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*** EIGEN UPDATE *** updated the eigen library to the 3.2.1 version (feb 2014)

This commit is contained in:
Paolo Cignoni 2014-05-14 15:20:00 +00:00
parent 53dbae6626
commit 51599b37ca
317 changed files with 23637 additions and 13926 deletions
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48
eigenlib/Eigen/Core
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@ -19,6 +19,12 @@
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// 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)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
#include <complex>
// this include file manages BLAS and MKL related macros
@ -44,7 +50,7 @@
#endif
#else
// Remember that usage of defined() in a #define is undefined by the standard
#if (defined __SSE2__) && ( (!defined __GNUC__) || EIGEN_GNUC_AT_LEAST(4,2) )
#if (defined __SSE2__) && ( (!defined __GNUC__) || (defined __INTEL_COMPILER) || EIGEN_GNUC_AT_LEAST(4,2) )
#define EIGEN_SSE2_ON_NON_MSVC_BUT_NOT_OLD_GCC
#endif
#endif
@ -87,19 +93,25 @@
// so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
// notice that since these are C headers, the extern "C" is theoretically needed anyways.
extern "C" {
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
// 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:
#ifdef __INTEL_COMPILER
#include <immintrin.h>
#else
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
#include <pmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSSE3
#include <tmmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_1
#include <smmintrin.h>
#endif
#ifdef EIGEN_VECTORIZE_SSE4_2
#include <nmmintrin.h>
#endif
#endif
} // end extern "C"
#elif defined __ALTIVEC__
@ -236,15 +248,11 @@ using std::ptrdiff_t;
* \endcode
*/
/** \defgroup Support_modules Support modules [category]
* Category of modules which add support for external libraries.
*/
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/NumTraits.h"
@ -297,6 +305,7 @@ using std::ptrdiff_t;
#include "src/Core/Map.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"
@ -330,6 +339,7 @@ using std::ptrdiff_t;
#include "src/Core/products/TriangularSolverMatrix.h"
#include "src/Core/products/TriangularSolverVector.h"
#include "src/Core/BandMatrix.h"
#include "src/Core/CoreIterators.h"
#include "src/Core/BooleanRedux.h"
#include "src/Core/Select.h"

15
eigenlib/Eigen/Eigen2Support
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@ -14,12 +14,25 @@
#error Eigen2 support must be enabled by defining EIGEN2_SUPPORT before including any Eigen header
#endif
#ifndef EIGEN_NO_EIGEN2_DEPRECATED_WARNING
#if defined(__GNUC__) || defined(__INTEL_COMPILER) || defined(__clang__)
#warning "Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3. (Define EIGEN_NO_EIGEN2_DEPRECATED_WARNING to disable this warning)"
#else
#pragma message ("Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3. (Define EIGEN_NO_EIGEN2_DEPRECATED_WARNING to disable this warning)")
#endif
#endif // EIGEN_NO_EIGEN2_DEPRECATED_WARNING
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Support_modules
* \defgroup Eigen2Support_Module Eigen2 support module
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* \warning Eigen2 support is deprecated in Eigen 3.2.x and it will be removed in Eigen 3.3.
*
* This module provides a couple of deprecated functions improving the compatibility with Eigen2.
*
* To use it, define EIGEN2_SUPPORT before including any Eigen header
* \code
* #define EIGEN2_SUPPORT

2
eigenlib/Eigen/Eigenvalues
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@ -33,6 +33,8 @@
#include "src/Eigenvalues/HessenbergDecomposition.h"
#include "src/Eigenvalues/ComplexSchur.h"
#include "src/Eigenvalues/ComplexEigenSolver.h"
#include "src/Eigenvalues/RealQZ.h"
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Eigenvalues/RealSchur_MKL.h"

2
eigenlib/Eigen/IterativeLinearSolvers
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@ -6,7 +6,7 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
*
* 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.

28
eigenlib/Eigen/MetisSupport Normal file
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@ -0,0 +1,28 @@
#ifndef EIGEN_METISSUPPORT_MODULE_H
#define EIGEN_METISSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
extern "C" {
#include <metis.h>
}
/** \ingroup Support_modules
* \defgroup MetisSupport_Module MetisSupport module
*
* \code
* #include <Eigen/MetisSupport>
* \endcode
* This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
* It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
*/
#include "src/MetisSupport/MetisSupport.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_METISSUPPORT_MODULE_H

51
eigenlib/Eigen/OrderingMethods
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@ -5,19 +5,62 @@
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup OrderingMethods_Module OrderingMethods module
*
* This module is currently for internal use only.
*
*
* This module is currently for internal use only
*
* It defines various built-in and external ordering methods for sparse matrices.
* They are typically used to reduce the number of elements during
* the sparse matrix decomposition (LLT, LU, QR).
* Precisely, in a preprocessing step, a permutation matrix P is computed using
* those ordering methods and applied to the columns of the matrix.
* Using for instance the sparse Cholesky decomposition, it is expected that
* the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
*
*
* Usage :
* \code
* #include <Eigen/OrderingMethods>
* \endcode
*
* A simple usage is as a template parameter in the sparse decomposition classes :
*
* \code
* SparseLU<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* \code
* SparseQR<MatrixType, COLAMDOrdering<int> > solver;
* \endcode
*
* It is possible as well to call directly a particular ordering method for your own purpose,
* \code
* AMDOrdering<int> ordering;
* PermutationMatrix<Dynamic, Dynamic, int> perm;
* SparseMatrix<double> A;
* //Fill the matrix ...
*
* ordering(A, perm); // Call AMD
* \endcode
*
* \note Some of these methods (like AMD or METIS), need the sparsity pattern
* of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
* Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
* If your matrix is already symmetric (at leat in structure), you can avoid that
* by calling the method with a SelfAdjointView type.
*
* \code
* // Call the ordering on the pattern of the lower triangular matrix A
* ordering(A.selfadjointView<Lower>(), perm);
* \endcode
*/
#ifndef EIGEN_MPL2_ONLY
#include "src/OrderingMethods/Amd.h"
#endif
#include "src/OrderingMethods/Ordering.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_ORDERINGMETHODS_MODULE_H

29
eigenlib/Eigen/SPQRSupport Normal file
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@ -0,0 +1,29 @@
#ifndef EIGEN_SPQRSUPPORT_MODULE_H
#define EIGEN_SPQRSUPPORT_MODULE_H
#include "SparseCore"
#include "src/Core/util/DisableStupidWarnings.h"
#include "SuiteSparseQR.hpp"
/** \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.
*
* \code
* #include <Eigen/SPQRSupport>
* \endcode
*
* In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
* For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif

14
eigenlib/Eigen/Sparse
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@ -1,13 +1,15 @@
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
/** \defgroup Sparse_modules Sparse modules
/** \defgroup Sparse_Module Sparse meta-module
*
* Meta-module including all related modules:
* - SparseCore
* - OrderingMethods
* - SparseCholesky
* - IterativeLinearSolvers
* - \ref SparseCore_Module
* - \ref OrderingMethods_Module
* - \ref SparseCholesky_Module
* - \ref SparseLU_Module
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>
@ -17,6 +19,8 @@
#include "SparseCore"
#include "OrderingMethods"
#include "SparseCholesky"
#include "SparseLU"
#include "SparseQR"
#include "IterativeLinearSolvers"
#endif // EIGEN_SPARSE_MODULE_H

21
eigenlib/Eigen/SparseCholesky
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@ -1,11 +1,21 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2013 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_SPARSECHOLESKY_MODULE_H
#define EIGEN_SPARSECHOLESKY_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \ingroup Sparse_modules
/**
* \defgroup SparseCholesky_Module SparseCholesky module
*
* This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
@ -20,11 +30,18 @@
* \endcode
*/
#ifdef EIGEN_MPL2_ONLY
#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
#include "src/SparseCholesky/SimplicialCholesky_impl.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SPARSECHOLESKY_MODULE_H

4
eigenlib/Eigen/SparseCore
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@ -11,7 +11,7 @@
#include <cstring>
#include <algorithm>
/** \ingroup Sparse_modules
/**
* \defgroup SparseCore_Module SparseCore module
*
* This module provides a sparse matrix representation, and basic associatd matrix manipulations
@ -40,14 +40,12 @@ struct Sparse {};
#include "src/SparseCore/SparseMatrix.h"
#include "src/SparseCore/MappedSparseMatrix.h"
#include "src/SparseCore/SparseVector.h"
#include "src/SparseCore/CoreIterators.h"
#include "src/SparseCore/SparseBlock.h"
#include "src/SparseCore/SparseTranspose.h"
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"

49
eigenlib/Eigen/SparseLU Normal file
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@ -0,0 +1,49 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 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_SPARSELU_MODULE_H
#define EIGEN_SPARSELU_MODULE_H
#include "SparseCore"
/**
* \defgroup SparseLU_Module SparseLU module
* This module defines a supernodal factorization of general sparse matrices.
* The code is fully optimized for supernode-panel updates with specialized kernels.
* 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"
#include "src/SparseLU/SparseLU_gemm_kernel.h"
#include "src/SparseLU/SparseLU_Structs.h"
#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
#include "src/SparseLU/SparseLUImpl.h"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseLU/SparseLU_Memory.h"
#include "src/SparseLU/SparseLU_heap_relax_snode.h"
#include "src/SparseLU/SparseLU_relax_snode.h"
#include "src/SparseLU/SparseLU_pivotL.h"
#include "src/SparseLU/SparseLU_panel_dfs.h"
#include "src/SparseLU/SparseLU_kernel_bmod.h"
#include "src/SparseLU/SparseLU_panel_bmod.h"
#include "src/SparseLU/SparseLU_column_dfs.h"
#include "src/SparseLU/SparseLU_column_bmod.h"
#include "src/SparseLU/SparseLU_copy_to_ucol.h"
#include "src/SparseLU/SparseLU_pruneL.h"
#include "src/SparseLU/SparseLU_Utils.h"
#include "src/SparseLU/SparseLU.h"
#endif // EIGEN_SPARSELU_MODULE_H

33
eigenlib/Eigen/SparseQR Normal file
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@ -0,0 +1,33 @@
#ifndef EIGEN_SPARSEQR_MODULE_H
#define EIGEN_SPARSEQR_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
/** \defgroup SparseQR_Module SparseQR module
* \brief Provides QR decomposition for sparse matrices
*
* This module provides a simplicial version of the left-looking Sparse QR decomposition.
* The columns of the input matrix should be reordered to limit the fill-in during the
* decomposition. Built-in methods (COLAMD, AMD) or external methods (METIS) can be used to this end.
* See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
* of built-in and external ordering methods.
*
* \code
* #include <Eigen/SparseQR>
* \endcode
*
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"
#include "src/Core/util/ReenableStupidWarnings.h"
#endif

