Index j=0; j<outerSize; ++j)
for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned>(j,i));
packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned,PacketType>(j,i));
res = func.predux(packet_res);
for(Index j=0; j<outerSize; ++j)
@@ -291,22 +307,90 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
enum {
PacketSize = packet_traits<Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
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");
Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
if (VectorizedSize != Size)
res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
return res;
if (VectorizedSize > 0) {
Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
if (VectorizedSize != Size)
res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
return res;
}
else {
return redux_novec_unroller<Func, Derived, 0, Size>::run(mat,func);
}
}
};
// 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::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,
Alignment = evaluator<XprType>::Alignment
};
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, typename PacketType>
PacketType packet(Index row, Index col) const
{ return m_evaluator.template packet<LoadMode,PacketType>(row, col); }
template<int LoadMode, typename PacketType>
PacketType packet(Index index) const
{ return m_evaluator.template packet<LoadMode,PacketType>(index); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{ return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
template<int LoadMode, typename PacketType>
PacketType packetByOuterInner(Index outer, Index inner) const
{ return m_evaluator.template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
const XprType & nestedExpression() const { return m_xpr; }
protected:
internal::evaluator<XprType> m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/***************************************************************************
@@ -317,18 +401,21 @@ 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()
*/
template<typename Derived>
template<typename Func>
EIGEN_STRONG_INLINE typename internal::result_of<Func(typename internal::traits<Derived>::Scalar)>::type
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
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");
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.
@@ -338,7 +425,7 @@ 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,Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@@ -348,10 +435,12 @@ 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,Scalar>());
}
/** \returns the sum of all coefficients of *this
/** \returns the sum of all coefficients of \c *this
*
* If \c *this is empty, then the value 0 is returned.
*
* \sa trace(), prod(), mean()
*/
@@ -361,7 +450,7 @@ 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,Scalar>());
}
/** \returns the mean of all coefficients of *this
@@ -372,7 +461,14 @@ 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());
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
}
/** \returns the product of all coefficients of *this
@@ -388,7 +484,7 @@ 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.

201
extern/Eigen3/Eigen/src/Core/Ref.h vendored
View File

@@ -12,79 +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
*
* \brief A matrix or vector expression mapping an existing expressions
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam Options specifies whether the pointer is \c #Aligned, or \c #Unaligned.
* The default is \c #Unaligned.
* \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
* but accept a variable outer stride (leading dimension).
* This can be overridden by specifying strides.
* The type passed here must be a specialization of the Stride template, see examples below.
*
* This class permits to write non template functions taking Eigen's object as parameters while limiting the number of copies.
* A Ref<> object can represent either a const expression or a l-value:
* \code
* // in-out argument:
* void foo1(Ref<VectorXf> x);
*
* // read-only const argument:
* void foo2(const Ref<const VectorXf>& x);
* \endcode
*
* In the in-out case, the input argument must satisfies the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
* By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
* Likewise, a Ref<MatrixXf> can reference any column major dense matrix expression of float whose column's elements are contiguously stored with
* the possibility to have a constant space inbetween each column, i.e.: the inner stride mmust be equal to 1, but the outer-stride (or leading dimension),
* can be greater than the number of rows.
*
* In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
* Here are some examples:
* \code
* MatrixXf A;
* VectorXf a;
* foo1(a.head()); // OK
* foo1(A.col()); // OK
* foo1(A.row()); // compilation error because here innerstride!=1
* foo2(A.row()); // The row is copied into a contiguous temporary
* foo2(2*a); // The expression is evaluated into a temporary
* foo2(A.col().segment(2,4)); // No temporary
* \endcode
*
* The range of inputs that can be referenced without temporary can be enlarged using the last two template parameter.
* Here is an example accepting an innerstride!=1:
* \code
* // in-out argument:
* void foo3(Ref<VectorXf,0,InnerStride<> > x);
* foo3(A.row()); // OK
* \endcode
* The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involved more
* expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overloads internally calling a
* template function, e.g.:
* \code
* // in the .h:
* void foo(const Ref<MatrixXf>& A);
* void foo(const Ref<MatrixXf,0,Stride<> >& A);
*
* // in the .cpp:
* template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
* ... // crazy code goes here
* }
* void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
* void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
* \endcode
*
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
namespace internal {
template<typename _PlainObjectType, int _Options, typename _StrideType>
@@ -95,7 +22,8 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
typedef _StrideType StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit,
Alignment = traits<Map<_PlainObjectType, _Options, _StrideType> >::Alignment
};
template<typename Derived> struct match {
@@ -107,7 +35,13 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
|| (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
OuterStrideMatch = Derived::IsVectorAtCompileTime
|| int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
AlignmentMatch = (_Options!=Aligned) || ((PlainObjectType::Flags&AlignedBit)==0) || ((traits<Derived>::Flags&AlignedBit)==AlignedBit),
// NOTE, this indirection of evaluator<Derived>::Alignment is needed
// to workaround a very strange bug in MSVC related to the instantiation
// of has_*ary_operator in evaluator<CwiseNullaryOp>.
// This line is surprisingly very sensitive. For instance, simply adding parenthesis
// as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
DerivedAlignment = int(evaluator<Derived>::Alignment),
AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
};
@@ -132,12 +66,12 @@ 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()
@@ -145,7 +79,7 @@ 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,
@@ -159,8 +93,10 @@ protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
void construct(Expression& expr)
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(PlainObjectType,Expression);
if(PlainObjectType::RowsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);
@@ -184,15 +120,83 @@ protected:
StrideBase m_stride;
};
/** \class Ref
* \ingroup Core_Module
*
* \brief A matrix or vector expression mapping an existing expression
*
* \tparam PlainObjectType the equivalent matrix type of the mapped data
* \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
* The default is \c #Unaligned.
* \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
* but accepts a variable outer stride (leading dimension).
* This can be overridden by specifying strides.
* The type passed here must be a specialization of the Stride template, see examples below.
*
* This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies.
* A Ref<> object can represent either a const expression or a l-value:
* \code
* // in-out argument:
* void foo1(Ref<VectorXf> x);
*
* // read-only const argument:
* void foo2(const Ref<const VectorXf>& x);
* \endcode
*
* In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
* By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
* Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with
* the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension)
* can be greater than the number of rows.
*
* In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
* Here are some examples:
* \code
* MatrixXf A;
* VectorXf a;
* foo1(a.head()); // OK
* foo1(A.col()); // OK
* foo1(A.row()); // Compilation error because here innerstride!=1
* foo2(A.row()); // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
* foo2(A.row().transpose()); // The row is copied into a contiguous temporary
* foo2(2*a); // The expression is evaluated into a temporary
* foo2(A.col().segment(2,4)); // No temporary
* \endcode
*
* The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
* Here is an example accepting an innerstride!=1:
* \code
* // in-out argument:
* void foo3(Ref<VectorXf,0,InnerStride<> > x);
* foo3(A.row()); // OK
* \endcode
* The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more
* expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a
* template function, e.g.:
* \code
* // in the .h:
* void foo(const Ref<MatrixXf>& A);
* void foo(const Ref<MatrixXf,0,Stride<> >& A);
*
* // in the .cpp:
* template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
* ... // crazy code goes here
* }
* void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
* void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
* \endcode
*
*
* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
*/
template<typename PlainObjectType, int Options, typename StrideType> class Ref
: public RefBase<Ref<PlainObjectType, Options, StrideType> >
{
private:
typedef internal::traits<Ref> Traits;
template<typename Derived>
inline Ref(const PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0);
public:
typedef RefBase<Ref> Base;
@@ -201,23 +205,24 @@ 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,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
{
EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
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(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
#else
/** Implicit constructor from any dense expression */
template<typename Derived>
inline Ref(DenseBase<Derived>& expr)
#endif
{
EIGEN_STATIC_ASSERT(static_cast<bool>(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
enum { THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY = Derived::ThisConstantIsPrivateInPlainObjectBase};
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());
}
@@ -236,36 +241,36 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>::type* = 0)
{
// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
// std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
construct(expr.derived(), typename Traits::template match<Derived>::type());
}
inline Ref(const Ref& other) : Base(other) {
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>
inline Ref(const RefBase<OtherRef>& other) {
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,Scalar>());
Base::construct(m_object);
}

95
extern/Eigen3/Eigen/src/Core/Replicate.h vendored
View File

@@ -12,21 +12,6 @@
namespace Eigen {
/**
* \class Replicate
* \ingroup Core_Module
*
* \brief Expression of the multiple replication of a matrix or vector
*
* \param MatrixType the type of the object we are replicating
*
* This class represents an expression of the multiple replication of a matrix or vector.
* It is the return type of DenseBase::replicate() and most of the time
* this is the only way it is used.
*
* \sa DenseBase::replicate()
*/
namespace internal {
template<typename MatrixType,int RowFactor,int ColFactor>
struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
@@ -35,10 +20,7 @@ struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
typedef typename MatrixType::Scalar Scalar;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
enum {
Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
};
typedef typename nested<MatrixType,Factor>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
@@ -53,12 +35,29 @@ 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
};
};
}
/**
* \class Replicate
* \ingroup Core_Module
*
* \brief Expression of the multiple replication of a matrix or vector
*
* \tparam MatrixType the type of the object we are replicating
* \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
* \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
*
* This class represents an expression of the multiple replication of a matrix or vector.
* It is the return type of DenseBase::replicate() and most of the time
* this is the only way it is used.
*
* \sa DenseBase::replicate()
*/
template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
: public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
{
@@ -68,10 +67,12 @@ 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)
: m_matrix(a_matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
@@ -79,41 +80,20 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
}
template<typename OriginalMatrixType>
inline Replicate(const OriginalMatrixType& a_matrix, Index rowFactor, Index colFactor)
: m_matrix(a_matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
EIGEN_DEVICE_FUNC
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value),
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
inline Scalar coeff(Index rowId, Index colId) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const
{
return m_matrix;
@@ -141,21 +121,6 @@ DenseBase<Derived>::replicate() const
return Replicate<Derived,RowFactor,ColFactor>(derived());
}
/**
* \return an expression of the replication of \c *this
*
* Example: \include MatrixBase_replicate_int_int.cpp
* Output: \verbinclude MatrixBase_replicate_int_int.out
*
* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
*/
template<typename Derived>
const typename DenseBase<Derived>::ReplicateReturnType
DenseBase<Derived>::replicate(Index rowFactor,Index colFactor) const
{
return Replicate<Derived,Dynamic,Dynamic>(derived(),rowFactor,colFactor);
}
/**
* \return an expression of the replication of each column (or row) of \c *this
*