74
eigenlib/Eigen/src/Cholesky/LDLT.h
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@ -16,7 +16,10 @@
namespace Eigen {
namespace internal {
template<typename MatrixType, int UpLo> struct LDLT_Traits;
template<typename MatrixType, int UpLo> struct LDLT_Traits;
// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
}
/** \ingroup Cholesky_Module
@ -69,7 +72,12 @@ 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(), m_isInitialized(false) {}
LDLT()
: m_matrix(),
m_transpositions(),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
*
@ -81,6 +89,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
: m_matrix(size, size),
m_transpositions(size),
m_temporary(size),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{}
@ -93,6 +102,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix);
@ -139,7 +149,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
inline bool isPositive() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == 1;
return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
}
#ifdef EIGEN2_SUPPORT
@ -153,7 +163,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
inline bool isNegative(void) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return m_sign == -1;
return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
}
/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
@ -196,7 +206,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
LDLT& compute(const MatrixType& matrix);
template <typename Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w,RealScalar alpha=1);
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
/** \returns the internal LDLT decomposition matrix
*
@ -235,7 +245,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType m_matrix;
TranspositionType m_transpositions;
TmpMatrixType m_temporary;
int m_sign;
internal::SignMatrix m_sign;
bool m_isInitialized;
};
@ -246,8 +256,9 @@ template<int UpLo> struct ldlt_inplace;
template<> struct ldlt_inplace<Lower>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
using std::abs;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
@ -257,8 +268,9 @@ template<> struct ldlt_inplace<Lower>
if (size <= 1)
{
transpositions.setIdentity();
if(sign)
*sign = real(mat.coeff(0,0))>0 ? 1:-1;
if (numext::real(mat.coeff(0,0)) > 0) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < 0) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;
}
@ -277,9 +289,6 @@ template<> struct ldlt_inplace<Lower>
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails.
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
if(sign)
*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
}
// Finish early if the matrix is not full rank.
@ -301,11 +310,11 @@ template<> struct ldlt_inplace<Lower>
for(int i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
@ -326,6 +335,16 @@ template<> struct ldlt_inplace<Lower>
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
if (sign == PositiveSemiDef) {
if (realAkk < 0) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
if (realAkk > 0) sign = Indefinite;
} else if (sign == ZeroSign) {
if (realAkk > 0) sign = PositiveSemiDef;
else if (realAkk < 0) sign = NegativeSemiDef;
}
}
return true;
@ -339,9 +358,9 @@ template<> struct ldlt_inplace<Lower>
// Here only rank-1 updates are implemented, to reduce the
// requirement for intermediate storage and improve accuracy
template<typename MatrixType, typename WDerived>
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, typename MatrixType::RealScalar sigma=1)
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
{
using internal::isfinite;
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
@ -359,9 +378,9 @@ template<> struct ldlt_inplace<Lower>
break;
// Update the diagonal terms
RealScalar dj = real(mat.coeff(j,j));
RealScalar dj = numext::real(mat.coeff(j,j));
Scalar wj = w.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*alpha + swj2;
mat.coeffRef(j,j) += swj2/alpha;
@ -372,13 +391,13 @@ template<> struct ldlt_inplace<Lower>
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs);
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1)
static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
{
// Apply the permutation to the input w
tmp = transpositions * w;
@ -390,14 +409,14 @@ template<> struct ldlt_inplace<Lower>
template<> struct ldlt_inplace<Upper>
{
template<typename MatrixType, typename TranspositionType, typename Workspace>
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0)
static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
}
template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1)
static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
{
Transpose<MatrixType> matt(mat);
return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
@ -436,7 +455,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
m_isInitialized = false;
m_temporary.resize(size);
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign);
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign);
m_isInitialized = true;
return *this;
@ -449,7 +468,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w,typename NumTraits<typename MatrixType::Scalar>::Real sigma)
LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename NumTraits<typename MatrixType::Scalar>::Real& sigma)
{
const Index size = w.rows();
if (m_isInitialized)
@ -464,7 +483,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Deri
for (Index i = 0; i < size; i++)
m_transpositions.coeffRef(i) = i;
m_temporary.resize(size);
m_sign = sigma>=0 ? 1 : -1;
m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
m_isInitialized = true;
}
@ -534,8 +553,7 @@ template<typename Derived>
bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
const Index size = m_matrix.rows();
eigen_assert(size == bAndX.rows());
eigen_assert(m_matrix.rows() == bAndX.rows());
bAndX = this->solve(bAndX);

20
eigenlib/Eigen/src/Cholesky/LLT.h
View File

@ -190,6 +190,7 @@ 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)
{
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
@ -199,7 +200,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
typedef Matrix<Scalar,Dynamic,1> TempVectorType;
typedef typename TempVectorType::SegmentReturnType TempVecSegment;
int n = mat.cols();
Index n = mat.cols();
eigen_assert(mat.rows()==n && vec.size()==n);
TempVectorType temp;
@ -211,12 +212,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
// i.e., for sigma > 0
temp = sqrt(sigma) * vec;
for(int i=0; i<n; ++i)
for(Index i=0; i<n; ++i)
{
JacobiRotation<Scalar> g;
g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
int rs = n-i-1;
Index rs = n-i-1;
if(rs>0)
{
ColXprSegment x(mat.col(i).tail(rs));
@ -229,12 +230,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp = vec;
RealScalar beta = 1;
for(int j=0; j<n; ++j)
for(Index j=0; j<n; ++j)
{
RealScalar Ljj = real(mat.coeff(j,j));
RealScalar dj = abs2(Ljj);
RealScalar Ljj = numext::real(mat.coeff(j,j));
RealScalar dj = numext::abs2(Ljj);
Scalar wj = temp.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*beta + swj2;
RealScalar x = dj + swj2/beta;
@ -250,7 +251,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs);
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
}
@ -263,6 +264,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
template<typename MatrixType>
static typename MatrixType::Index unblocked(MatrixType& mat)
{
using std::sqrt;
typedef typename MatrixType::Index Index;
eigen_assert(mat.rows()==mat.cols());
@ -275,7 +277,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = real(mat.coeff(k,k));
RealScalar x = numext::real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;

66
eigenlib/Eigen/src/CholmodSupport/CholmodSupport.h
View File

@ -51,7 +51,6 @@ void cholmod_configure_matrix(CholmodType& mat)
template<typename _Scalar, int _Options, typename _Index>
cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
{
typedef SparseMatrix<_Scalar,_Options,_Index> MatrixType;
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();;
@ -59,10 +58,12 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();
res.x = mat.valuePtr();
res.z = 0;
res.sorted = 1;
if(mat.isCompressed())
{
res.packed = 1;
res.nz = 0;
}
else
{
@ -77,9 +78,13 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
{
res.itype = CHOLMOD_INT;
}
else if (internal::is_same<_Index,UF_long>::value)
{
res.itype = CHOLMOD_LONG;
}
else
{
eigen_assert(false && "Index type different than int is not supported yet");
eigen_assert(false && "Index type not supported yet");
}
// setup res.xtype
@ -123,7 +128,7 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
res.x = mat.derived().data();
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>(res);
@ -137,8 +142,8 @@ template<typename Scalar, int Flags, typename Index>
MappedSparseMatrix<Scalar,Flags,Index> viewAsEigen(cholmod_sparse& cm)
{
return MappedSparseMatrix<Scalar,Flags,Index>
(cm.nrow, cm.ncol, reinterpret_cast<Index*>(cm.p)[cm.ncol],
reinterpret_cast<Index*>(cm.p), reinterpret_cast<Index*>(cm.i),reinterpret_cast<Scalar*>(cm.x) );
(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) );
}
enum CholmodMode {
@ -167,12 +172,14 @@ class CholmodBase : internal::noncopyable
CholmodBase()
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
cholmod_start(&m_cholmod);
}
CholmodBase(const MatrixType& matrix)
: m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
{
m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0);
cholmod_start(&m_cholmod);
compute(matrix);
}
@ -237,7 +244,7 @@ class CholmodBase : internal::noncopyable
return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
/** 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.
*
@ -261,7 +268,7 @@ class CholmodBase : internal::noncopyable
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
* The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
*
* \sa analyzePattern()
*/
@ -269,9 +276,10 @@ class CholmodBase : internal::noncopyable
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
cholmod_factorize(&A, m_cholmodFactor, &m_cholmod);
cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod);
this->m_info = Success;
// 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;
}
@ -286,16 +294,18 @@ class CholmodBase : internal::noncopyable
{
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());
// note: cd stands for Cholmod Dense
cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived());
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;
}
// TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
// 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());
cholmod_free_dense(&x_cd, &m_cholmod);
}
@ -306,6 +316,7 @@ class CholmodBase : internal::noncopyable
{
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());
// note: cs stands for Cholmod Sparse
@ -315,19 +326,36 @@ class CholmodBase : internal::noncopyable
{
this->m_info = NumericalIssue;
}
// TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
// TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
cholmod_free_sparse(&x_cs, &m_cholmod);
}
#endif // EIGEN_PARSED_BY_DOXYGEN
/** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
*
* During the numerical factorization, an offset term is added to the diagonal coefficients:\n
* \c d_ii = \a offset + \c d_ii
*
* The default is \a offset=0.
*
* \returns a reference to \c *this.
*/
Derived& setShift(const RealScalar& offset)
{
m_shiftOffset[0] = offset;
return derived();
}
template<typename Stream>
void dumpMemory(Stream& s)
void dumpMemory(Stream& /*s*/)
{}
protected:
mutable cholmod_common m_cholmod;
cholmod_factor* m_cholmodFactor;
RealScalar m_shiftOffset[2];
mutable ComputationInfo m_info;
bool m_isInitialized;
int m_factorizationIsOk;
@ -340,8 +368,8 @@ class CholmodBase : internal::noncopyable
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@ -387,8 +415,8 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl
*
* This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
* using the Cholmod library.
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@ -433,7 +461,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp
* This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
* using the Cholmod library.
* This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
* The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
@ -476,7 +504,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper
* \brief A general Cholesky factorization and solver based on Cholmod
*
* This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
* using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
* using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
* X and B can be either dense or sparse.
*
* This variant permits to change the underlying Cholesky method at runtime.

34
eigenlib/Eigen/src/Core/Array.h
View File

@ -107,10 +107,10 @@ class Array
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Array() : Base()
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -120,7 +120,7 @@ class Array
: Base(internal::constructor_without_unaligned_array_assert())
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
@ -137,15 +137,15 @@ class Array
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename T0, typename T1>
EIGEN_STRONG_INLINE Array(const T0& x, const T1& y)
EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
{
Base::_check_template_params();
this->template _init2<T0,T1>(x, y);
this->template _init2<T0,T1>(val0, val1);
}
#else
/** constructs an uninitialized matrix with \a rows rows and \a cols columns.
@ -155,27 +155,27 @@ class Array
* Matrix() instead. */
Array(Index rows, Index cols);
/** constructs an initialized 2D vector with given coefficients */
Array(const Scalar& x, const Scalar& y);
Array(const Scalar& val0, const Scalar& val1);
#endif
/** constructs an initialized 3D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z)
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
}
/** constructs an initialized 4D vector with given coefficients */
EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
{
Base::_check_template_params();
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[2] = z;
m_storage.data()[3] = w;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[2] = val2;
m_storage.data()[3] = val3;
}
explicit Array(const Scalar *data);
@ -210,7 +210,7 @@ class Array
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
Base::_resize_to_match(other);
*this = other;
}

2
eigenlib/Eigen/src/Core/ArrayBase.h
View File

@ -143,7 +143,7 @@ template<typename Derived> class ArrayBase
ArrayBase<Derived>& array() { return *this; }
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link MatrixBase Matrix \endlink expression of this array
/** \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(); }

54
eigenlib/Eigen/src/Core/ArrayWrapper.h
View File

@ -55,22 +55,22 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline CoeffReturnType coeff(Index index) const
@ -89,15 +89,15 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(row, col);
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@ -107,9 +107,9 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
}
template<typename Dest>
@ -168,29 +168,29 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_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(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index row, Index col) const
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_expression.coeff(row, col);
return m_expression.coeff(rowId, colId);
}
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.derived().coeffRef(row, col);
return m_expression.derived().coeffRef(rowId, colId);
}
inline CoeffReturnType coeff(Index index) const
@ -209,15 +209,15 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
inline const PacketScalar packet(Index rowId, Index colId) const
{
return m_expression.template packet<LoadMode>(row, col);
return m_expression.template packet<LoadMode>(rowId, colId);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@ -227,9 +227,9 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, x);
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
}
const typename internal::remove_all<NestedExpressionType>::type&

32
eigenlib/Eigen/src/Core/Assign.h
View File

@ -155,7 +155,7 @@ struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_DefaultTraversal_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer)
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);
@ -165,7 +165,7 @@ struct assign_DefaultTraversal_InnerUnrolling
template<typename Derived1, typename Derived2, int Stop>
struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***********************
@ -218,7 +218,7 @@ struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop>
template<typename Derived1, typename Derived2, int Index, int Stop>
struct assign_innervec_InnerUnrolling
{
static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer)
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,
@ -229,7 +229,7 @@ struct assign_innervec_InnerUnrolling
template<typename Derived1, typename Derived2, int Stop>
struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop>
{
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {}
static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {}
};
/***************************************************************************
@ -507,19 +507,19 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = Derived::IsVectorAtCompileTime
&& OtherDerived::IsVectorAtCompileTime
&& ((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>
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> {
@ -528,6 +528,8 @@ struct assign_selector<Derived,OtherDerived,true,false> {
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> {
@ -566,16 +568,14 @@ template<typename Derived>
template <typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
other.derived().evalTo(derived());
return derived();
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
return derived();
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
}
} // end namespace Eigen

2
eigenlib/Eigen/src/Core/Assign_MKL.h
View File

@ -210,7 +210,7 @@ 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.
#ifdef _WIN32
#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)

184
eigenlib/Eigen/src/Core/Block.h
View File

@ -21,7 +21,6 @@ namespace Eigen {
* \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)
* \param _DirectAccessStatus \internal used for partial specialization
*
* 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
@ -47,8 +46,8 @@ namespace Eigen {
*/
namespace internal {
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> > : traits<XprType>
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;
@ -92,21 +91,92 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess>
Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit
};
};
}
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class Block
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess> >::type
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>
{
typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
public:
//typedef typename Impl::Base Base;
typedef Impl Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i) : Impl(xpr,i)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index a_startRow, Index a_startCol)
: Impl(xpr, a_startRow, a_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());
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
Index a_startRow, Index a_startCol,
Index blockRows, Index blockCols)
: Impl(xpr, a_startRow, a_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);
}
};
// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
// that must be specialized for direct and non-direct access...
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
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;
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) {}
};
namespace internal {
/** \internal Internal implementation of dense Blocks in the general case. */
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
: public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
public:
typedef typename internal::dense_xpr_base<Block>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
typedef typename internal::dense_xpr_base<BlockType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
class InnerIterator;
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
inline BlockImpl_dense(XprType& xpr, Index i)
: m_xpr(xpr),
// It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
// and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
@ -116,58 +186,43 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
m_blockCols(BlockCols==1 ? 1 : xpr.cols())
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
}
{}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
}
inline BlockImpl_dense(XprType& xpr, Index a_startRow, Index a_startCol)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
m_blockRows(BlockRows), m_blockCols(BlockCols)
{}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
Index startRow, Index startCol,
inline BlockImpl_dense(XprType& xpr,
Index a_startRow, Index a_startCol,
Index blockRows, Index blockCols)
: m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
: m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol),
m_blockRows(blockRows), m_blockCols(blockCols)
{}
inline Index rows() const { return m_blockRows.value(); }
inline Index cols() const { return m_blockCols.value(); }
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
{
return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value());
return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
}
inline Scalar& coeffRef(Index index)
@ -193,17 +248,17 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(row + m_startRow.value(), col + m_startCol.value());
(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(row + m_startRow.value(), col + m_startCol.value(), x);
(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@ -215,11 +270,11 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
}
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x);
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
#ifdef EIGEN_PARSED_BY_DOXYGEN
@ -253,21 +308,21 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
};
/** \internal */
/** \internal Internal implementation of dense Blocks in the direct access case.*/
template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel, true> >
class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
: public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
{
typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
public:
typedef MapBase<Block> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Block)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
typedef MapBase<BlockType> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
/** Column or Row constructor
*/
inline Block(XprType& xpr, Index i)
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)),
@ -275,34 +330,25 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
BlockCols==1 ? 1 : xpr.cols()),
m_xpr(xpr)
{
eigen_assert( (i>=0) && (
((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
init();
}
/** Fixed-size constructor
*/
inline Block(XprType& xpr, Index startRow, Index startCol)
inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
: Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr)
{
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
init();
}
/** Dynamic-size constructor
*/
inline Block(XprType& xpr,
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)
{
eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
&& (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows()
&& startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols());
init();
}
@ -314,7 +360,7 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
/** \sa MapBase::innerStride() */
inline Index innerStride() const
{
return internal::traits<Block>::HasSameStorageOrderAsXprType
return internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.innerStride()
: m_xpr.outerStride();
}
@ -333,7 +379,7 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal used by allowAligned() */
inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
: Base(data, blockRows, blockCols), m_xpr(xpr)
{
init();
@ -343,7 +389,7 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
protected:
void init()
{
m_outerStride = internal::traits<Block>::HasSameStorageOrderAsXprType
m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
? m_xpr.outerStride()
: m_xpr.innerStride();
}
@ -352,6 +398,8 @@ class Block<XprType,BlockRows,BlockCols, InnerPanel,true>
Index m_outerStride;
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_BLOCK_H

36
eigenlib/Eigen/src/Core/BooleanRedux.h
View File

@ -29,9 +29,9 @@ struct all_unroller
};
template<typename Derived>
struct all_unroller<Derived, 1>
struct all_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived &/*mat*/) { return true; }
};
template<typename Derived>
@ -55,9 +55,9 @@ struct any_unroller
};
template<typename Derived>
struct any_unroller<Derived, 1>
struct any_unroller<Derived, 0>
{
static inline bool run(const Derived &mat) { return mat.coeff(0, 0); }
static inline bool run(const Derived & /*mat*/) { return false; }
};
template<typename Derived>
@ -85,9 +85,7 @@ inline bool DenseBase<Derived>::all() const
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
if(unroll)
return internal::all_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
return internal::all_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived());
else
{
for(Index j = 0; j < cols(); ++j)
@ -111,9 +109,7 @@ inline bool DenseBase<Derived>::any() const
&& SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT
};
if(unroll)
return internal::any_unroller<Derived,
unroll ? int(SizeAtCompileTime) : Dynamic
>::run(derived());
return internal::any_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived());
else
{
for(Index j = 0; j < cols(); ++j)
@ -133,6 +129,26 @@ inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const
return derived().template cast<bool>().template cast<Index>().sum();
}
/** \returns true is \c *this contains at least one Not A Number (NaN).
*
* \sa allFinite()
*/
template<typename Derived>
inline bool DenseBase<Derived>::hasNaN() const
{
return !((derived().array()==derived().array()).all());
}
/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
*
* \sa hasNaN()
*/
template<typename Derived>
inline bool DenseBase<Derived>::allFinite() const
{
return !((derived()-derived()).hasNaN());
}
} // end namespace Eigen
#endif // EIGEN_ALLANDANY_H

2
eigenlib/Eigen/src/Core/CommaInitializer.h
View File

@ -118,6 +118,8 @@ struct CommaInitializer
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/

0
eigenlib/Eigen/src/SparseCore/CoreIterators.h → eigenlib/Eigen/src/Core/CoreIterators.h
View File

22
eigenlib/Eigen/src/Core/CwiseBinaryOp.h
View File

@ -94,8 +94,8 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
// 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_allows_mixing_real_and_complex<BINOP>::ret \
? int(internal::is_same<typename NumTraits<LHS>::Real, typename NumTraits<RHS>::Real>::value) \
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)
@ -122,13 +122,13 @@ class CwiseBinaryOp : internal::no_assignment_operator,
typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
typedef typename internal::remove_reference<RhsNested>::type _RhsNested;
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp())
: m_lhs(lhs), m_rhs(rhs), m_functor(func)
EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
: m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
{
EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar);
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols());
eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
}
EIGEN_STRONG_INLINE Index rows() const {
@ -169,17 +169,17 @@ class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense>
typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE( Derived )
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().lhs().coeff(row, col),
derived().rhs().coeff(row, col));
return derived().functor()(derived().lhs().coeff(rowId, colId),
derived().rhs().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(row, col),
derived().rhs().template packet<LoadMode>(row, col));
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

134
eigenlib/Eigen/src/Core/CwiseNullaryOp.h
View File

@ -54,27 +54,27 @@ class CwiseNullaryOp : internal::no_assignment_operator,
typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
: m_rows(rows), m_cols(cols), m_functor(func)
CwiseNullaryOp(Index nbRows, Index nbCols, const NullaryOp& func = NullaryOp())
: m_rows(nbRows), m_cols(nbCols), m_functor(func)
{
eigen_assert(rows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
eigen_assert(nbRows >= 0
&& (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0
&& (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols));
}
EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); }
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_STRONG_INLINE const Scalar coeff(Index rows, Index cols) const
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return m_functor(rows, cols);
return m_functor(rowId, colId);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return m_functor.packetOp(row, col);
return m_functor.packetOp(rowId, colId);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
@ -163,11 +163,11 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
/** \returns an expression of a constant matrix of value \a value
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols 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 rows and \a cols as arguments, so Zero() should be used
* it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@ -176,9 +176,9 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
@ -292,14 +292,14 @@ DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
/** \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
(const Scalar& value, RealScalar prec) const
(const Scalar& val, const RealScalar& prec) const
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
if(!internal::isApprox(this->coeff(i, j), value, prec))
if(!internal::isApprox(this->coeff(i, j), val, prec))
return false;
return true;
}
@ -309,19 +309,19 @@ 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
(const Scalar& value, RealScalar prec) const
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(value, prec);
return isApproxToConstant(val, prec);
}
/** Alias for setConstant(): sets all coefficients in this expression to \a value.
/** Alias for setConstant(): sets all coefficients in this expression to \a val.
*
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(value);
setConstant(val);
}
/** Sets all coefficients in this expression to \a value.
@ -329,9 +329,9 @@ EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& value)
* \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& value)
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), value);
return derived() = Constant(rows(), cols(), val);
}
/** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value.
@ -345,17 +345,17 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& value
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
return setConstant(value);
return setConstant(val);
}
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param value the value to which all coefficients are set
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
@ -364,10 +364,10 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& value)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& value)
PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar& val)
{
resize(rows, cols);
return setConstant(value);
resize(nbRows, nbCols);
return setConstant(val);
}
/**
@ -384,10 +384,10 @@ PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& valu
* \sa CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index size, const Scalar& low, const Scalar& high)
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(size, internal::linspaced_op<Scalar,false>(low,high,size));
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,false>(low,high,newSize));
}
/**
@ -425,9 +425,9 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
DenseBase<Derived>::Zero(Index nbRows, Index nbCols)
{
return Constant(rows, cols, Scalar(0));
return Constant(nbRows, nbCols, Scalar(0));
}
/** \returns an expression of a zero vector.
@ -479,7 +479,7 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(RealScalar prec) const
bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
for(Index j = 0; j < cols(); ++j)
for(Index i = 0; i < rows(); ++i)
@ -512,16 +512,16 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index size)
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(0));
}
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
@ -530,9 +530,9 @@ PlainObjectBase<Derived>::setZero(Index size)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
{
resize(rows, cols);
resize(nbRows, nbCols);
return setConstant(Scalar(0));
}
@ -540,7 +540,7 @@ PlainObjectBase<Derived>::setZero(Index rows, Index cols)
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols 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,
@ -554,14 +554,14 @@ PlainObjectBase<Derived>::setZero(Index rows, Index cols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
{
return Constant(rows, cols, Scalar(1));
return Constant(nbRows, nbCols, Scalar(1));
}
/** \returns an expression of a vector where all coefficients equal one.
*
* The parameter \a size is the size of the returned vector.
* The parameter \a newSize is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
@ -577,9 +577,9 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index size)
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(size, Scalar(1));
return Constant(newSize, Scalar(1));
}
/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
@ -609,7 +609,7 @@ DenseBase<Derived>::Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
(RealScalar prec) const
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
}
@ -627,7 +627,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
return setConstant(Scalar(1));
}
/** Resizes to the given \a size, and sets all coefficients in this expression to one.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
*
* \only_for_vectors
*
@ -638,16 +638,16 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index size)
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(size);
resize(newSize);
return setConstant(Scalar(1));
}
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
@ -656,9 +656,9 @@ PlainObjectBase<Derived>::setOnes(Index size)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
{
resize(rows, cols);
resize(nbRows, nbCols);
return setConstant(Scalar(1));
}
@ -666,7 +666,7 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols 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,
@ -680,9 +680,9 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
MatrixBase<Derived>::Identity(Index nbRows, Index nbCols)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_identity_op<Scalar>());
}
/** \returns an expression of the identity matrix (not necessarily square).
@ -714,7 +714,7 @@ MatrixBase<Derived>::Identity()
*/
template<typename Derived>
bool MatrixBase<Derived>::isIdentity
(RealScalar prec) const
(const RealScalar& prec) const
{
for(Index j = 0; j < cols(); ++j)
{
@ -776,8 +776,8 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out
@ -785,9 +785,9 @@ 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 rows, Index cols)
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index nbRows, Index nbCols)
{
derived().resize(rows, cols);
derived().resize(nbRows, nbCols);
return setIdentity();
}
@ -798,10 +798,10 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index
* \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 size, Index i)
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(size,size), i);
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
}
/** \returns an expression of the i-th unit (basis) vector.