54
extern/Eigen3/Eigen/src/Core/ReturnByValue.h vendored
View File

@@ -13,11 +13,6 @@
namespace Eigen {
/** \class ReturnByValue
* \ingroup Core_Module
*
*/
namespace internal {
template<typename Derived>
@@ -38,17 +33,22 @@ 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;
};
} // end namespace internal
/** \class ReturnByValue
* \ingroup Core_Module
*
*/
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;
@@ -57,10 +57,11 @@ template<typename Derived> class ReturnByValue
EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
template<typename Dest>
EIGEN_DEVICE_FUNC
inline void evalTo(Dest& dst) const
{ static_cast<const Derived*>(this)->evalTo(dst); }
inline Index rows() const { return static_cast<const Derived*>(this)->rows(); }
inline Index cols() const { return static_cast<const Derived*>(this)->cols(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return static_cast<const Derived*>(this)->rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return static_cast<const Derived*>(this)->cols(); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
@@ -72,8 +73,7 @@ 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); }
template<int LoadMode> Unusable& packet(Index) const;
template<int LoadMode> Unusable& packet(Index, Index) const;
#undef Unusable
#endif
};
@@ -85,14 +85,32 @@ Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
return derived();
}
template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::lazyAssign(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
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>
{
typedef ReturnByValue<Derived> XprType;
typedef typename internal::traits<Derived>::ReturnType PlainObject;
typedef evaluator<PlainObject> Base;
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

209
extern/Eigen3/Eigen/src/Core/Reverse.h vendored
View File

@@ -14,20 +14,6 @@
namespace Eigen {
/** \class Reverse
* \ingroup Core_Module
*
* \brief Expression of the reverse of a vector or matrix
*
* \param MatrixType the type of the object of which we are taking the reverse
*
* This class represents an expression of the reverse of a vector.
* It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::reverse(), VectorwiseOp::reverse()
*/
namespace internal {
template<typename MatrixType, int Direction>
@@ -37,36 +23,43 @@ struct traits<Reverse<MatrixType, Direction> >
typedef typename MatrixType::Scalar Scalar;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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)
};
};
template<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond
template<typename PacketType, bool ReversePacket> struct reverse_packet_cond
{
static inline PacketScalar run(const PacketScalar& x) { return preverse(x); }
static inline PacketType run(const PacketType& x) { return preverse(x); }
};
template<typename PacketScalar> struct reverse_packet_cond<PacketScalar,false>
template<typename PacketType> struct reverse_packet_cond<PacketType,false>
{
static inline PacketScalar run(const PacketScalar& x) { return x; }
static inline PacketType run(const PacketType& x) { return x; }
};
} // end namespace internal
/** \class Reverse
* \ingroup Core_Module
*
* \brief Expression of the reverse of a vector or matrix
*
* \tparam MatrixType the type of the object of which we are taking the reverse
* \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections
*
* This class represents an expression of the reverse of a vector.
* It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::reverse(), VectorwiseOp::reverse()
*/
template<typename MatrixType, int Direction> class Reverse
: public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
{
@@ -74,12 +67,9 @@ 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()
// is hidden by non-const version defined in this file
using Base::operator();
protected:
enum {
PacketSize = internal::packet_traits<Scalar>::size,
@@ -95,82 +85,19 @@ 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_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
return coeffRef(row, col);
}
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
{
return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row,
ReverseCol ? m_matrix.cols() - col - 1 : col);
}
inline CoeffReturnType coeff(Index index) const
{
return m_matrix.coeff(m_matrix.size() - index - 1);
}
inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1);
}
inline Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < m_matrix.size());
return coeffRef(index);
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
{
return reverse_packet::run(m_matrix.template packet<LoadMode>(
ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
ReverseCol ? m_matrix.cols() - col - OffsetCol : col));
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
{
m_matrix.const_cast_derived().template writePacket<LoadMode>(
ReverseRow ? m_matrix.rows() - row - OffsetRow : row,
ReverseCol ? m_matrix.cols() - col - OffsetCol : col,
reverse_packet::run(x));
}
template<int LoadMode>
inline const PacketScalar packet(Index index) const
{
return internal::preverse(m_matrix.template packet<LoadMode>( m_matrix.size() - index - PacketSize ));
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
{
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;
@@ -190,33 +117,93 @@ template<typename Derived>
inline typename DenseBase<Derived>::ReverseReturnType
DenseBase<Derived>::reverse()
{
return derived();
return ReverseReturnType(derived());
}
/** This is the const version of reverse(). */
template<typename Derived>
inline const typename DenseBase<Derived>::ConstReverseReturnType
DenseBase<Derived>::reverse() const
{
return derived();
}
//reverse const overload moved DenseBase.h due to a CUDA compiler bug
/** This is the "in place" version of reverse: it reverses \c *this.
*
* In most cases it is probably better to simply use the reversed expression
* of a matrix. However, when reversing the matrix data itself is really needed,
* then this "in-place" version is probably the right choice because it provides
* the following additional features:
* the following additional benefits:
* - less error prone: doing the same operation with .reverse() requires special care:
* \code m = m.reverse().eval(); \endcode
* - this API allows to avoid creating a temporary (the current implementation creates a temporary, but that could be avoided using swap)
* - this API enables reverse operations without the need for a temporary
* - it allows future optimizations (cache friendliness, etc.)
*
* \sa reverse() */
* \sa VectorwiseOp::reverseInPlace(), reverse() */
template<typename Derived>
inline void DenseBase<Derived>::reverseInPlace()
{
derived() = derived().reverse().eval();
if(cols()>rows())
{
Index half = cols()/2;
leftCols(half).swap(rightCols(half).reverse());
if((cols()%2)==1)
{
Index half2 = rows()/2;
col(half).head(half2).swap(col(half).tail(half2).reverse());
}
}
else
{
Index half = rows()/2;
topRows(half).swap(bottomRows(half).reverse());
if((rows()%2)==1)
{
Index half2 = cols()/2;
row(half).head(half2).swap(row(half).tail(half2).reverse());
}
}
}
namespace internal {
template<int Direction>
struct vectorwise_reverse_inplace_impl;
template<>
struct vectorwise_reverse_inplace_impl<Vertical>
{
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
Index half = xpr.rows()/2;
xpr.topRows(half).swap(xpr.bottomRows(half).colwise().reverse());
}
};
template<>
struct vectorwise_reverse_inplace_impl<Horizontal>
{
template<typename ExpressionType>
static void run(ExpressionType &xpr)
{
Index half = xpr.cols()/2;
xpr.leftCols(half).swap(xpr.rightCols(half).rowwise().reverse());
}
};
} // end namespace internal
/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
*
* In most cases it is probably better to simply use the reversed expression
* of a matrix. However, when reversing the matrix data itself is really needed,
* then this "in-place" version is probably the right choice because it provides
* the following additional benefits:
* - less error prone: doing the same operation with .reverse() requires special care:
* \code m = m.reverse().eval(); \endcode
* - this API enables reverse operations without the need for a temporary
*
* \sa DenseBase::reverseInPlace(), reverse() */
template<typename ExpressionType, int Direction>
void VectorwiseOp<ExpressionType,Direction>::reverseInPlace()
{
internal::vectorwise_reverse_inplace_impl<Direction>::run(_expression().const_cast_derived());
}
} // end namespace Eigen

22
extern/Eigen3/Eigen/src/Core/Select.h vendored
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;
}

300
extern/Eigen3/Eigen/src/Core/SelfAdjointView.h vendored
View File

@@ -32,54 +32,62 @@ namespace internal {
template<typename MatrixType, unsigned int UpLo>
struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
{
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::non_const_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;
typedef MatrixTypeNestedCleaned NestedExpression;
/** \brief The type of coefficients in this matrix */
typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType;
enum {
Mode = internal::traits<SelfAdjointView>::Mode
Mode = internal::traits<SelfAdjointView>::Mode,
Flags = internal::traits<SelfAdjointView>::Flags,
TransposeMode = ((Mode & Upper) ? Lower : 0) | ((Mode & Lower) ? Upper : 0)
};
typedef typename MatrixType::PlainObject PlainObject;
inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{}
EIGEN_DEVICE_FUNC
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{
EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY);
}
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(); }
/** \sa MatrixBase::coeff()
* \warning the coordinates must fit into the referenced triangular part
*/
EIGEN_DEVICE_FUNC
inline Scalar coeff(Index row, Index col) const
{
Base::check_coordinates_internal(row, col);
@@ -89,36 +97,46 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
/** \sa MatrixBase::coeffRef()
* \warning the coordinates must fit into the referenced triangular part
*/
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);
return m_matrix.coeffRef(row, col);
}
/** \internal */
EIGEN_DEVICE_FUNC
const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
EIGEN_DEVICE_FUNC
const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); }
EIGEN_DEVICE_FUNC
MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
/** Efficient self-adjoint matrix times vector/matrix product */
/** Efficient triangular matrix times vector/matrix product */
template<typename OtherDerived>
SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime>
EIGEN_DEVICE_FUNC
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
SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false>
EIGEN_DEVICE_FUNC
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 EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),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:
@@ -132,6 +150,7 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
*/
template<typename DerivedU, typename DerivedV>
EIGEN_DEVICE_FUNC
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1));
/** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
@@ -145,8 +164,74 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
*/
template<typename DerivedU>
EIGEN_DEVICE_FUNC
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
/** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
*
* The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
* \c #Lower, \c #StrictlyLower, \c #UnitLower.
*
* If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView of the nested expression,
* otherwise, the nested expression is first transposed, thus returning a \c TriangularView<Transpose<MatrixType>> object.
*
* \sa MatrixBase::triangularView(), class TriangularView
*/
template<unsigned int TriMode>
EIGEN_DEVICE_FUNC
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type
triangularView() const
{
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType>::type tmp1(m_matrix);
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType>::type tmp2(tmp1);
return typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
}
typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
/** \sa MatrixBase::conjugate() const */
EIGEN_DEVICE_FUNC
inline const ConjugateReturnType conjugate() const
{ return ConjugateReturnType(m_matrix.conjugate()); }
typedef SelfAdjointView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
/** \sa MatrixBase::adjoint() const */
EIGEN_DEVICE_FUNC
inline const AdjointReturnType adjoint() const
{ return AdjointReturnType(m_matrix.adjoint()); }
typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
/** \sa MatrixBase::transpose() */
EIGEN_DEVICE_FUNC
inline TransposeReturnType transpose()
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
typename MatrixType::TransposeReturnType tmp(m_matrix);
return TransposeReturnType(tmp);
}
typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
/** \sa MatrixBase::transpose() const */
EIGEN_DEVICE_FUNC
inline const ConstTransposeReturnType transpose() const
{
return ConstTransposeReturnType(m_matrix.transpose());
}
/** \returns a const expression of the main diagonal of the matrix \c *this
*
* This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
*
* \sa MatrixBase::diagonal(), class Diagonal */
EIGEN_DEVICE_FUNC
typename MatrixType::ConstDiagonalReturnType diagonal() const
{
return typename MatrixType::ConstDiagonalReturnType(m_matrix);
}
/////////// Cholesky module ///////////
const LLT<PlainObject, UpLo> llt() const;
@@ -159,31 +244,10 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
/** Return type of eigenvalues() */
typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
EIGEN_DEVICE_FUNC
EigenvaluesReturnType eigenvalues() const;
EIGEN_DEVICE_FUNC
RealScalar operatorNorm() const;
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
SelfAdjointView& operator=(const MatrixBase<OtherDerived>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other;
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint();
return *this;
}
template<typename OtherMatrixType, unsigned int OtherMode>
SelfAdjointView& operator=(const TriangularView<OtherMatrixType, OtherMode>& other)
{
enum {
OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper
};
m_matrix.const_cast_derived().template triangularView<UpLo>() = other.toDenseMatrix();
m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint();
return *this;
}
#endif
protected:
MatrixTypeNested m_matrix;
@@ -201,90 +265,54 @@ 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 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::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>
{
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
};
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>
{
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;
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>
{
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
@@ -293,20 +321,30 @@ struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dyn
* Implementation of MatrixBase methods
***************************************************************************/
/** This is the const version of MatrixBase::selfadjointView() */
template<typename Derived>
template<unsigned int UpLo>
typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
MatrixBase<Derived>::selfadjointView() const
{
return derived();
return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
}
/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the current matrix
*
* The parameter \a UpLo can be either \c #Upper or \c #Lower
*
* Example: \include MatrixBase_selfadjointView.cpp
* Output: \verbinclude MatrixBase_selfadjointView.out
*
* \sa class SelfAdjointView
*/
template<typename Derived>
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