8
eigenlib/Eigen/src/Core/CwiseUnaryOp.h
View File

@ -98,15 +98,15 @@ class CwiseUnaryOpImpl<UnaryOp,XprType,Dense>
typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return derived().functor()(derived().nestedExpression().coeff(row, col));
return derived().functor()(derived().nestedExpression().coeff(rowId, colId));
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(row, col));
return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(rowId, colId));
}
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const

16
eigenlib/Eigen/src/Core/CwiseUnaryView.h
View File

@ -44,9 +44,10 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
// "error: no integral type can represent all of the enumerator values
InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic
? int(Dynamic)
: int(MatrixTypeInnerStride)
* int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
: int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)),
OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic
? int(Dynamic)
: outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar))
};
};
}
@ -55,8 +56,7 @@ template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : internal::no_assignment_operator,
public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
@ -98,6 +98,10 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
inline Scalar* data() { return &coeffRef(0); }
inline const Scalar* data() const { return &coeff(0); }
inline Index innerStride() const
{
@ -106,7 +110,7 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
inline Index outerStride() const
{
return derived().nestedExpression().outerStride();
return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
}
EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const

72
eigenlib/Eigen/src/Core/DenseBase.h
View File

@ -13,6 +13,16 @@
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
}
} // end namespace internal
/** \class DenseBase
* \ingroup Core_Module
*
@ -204,21 +214,21 @@ 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 size)
void resize(Index newSize)
{
EIGEN_ONLY_USED_FOR_DEBUG(size);
eigen_assert(size == this->size()
EIGEN_ONLY_USED_FOR_DEBUG(newSize);
eigen_assert(newSize == this->size()
&& "DenseBase::resize() does not actually allow to resize.");
}
/** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
* 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 rows, Index cols)
void resize(Index nbRows, Index nbCols)
{
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
eigen_assert(rows == this->rows() && cols == this->cols()
EIGEN_ONLY_USED_FOR_DEBUG(nbRows);
EIGEN_ONLY_USED_FOR_DEBUG(nbCols);
eigen_assert(nbRows == this->rows() && nbCols == this->cols()
&& "DenseBase::resize() does not actually allow to resize.");
}
@ -271,7 +281,7 @@ template<typename Derived> class DenseBase
CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
Eigen::Transpose<Derived> transpose();
typedef const Transpose<const Derived> ConstTransposeReturnType;
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
ConstTransposeReturnType transpose() const;
void transposeInPlace();
#ifndef EIGEN_NO_DEBUG
@ -281,29 +291,6 @@ template<typename Derived> class DenseBase
public:
#endif
typedef VectorBlock<Derived> SegmentReturnType;
typedef const VectorBlock<const Derived> ConstSegmentReturnType;
template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
// Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
SegmentReturnType segment(Index start, Index size);
typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const;
SegmentReturnType head(Index size);
typename DenseBase::ConstSegmentReturnType head(Index size) const;
SegmentReturnType tail(Index size);
typename DenseBase::ConstSegmentReturnType tail(Index size) const;
template<int Size> typename FixedSegmentReturnType<Size>::Type head();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type head() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type tail();
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type tail() const;
template<int Size> typename FixedSegmentReturnType<Size>::Type segment(Index start);
template<int Size> typename ConstFixedSegmentReturnType<Size>::Type segment(Index start) const;
static const ConstantReturnType
Constant(Index rows, Index cols, const Scalar& value);
@ -348,17 +335,20 @@ template<typename Derived> class DenseBase
template<typename OtherDerived>
bool isApprox(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isMuchSmallerThan(const RealScalar& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isOnes(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;
inline bool hasNaN() const;
inline bool allFinite() const;
inline Derived& operator*=(const Scalar& other);
inline Derived& operator/=(const Scalar& other);
@ -438,8 +428,6 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
/////////// Array module ///////////
bool all(void) const;
bool any(void) const;
Index count() const;
@ -465,11 +453,11 @@ template<typename Derived> class DenseBase
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
select(const DenseBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;

12
eigenlib/Eigen/src/Core/DenseCoeffsBase.h
View File

@ -427,22 +427,22 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& x)
(Index row, Index col, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
derived().template writePacket<StoreMode>(row,col,x);
derived().template writePacket<StoreMode>(row,col,val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacketByOuterInner
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& x)
(Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& val)
{
writePacket<StoreMode>(rowIndexByOuterInner(outer, inner),
colIndexByOuterInner(outer, inner),
x);
val);
}
/** \internal
@ -456,10 +456,10 @@ class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived,
*/
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket
(Index index, const typename internal::packet_traits<Scalar>::type& x)
(Index index, const typename internal::packet_traits<Scalar>::type& val)
{
eigen_internal_assert(index >= 0 && index < size());
derived().template writePacket<StoreMode>(index,x);
derived().template writePacket<StoreMode>(index,val);
}
#ifndef EIGEN_PARSED_BY_DOXYGEN

114
eigenlib/Eigen/src/Core/DenseStorage.h
View File

@ -35,17 +35,36 @@ template <typename T, int Size, int MatrixOrArrayOptions,
struct plain_array
{
T array[Size];
plain_array() {}
plain_array(constructor_without_unaligned_array_assert) {}
plain_array()
{
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= 128 * 128 * 8, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
}
plain_array(constructor_without_unaligned_array_assert)
{
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= 128 * 128 * 8, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
}
};
#ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
#elif EIGEN_GNUC_AT_LEAST(4,7)
// GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned.
// See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900
// Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined:
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(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 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#endif
@ -53,8 +72,17 @@ template <typename T, int Size, int MatrixOrArrayOptions>
struct plain_array<T, Size, MatrixOrArrayOptions, 16>
{
EIGEN_USER_ALIGN16 T array[Size];
plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) }
plain_array(constructor_without_unaligned_array_assert) {}
plain_array()
{
EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf);
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= 128 * 128 * 8, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
}
plain_array(constructor_without_unaligned_array_assert)
{
EIGEN_STATIC_ASSERT(Size * sizeof(T) <= 128 * 128 * 8, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
}
};
template <typename T, int MatrixOrArrayOptions, int Alignment>
@ -86,7 +114,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
inline explicit DenseStorage() {}
inline DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
@ -103,7 +131,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
inline explicit DenseStorage() {}
inline DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& ) {}
@ -132,16 +160,16 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
DenseIndex m_rows;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {}
inline DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {}
inline 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); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; }
inline DenseIndex rows() const {return m_rows;}
inline DenseIndex cols() const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
inline void resize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
@ -152,15 +180,15 @@ template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Si
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_rows(0) {}
inline DenseStorage() : m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {}
inline DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return _Cols;}
inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; }
inline void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
inline void resize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
@ -171,15 +199,15 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_cols(0) {}
inline DenseStorage() : m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {}
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
inline DenseIndex rows(void) const {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; }
inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
inline void resize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; }
inline const T *data() const { return m_data.array; }
inline T *data() { return m_data.array; }
};
@ -191,24 +219,24 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
DenseIndex m_rows;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols)
: m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows), m_cols(cols)
inline 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 }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); }
inline 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); }
inline DenseIndex rows(void) const {return m_rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols)
inline void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols);
m_rows = rows;
m_cols = cols;
m_rows = nbRows;
m_cols = nbCols;
}
void resize(DenseIndex size, DenseIndex rows, DenseIndex cols)
void resize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
{
if(size != m_rows*m_cols)
{
@ -219,8 +247,8 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = rows;
m_cols = cols;
m_rows = nbRows;
m_cols = nbCols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
@ -232,20 +260,20 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
T *m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(cols)
inline 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 }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); }
static inline DenseIndex rows(void) {return _Rows;}
inline DenseIndex cols(void) const {return m_cols;}
inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols)
inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex nbCols)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols);
m_cols = cols;
m_cols = nbCols;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols)
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex nbCols)
{
if(size != _Rows*m_cols)
{
@ -256,7 +284,7 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_cols = cols;
m_cols = nbCols;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }
@ -268,20 +296,20 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
T *m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(rows)
inline 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 }
inline ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); }
inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); }
inline DenseIndex rows(void) const {return m_rows;}
static inline DenseIndex cols(void) {return _Cols;}
inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex)
inline void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols);
m_rows = rows;
m_rows = nbRows;
}
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex)
EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex nbRows, DenseIndex)
{
if(size != m_rows*_Cols)
{
@ -292,7 +320,7 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
m_data = 0;
EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN
}
m_rows = rows;
m_rows = nbRows;
}
inline const T *data() const { return m_data; }
inline T *data() { return m_data; }

41
eigenlib/Eigen/src/Core/Diagonal.h
View File

@ -41,12 +41,12 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
typedef typename MatrixType::StorageKind StorageKind;
enum {
RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
ColsAtCompileTime = 1,
MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
: DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
: DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,
MatrixType::MaxColsAtCompileTime)
: (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0),
MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))),
@ -61,20 +61,21 @@ struct traits<Diagonal<MatrixType,DiagIndex> >
};
}
template<typename MatrixType, int DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex> >::type
template<typename MatrixType, int _DiagIndex> class Diagonal
: public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type
{
public:
enum { DiagIndex = _DiagIndex };
typedef typename internal::dense_xpr_base<Diagonal>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {}
inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
inline Index rows() const
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{ 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()); }
inline Index cols() const { return 1; }
@ -113,20 +114,20 @@ template<typename MatrixType, int DiagIndex> class Diagonal
return m_matrix.coeff(row+rowOffset(), row+colOffset());
}
inline Scalar& coeffRef(Index index)
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
}
inline const Scalar& coeffRef(Index index) const
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset());
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
}
inline CoeffReturnType coeff(Index index) const
inline CoeffReturnType coeff(Index idx) const
{
return m_matrix.coeff(index+rowOffset(), index+colOffset());
return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
}
const typename internal::remove_all<typename MatrixType::Nested>::type&
@ -142,7 +143,7 @@ template<typename MatrixType, int DiagIndex> class Diagonal
protected:
typename MatrixType::Nested m_matrix;
const internal::variable_if_dynamic<Index, DiagIndex> m_index;
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...
@ -171,7 +172,7 @@ MatrixBase<Derived>::diagonal()
/** This is the const version of diagonal(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
inline typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());
@ -189,18 +190,18 @@ MatrixBase<Derived>::diagonal() const
*
* \sa MatrixBase::diagonal(), class Diagonal */
template<typename Derived>
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<DynamicIndex>::Type
MatrixBase<Derived>::diagonal(Index index)
{
return typename DiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return typename DiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
}
/** This is the const version of diagonal(Index). */
template<typename Derived>
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Dynamic>::Type
inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<DynamicIndex>::Type
MatrixBase<Derived>::diagonal(Index index) const
{
return typename ConstDiagonalIndexReturnType<Dynamic>::Type(derived(), index);
return typename ConstDiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
}
/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this

14
eigenlib/Eigen/src/Core/DiagonalMatrix.h
View File

@ -56,9 +56,14 @@ class DiagonalBase : public EigenBase<Derived>
inline Index rows() const { return diagonal().size(); }
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>
operator*(const MatrixBase<MatrixDerived> &matrix) const;
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), derived());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
inverse() const
@ -250,7 +255,7 @@ class DiagonalWrapper
#endif
/** Constructor from expression of diagonal coefficients to wrap. */
inline DiagonalWrapper(DiagonalVectorType& diagonal) : m_diagonal(diagonal) {}
inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
/** \returns a const reference to the wrapped expression of diagonal coefficients. */
const DiagonalVectorType& diagonal() const { return m_diagonal; }
@ -284,13 +289,14 @@ MatrixBase<Derived>::asDiagonal() const
* \sa asDiagonal()
*/
template<typename Derived>
bool MatrixBase<Derived>::isDiagonal(RealScalar prec) const
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 = internal::abs(coeff(j,j));
RealScalar absOnDiagonal = abs(coeff(j,j));
if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
}
for(Index j = 0; j < cols(); ++j)

51
eigenlib/Eigen/src/Core/DiagonalProduct.h
View File

@ -26,14 +26,15 @@ struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
_PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
_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::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
//_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 & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0) | AlignedBit,//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
};
};
@ -54,13 +55,21 @@ class DiagonalProduct : internal::no_assignment_operator,
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
const Scalar coeff(Index row, Index col) const
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
@ -69,11 +78,19 @@ class DiagonalProduct : internal::no_assignment_operator,
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>
@ -88,7 +105,7 @@ class DiagonalProduct : internal::no_assignment_operator,
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
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));
@ -103,19 +120,9 @@ class DiagonalProduct : internal::no_assignment_operator,
template<typename Derived>
template<typename DiagonalDerived>
inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight>
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &diagonal) const
MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
{
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), diagonal.derived());
}
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename DiagonalDerived>
template<typename MatrixDerived>
inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived());
}
} // end namespace Eigen

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

@ -112,7 +112,7 @@ MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return numext::real((*this).cwiseAbs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
@ -124,7 +124,8 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return internal::sqrt(squaredNorm());
using std::sqrt;
return sqrt(squaredNorm());
}
/** \returns an expression of the quotient of *this by its own norm.
@ -165,6 +166,7 @@ struct lpNorm_selector
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
static inline RealScalar run(const MatrixBase<Derived>& m)
{
using std::pow;
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@ -223,11 +225,11 @@ MatrixBase<Derived>::lpNorm() const
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isOrthogonal
(const MatrixBase<OtherDerived>& other, RealScalar prec) const
(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());
return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,
@ -242,7 +244,7 @@ bool MatrixBase<Derived>::isOrthogonal
* Output: \verbinclude MatrixBase_isUnitary.out
*/
template<typename Derived>
bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
{
typename Derived::Nested nested(derived());
for(Index i = 0; i < cols(); ++i)

29
eigenlib/Eigen/src/Core/EigenBase.h
View File

@ -126,35 +126,6 @@ Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
return derived();
}
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \c *this * \a other. */
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_EIGENBASE_H

86
eigenlib/Eigen/src/Core/Functors.h
View File

@ -154,6 +154,7 @@ template<typename Scalar> struct scalar_hypot_op {
{
using std::max;
using std::min;
using std::sqrt;
Scalar p = (max)(_x, _y);
Scalar q = (min)(_x, _y);
Scalar qp = q/p;
@ -170,7 +171,7 @@ struct functor_traits<scalar_hypot_op<Scalar> > {
*/
template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op)
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return internal::pow(a, b); }
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); }
};
template<typename Scalar, typename OtherScalar>
struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
@ -204,21 +205,28 @@ struct functor_traits<scalar_difference_op<Scalar> > {
*
* \sa class CwiseBinaryOp, Cwise::operator/()
*/
template<typename Scalar> struct scalar_quotient_op {
template<typename LhsScalar,typename RhsScalar> struct scalar_quotient_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; }
EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pdiv(a,b); }
};
template<typename Scalar>
struct functor_traits<scalar_quotient_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
enum {
Cost = 2 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasDiv
Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost), // rough estimate!
PacketAccess = scalar_quotient_op<LhsScalar,RhsScalar>::Vectorizable
};
};
/** \internal
* \brief Template functor to compute the and of two booleans
*
@ -280,7 +288,7 @@ struct functor_traits<scalar_opposite_op<Scalar> >
template<typename Scalar> struct scalar_abs_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs(a); }
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { using std::abs; return abs(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pabs(a); }
@ -302,7 +310,7 @@ struct functor_traits<scalar_abs_op<Scalar> >
template<typename Scalar> struct scalar_abs2_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs2(a); }
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a,a); }
@ -318,7 +326,7 @@ struct functor_traits<scalar_abs2_op<Scalar> >
*/
template<typename Scalar> struct scalar_conjugate_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return internal::conj(a); }
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using numext::conj; return conj(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
};
@ -355,7 +363,7 @@ template<typename Scalar>
struct scalar_real_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::real(a); }
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); }
};
template<typename Scalar>
struct functor_traits<scalar_real_op<Scalar> >
@ -370,7 +378,7 @@ template<typename Scalar>
struct scalar_imag_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::imag(a); }
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_op<Scalar> >
@ -385,7 +393,7 @@ template<typename Scalar>
struct scalar_real_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::real_ref(*const_cast<Scalar*>(&a)); }
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_real_ref_op<Scalar> >
@ -400,7 +408,7 @@ template<typename Scalar>
struct scalar_imag_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::imag_ref(*const_cast<Scalar*>(&a)); }
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_ref_op<Scalar> >
@ -414,7 +422,7 @@ struct functor_traits<scalar_imag_ref_op<Scalar> >
*/
template<typename Scalar> struct scalar_exp_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op)
inline const Scalar operator() (const Scalar& a) const { return internal::exp(a); }
inline const Scalar operator() (const Scalar& a) const { using std::exp; return exp(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
};
@ -430,7 +438,7 @@ struct functor_traits<scalar_exp_op<Scalar> >
*/
template<typename Scalar> struct scalar_log_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
inline const Scalar operator() (const Scalar& a) const { return internal::log(a); }
inline const Scalar operator() (const Scalar& a) const { using std::log; return log(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
};
@ -533,20 +541,28 @@ template <typename Scalar, bool RandomAccess> struct linspaced_op_impl;
// linear access for packet ops:
// 1) initialization
// base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0])
// 2) each step
// 2) each step (where size is 1 for coeff access or PacketSize for packet access)
// base += [size*step, ..., size*step]
//
// TODO: Perhaps it's better to initialize lazily (so not in the constructor but in packetOp)
// in order to avoid the padd() in operator() ?
template <typename Scalar>
struct linspaced_op_impl<Scalar,false>
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op_impl(Scalar low, Scalar step) :
linspaced_op_impl(const Scalar& low, const Scalar& step) :
m_low(low), m_step(step),
m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)),
m_base(padd(pset1<Packet>(low),pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {}
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const
{
m_base = padd(m_base, pset1<Packet>(m_step));
return m_low+Scalar(i)*m_step;
}
template<typename Index>
EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); }
@ -564,7 +580,7 @@ struct linspaced_op_impl<Scalar,true>
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op_impl(Scalar low, Scalar step) :
linspaced_op_impl(const Scalar& low, const Scalar& step) :
m_low(low), m_step(step),
m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Scalar>(0)) {}
@ -593,7 +609,7 @@ template <typename Scalar, bool RandomAccess> struct functor_traits< linspaced_o
template <typename Scalar, bool RandomAccess> struct linspaced_op
{
typedef typename packet_traits<Scalar>::type Packet;
linspaced_op(Scalar low, Scalar high, int num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {}
linspaced_op(const Scalar& low, const Scalar& high, DenseIndex num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {}
template<typename Index>
EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); }
@ -632,12 +648,14 @@ template <typename Scalar, bool RandomAccess> struct linspaced_op
template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; };
template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; };
// in CwiseBinaryOp, we require the Lhs and Rhs to have the same scalar type, except for multiplication
// where we only require them to have the same _real_ scalar type so one may multiply, say, float by complex<float>.
// In Eigen, any binary op (Product, CwiseBinaryOp) require the Lhs and Rhs to have the same scalar type, except for multiplication
// where the mixing of different types is handled by scalar_product_traits
// In particular, real * complex<real> is allowed.
// FIXME move this to functor_traits adding a functor_default
template<typename Functor> struct functor_allows_mixing_real_and_complex { enum { ret = 0 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_allows_mixing_real_and_complex<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename Functor> struct functor_is_product_like { enum { ret = 0 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_quotient_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
/** \internal
@ -666,7 +684,7 @@ struct functor_traits<scalar_add_op<Scalar> >
*/
template<typename Scalar> struct scalar_sqrt_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op)
inline const Scalar operator() (const Scalar& a) const { return internal::sqrt(a); }
inline const Scalar operator() (const Scalar& a) const { using std::sqrt; return sqrt(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
};
@ -684,7 +702,7 @@ struct functor_traits<scalar_sqrt_op<Scalar> >
*/
template<typename Scalar> struct scalar_cos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
inline Scalar operator() (const Scalar& a) const { return internal::cos(a); }
inline Scalar operator() (const Scalar& a) const { using std::cos; return cos(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
};
@ -703,7 +721,7 @@ struct functor_traits<scalar_cos_op<Scalar> >
*/
template<typename Scalar> struct scalar_sin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
inline const Scalar operator() (const Scalar& a) const { return internal::sin(a); }
inline const Scalar operator() (const Scalar& a) const { using std::sin; return sin(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
};
@ -723,7 +741,7 @@ struct functor_traits<scalar_sin_op<Scalar> >
*/
template<typename Scalar> struct scalar_tan_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
inline const Scalar operator() (const Scalar& a) const { return internal::tan(a); }
inline const Scalar operator() (const Scalar& a) const { using std::tan; return tan(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
};
@ -742,7 +760,7 @@ struct functor_traits<scalar_tan_op<Scalar> >
*/
template<typename Scalar> struct scalar_acos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
inline const Scalar operator() (const Scalar& a) const { return internal::acos(a); }
inline const Scalar operator() (const Scalar& a) const { using std::acos; return acos(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
};
@ -761,7 +779,7 @@ struct functor_traits<scalar_acos_op<Scalar> >
*/
template<typename Scalar> struct scalar_asin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
inline const Scalar operator() (const Scalar& a) const { return internal::asin(a); }
inline const Scalar operator() (const Scalar& a) const { using std::asin; return asin(a); }
typedef typename packet_traits<Scalar>::type Packet;
inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
};
@ -783,7 +801,7 @@ struct scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
inline Scalar operator() (const Scalar& a) const { return internal::pow(a, m_exponent); }
inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); }
const Scalar m_exponent;
};
template<typename Scalar>

22
eigenlib/Eigen/src/Core/Fuzzy.h
View File

@ -19,7 +19,7 @@ namespace internal
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isApprox_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
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);
@ -31,7 +31,7 @@ struct isApprox_selector
template<typename Derived, typename OtherDerived>
struct isApprox_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar)
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
{
return x.matrix() == y.matrix();
}
@ -40,16 +40,16 @@ struct isApprox_selector<Derived, OtherDerived, true>
template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_object_selector
{
static bool run(const Derived& x, const OtherDerived& y, typename Derived::RealScalar prec)
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
}
};
template<typename Derived, typename OtherDerived>
struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
{
static bool run(const Derived& x, const OtherDerived&, typename Derived::RealScalar)
static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@ -58,16 +58,16 @@ struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
struct isMuchSmallerThan_scalar_selector
{
static bool run(const Derived& x, const typename Derived::RealScalar& y, typename Derived::RealScalar prec)
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec * y);
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
}
};
template<typename Derived>
struct isMuchSmallerThan_scalar_selector<Derived, true>
{
static bool run(const Derived& x, const typename Derived::RealScalar&, typename Derived::RealScalar)
static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
{
return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
}
@ -97,7 +97,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isApprox(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
@ -119,7 +119,7 @@ bool DenseBase<Derived>::isApprox(
template<typename Derived>
bool DenseBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
@ -139,7 +139,7 @@ template<typename Derived>
template<typename OtherDerived>
bool DenseBase<Derived>::isMuchSmallerThan(
const DenseBase<OtherDerived>& other,
RealScalar prec
const RealScalar& prec
) const
{
return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);