182
extern/Eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h vendored
View File

@@ -12,177 +12,33 @@
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&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;
inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {}
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(); }
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()
inline Scalar& coeffRef(Index row, Index col)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(row, col);
}
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()
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(Lhs)
return m_matrix.const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index index) const
{
return m_matrix.const_cast_derived().coeffRef(index);
}
template<typename OtherDerived>
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>
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_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...
SelfCwiseBinaryOp& operator=(const Rhs& _rhs)
{
typename internal::nested<Rhs>::type rhs(_rhs);
return Base::operator=(rhs);
}
Lhs& expression() const
{
return m_matrix;
}
const BinaryOp& functor() const
{
return m_functor;
}
protected:
Lhs& m_matrix;
const BinaryOp& m_functor;
private:
SelfCwiseBinaryOp& operator=(const SelfCwiseBinaryOp&);
};
// TODO generalize the scalar type of 'other'
template<typename Derived>
inline Derived& DenseBase<Derived>::operator*=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_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,Scalar>());
return derived();
}
template<typename Derived>
inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<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::add_assign_op<Scalar,Scalar>());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
return derived();
}
template<typename Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
return derived();
}

188
extern/Eigen3/Eigen/src/Core/Solve.h vendored Normal file
View File

@@ -0,0 +1,188 @@
// 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 make_proper_matrix_type<typename RhsType::Scalar,
Decomposition::ColsAtCompileTime,
RhsType::ColsAtCompileTime,
RhsType::PlainObject::Options,
Decomposition::MaxColsAtCompileTime,
RhsType::MaxColsAtCompileTime>::type 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 typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type StorageIndex;
typedef traits<PlainObject> BaseTraits;
enum {
Flags = BaseTraits::Flags & RowMajorBit,
CoeffReadCost = HugeCost
};
};
}
template<typename Decomposition, typename RhsType>
class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
{
public:
typedef typename internal::traits<Solve>::PlainObject PlainObject;
typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
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>
{
typedef Solve<Decomposition,RhsType> SolveType;
typedef typename SolveType::PlainObject PlainObject;
typedef evaluator<PlainObject> Base;
enum { Flags = Base::Flags | EvalBeforeNestingBit };
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,Scalar>, Dense2Dense>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
src.dec()._solve_impl(src.rhs(), dst);
}
};
// Specialization for "dst = dec.transpose().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
{
typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
}
};
// Specialization for "dst = dec.adjoint().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
internal::assign_op<Scalar,Scalar>, Dense2Dense>
{
typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
dst.resize(dstRows, dstCols);
src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
}
};
} // end namepsace internal
} // end namespace Eigen
#endif // EIGEN_SOLVE_H

79
extern/Eigen3/Eigen/src/Core/SolveTriangular.h vendored
View File

@@ -68,7 +68,7 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
if(!useRhsDirectly)
MappedRhs(actualRhs,rhs.size()) = rhs;
triangular_solve_vector<LhsScalar, RhsScalar, typename Lhs::Index, Side, Mode, LhsProductTraits::NeedToConjugate,
triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
@@ -82,7 +82,6 @@ template<typename Lhs, typename Rhs, int Side, int Mode>
struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
{
typedef typename Rhs::Scalar Scalar;
typedef typename Rhs::Index Index;
typedef blas_traits<Lhs> LhsProductTraits;
typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
@@ -96,7 +95,7 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType;
BlockingType blocking(rhs.rows(), rhs.cols(), size);
BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor>
@@ -108,32 +107,32 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
* meta-unrolling implementation
***************************************************************************/
template<typename Lhs, typename Rhs, int Mode, int Index, int Size,
bool Stop = Index==Size>
template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size,
bool Stop = LoopIndex==Size>
struct triangular_solver_unroller;
template<typename Lhs, typename Rhs, int Mode, int Index, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,false> {
template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,false> {
enum {
IsLower = ((Mode&Lower)==Lower),
I = IsLower ? Index : Size - Index - 1,
S = IsLower ? 0 : I+1
DiagIndex = IsLower ? LoopIndex : Size - LoopIndex - 1,
StartIndex = IsLower ? 0 : DiagIndex+1
};
static void run(const Lhs& lhs, Rhs& rhs)
{
if (Index>0)
rhs.coeffRef(I) -= lhs.row(I).template segment<Index>(S).transpose()
.cwiseProduct(rhs.template segment<Index>(S)).sum();
if (LoopIndex>0)
rhs.coeffRef(DiagIndex) -= lhs.row(DiagIndex).template segment<LoopIndex>(StartIndex).transpose()
.cwiseProduct(rhs.template segment<LoopIndex>(StartIndex)).sum();
if(!(Mode & UnitDiag))
rhs.coeffRef(I) /= lhs.coeff(I,I);
rhs.coeffRef(DiagIndex) /= lhs.coeff(DiagIndex,DiagIndex);
triangular_solver_unroller<Lhs,Rhs,Mode,Index+1,Size>::run(lhs,rhs);
triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex+1,Size>::run(lhs,rhs);
}
};
template<typename Lhs, typename Rhs, int Mode, int Index, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,true> {
template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,true> {
static void run(const Lhs&, Rhs&) {}
};
@@ -162,61 +161,38 @@ struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
* TriangularView methods
***************************************************************************/
/** "in-place" version of TriangularView::solve() where the result is written in \a other
*
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* See TriangularView:solve() for the details.
*/
#ifndef EIGEN_PARSED_BY_DOXYGEN
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)));
// If solving for a 0x0 matrix, nothing to do, simply return.
if (derived().cols() == 0)
return;
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit) && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
typedef typename internal::conditional<copy,
typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
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;
}
/** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
*
* This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
* \a Side==OnTheLeft (the default), or the right-inverse-multiply \a other * inverse(\c *this) if
* \a Side==OnTheRight.
*
* The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
* diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
* is an upper (resp. lower) triangular matrix.
*
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.out
*
* This function returns an expression of the inverse-multiply and can works in-place if it is assigned
* to the same matrix or vector \a other.
*
* For users coming from BLAS, this function (and more specifically solveInPlace()) offer
* all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
*
* \sa TriangularView::solveInPlace()
*/
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());
}
#endif
namespace internal {
@@ -232,7 +208,6 @@ template<int Side, typename TriangularType, typename Rhs> struct triangular_solv
{
typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
typedef ReturnByValue<triangular_solve_retval> Base;
typedef typename Base::Index Index;
triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)
: m_triangularMatrix(tri), m_rhs(rhs)
@@ -243,7 +218,7 @@ template<int Side, typename TriangularType, typename Rhs> struct triangular_solv
template<typename Dest> inline void evalTo(Dest& dst) const
{
if(!(is_same<RhsNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_rhs)))
if(!is_same_dense(dst,m_rhs))
dst = m_rhs;
m_triangularMatrix.template solveInPlace<Side>(dst);
}

130
extern/Eigen3/Eigen/src/Core/SolverBase.h vendored Normal file
View File

@@ -0,0 +1,130 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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_SOLVERBASE_H
#define EIGEN_SOLVERBASE_H
namespace Eigen {
namespace internal {
} // end namespace internal
/** \class SolverBase
* \brief A base class for matrix decomposition and solvers
*
* \tparam Derived the actual type of the decomposition/solver.
*
* Any matrix decomposition inheriting this base class provide the following API:
*
* \code
* MatrixType A, b, x;
* DecompositionType dec(A);
* x = dec.solve(b); // solve A * x = b
* x = dec.transpose().solve(b); // solve A^T * x = b
* x = dec.adjoint().solve(b); // solve A' * x = b
* \endcode
*
* \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation errors.
*
* \sa class PartialPivLU, class FullPivLU
*/
template<typename Derived>
class SolverBase : public EigenBase<Derived>
{
public:
typedef EigenBase<Derived> Base;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef Scalar CoeffReturnType;
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
internal::traits<Derived>::ColsAtCompileTime>::ret),
MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime,
internal::traits<Derived>::MaxColsAtCompileTime>::ret),
IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
|| internal::traits<Derived>::MaxColsAtCompileTime == 1
};
/** Default constructor */
SolverBase()
{}
~SolverBase()
{}
using Base::derived;
/** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
*/
template<typename Rhs>
inline const Solve<Derived, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
return Solve<Derived, Rhs>(derived(), b.derived());
}
/** \internal the return type of transpose() */
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
/** \returns an expression of the transposed of the factored matrix.
*
* A typical usage is to solve for the transposed problem A^T x = b:
* \code x = dec.transpose().solve(b); \endcode
*
* \sa adjoint(), solve()
*/
inline ConstTransposeReturnType transpose() const
{
return ConstTransposeReturnType(derived());
}
/** \internal the return type of adjoint() */
typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
ConstTransposeReturnType
>::type AdjointReturnType;
/** \returns an expression of the adjoint of the factored matrix
*
* A typical usage is to solve for the adjoint problem A' x = b:
* \code x = dec.adjoint().solve(b); \endcode
*
* For real scalar types, this function is equivalent to transpose().
*
* \sa transpose(), solve()
*/
inline AdjointReturnType adjoint() const
{
return AdjointReturnType(derived().transpose());
}
protected:
};
namespace internal {
template<typename Derived>
struct generic_xpr_base<Derived, MatrixXpr, SolverStorage>
{
typedef SolverBase<Derived> type;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_SOLVERBASE_H

54
extern/Eigen3/Eigen/src/Core/StableNorm.h vendored
View File

@@ -17,10 +17,9 @@ 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)
if(maxCoeff>scale)
{
ssq = ssq * numext::abs2(scale/maxCoeff);
Scalar tmp = Scalar(1)/maxCoeff;
@@ -29,12 +28,21 @@ inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& sc
invScale = NumTraits<Scalar>::highest();
scale = Scalar(1)/invScale;
}
else if(maxCoeff>NumTraits<Scalar>::highest()) // we got a INF
{
invScale = Scalar(1);
scale = maxCoeff;
}
else
{
scale = maxCoeff;
invScale = tmp;
}
}
else if(maxCoeff!=maxCoeff) // we got a NaN
{
scale = maxCoeff;
}
// TODO if the maxCoeff is much much smaller than the current scale,
// then we can neglect this sub vector
@@ -47,15 +55,12 @@ inline typename NumTraits<typename traits<Derived>::Scalar>::Real
blueNorm_impl(const EigenBase<Derived>& _vec)
{
typedef typename Derived::RealScalar RealScalar;
typedef typename Derived::Index Index;
using std::pow;
using std::min;
using std::max;
using std::sqrt;
using std::abs;
const Derived& vec(_vec.derived());
static bool initialized = false;
static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr;
static RealScalar b1, b2, s1m, s2m, rbig, relerr;
if(!initialized)
{
int ibeta, it, iemin, iemax, iexp;
@@ -84,7 +89,6 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
iexp = - ((iemax+it)/2);
s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range
overfl = rbig*s2m; // overflow boundary for abig
eps = RealScalar(pow(double(ibeta), 1-it));
relerr = sqrt(eps); // tolerance for neglecting asml
initialized = true;
@@ -101,13 +105,13 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
else if(ax < b1) asml += numext::abs2(ax*s1m);
else amed += numext::abs2(ax);
}
if(amed!=amed)
return amed; // we got a NaN
if(abig > RealScalar(0))
{
abig = sqrt(abig);
if(abig > overfl)
{
return rbig;
}
if(abig > rbig) // overflow, or *this contains INF values
return abig; // return INF
if(amed > RealScalar(0))
{
abig = abig/s2m;
@@ -128,8 +132,8 @@ 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
@@ -152,21 +156,35 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
using std::min;
using std::sqrt;
using std::abs;
const Index blockSize = 4096;
RealScalar scale(0);
RealScalar invScale(1);
RealScalar ssq(0); // sum of square
typedef typename internal::nested_eval<Derived,2>::type DerivedCopy;
typedef typename internal::remove_all<DerivedCopy>::type DerivedCopyClean;
const DerivedCopy copy(derived());
enum {
Alignment = (int(Flags)&DirectAccessBit) || (int(Flags)&AlignedBit) ? 1 : 0
CanAlign = ( (int(DerivedCopyClean::Flags)&DirectAccessBit)
|| (int(internal::evaluator<DerivedCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
&& (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
};
typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<DerivedCopyClean>::Alignment>,
typename DerivedCopyClean::ConstSegmentReturnType>::type SegmentWrapper;
Index n = size();
Index bi = internal::first_aligned(derived());
if(n==1)
return abs(this->coeff(0));
Index bi = internal::first_default_aligned(copy);
if (bi>0)
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
internal::stable_norm_kernel(copy.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(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
return scale * sqrt(ssq);
}

25
extern/Eigen3/Eigen/src/Core/Stride.h vendored
View File

@@ -31,8 +31,8 @@ namespace Eigen {
* arguments to the constructor.
*
* Indeed, this class takes two template parameters:
* \param _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
* \param _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
* \tparam _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime.
* \tparam _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime.
*
* Here is an example:
* \include Map_general_stride.cpp
@@ -44,13 +44,14 @@ template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
class Stride
{
public:
typedef DenseIndex Index;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
enum {
InnerStrideAtCompileTime = _InnerStrideAtCompileTime,
OuterStrideAtCompileTime = _OuterStrideAtCompileTime
};
/** Default constructor, for use when strides are fixed at compile time */
EIGEN_DEVICE_FUNC
Stride()
: m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
{
@@ -58,6 +59,7 @@ class Stride
}
/** Constructor allowing to pass the strides at runtime */
EIGEN_DEVICE_FUNC
Stride(Index outerStride, Index innerStride)
: m_outer(outerStride), m_inner(innerStride)
{
@@ -65,13 +67,16 @@ class Stride
}
/** Copy constructor */
EIGEN_DEVICE_FUNC
Stride(const Stride& other)
: m_outer(other.outer()), m_inner(other.inner())
{}
/** \returns the outer stride */
EIGEN_DEVICE_FUNC
inline Index outer() const { return m_outer.value(); }
/** \returns the inner stride */
EIGEN_DEVICE_FUNC
inline Index inner() const { return m_inner.value(); }
protected:
@@ -81,26 +86,24 @@ 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;
InnerStride() : Base() {}
InnerStride(Index v) : Base(0, v) {}
EIGEN_DEVICE_FUNC InnerStride() : Base() {}
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;
OuterStride() : Base() {}
OuterStride(Index v) : Base(v,0) {}
EIGEN_DEVICE_FUNC OuterStride() : Base() {}
EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
};
} // end namespace Eigen

149
extern/Eigen3/Eigen/src/Core/Swap.h vendored
View File

@@ -12,115 +12,56 @@
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;
inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {}
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(); }
typedef typename internal::conditional<
internal::is_lvalue<ExpressionType>::value,
Scalar,
const Scalar
>::type ScalarWithConstIfNotLvalue;
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeffRef(rowId, colId);
}
inline Scalar& coeffRef(Index index) const
{
return m_expression.coeffRef(index);
}
template<typename OtherDerived>
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>
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);
}
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;
using Base::m_dst;
using Base::m_src;
using Base::m_functor;
public:
typedef typename Base::Scalar Scalar;
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, typename PacketType>
void assignPacket(Index row, Index col)
{
PacketType tmp = m_src.template packet<LoadMode,PacketType>(row,col);
const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(row,col, m_dst.template packet<StoreMode,PacketType>(row,col));
m_dst.template writePacket<StoreMode>(row,col,tmp);
}
template<int StoreMode, int LoadMode, typename PacketType>
void assignPacket(Index index)
{
PacketType tmp = m_src.template packet<LoadMode,PacketType>(index);
const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(index, m_dst.template packet<StoreMode,PacketType>(index));
m_dst.template writePacket<StoreMode>(index,tmp);
}
// 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, typename PacketType>
void assignPacketByOuterInner(Index outer, Index inner)
{
Index row = Base::rowIndexByOuterInner(outer, inner);
Index col = Base::colIndexByOuterInner(outer, inner);
assignPacket<StoreMode,LoadMode,PacketType>(row, col);
}
};
} // namespace internal
} // end namespace Eigen
#endif // EIGEN_SWAP_H

184
extern/Eigen3/Eigen/src/Core/Transpose.h vendored
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
@@ -13,39 +13,21 @@
namespace Eigen {
/** \class Transpose
* \ingroup Core_Module
*
* \brief Expression of the transpose of a matrix
*
* \param MatrixType the type of the object of which we are taking the transpose
*
* This class represents an expression of the transpose of a matrix.
* It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::transpose(), MatrixBase::adjoint()
*/
namespace internal {
template<typename MatrixType>
struct traits<Transpose<MatrixType> > : traits<MatrixType>
struct traits<Transpose<MatrixType> > : public traits<MatrixType>
{
typedef typename MatrixType::Scalar Scalar;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain;
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
enum {
RowsAtCompileTime = MatrixType::ColsAtCompileTime,
ColsAtCompileTime = MatrixType::RowsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
Flags0 = MatrixTypeNestedPlain::Flags & ~(LvalueBit | NestByRefBit),
Flags0 = traits<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
};
@@ -54,31 +36,55 @@ struct traits<Transpose<MatrixType> > : traits<MatrixType>
template<typename MatrixType, typename StorageKind> class TransposeImpl;
/** \class Transpose
* \ingroup Core_Module
*
* \brief Expression of the transpose of a matrix
*
* \tparam MatrixType the type of the object of which we are taking the transpose
*
* This class represents an expression of the transpose of a matrix.
* It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::transpose(), MatrixBase::adjoint()
*/
template<typename MatrixType> class Transpose
: public TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>
{
public:
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {}
EIGEN_DEVICE_FUNC
explicit inline Transpose(MatrixType& matrix) : m_matrix(matrix) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
inline Index rows() const { return m_matrix.cols(); }
inline Index cols() const { return m_matrix.rows(); }
EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.cols(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.rows(); }
/** \returns the nested expression */
const typename internal::remove_all<typename MatrixType::Nested>::type&
EIGEN_DEVICE_FUNC
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
EIGEN_DEVICE_FUNC
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix; }
/** \internal */
void resize(Index nrows, Index ncols) {
m_matrix.resize(ncols,nrows);
}
protected:
typename MatrixType::Nested m_matrix;
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
};
namespace internal {
@@ -97,17 +103,27 @@ 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)
inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
EIGEN_DEVICE_FUNC inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
typedef typename internal::conditional<
internal::is_lvalue<MatrixType>::value,
@@ -115,64 +131,21 @@ 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(); }
inline ScalarWithConstIfNotLvalue& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(colId, rowId);
}
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
{
return derived().nestedExpression().coeffRef(colId, rowId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return derived().nestedExpression().coeffRef(index);
}
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return derived().nestedExpression().coeff(colId, rowId);
}
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.
@@ -198,7 +171,7 @@ template<typename Derived>
inline Transpose<Derived>
DenseBase<Derived>::transpose()
{
return derived();
return TransposeReturnType(derived());
}
/** This is the const version of transpose().
@@ -236,8 +209,7 @@ 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());
}
/***************************************************************************
@@ -247,18 +219,38 @@ 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;
const Index PacketSize = internal::packet_traits<Scalar>::size;
const Index Alignment = internal::evaluator<MatrixType>::Alignment;
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());
@@ -331,14 +323,6 @@ inline void MatrixBase<Derived>::adjointInPlace()
namespace internal {
template<typename BinOp,typename NestedXpr,typename Rhs>
struct blas_traits<SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> >
: blas_traits<NestedXpr>
{
typedef SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> XprType;
static inline const XprType extract(const XprType& x) { return x; }
};
template<bool DestIsTransposed, typename OtherDerived>
struct check_transpose_aliasing_compile_time_selector
{
@@ -404,15 +388,15 @@ 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