100
eigenlib/Eigen/src/Core/GeneralProduct.h
View File

@ -222,7 +222,29 @@ class GeneralProduct<Lhs, Rhs, InnerProduct>
***********************************************************************/
namespace internal {
template<int StorageOrder> struct outer_product_selector;
// 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(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) * prod.rhs());
}
template<typename Lhs, typename Rhs>
struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
@ -235,6 +257,8 @@ template<typename Lhs, typename Rhs>
class GeneralProduct<Lhs, Rhs, OuterProduct>
: public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
{
template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
public:
EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
@ -243,41 +267,39 @@ class GeneralProduct<Lhs, Rhs, OuterProduct>
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
}
struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } };
struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
struct adds {
Scalar m_scale;
adds(const Scalar& s) : m_scale(s) {}
template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const {
dst.const_cast_derived() += m_scale * src;
}
};
template<typename Dest>
inline void evalTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>());
}
template<typename Dest>
inline void addTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
template<typename Dest>
inline void subTo(Dest& dest) const {
internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>());
}
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
internal::outer_product_selector<(int(Dest::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dest, alpha);
internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>());
}
};
namespace internal {
template<> struct outer_product_selector<ColMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
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)
dest.col(j) += (alpha * prod.rhs().coeff(j)) * prod.lhs();
}
};
template<> struct outer_product_selector<RowMajor> {
template<typename ProductType, typename Dest>
static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
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)
dest.row(i) += (alpha * prod.lhs().coeff(i)) * prod.rhs();
}
};
} // end namespace internal
/***********************************************************************
* Implementation of General Matrix Vector Product
***********************************************************************/
@ -311,7 +333,7 @@ class GeneralProduct<Lhs, Rhs, GemvProduct>
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
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)
@ -320,7 +342,7 @@ class GeneralProduct<Lhs, Rhs, GemvProduct>
enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
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,
@ -335,7 +357,7 @@ template<int StorageOrder, bool BlasCompatible>
struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
Transpose<Dest> destT(dest);
enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
@ -384,7 +406,7 @@ struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
template<> struct gemv_selector<OnTheRight,ColMajor,true>
{
template<typename ProductType, typename Dest>
static inline void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
static inline void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
typedef typename ProductType::Index Index;
typedef typename ProductType::LhsScalar LhsScalar;
@ -413,7 +435,7 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
@ -457,7 +479,7 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
template<> struct gemv_selector<OnTheRight,RowMajor,true>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
typedef typename ProductType::LhsScalar LhsScalar;
typedef typename ProductType::RhsScalar RhsScalar;
@ -508,7 +530,7 @@ template<> struct gemv_selector<OnTheRight,RowMajor,true>
template<> struct gemv_selector<OnTheRight,ColMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure dest is sequentially stored in memory, otherwise use a temp
@ -521,7 +543,7 @@ template<> struct gemv_selector<OnTheRight,ColMajor,false>
template<> struct gemv_selector<OnTheRight,RowMajor,false>
{
template<typename ProductType, typename Dest>
static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha)
{
typedef typename Dest::Index Index;
// TODO makes sure rhs is sequentially stored in memory, otherwise use a temp

50
eigenlib/Eigen/src/Core/GenericPacketMath.h
View File

@ -106,7 +106,7 @@ pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> inline Packet
pconj(const Packet& a) { return conj(a); }
pconj(const Packet& a) { return numext::conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> inline Packet
@ -130,7 +130,7 @@ pmax(const Packet& a,
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet
pabs(const Packet& a) { return abs(a); }
pabs(const Packet& a) { using std::abs; return abs(a); }
/** \internal \returns the bitwise and of \a a and \a b */
template<typename Packet> inline Packet
@ -156,7 +156,11 @@ pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
template<typename Packet> inline Packet
ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from duplicated, e.g.: (from[0],from[0],from[1],from[1]) */
/** \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; }
@ -215,7 +219,12 @@ template<typename Packet> inline Packet preverse(const Packet& a)
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> inline Packet pcplxflip(const Packet& a)
{ return Packet(imag(a),real(a)); }
{
// FIXME: uncomment the following in case we drop the internal imag and real functions.
// using std::imag;
// using std::real;
return Packet(imag(a),real(a));
}
/**************************
* Special math functions
@ -223,35 +232,35 @@ template<typename Packet> inline Packet pcplxflip(const Packet& a)
/** \internal \returns the sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psin(const Packet& a) { return sin(a); }
Packet psin(const Packet& a) { using std::sin; return sin(a); }
/** \internal \returns the cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pcos(const Packet& a) { return cos(a); }
Packet pcos(const Packet& a) { using std::cos; return cos(a); }
/** \internal \returns the tan of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ptan(const Packet& a) { return tan(a); }
Packet ptan(const Packet& a) { using std::tan; return tan(a); }
/** \internal \returns the arc sine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pasin(const Packet& a) { return asin(a); }
Packet pasin(const Packet& a) { using std::asin; return asin(a); }
/** \internal \returns the arc cosine of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pacos(const Packet& a) { return acos(a); }
Packet pacos(const Packet& a) { using std::acos; return acos(a); }
/** \internal \returns the exp of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pexp(const Packet& a) { return exp(a); }
Packet pexp(const Packet& a) { using std::exp; return exp(a); }
/** \internal \returns the log of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { return log(a); }
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the square-root of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet psqrt(const Packet& a) { return sqrt(a); }
Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
@ -302,8 +311,21 @@ struct palign_impl
static inline void run(PacketType&, const PacketType&) {}
};
/** \internal update \a first using the concatenation of the \a Offset last elements
* of \a first and packet_size minus \a Offset first elements of \a second */
/** \internal update \a first using the concatenation of the packet_size minus \a Offset last elements
* of \a first and \a Offset first elements of \a second.
*
* This function is currently only used to optimize matrix-vector products on unligned matrices.
* It takes 2 packets that represent a contiguous memory array, and returns a packet starting
* at the position \a Offset. For instance, for packets of 4 elements, we have:
* Input:
* - first = {f0,f1,f2,f3}
* - second = {s0,s1,s2,s3}
* Output:
* - if Offset==0 then {f0,f1,f2,f3}
* - if Offset==1 then {f1,f2,f3,s0}
* - if Offset==2 then {f2,f3,s0,s1}
* - if Offset==3 then {f3,s0,s1,s3}
*/
template<int Offset,typename PacketType>
inline void palign(PacketType& first, const PacketType& second)
{

49
eigenlib/Eigen/src/Core/GlobalFunctions.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2012 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
@ -11,7 +11,7 @@
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR) \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
NAME(const Eigen::ArrayBase<Derived>& x) { \
@ -35,20 +35,21 @@
};
namespace std
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op)
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)
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) {
@ -64,16 +65,13 @@ namespace std
exponents.derived()
);
}
}
namespace Eigen
{
/**
* \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/(typename Derived::Scalar s, const Eigen::ArrayBase<Derived>& a)
operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
@ -85,19 +83,10 @@ namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op)
}
}
// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
#endif // EIGEN_GLOBAL_FUNCTIONS_H

13
eigenlib/Eigen/src/Core/IO.h
View File

@ -55,9 +55,8 @@ struct IOFormat
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
const std::string& _matPrefix="", const std::string& _matSuffix="")
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
rowSpacer = "";
int i = int(matSuffix.length())-1;
while (i>=0 && matSuffix[i]!='\n')
{
@ -129,6 +128,7 @@ struct significant_decimals_default_impl
static inline int run()
{
using std::ceil;
using std::log;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
@ -185,21 +185,22 @@ std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat&
explicit_precision = fmt.precision;
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
bool align_cols = !(fmt.flags & DontAlignCols);
if(align_cols)
{
// compute the largest width
for(Index j = 1; j < m.cols(); ++j)
for(Index j = 0; j < m.cols(); ++j)
for(Index i = 0; i < m.rows(); ++i)
{
std::stringstream sstr;
if(explicit_precision) sstr.precision(explicit_precision);
sstr.copyfmt(s);
sstr << m.coeff(i,j);
width = std::max<Index>(width, Index(sstr.str().length()));
}
}
std::streamsize old_precision = 0;
if(explicit_precision) old_precision = s.precision(explicit_precision);
s << fmt.matPrefix;
for(Index i = 0; i < m.rows(); ++i)
{

30
eigenlib/Eigen/src/Core/Map.h
View File

@ -133,36 +133,36 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the fixed-size case.
*
* \param data pointer to the array to map
* \param stride optional Stride object, passing the strides.
* \param dataPtr pointer to the array to map
* \param a_stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data)), m_stride(stride)
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
*
* \param data pointer to the array to map
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
* \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.
*/
inline Map(PointerArgType data, Index size, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), size), m_stride(stride)
inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size matrix case.
*
* \param data pointer to the array to map
* \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.
* \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.
*/
inline Map(PointerArgType data, Index rows, Index cols, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data), rows, cols), m_stride(stride)
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)
{
PlainObjectType::Base::_check_template_params();
}

50
eigenlib/Eigen/src/Core/MapBase.h
View File

@ -87,9 +87,9 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
*/
inline const Scalar* data() const { return m_data; }
inline const Scalar& coeff(Index row, Index col) const
inline const Scalar& coeff(Index rowId, Index colId) const
{
return m_data[col * colStride() + row * rowStride()];
return m_data[colId * colStride() + rowId * rowStride()];
}
inline const Scalar& coeff(Index index) const
@ -98,9 +98,9 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return m_data[index * innerStride()];
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return this->m_data[col * colStride() + row * rowStride()];
return this->m_data[colId * colStride() + rowId * rowStride()];
}
inline const Scalar& coeffRef(Index index) const
@ -110,10 +110,10 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_data + (col * colStride() + row * rowStride()));
(m_data + (colId * colStride() + rowId * rowStride()));
}
template<int LoadMode>
@ -123,29 +123,29 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
inline MapBase(PointerType data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
checkSanity();
}
inline MapBase(PointerType data, Index size)
: m_data(data),
m_rows(RowsAtCompileTime == Dynamic ? size : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? size : Index(ColsAtCompileTime))
inline MapBase(PointerType dataPtr, Index vecSize)
: m_data(dataPtr),
m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
eigen_assert(size >= 0);
eigen_assert(data == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
eigen_assert(vecSize >= 0);
eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
checkSanity();
}
inline MapBase(PointerType data, Index rows, Index cols)
: m_data(data), m_rows(rows), m_cols(cols)
inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols)
: m_data(dataPtr), m_rows(nbRows), m_cols(nbCols)
{
eigen_assert( (data == 0)
|| ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
eigen_assert( (dataPtr == 0)
|| ( nbRows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows)
&& nbCols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols)));
checkSanity();
}
@ -210,23 +210,23 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
}
template<int StoreMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index row, Index col, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + (col * colStride() + row * rowStride()), x);
(this->m_data + (col * colStride() + row * rowStride()), val);
}
template<int StoreMode>
inline void writePacket(Index index, const PacketScalar& x)
inline void writePacket(Index index, const PacketScalar& val)
{
EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
internal::pstoret<Scalar, PacketScalar, StoreMode>
(this->m_data + index * innerStride(), x);
(this->m_data + index * innerStride(), val);
}
explicit inline MapBase(PointerType data) : Base(data) {}
inline MapBase(PointerType data, Index size) : Base(data, size) {}
inline MapBase(PointerType data, Index rows, Index cols) : Base(data, rows, cols) {}
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) {}
Derived& operator=(const MapBase& other)
{