245
extern/Eigen3/Eigen/src/Core/Transpositions.h vendored
View File

@@ -12,39 +12,6 @@
namespace Eigen {
/** \class Transpositions
* \ingroup Core_Module
*
* \brief Represents a sequence of transpositions (row/column interchange)
*
* \param SizeAtCompileTime the number of transpositions, or Dynamic
* \param MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
*
* This class represents a permutation transformation as a sequence of \em n transpositions
* \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
* Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
* the rows \c i and \c indices[i] of the matrix \c M.
* A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
*
* Compared to the class PermutationMatrix, such a sequence of transpositions is what is
* computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
*
* To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
* \code
* Transpositions tr;
* MatrixXf mat;
* mat = tr * mat;
* \endcode
* In this example, we detect that the matrix appears on both side, and so the transpositions
* are applied in-place without any temporary or extra copy.
*
* \sa class PermutationMatrix
*/
namespace internal {
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed=false> struct transposition_matrix_product_retval;
}
template<typename Derived>
class TranspositionsBase
{
@@ -53,7 +20,8 @@ class TranspositionsBase
public:
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
Derived& derived() { return *static_cast<Derived*>(this); }
const Derived& derived() const { return *static_cast<const Derived*>(this); }
@@ -65,7 +33,7 @@ class TranspositionsBase
indices() = other.indices();
return derived();
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is a special case of the templated operator=. Its purpose is to
* prevent a default operator= from hiding the templated operator=.
@@ -78,20 +46,24 @@ class TranspositionsBase
#endif
/** \returns the number of transpositions */
inline Index size() const { return indices().size(); }
Index size() const { return indices().size(); }
/** \returns the number of rows of the equivalent permutation matrix */
Index rows() const { return indices().size(); }
/** \returns the number of columns of the equivalent permutation matrix */
Index cols() const { return indices().size(); }
/** Direct access to the underlying index vector */
inline const Index& coeff(Index i) const { return indices().coeff(i); }
inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
/** Direct access to the underlying index vector */
inline Index& coeffRef(Index i) { return indices().coeffRef(i); }
inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
/** Direct access to the underlying index vector */
inline const Index& operator()(Index i) const { return indices()(i); }
inline const StorageIndex& operator()(Index i) const { return indices()(i); }
/** Direct access to the underlying index vector */
inline Index& operator()(Index i) { return indices()(i); }
inline StorageIndex& operator()(Index i) { return indices()(i); }
/** Direct access to the underlying index vector */
inline const Index& operator[](Index i) const { return indices()(i); }
inline const StorageIndex& operator[](Index i) const { return indices()(i); }
/** Direct access to the underlying index vector */
inline Index& operator[](Index i) { return indices()(i); }
inline StorageIndex& operator[](Index i) { return indices()(i); }
/** const version of indices(). */
const IndicesType& indices() const { return derived().indices(); }
@@ -99,7 +71,7 @@ class TranspositionsBase
IndicesType& indices() { return derived().indices(); }
/** Resizes to given size. */
inline void resize(int newSize)
inline void resize(Index newSize)
{
indices().resize(newSize);
}
@@ -107,7 +79,7 @@ class TranspositionsBase
/** Sets \c *this to represents an identity transformation */
void setIdentity()
{
for(int i = 0; i < indices().size(); ++i)
for(StorageIndex i = 0; i < indices().size(); ++i)
coeffRef(i) = i;
}
@@ -144,23 +116,53 @@ class TranspositionsBase
};
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef IndexType Index;
typedef Matrix<Index, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef Matrix<_StorageIndex, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
typedef TranspositionsStorage StorageKind;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType> >
/** \class Transpositions
* \ingroup Core_Module
*
* \brief Represents a sequence of transpositions (row/column interchange)
*
* \tparam SizeAtCompileTime the number of transpositions, or Dynamic
* \tparam MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
*
* This class represents a permutation transformation as a sequence of \em n transpositions
* \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
* Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
* the rows \c i and \c indices[i] of the matrix \c M.
* A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
*
* Compared to the class PermutationMatrix, such a sequence of transpositions is what is
* computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
*
* To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
* \code
* Transpositions tr;
* MatrixXf mat;
* mat = tr * mat;
* \endcode
* In this example, we detect that the matrix appears on both side, and so the transpositions
* are applied in-place without any temporary or extra copy.
*
* \sa class PermutationMatrix
*/
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex>
class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef internal::traits<Transpositions> Traits;
public:
typedef TranspositionsBase<Transpositions> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
inline Transpositions() {}
@@ -177,7 +179,7 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
/** Generic constructor from expression of the transposition indices. */
template<typename Other>
explicit inline Transpositions(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
{}
/** Copies the \a other transpositions into \c *this */
@@ -215,30 +217,32 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
namespace internal {
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,_PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int _PacketAccess>
struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,_PacketAccess> >
: traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex> >
{
typedef IndexType Index;
typedef Map<const Matrix<Index,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
typedef Map<const Matrix<_StorageIndex,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType;
typedef _StorageIndex StorageIndex;
typedef TranspositionsStorage StorageKind;
};
}
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,PacketAccess> >
template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename _StorageIndex, int PacketAccess>
class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess>
: public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,_StorageIndex>,PacketAccess> >
{
typedef internal::traits<Map> Traits;
public:
typedef TranspositionsBase<Map> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
inline Map(const Index* indicesPtr)
explicit inline Map(const StorageIndex* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indicesPtr, Index size)
inline Map(const StorageIndex* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
@@ -274,9 +278,9 @@ class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,Packe
namespace internal {
template<typename _IndicesType>
struct traits<TranspositionsWrapper<_IndicesType> >
: traits<PermutationWrapper<_IndicesType> >
{
typedef typename _IndicesType::Scalar Index;
typedef _IndicesType IndicesType;
typedef TranspositionsStorage StorageKind;
};
}
@@ -289,10 +293,10 @@ class TranspositionsWrapper
typedef TranspositionsBase<TranspositionsWrapper> Base;
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
typedef typename IndicesType::Scalar StorageIndex;
inline TranspositionsWrapper(IndicesType& a_indices)
: m_indices(a_indices)
explicit inline TranspositionsWrapper(IndicesType& indices)
: m_indices(indices)
{}
/** Copies the \a other transpositions into \c *this */
@@ -321,83 +325,46 @@ class TranspositionsWrapper
protected:
const typename IndicesType::Nested m_indices;
typename IndicesType::Nested m_indices;
};
/** \returns the \a matrix with the \a transpositions applied to the columns.
*/
template<typename Derived, typename TranspositionsDerived>
inline const internal::transposition_matrix_product_retval<TranspositionsDerived, Derived, OnTheRight>
operator*(const MatrixBase<Derived>& matrix,
const TranspositionsBase<TranspositionsDerived> &transpositions)
template<typename MatrixDerived, typename TranspositionsDerived>
EIGEN_DEVICE_FUNC
const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
operator*(const MatrixBase<MatrixDerived> &matrix,
const TranspositionsBase<TranspositionsDerived>& transpositions)
{
return internal::transposition_matrix_product_retval
<TranspositionsDerived, Derived, OnTheRight>
(transpositions.derived(), matrix.derived());
return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
(matrix.derived(), transpositions.derived());
}
/** \returns the \a matrix with the \a transpositions applied to the rows.
*/
template<typename Derived, typename TranspositionDerived>
inline const internal::transposition_matrix_product_retval
<TranspositionDerived, Derived, OnTheLeft>
operator*(const TranspositionsBase<TranspositionDerived> &transpositions,
const MatrixBase<Derived>& matrix)
template<typename TranspositionsDerived, typename MatrixDerived>
EIGEN_DEVICE_FUNC
const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
operator*(const TranspositionsBase<TranspositionsDerived> &transpositions,
const MatrixBase<MatrixDerived>& matrix)
{
return internal::transposition_matrix_product_retval
<TranspositionDerived, Derived, OnTheLeft>
(transpositions.derived(), matrix.derived());
return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
(transpositions.derived(), matrix.derived());
}
// Template partial specialization for transposed/inverse transpositions
namespace internal {
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed>
struct traits<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> >
{
typedef typename MatrixType::PlainObject ReturnType;
};
template<typename TranspositionType, typename MatrixType, int Side, bool Transposed>
struct transposition_matrix_product_retval
: public ReturnByValue<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
typedef typename TranspositionType::Index Index;
transposition_matrix_product_retval(const TranspositionType& tr, const MatrixType& matrix)
: m_transpositions(tr), m_matrix(matrix)
{}
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
const int size = m_transpositions.size();
Index j = 0;
if(!(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix)))
dst = m_matrix;
for(int k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
if((j=m_transpositions.coeff(k))!=k)
{
if(Side==OnTheLeft)
dst.row(k).swap(dst.row(j));
else if(Side==OnTheRight)
dst.col(k).swap(dst.col(j));
}
}
protected:
const TranspositionType& m_transpositions;
typename MatrixType::Nested m_matrix;
};
template<typename Derived>
struct traits<Transpose<TranspositionsBase<Derived> > >
: traits<Derived>
{};
} // end namespace internal
/* Template partial specialization for transposed/inverse transpositions */
template<typename TranspositionsDerived>
class Transpose<TranspositionsBase<TranspositionsDerived> >
{
@@ -405,27 +372,31 @@ 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(); }
Index size() const { return m_transpositions.size(); }
Index rows() const { return m_transpositions.size(); }
Index cols() const { return m_transpositions.size(); }
/** \returns the \a matrix with the inverse transpositions applied to the columns.
*/
template<typename Derived> friend
inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true>
operator*(const MatrixBase<Derived>& matrix, const Transpose& trt)
template<typename OtherDerived> friend
const Product<OtherDerived, Transpose, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
{
return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true>(trt.m_transpositions, matrix.derived());
return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
}
/** \returns the \a matrix with the inverse transpositions applied to the rows.
*/
template<typename Derived>
inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true>
operator*(const MatrixBase<Derived>& matrix) const
template<typename OtherDerived>
const Product<Transpose, OtherDerived, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix) const
{
return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true>(m_transpositions, matrix.derived());
return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
}
const TranspositionType& nestedExpression() const { return m_transpositions; }
protected:
const TranspositionType& m_transpositions;

1166
extern/Eigen3/Eigen/src/Core/TriangularMatrix.h vendored
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27
extern/Eigen3/Eigen/src/Core/VectorBlock.h vendored
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@@ -13,13 +13,23 @@
namespace Eigen {
namespace internal {
template<typename VectorType, int Size>
struct traits<VectorBlock<VectorType, Size> >
: public traits<Block<VectorType,
traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
traits<VectorType>::Flags & RowMajorBit ? Size : 1> >
{
};
}
/** \class VectorBlock
* \ingroup Core_Module
*
* \brief Expression of a fixed-size or dynamic-size sub-vector
*
* \param VectorType the type of the object in which we are taking a sub-vector
* \param Size size of the sub-vector we are taking at compile time (optional)
* \tparam VectorType the type of the object in which we are taking a sub-vector
* \tparam Size size of the sub-vector we are taking at compile time (optional)
*
* This class represents an expression of either a fixed-size or dynamic-size sub-vector.
* It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and
@@ -43,17 +53,6 @@ namespace Eigen {
*
* \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index)
*/
namespace internal {
template<typename VectorType, int Size>
struct traits<VectorBlock<VectorType, Size> >
: public traits<Block<VectorType,
traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
traits<VectorType>::Flags & RowMajorBit ? Size : 1> >
{
};
}
template<typename VectorType, int Size> class VectorBlock
: public Block<VectorType,
internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
@@ -72,6 +71,7 @@ template<typename VectorType, int Size> class VectorBlock
/** Dynamic-size constructor
*/
EIGEN_DEVICE_FUNC
inline VectorBlock(VectorType& vector, Index start, Index size)
: Base(vector,
IsColVector ? start : 0, IsColVector ? 0 : start,
@@ -82,6 +82,7 @@ template<typename VectorType, int Size> class VectorBlock
/** Fixed-size constructor
*/
EIGEN_DEVICE_FUNC
inline VectorBlock(VectorType& vector, Index start)
: Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start)
{

339
extern/Eigen3/Eigen/src/Core/VectorwiseOp.h vendored
View File

@@ -11,7 +11,7 @@
#ifndef EIGEN_PARTIAL_REDUX_H
#define EIGEN_PARTIAL_REDUX_H
namespace Eigen {
namespace Eigen {
/** \class PartialReduxExpr
* \ingroup Core_Module
@@ -41,64 +41,43 @@ struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
typedef typename traits<MatrixType>::StorageKind StorageKind;
typedef typename traits<MatrixType>::XprKind XprKind;
typedef typename MatrixType::Scalar InputScalar;
typedef typename nested<MatrixType>::type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
enum {
RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime,
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:
typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
typedef typename internal::traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested;
PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
EIGEN_DEVICE_FUNC
explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
: m_matrix(mat), m_functor(func) {}
EIGEN_DEVICE_FUNC
Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); }
EIGEN_DEVICE_FUNC
Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const
{
if (Direction==Vertical)
return m_functor(m_matrix.col(j));
else
return m_functor(m_matrix.row(i));
}
EIGEN_DEVICE_FUNC
typename MatrixType::Nested nestedExpression() const { return m_matrix; }
const Scalar coeff(Index index) const
{
if (Direction==Vertical)
return m_functor(m_matrix.col(index));
else
return m_functor(m_matrix.row(index));
}
EIGEN_DEVICE_FUNC
const MemberOp& functor() const { return m_functor; }
protected:
MatrixTypeNested m_matrix;
typename MatrixType::Nested m_matrix;
const MemberOp m_functor;
};
@@ -110,7 +89,8 @@ class PartialReduxExpr : internal::no_assignment_operator,
template<typename Scalar, int Size> struct Cost \
{ enum { value = COST }; }; \
template<typename XprType> \
EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
ResultType operator()(const XprType& mat) const \
{ return mat.MEMBER(); } \
}
@@ -130,17 +110,27 @@ EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost);
template <int p, typename ResultType>
struct member_lpnorm {
typedef ResultType result_type;
template<typename Scalar, int Size> struct Cost
{ enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
EIGEN_DEVICE_FUNC member_lpnorm() {}
template<typename XprType>
EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
{ return mat.template lpNorm<p>(); }
};
template <typename BinaryOp, typename Scalar>
struct member_redux {
typedef typename result_of<
BinaryOp(Scalar)
BinaryOp(const Scalar&,const Scalar&)
>::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) {}
EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
template<typename Derived>
inline result_type operator()(const DenseBase<Derived>& mat) const
EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
{ return mat.redux(m_functor); }
const BinaryOp m_functor;
};
@@ -151,8 +141,8 @@ struct member_redux {
*
* \brief Pseudo expression providing partial reduction operations
*
* \param ExpressionType the type of the object on which to do partial reductions
* \param Direction indicates the direction of the redux (#Vertical or #Horizontal)
* \tparam ExpressionType the type of the object on which to do partial reductions
* \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
*
* This class represents a pseudo expression with partial reduction features.
* It is the return type of DenseBase::colwise() and DenseBase::rowwise()
@@ -169,16 +159,15 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename ExpressionType::Scalar Scalar;
typedef typename ExpressionType::RealScalar RealScalar;
typedef typename ExpressionType::Index Index;
typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret,
ExpressionType, ExpressionType&>::type ExpressionTypeNested;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned;
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;
};
@@ -186,23 +175,24 @@ 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;
};
enum {
IsVertical = (Direction==Vertical) ? 1 : 0,
IsHorizontal = (Direction==Horizontal) ? 1 : 0
isVertical = (Direction==Vertical) ? 1 : 0,
isHorizontal = (Direction==Horizontal) ? 1 : 0
};
protected:
/** \internal
* \returns the i-th subvector according to the \c Direction */
typedef typename internal::conditional<Direction==Vertical,
typedef typename internal::conditional<isVertical,
typename ExpressionType::ColXpr,
typename ExpressionType::RowXpr>::type SubVector;
/** \internal
* \returns the i-th subvector according to the \c Direction */
EIGEN_DEVICE_FUNC
SubVector subVector(Index i)
{
return SubVector(m_matrix.derived(),i);
@@ -210,58 +200,62 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** \internal
* \returns the number of subvectors in the direction \c Direction */
EIGEN_DEVICE_FUNC
Index subVectors() const
{ return Direction==Vertical?m_matrix.cols():m_matrix.rows(); }
{ return isVertical?m_matrix.cols():m_matrix.rows(); }
template<typename OtherDerived> struct ExtendedType {
typedef Replicate<OtherDerived,
Direction==Vertical ? 1 : ExpressionType::RowsAtCompileTime,
Direction==Horizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
isVertical ? 1 : ExpressionType::RowsAtCompileTime,
isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename ExtendedType<OtherDerived>::Type
extendedTo(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename ExtendedType<OtherDerived>::Type
(other.derived(),
Direction==Vertical ? 1 : m_matrix.rows(),
Direction==Horizontal ? 1 : m_matrix.cols());
isVertical ? 1 : m_matrix.rows(),
isHorizontal ? 1 : m_matrix.cols());
}
template<typename OtherDerived> struct OppositeExtendedType {
typedef Replicate<OtherDerived,
Direction==Horizontal ? 1 : ExpressionType::RowsAtCompileTime,
Direction==Vertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
isVertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector in the opposite direction to match the size of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename OppositeExtendedType<OtherDerived>::Type
extendedToOpposite(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxColsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxRowsAtCompileTime==1),
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
return typename OppositeExtendedType<OtherDerived>::Type
(other.derived(),
Direction==Horizontal ? 1 : m_matrix.rows(),
Direction==Vertical ? 1 : m_matrix.cols());
isHorizontal ? 1 : m_matrix.rows(),
isVertical ? 1 : m_matrix.cols());
}
public:
inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
EIGEN_DEVICE_FUNC
explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
/** \internal */
EIGEN_DEVICE_FUNC
inline const ExpressionType& _expression() const { return m_matrix; }
/** \returns a row or column vector expression of \c *this reduxed by \a func
@@ -272,80 +266,126 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
*/
template<typename BinaryOp>
EIGEN_DEVICE_FUNC
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<const ExpressionType, Direction> ConstReverseReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType;
template<int p> struct LpNormReturnType {
typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar>,Direction> Type;
};
/** \returns a row (or column) vector expression of the smallest coefficient
* of each column (or row) of the referenced expression.
*
*
* \warning the result is undefined if \c *this contains NaN.
*
* Example: \include PartialRedux_minCoeff.cpp
* Output: \verbinclude PartialRedux_minCoeff.out
*
* \sa DenseBase::minCoeff() */
const typename ReturnType<internal::member_minCoeff>::Type minCoeff() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
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.
*
*
* \warning the result is undefined if \c *this contains NaN.
*
* Example: \include PartialRedux_maxCoeff.cpp
* Output: \verbinclude PartialRedux_maxCoeff.out
*
* \sa DenseBase::maxCoeff() */
const typename ReturnType<internal::member_maxCoeff>::Type maxCoeff() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
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.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* Example: \include PartialRedux_squaredNorm.cpp
* Output: \verbinclude PartialRedux_squaredNorm.out
*
* \sa DenseBase::squaredNorm() */
const typename ReturnType<internal::member_squaredNorm,RealScalar>::Type squaredNorm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
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.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* Example: \include PartialRedux_norm.cpp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa DenseBase::norm() */
const typename ReturnType<internal::member_norm,RealScalar>::Type norm() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
const NormReturnType norm() const
{ return NormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression.
* This is a vector with real entries, even if the original matrix has complex entries.
*
* Example: \include PartialRedux_norm.cpp
* Output: \verbinclude PartialRedux_norm.out
*
* \sa DenseBase::norm() */
template<int p>
EIGEN_DEVICE_FUNC
const typename LpNormReturnType<p>::Type lpNorm() const
{ return typename LpNormReturnType<p>::Type(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, using
* blue's algorithm.
* Blue's algorithm.
* 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(); }
EIGEN_DEVICE_FUNC
const BlueNormReturnType blueNorm() const
{ return BlueNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, avoiding
* underflow and overflow.
* 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(); }
EIGEN_DEVICE_FUNC
const StableNormReturnType stableNorm() const
{ return StableNormReturnType(_expression()); }
/** \returns a row (or column) vector expression of the norm
* of each column (or row) of the referenced expression, avoiding
* underflow and overflow using a concatenation of hypot() calls.
* 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(); }
EIGEN_DEVICE_FUNC
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.
@@ -354,39 +394,48 @@ 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(); }
EIGEN_DEVICE_FUNC
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(); }
EIGEN_DEVICE_FUNC
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(); }
EIGEN_DEVICE_FUNC
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(); }
EIGEN_DEVICE_FUNC
const AnyReturnType any() const
{ return AnyReturnType(_expression()); }
/** \returns a row (or column) vector expression representing
* the number of \c true coefficients of each respective column (or row).
* This expression can be assigned to a vector whose entries have the same type as is used to
* index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
*
* Example: \include PartialRedux_count.cpp
* Output: \verbinclude PartialRedux_count.out
*
* \sa DenseBase::count() */
const PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> count() const
{ return _expression(); }
EIGEN_DEVICE_FUNC
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.
@@ -395,8 +444,9 @@ 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(); }
EIGEN_DEVICE_FUNC
const ProdReturnType prod() const
{ return ProdReturnType(_expression()); }
/** \returns a matrix expression
@@ -406,10 +456,20 @@ 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() ); }
EIGEN_DEVICE_FUNC
const ConstReverseReturnType reverse() const
{ return ConstReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,Direction==Vertical?Dynamic:1,Direction==Horizontal?Dynamic:1> ReplicateReturnType;
/** \returns a writable matrix expression
* where each column (or row) are reversed.
*
* \sa reverse() const */
EIGEN_DEVICE_FUNC
ReverseReturnType reverse()
{ return ReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
EIGEN_DEVICE_FUNC
const ReplicateReturnType replicate(Index factor) const;
/**
@@ -421,17 +481,20 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
* \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
*/
// NOTE implemented here because of sunstudio's compilation errors
template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)>
// isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
EIGEN_DEVICE_FUNC
replicate(Index factor = Factor) const
{
return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1>
(_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
(_expression(),isVertical?factor:1,isHorizontal?factor:1);
}
/////////// Artithmetic operators ///////////
/** Copies the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -442,6 +505,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Adds the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -451,6 +515,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Substracts the vector \a other to each subvector of \c *this */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -460,6 +525,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Multiples each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -471,6 +537,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Divides each subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -481,8 +548,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
}
/** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE
CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator+(const DenseBase<OtherDerived>& other) const
@@ -494,7 +561,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
template<typename OtherDerived>
CwiseBinaryOp<internal::scalar_difference_op<Scalar>,
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator-(const DenseBase<OtherDerived>& other) const
@@ -506,10 +574,11 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression where each subvector is the product of the vector \a other
* by the corresponding subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_product_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
EIGEN_DEVICE_FUNC
operator*(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
@@ -521,6 +590,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression where each subvector is the quotient of the corresponding
* subvector of \c *this by the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
@@ -531,32 +601,36 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
return m_matrix / extendedTo(other.derived());
}
/** \returns an expression where each column of row of the referenced matrix are normalized.
/** \returns an expression where each column (or row) of the referenced matrix are normalized.
* The referenced matrix is \b not modified.
* \sa MatrixBase::normalized(), normalize()
*/
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
const ExpressionTypeNestedCleaned,
const typename OppositeExtendedType<typename ReturnType<internal::member_norm,RealScalar>::Type>::Type>
normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); }
/** Normalize in-place each row or columns of the referenced matrix.
* \sa MatrixBase::normalize(), normalized()
*/
void normalize() {
EIGEN_DEVICE_FUNC void normalize() {
m_matrix = this->normalized();
}
EIGEN_DEVICE_FUNC inline void reverseInPlace();
/////////// Geometry module ///////////
#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS
Homogeneous<ExpressionType,Direction> homogeneous() const;
#endif
typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
EIGEN_DEVICE_FUNC
HomogeneousReturnType homogeneous() const;
typedef typename ExpressionType::PlainObject CrossReturnType;
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
enum {
@@ -581,25 +655,15 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
Direction==Horizontal ? HNormalized_SizeMinusOne : 1> >
HNormalizedReturnType;
EIGEN_DEVICE_FUNC
const HNormalizedReturnType hnormalized() const;
protected:
ExpressionTypeNested m_matrix;
};
/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations
*
* Example: \include MatrixBase_colwise.cpp
* Output: \verbinclude MatrixBase_colwise.out
*
* \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
template<typename Derived>
inline const typename DenseBase<Derived>::ConstColwiseReturnType
DenseBase<Derived>::colwise() const
{
return derived();
}
//const colwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
@@ -609,22 +673,11 @@ 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
*
* Example: \include MatrixBase_rowwise.cpp
* Output: \verbinclude MatrixBase_rowwise.out
*
* \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
*/
template<typename Derived>
inline const typename DenseBase<Derived>::ConstRowwiseReturnType
DenseBase<Derived>::rowwise() const
{
return derived();
}
//const rowwise moved to DenseBase.h due to CUDA compiler bug
/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
*
@@ -634,7 +687,7 @@ template<typename Derived>
inline typename DenseBase<Derived>::RowwiseReturnType
DenseBase<Derived>::rowwise()
{
return derived();
return RowwiseReturnType(derived());
}
} // end namespace Eigen