354
eigenlib/Eigen/src/Core/MathFunctions.h
View File

@ -51,16 +51,15 @@ struct global_math_functions_filtering_base
typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
};
#define EIGEN_MATHFUNC_IMPL(func, scalar) func##_impl<typename global_math_functions_filtering_base<scalar>::type>
#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename func##_retval<typename global_math_functions_filtering_base<scalar>::type>::type
#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
/****************************************************************************
* Implementation of real *
****************************************************************************/
template<typename Scalar>
struct real_impl
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct real_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x)
@ -69,34 +68,32 @@ struct real_impl
}
};
template<typename RealScalar>
struct real_impl<std::complex<RealScalar> >
template<typename Scalar>
struct real_default_impl<Scalar,true>
{
static inline RealScalar run(const std::complex<RealScalar>& x)
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x)
{
using std::real;
return real(x);
}
};
template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
template<typename Scalar>
struct real_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
}
/****************************************************************************
* Implementation of imag *
****************************************************************************/
template<typename Scalar>
struct imag_impl
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct imag_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar&)
@ -105,28 +102,25 @@ struct imag_impl
}
};
template<typename RealScalar>
struct imag_impl<std::complex<RealScalar> >
template<typename Scalar>
struct imag_default_impl<Scalar,true>
{
static inline RealScalar run(const std::complex<RealScalar>& x)
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x)
{
using std::imag;
return imag(x);
}
};
template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
template<typename Scalar>
struct imag_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
}
/****************************************************************************
* Implementation of real_ref *
****************************************************************************/
@ -151,18 +145,6 @@ struct real_ref_retval
typedef typename NumTraits<Scalar>::Real & type;
};
template<typename Scalar>
inline typename add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
{
return real_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
}
/****************************************************************************
* Implementation of imag_ref *
****************************************************************************/
@ -203,23 +185,11 @@ struct imag_ref_retval
typedef typename NumTraits<Scalar>::Real & type;
};
template<typename Scalar>
inline typename add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
{
return imag_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
}
/****************************************************************************
* Implementation of conj *
****************************************************************************/
template<typename Scalar>
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct conj_impl
{
static inline Scalar run(const Scalar& x)
@ -228,10 +198,10 @@ struct conj_impl
}
};
template<typename RealScalar>
struct conj_impl<std::complex<RealScalar> >
template<typename Scalar>
struct conj_impl<Scalar,true>
{
static inline std::complex<RealScalar> run(const std::complex<RealScalar>& x)
static inline Scalar run(const Scalar& x)
{
using std::conj;
return conj(x);
@ -244,39 +214,6 @@ struct conj_retval
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
}
/****************************************************************************
* Implementation of abs *
****************************************************************************/
template<typename Scalar>
struct abs_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x)
{
using std::abs;
return abs(x);
}
};
template<typename Scalar>
struct abs_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(abs, Scalar) abs(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(abs, Scalar)::run(x);
}
/****************************************************************************
* Implementation of abs2 *
****************************************************************************/
@ -306,12 +243,6 @@ struct abs2_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
}
/****************************************************************************
* Implementation of norm1 *
****************************************************************************/
@ -322,6 +253,7 @@ struct norm1_default_impl
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x)
{
using std::abs;
return abs(real(x)) + abs(imag(x));
}
};
@ -331,6 +263,7 @@ struct norm1_default_impl<Scalar, false>
{
static inline Scalar run(const Scalar& x)
{
using std::abs;
return abs(x);
}
};
@ -344,12 +277,6 @@ struct norm1_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
}
/****************************************************************************
* Implementation of hypot *
****************************************************************************/
@ -362,9 +289,12 @@ struct hypot_impl
{
using std::max;
using std::min;
using std::abs;
using std::sqrt;
RealScalar _x = abs(x);
RealScalar _y = abs(y);
RealScalar p = (max)(_x, _y);
if(p==RealScalar(0)) return 0;
RealScalar q = (min)(_x, _y);
RealScalar qp = q/p;
return p * sqrt(RealScalar(1) + qp*qp);
@ -377,12 +307,6 @@ struct hypot_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of cast *
****************************************************************************/
@ -405,97 +329,29 @@ inline NewType cast(const OldType& x)
}
/****************************************************************************
* Implementation of sqrt *
* Implementation of atanh2 *
****************************************************************************/
template<typename Scalar, bool IsInteger>
struct sqrt_default_impl
{
static inline Scalar run(const Scalar& x)
{
using std::sqrt;
return sqrt(x);
}
};
template<typename Scalar>
struct sqrt_default_impl<Scalar, true>
{
static inline Scalar run(const Scalar&)
{
#ifdef EIGEN2_SUPPORT
eigen_assert(!NumTraits<Scalar>::IsInteger);
#else
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#endif
return Scalar(0);
}
};
template<typename Scalar>
struct sqrt_impl : sqrt_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
template<typename Scalar>
struct sqrt_retval
{
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x);
}
/****************************************************************************
* Implementation of standard unary real functions (exp, log, sin, cos, ... *
****************************************************************************/
// This macro instanciate all the necessary template mechanism which is common to all unary real functions.
#define EIGEN_MATHFUNC_STANDARD_REAL_UNARY(NAME) \
template<typename Scalar, bool IsInteger> struct NAME##_default_impl { \
static inline Scalar run(const Scalar& x) { using std::NAME; return NAME(x); } \
}; \
template<typename Scalar> struct NAME##_default_impl<Scalar, true> { \
static inline Scalar run(const Scalar&) { \
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) \
return Scalar(0); \
} \
}; \
template<typename Scalar> struct NAME##_impl \
: NAME##_default_impl<Scalar, NumTraits<Scalar>::IsInteger> \
{}; \
template<typename Scalar> struct NAME##_retval { typedef Scalar type; }; \
template<typename Scalar> \
inline EIGEN_MATHFUNC_RETVAL(NAME, Scalar) NAME(const Scalar& x) { \
return EIGEN_MATHFUNC_IMPL(NAME, Scalar)::run(x); \
}
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(exp)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(log)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(sin)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(cos)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(tan)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(asin)
EIGEN_MATHFUNC_STANDARD_REAL_UNARY(acos)
/****************************************************************************
* Implementation of atan2 *
****************************************************************************/
template<typename Scalar, bool IsInteger>
struct atan2_default_impl
struct atanh2_default_impl
{
typedef Scalar retval;
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline Scalar run(const Scalar& x, const Scalar& y)
{
using std::atan2;
return atan2(x, y);
using std::abs;
using std::log;
using std::sqrt;
Scalar z = x / y;
if (y == Scalar(0) || abs(z) > sqrt(NumTraits<RealScalar>::epsilon()))
return RealScalar(0.5) * log((y + x) / (y - x));
else
return z + z*z*z / RealScalar(3);
}
};
template<typename Scalar>
struct atan2_default_impl<Scalar, true>
struct atanh2_default_impl<Scalar, true>
{
static inline Scalar run(const Scalar&, const Scalar&)
{
@ -505,20 +361,14 @@ struct atan2_default_impl<Scalar, true>
};
template<typename Scalar>
struct atan2_impl : atan2_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
struct atanh2_impl : atanh2_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
template<typename Scalar>
struct atan2_retval
struct atanh2_retval
{
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(atan2, Scalar) atan2(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(atan2, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of pow *
****************************************************************************/
@ -562,12 +412,6 @@ struct pow_retval
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of random *
****************************************************************************/
@ -666,11 +510,10 @@ struct random_default_impl<Scalar, false, true>
#else
enum { rand_bits = floor_log2<(unsigned int)(RAND_MAX)+1>::value,
scalar_bits = sizeof(Scalar) * CHAR_BIT,
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits))
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
};
Scalar x = Scalar(std::rand() >> shift);
Scalar offset = NumTraits<Scalar>::IsSigned ? Scalar(1 << (rand_bits-1)) : Scalar(0);
return x - offset;
return Scalar((std::rand() >> shift) - offset);
#endif
}
};
@ -702,6 +545,97 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
}
} // end namespace internal
/****************************************************************************
* Generic math function *
****************************************************************************/
namespace numext {
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
}
template<typename Scalar>
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
{
return internal::real_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
}
template<typename Scalar>
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
{
return internal::imag_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
}
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
}
// std::isfinite is non standard, so let's define our own version,
// even though it is not very efficient.
template<typename T> bool (isfinite)(const T& x)
{
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
}
} // end namespace numext
namespace internal {
/****************************************************************************
* Implementation of fuzzy comparisons *
****************************************************************************/
@ -718,11 +652,13 @@ struct scalar_fuzzy_default_impl<Scalar, false, false>
template<typename OtherScalar>
static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
{
using std::abs;
return abs(x) <= abs(y) * prec;
}
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
using std::min;
using std::abs;
return abs(x - y) <= (min)(abs(x), abs(y)) * prec;
}
static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
@ -757,12 +693,12 @@ struct scalar_fuzzy_default_impl<Scalar, true, false>
template<typename OtherScalar>
static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
{
return abs2(x) <= abs2(y) * prec * prec;
return numext::abs2(x) <= numext::abs2(y) * prec * prec;
}
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
using std::min;
return abs2(x - y) <= (min)(abs2(x), abs2(y)) * prec * prec;
return numext::abs2(x - y) <= (min)(numext::abs2(x), numext::abs2(y)) * prec * prec;
}
};
@ -824,17 +760,7 @@ template<> struct scalar_fuzzy_impl<bool>
};
/****************************************************************************
* Special functions *
****************************************************************************/
// std::isfinite is non standard, so let's define our own version,
// even though it is not very efficient.
template<typename T> bool (isfinite)(const T& x)
{
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
}
} // end namespace internal
} // end namespace Eigen

10
eigenlib/Eigen/src/Core/Matrix.h
View File

@ -200,16 +200,16 @@ class Matrix
*
* \sa resize(Index,Index)
*/
EIGEN_STRONG_INLINE explicit Matrix() : Base()
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
// FIXME is it still needed
Matrix(internal::constructor_without_unaligned_array_assert)
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED }
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
/** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
*
@ -224,7 +224,7 @@ class Matrix
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
eigen_assert(dim >= 0);
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -304,7 +304,7 @@ class Matrix
: Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
Base::_check_template_params();
Base::resize(other.rows(), other.cols());
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;

77
eigenlib/Eigen/src/Core/MatrixBase.h
View File

@ -162,6 +162,9 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
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);
#endif // not EIGEN_PARSED_BY_DOXYGEN
template<typename OtherDerived>
@ -212,8 +215,8 @@ template<typename Derived> class MatrixBase
typedef Diagonal<Derived> DiagonalReturnType;
DiagonalReturnType diagonal();
typedef const Diagonal<const Derived> ConstDiagonalReturnType;
const ConstDiagonalReturnType diagonal() const;
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
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; };
@ -224,11 +227,11 @@ template<typename Derived> class MatrixBase
// Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
// On the other hand they confuse MSVC8...
#if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later
typename MatrixBase::template DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
typename MatrixBase::template DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
typename MatrixBase::template ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#else
typename DiagonalIndexReturnType<Dynamic>::Type diagonal(Index index);
typename ConstDiagonalIndexReturnType<Dynamic>::Type diagonal(Index index) const;
typename DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
#endif
#ifdef EIGEN2_SUPPORT
@ -255,7 +258,7 @@ template<typename Derived> class MatrixBase
template<unsigned int UpLo> typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
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);
@ -271,16 +274,16 @@ template<typename Derived> class MatrixBase
Derived& setIdentity();
Derived& setIdentity(Index rows, Index cols);
bool isIdentity(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
template<typename OtherDerived>
bool isOrthogonal(const MatrixBase<OtherDerived>& other,
RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
/** \returns true if each coefficients of \c *this and \a other are all exactly equal.
* \warning When using floating point scalar values you probably should rather use a
@ -314,7 +317,7 @@ template<typename Derived> class MatrixBase
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link ArrayBase Array \endlink expression of this matrix
/** \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(); }
@ -454,6 +457,7 @@ template<typename Derived> class MatrixBase
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>
@ -506,6 +510,51 @@ template<typename Derived> class MatrixBase
{EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
};
/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/
/** replaces \c *this by \c *this * \a other.
*
* \returns a reference to \c *this
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline Derived&
MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
*
* Example: \include MatrixBase_applyOnTheRight.cpp
* Output: \verbinclude MatrixBase_applyOnTheRight.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheRight(derived());
}
/** replaces \c *this by \a other * \c *this.
*
* Example: \include MatrixBase_applyOnTheLeft.cpp
* Output: \verbinclude MatrixBase_applyOnTheLeft.out
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
{
other.derived().applyThisOnTheLeft(derived());
}
} // end namespace Eigen
#endif // EIGEN_MATRIXBASE_H

9
eigenlib/Eigen/src/Core/NoAlias.h
View File

@ -80,8 +80,17 @@ class NoAlias
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 OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
ExpressionType& expression() const
{
return m_expression;
}
protected:
ExpressionType& m_expression;
};

3
eigenlib/Eigen/src/Core/NumTraits.h
View File

@ -140,6 +140,9 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
};
static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
};
} // end namespace Eigen

46
eigenlib/Eigen/src/Core/PermutationMatrix.h
View File

@ -105,13 +105,13 @@ class PermutationBase : public EigenBase<Derived>
#endif
/** \returns the number of rows */
inline Index rows() const { return indices().size(); }
inline Index rows() const { return Index(indices().size()); }
/** \returns the number of columns */
inline Index cols() const { return indices().size(); }
inline Index cols() const { return Index(indices().size()); }
/** \returns the size of a side of the respective square matrix, i.e., the number of indices */
inline Index size() const { return indices().size(); }
inline Index size() const { return Index(indices().size()); }
#ifndef EIGEN_PARSED_BY_DOXYGEN
template<typename DenseDerived>
@ -139,9 +139,9 @@ class PermutationBase : public EigenBase<Derived>
/** Resizes to given size.
*/
inline void resize(Index size)
inline void resize(Index newSize)
{
indices().resize(size);
indices().resize(newSize);
}
/** Sets *this to be the identity permutation matrix */
@ -153,9 +153,9 @@ class PermutationBase : public EigenBase<Derived>
/** Sets *this to be the identity permutation matrix of given size.
*/
void setIdentity(Index size)
void setIdentity(Index newSize)
{
resize(size);
resize(newSize);
setIdentity();
}
@ -317,7 +317,7 @@ class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompile
* array's size.
*/
template<typename Other>
explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
explicit inline PermutationMatrix(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
{}
/** Convert the Transpositions \a tr to a permutation matrix */
@ -406,12 +406,12 @@ class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,
typedef typename IndicesType::Scalar Index;
#endif
inline Map(const Index* indices)
: m_indices(indices)
inline Map(const Index* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indices, Index size)
: m_indices(indices,size)
inline Map(const Index* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
/** Copies the other permutation into *this */
@ -490,8 +490,8 @@ class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesTyp
typedef typename Traits::IndicesType IndicesType;
#endif
inline PermutationWrapper(const IndicesType& indices)
: m_indices(indices)
inline PermutationWrapper(const IndicesType& a_indices)
: m_indices(a_indices)
{}
/** const version of indices(). */
@ -541,24 +541,26 @@ 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 int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
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 int n = Side==OnTheLeft ? rows() : cols();
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 && 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);
int r = 0;
Index r = 0;
while(r < m_permutation.size())
{
// search for the next seed
@ -566,10 +568,10 @@ struct permut_matrix_product_retval
if(r>=m_permutation.size())
break;
// we got one, let's follow it until we are back to the seed
int k0 = r++;
int kPrev = k0;
Index k0 = r++;
Index kPrev = k0;
mask.coeffRef(k0) = true;
for(int k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k))
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>