74
extern/Eigen3/Eigen/src/Core/Visitor.h vendored
View File

@@ -22,6 +22,7 @@ struct visitor_impl
row = (UnrollCount-1) % Derived::RowsAtCompileTime
};
EIGEN_DEVICE_FUNC
static inline void run(const Derived &mat, Visitor& visitor)
{
visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor);
@@ -32,6 +33,7 @@ struct visitor_impl
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, 1>
{
EIGEN_DEVICE_FUNC
static inline void run(const Derived &mat, Visitor& visitor)
{
return visitor.init(mat.coeff(0, 0), 0, 0);
@@ -41,7 +43,7 @@ struct visitor_impl<Visitor, Derived, 1>
template<typename Visitor, typename Derived>
struct visitor_impl<Visitor, Derived, Dynamic>
{
typedef typename Derived::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Derived& mat, Visitor& visitor)
{
visitor.init(mat.coeff(0,0), 0, 0);
@@ -53,6 +55,33 @@ struct visitor_impl<Visitor, Derived, Dynamic>
}
};
// evaluator adaptor
template<typename XprType>
class visitor_evaluator
{
public:
EIGEN_DEVICE_FUNC
explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = XprType::RowsAtCompileTime,
CoeffReadCost = internal::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 CoeffReturnType coeff(Index row, Index col) const
{ return m_evaluator.coeff(row, col); }
protected:
internal::evaluator<XprType> m_evaluator;
const XprType &m_xpr;
};
} // end namespace internal
/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
@@ -74,16 +103,17 @@ struct visitor_impl<Visitor, Derived, Dynamic>
*/
template<typename Derived>
template<typename Visitor>
EIGEN_DEVICE_FUNC
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,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived(), visitor);
typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
enum {
unroll = SizeAtCompileTime != 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(thisEval, visitor);
}
namespace internal {
@@ -94,10 +124,10 @@ namespace internal {
template <typename Derived>
struct coeff_visitor
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
Index row, col;
Scalar res;
EIGEN_DEVICE_FUNC
inline void init(const Scalar& value, Index i, Index j)
{
res = value;
@@ -114,8 +144,8 @@ struct coeff_visitor
template <typename Derived>
struct min_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value < this->res)
@@ -142,8 +172,8 @@ struct functor_traits<min_coeff_visitor<Scalar> > {
template <typename Derived>
struct max_coeff_visitor : coeff_visitor<Derived>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Scalar Scalar;
EIGEN_DEVICE_FUNC
void operator() (const Scalar& value, Index i, Index j)
{
if(value > this->res)
@@ -164,13 +194,15 @@ struct functor_traits<max_coeff_visitor<Scalar> > {
} // end namespace internal
/** \returns the minimum of all coefficients of *this and puts in *row and *col its location.
/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
* \returns the minimum of all coefficients of *this and puts in *row and *col its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visitor(), DenseBase::minCoeff()
* \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
{
@@ -184,27 +216,30 @@ DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
/** \returns the minimum of all coefficients of *this and puts in *index its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::minCoeff()
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::minCoeff()
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff(IndexType* index) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
internal::min_coeff_visitor<Derived> minVisitor;
this->visit(minVisitor);
*index = (RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row;
*index = IndexType((RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row);
return minVisitor.res;
}
/** \returns the maximum of all coefficients of *this and puts in *row and *col its location.
/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
* \returns the maximum of all coefficients of *this and puts in *row and *col its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
{
@@ -222,6 +257,7 @@ DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
*/
template<typename Derived>
template<typename IndexType>
EIGEN_DEVICE_FUNC
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff(IndexType* index) const
{

451
extern/Eigen3/Eigen/src/Core/arch/AVX/Complex.h vendored Normal file
View File

@@ -0,0 +1,451 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COMPLEX_AVX_H
#define EIGEN_COMPLEX_AVX_H
namespace Eigen {
namespace internal {
//---------- float ----------
struct Packet4cf
{
EIGEN_STRONG_INLINE Packet4cf() {}
EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {}
__m256 v;
};
template<> struct packet_traits<std::complex<float> > : default_packet_traits
{
typedef Packet4cf type;
typedef Packet2cf half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size = 4,
HasHalfPacket = 1,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasNegate = 1,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasSetLinear = 0
};
};
template<> struct unpacket_traits<Packet4cf> { typedef std::complex<float> type; enum {size=4, alignment=Aligned32}; typedef Packet2cf half; };
template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a)
{
return Packet4cf(pnegate(a.v));
}
template<> EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a)
{
const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
return Packet4cf(_mm256_xor_ps(a.v,mask));
}
template<> EIGEN_STRONG_INLINE Packet4cf pmul<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
{
__m256 tmp1 = _mm256_mul_ps(_mm256_moveldup_ps(a.v), b.v);
__m256 tmp2 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
__m256 result = _mm256_addsub_ps(tmp1, tmp2);
return Packet4cf(result);
}
template<> EIGEN_STRONG_INLINE Packet4cf pand <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_and_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf por <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_or_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf pxor <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_xor_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_andnot_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet4cf pload <Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(pload<Packet8f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(ploadu<Packet8f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
{
return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from)));
}
template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
{
// FIXME The following might be optimized using _mm256_movedup_pd
Packet2cf a = ploaddup<Packet2cf>(from);
Packet2cf b = ploaddup<Packet2cf>(from+1);
return Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1));
}
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, Index stride)
{
return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]),
std::imag(from[2*stride]), std::real(from[2*stride]),
std::imag(from[1*stride]), std::real(from[1*stride]),
std::imag(from[0*stride]), std::real(from[0*stride])));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index stride)
{
__m128 low = _mm256_extractf128_ps(from.v, 0);
to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)),
_mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)));
to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)),
_mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)));
__m128 high = _mm256_extractf128_ps(from.v, 1);
to[stride*2] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)),
_mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)));
to[stride*3] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)),
_mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)));
}
template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet4cf>(const Packet4cf& a)
{
return pfirst(Packet2cf(_mm256_castps256_ps128(a.v)));
}
template<> EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) {
__m128 low = _mm256_extractf128_ps(a.v, 0);
__m128 high = _mm256_extractf128_ps(a.v, 1);
__m128d lowd = _mm_castps_pd(low);
__m128d highd = _mm_castps_pd(high);
low = _mm_castpd_ps(_mm_shuffle_pd(lowd,lowd,0x1));
high = _mm_castpd_ps(_mm_shuffle_pd(highd,highd,0x1));
__m256 result = _mm256_setzero_ps();
result = _mm256_insertf128_ps(result, low, 1);
result = _mm256_insertf128_ps(result, high, 0);
return Packet4cf(result);
}
template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a)
{
return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v,0)),
Packet2cf(_mm256_extractf128_ps(a.v,1))));
}
template<> EIGEN_STRONG_INLINE Packet4cf preduxp<Packet4cf>(const Packet4cf* vecs)
{
Packet8f t0 = _mm256_shuffle_ps(vecs[0].v, vecs[0].v, _MM_SHUFFLE(3, 1, 2 ,0));
Packet8f t1 = _mm256_shuffle_ps(vecs[1].v, vecs[1].v, _MM_SHUFFLE(3, 1, 2 ,0));
t0 = _mm256_hadd_ps(t0,t1);
Packet8f t2 = _mm256_shuffle_ps(vecs[2].v, vecs[2].v, _MM_SHUFFLE(3, 1, 2 ,0));
Packet8f t3 = _mm256_shuffle_ps(vecs[3].v, vecs[3].v, _MM_SHUFFLE(3, 1, 2 ,0));
t2 = _mm256_hadd_ps(t2,t3);
t1 = _mm256_permute2f128_ps(t0,t2, 0 + (2<<4));
t3 = _mm256_permute2f128_ps(t0,t2, 1 + (3<<4));
return Packet4cf(_mm256_add_ps(t1,t3));
}
template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a)
{
return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)),
Packet2cf(_mm256_extractf128_ps(a.v, 1))));
}
template<int Offset>
struct palign_impl<Offset,Packet4cf>
{
static EIGEN_STRONG_INLINE void run(Packet4cf& first, const Packet4cf& second)
{
if (Offset==0) return;
palign_impl<Offset*2,Packet8f>::run(first.v, second.v);
}
};
template<> struct conj_helper<Packet4cf, Packet4cf, false,true>
{
EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet4cf, Packet4cf, true,false>
{
EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet4cf, Packet4cf, true,true>
{
EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const
{
return pconj(internal::pmul(a, b));
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
{
Packet4cf num = pmul(a, pconj(b));
__m256 tmp = _mm256_mul_ps(b.v, b.v);
__m256 tmp2 = _mm256_shuffle_ps(tmp,tmp,0xB1);
__m256 denom = _mm256_add_ps(tmp, tmp2);
return Packet4cf(_mm256_div_ps(num.v, denom));
}
template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
{
return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
}
//---------- double ----------
struct Packet2cd
{
EIGEN_STRONG_INLINE Packet2cd() {}
EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {}
__m256d v;
};
template<> struct packet_traits<std::complex<double> > : default_packet_traits
{
typedef Packet2cd type;
typedef Packet1cd half;
enum {
Vectorizable = 1,
AlignedOnScalar = 0,
size = 2,
HasHalfPacket = 1,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasNegate = 1,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasSetLinear = 0
};
};
template<> struct unpacket_traits<Packet2cd> { typedef std::complex<double> type; enum {size=2, alignment=Aligned32}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) { return Packet2cd(pnegate(a.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a)
{
const __m256d mask = _mm256_castsi256_pd(_mm256_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
return Packet2cd(_mm256_xor_pd(a.v,mask));
}
template<> EIGEN_STRONG_INLINE Packet2cd pmul<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
{
__m256d tmp1 = _mm256_shuffle_pd(a.v,a.v,0x0);
__m256d even = _mm256_mul_pd(tmp1, b.v);
__m256d tmp2 = _mm256_shuffle_pd(a.v,a.v,0xF);
__m256d tmp3 = _mm256_shuffle_pd(b.v,b.v,0x5);
__m256d odd = _mm256_mul_pd(tmp2, tmp3);
return Packet2cd(_mm256_addsub_pd(even, odd));
}
template<> EIGEN_STRONG_INLINE Packet2cd pand <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_and_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd por <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_or_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd pxor <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_xor_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_andnot_pd(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cd pload <Packet2cd>(const std::complex<double>* from)
{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(pload<Packet4d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from)
{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(ploadu<Packet4d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from)
{
// in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though)
// return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from));
return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from));
}
template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) { return pset1<Packet2cd>(*from); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, Index stride)
{
return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]),
std::imag(from[0*stride]), std::real(from[0*stride])));
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, Index stride)
{
__m128d low = _mm256_extractf128_pd(from.v, 0);
to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));
__m128d high = _mm256_extractf128_pd(from.v, 1);
to[stride*1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)));
}
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a)
{
__m128d low = _mm256_extractf128_pd(a.v, 0);
EIGEN_ALIGN16 double res[2];
_mm_store_pd(res, low);
return std::complex<double>(res[0],res[1]);
}
template<> EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) {
__m256d result = _mm256_permute2f128_pd(a.v, a.v, 1);
return Packet2cd(result);
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a)
{
return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v,0)),
Packet1cd(_mm256_extractf128_pd(a.v,1))));
}
template<> EIGEN_STRONG_INLINE Packet2cd preduxp<Packet2cd>(const Packet2cd* vecs)
{
Packet4d t0 = _mm256_permute2f128_pd(vecs[0].v,vecs[1].v, 0 + (2<<4));
Packet4d t1 = _mm256_permute2f128_pd(vecs[0].v,vecs[1].v, 1 + (3<<4));
return Packet2cd(_mm256_add_pd(t0,t1));
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a)
{
return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v,0)),
Packet1cd(_mm256_extractf128_pd(a.v,1))));
}
template<int Offset>
struct palign_impl<Offset,Packet2cd>
{
static EIGEN_STRONG_INLINE void run(Packet2cd& first, const Packet2cd& second)
{
if (Offset==0) return;
palign_impl<Offset*2,Packet4d>::run(first.v, second.v);
}
};
template<> struct conj_helper<Packet2cd, Packet2cd, false,true>
{
EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const
{
return internal::pmul(a, pconj(b));
}
};
template<> struct conj_helper<Packet2cd, Packet2cd, true,false>
{
EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const
{
return internal::pmul(pconj(a), b);
}
};
template<> struct conj_helper<Packet2cd, Packet2cd, true,true>
{
EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const
{ return padd(pmul(x,y),c); }
EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const
{
return pconj(internal::pmul(a, b));
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
{
Packet2cd num = pmul(a, pconj(b));
__m256d tmp = _mm256_mul_pd(b.v, b.v);
__m256d denom = _mm256_hadd_pd(tmp, tmp);
return Packet2cd(_mm256_div_pd(num.v, denom));
}
template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)
{
return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5));
}
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet4cf,4>& kernel) {
__m256d P0 = _mm256_castps_pd(kernel.packet[0].v);
__m256d P1 = _mm256_castps_pd(kernel.packet[1].v);
__m256d P2 = _mm256_castps_pd(kernel.packet[2].v);
__m256d P3 = _mm256_castps_pd(kernel.packet[3].v);
__m256d T0 = _mm256_shuffle_pd(P0, P1, 15);
__m256d T1 = _mm256_shuffle_pd(P0, P1, 0);
__m256d T2 = _mm256_shuffle_pd(P2, P3, 15);
__m256d T3 = _mm256_shuffle_pd(P2, P3, 0);
kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32));
kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49));
kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32));
kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49));
}
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<Packet2cd,2>& kernel) {
__m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0+(2<<4));
kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1+(3<<4));
kernel.packet[0].v = tmp;
}
template<> EIGEN_STRONG_INLINE Packet4cf pinsertfirst(const Packet4cf& a, std::complex<float> b)
{
return Packet4cf(_mm256_blend_ps(a.v,pset1<Packet4cf>(b).v,1|2));
}
template<> EIGEN_STRONG_INLINE Packet2cd pinsertfirst(const Packet2cd& a, std::complex<double> b)
{
return Packet2cd(_mm256_blend_pd(a.v,pset1<Packet2cd>(b).v,1|2));
}
template<> EIGEN_STRONG_INLINE Packet4cf pinsertlast(const Packet4cf& a, std::complex<float> b)
{
return Packet4cf(_mm256_blend_ps(a.v,pset1<Packet4cf>(b).v,(1<<7)|(1<<6)));
}
template<> EIGEN_STRONG_INLINE Packet2cd pinsertlast(const Packet2cd& a, std::complex<double> b)
{
return Packet2cd(_mm256_blend_pd(a.v,pset1<Packet2cd>(b).v,(1<<3)|(1<<2)));
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COMPLEX_AVX_H

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