175
eigenlib/Eigen/src/Core/PlainObjectBase.h
View File

@ -11,30 +11,46 @@
#ifndef EIGEN_DENSESTORAGEBASE_H
#define EIGEN_DENSESTORAGEBASE_H
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
# define EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
#else
# define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
# undef EIGEN_INITIALIZE_COEFFS
# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
namespace Eigen {
namespace internal {
template<typename Index>
EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(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
bool error = (rows < 0 || cols < 0) ? true
: (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow {
template<typename Index>
static EIGEN_ALWAYS_INLINE void run(Index, Index)
{
}
};
template <typename Derived, typename OtherDerived = Derived, bool IsVector = bool(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
template<> struct check_rows_cols_for_overflow<Dynamic> {
template<typename Index>
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
bool error = (rows == 0 || cols == 0) ? false
: (rows > max_index / cols);
if (error)
throw_std_bad_alloc();
}
};
template <typename Derived,
typename OtherDerived = Derived,
bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
struct conservative_resize_like_impl;
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
@ -119,12 +135,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
EIGEN_STRONG_INLINE const Scalar& coeff(Index row, Index col) const
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
@ -132,12 +148,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
@ -145,12 +161,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[index];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index row, Index col) const
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
if(Flags & RowMajorBit)
return m_storage.data()[col + row * m_storage.cols()];
return m_storage.data()[colId + rowId * m_storage.cols()];
else // column-major
return m_storage.data()[row + col * m_storage.rows()];
return m_storage.data()[rowId + colId * m_storage.rows()];
}
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
@ -160,12 +176,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return internal::ploadt<PacketScalar, LoadMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()));
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()));
}
/** \internal */
@ -177,19 +193,19 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>
(m_storage.data() + (Flags & RowMajorBit
? col + row * m_storage.cols()
: row + col * m_storage.rows()), x);
? colId + rowId * m_storage.cols()
: rowId + colId * m_storage.rows()), val);
}
/** \internal */
template<int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& x)
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
{
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, x);
internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
}
/** \returns a const pointer to the data array of this matrix */
@ -216,17 +232,22 @@ 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 rows, Index cols)
EIGEN_STRONG_INLINE void resize(Index nbRows, Index nbCols)
{
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
internal::check_rows_cols_for_overflow(rows, cols);
Index size = rows*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);
#ifdef EIGEN_INITIALIZE_COEFFS
Index size = nbRows*nbCols;
bool size_changed = size != this->size();
m_storage.resize(size, rows, cols);
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
m_storage.resize(size, nbRows, nbCols);
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols, rows, cols);
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols);
m_storage.resize(nbRows*nbCols, nbRows, nbCols);
#endif
}
@ -244,16 +265,16 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
inline void resize(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0);
#ifdef EIGEN_INITIALIZE_COEFFS
bool size_changed = size != this->size();
#endif
if(RowsAtCompileTime == 1)
m_storage.resize(size, 1, size);
else
m_storage.resize(size, size, 1);
#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
#ifdef EIGEN_INITIALIZE_COEFFS
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#endif
}
@ -265,9 +286,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(NoChange_t, Index cols)
inline void resize(NoChange_t, Index nbCols)
{
resize(rows(), cols);
resize(rows(), nbCols);
}
/** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
@ -278,9 +299,9 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \sa resize(Index,Index)
*/
inline void resize(Index rows, NoChange_t)
inline void resize(Index nbRows, NoChange_t)
{
resize(rows, cols());
resize(nbRows, cols());
}
/** Resizes \c *this to have the same dimensions as \a other.
@ -294,7 +315,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
internal::check_rows_cols_for_overflow(other.rows(), other.cols());
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols());
const Index othersize = other.rows()*other.cols();
if(RowsAtCompileTime == 1)
{
@ -318,9 +339,9 @@ 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 rows, Index cols)
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, Index nbCols)
{
internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
internal::conservative_resize_like_impl<Derived>::run(*this, nbRows, nbCols);
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@ -330,10 +351,10 @@ 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 rows, NoChange_t)
EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, NoChange_t)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows, cols());
conservativeResize(nbRows, cols());
}
/** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
@ -343,10 +364,10 @@ 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 cols)
EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index nbCols)
{
// Note: see the comment in conservativeResize(Index,Index)
conservativeResize(rows(), cols);
conservativeResize(rows(), nbCols);
}
/** Resizes the vector to \a size while retaining old values.
@ -400,10 +421,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return Base::operator=(func);
}
EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage()
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -412,15 +433,15 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
PlainObjectBase(internal::constructor_without_unaligned_array_assert)
: m_storage(internal::constructor_without_unaligned_array_assert())
{
// _check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
#endif
EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
: m_storage(size, rows, cols)
EIGEN_STRONG_INLINE PlainObjectBase(Index a_size, Index nbRows, Index nbCols)
: m_storage(a_size, nbRows, nbCols)
{
// _check_template_params();
// EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
/** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&)
@ -439,7 +460,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
{
_check_template_params();
internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols());
internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.derived().rows(), other.derived().cols());
Base::operator=(other.derived());
}
@ -551,6 +572,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
: (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);
#else
resizeLike(other);
#endif
@ -600,23 +622,19 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(Index nbRows, Index nbCols, 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)
eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
internal::check_rows_cols_for_overflow(rows, cols);
m_storage.resize(rows*cols,rows,cols);
EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
resize(nbRows,nbCols);
}
template<typename T0, typename T1>
EIGEN_STRONG_INLINE void _init2(const Scalar& x, const Scalar& y, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = x;
m_storage.data()[1] = y;
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
}
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
@ -653,8 +671,10 @@ private:
enum { ThisConstantIsPrivateInPlainObjectBase };
};
namespace internal {
template <typename Derived, typename OtherDerived, bool IsVector>
struct internal::conservative_resize_like_impl
struct conservative_resize_like_impl
{
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index rows, Index cols)
@ -665,7 +685,7 @@ struct internal::conservative_resize_like_impl
if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows
(!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns
{
internal::check_rows_cols_for_overflow(rows, cols);
internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols);
_this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
}
else
@ -714,11 +734,14 @@ struct internal::conservative_resize_like_impl
}
};
namespace internal {
// Here, the specialization for vectors inherits from the general matrix case
// to allow calling .conservativeResize(rows,cols) on vectors.
template <typename Derived, typename OtherDerived>
struct conservative_resize_like_impl<Derived,OtherDerived,true>
: conservative_resize_like_impl<Derived,OtherDerived,false>
{
using conservative_resize_like_impl<Derived,OtherDerived,false>::run;
typedef typename Derived::Index Index;
static void run(DenseBase<Derived>& _this, Index size)
{

98
eigenlib/Eigen/src/Core/Product.h
View File

@ -1,98 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
#ifndef EIGEN_PRODUCT_H
#define EIGEN_PRODUCT_H
template<typename Lhs, typename Rhs> class Product;
template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl;
/** \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
*
* This class represents an expression of the product of two arbitrary matrices.
*
*/
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>
{
public:
typedef typename ProductImpl<
Lhs, Rhs,
typename internal::promote_storage_type<typename Lhs::StorageKind,
typename Rhs::StorageKind>::ret>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
typedef typename Lhs::Nested LhsNested;
typedef typename Rhs::Nested 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_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(); }
const LhsNestedCleaned& lhs() const { return m_lhs; }
const RhsNestedCleaned& rhs() const { return m_rhs; }
protected:
const LhsNested m_lhs;
const RhsNested m_rhs;
};
template<typename Lhs, typename Rhs>
class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type
{
typedef Product<Lhs, Rhs> Derived;
public:
typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
};
#endif // EIGEN_PRODUCT_H

20
eigenlib/Eigen/src/Core/ProductBase.h
View File

@ -87,10 +87,10 @@ class ProductBase : public MatrixBase<Derived>
typedef typename Base::PlainObject PlainObject;
ProductBase(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
ProductBase(const Lhs& a_lhs, const Rhs& a_rhs)
: m_lhs(a_lhs), m_rhs(a_rhs)
{
eigen_assert(lhs.cols() == rhs.rows()
eigen_assert(a_lhs.cols() == a_rhs.rows()
&& "invalid matrix product"
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
@ -108,7 +108,7 @@ class ProductBase : public MatrixBase<Derived>
inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { derived().scaleAndAddTo(dst,alpha); }
inline void scaleAndAddTo(Dest& dst, const Scalar& alpha) const { derived().scaleAndAddTo(dst,alpha); }
const _LhsNested& lhs() const { return m_lhs; }
const _RhsNested& rhs() const { return m_rhs; }
@ -195,25 +195,25 @@ class ScaledProduct;
// Also note that here we accept any compatible scalar types
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::Scalar x)
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::Scalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, typename Derived::RealScalar x)
operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::RealScalar& x)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
const ScaledProduct<Derived>
operator*(typename Derived::Scalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
operator*(const typename Derived::Scalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
template<typename Derived,typename Lhs,typename Rhs>
typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value,
const ScaledProduct<Derived> >::type
operator*(typename Derived::RealScalar x,const ProductBase<Derived,Lhs,Rhs>& prod)
operator*(const typename Derived::RealScalar& x,const ProductBase<Derived,Lhs,Rhs>& prod)
{ return ScaledProduct<Derived>(prod.derived(), x); }
namespace internal {
@ -241,7 +241,7 @@ class ScaledProduct
typedef typename Base::PlainObject PlainObject;
// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct)
ScaledProduct(const NestedProduct& prod, Scalar x)
ScaledProduct(const NestedProduct& prod, const Scalar& x)
: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
template<typename Dest>
@ -254,7 +254,7 @@ class ScaledProduct
inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
template<typename Dest>
inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); }
inline void scaleAndAddTo(Dest& dst, const Scalar& a_alpha) const { m_prod.derived().scaleAndAddTo(dst,a_alpha * m_alpha); }
const Scalar& alpha() const { return m_alpha; }

14
eigenlib/Eigen/src/Core/Random.h
View File

@ -112,7 +112,7 @@ inline Derived& DenseBase<Derived>::setRandom()
return *this = Random(rows(), cols());
}
/** Resizes to the given \a size, and sets all coefficients in this expression to random values.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
*
* \only_for_vectors
*
@ -123,16 +123,16 @@ inline Derived& DenseBase<Derived>::setRandom()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index size)
PlainObjectBase<Derived>::setRandom(Index newSize)
{
resize(size);
resize(newSize);
return setRandom();
}
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out
@ -141,9 +141,9 @@ PlainObjectBase<Derived>::setRandom(Index size)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
PlainObjectBase<Derived>::setRandom(Index nbRows, Index nbCols)
{
resize(rows, cols);
resize(nbRows, nbCols);
return setRandom();
}

6
eigenlib/Eigen/src/Core/Redux.h
View File

@ -330,7 +330,8 @@ DenseBase<Derived>::redux(const Func& func) const
::run(derived(), func);
}
/** \returns the minimum of all coefficients of *this
/** \returns the minimum of all coefficients of \c *this.
* \warning the result is undefined if \c *this contains NaN.
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
@ -339,7 +340,8 @@ DenseBase<Derived>::minCoeff() const
return this->redux(Eigen::internal::scalar_min_op<Scalar>());
}
/** \returns the maximum of all coefficients of *this
/** \returns the maximum of all coefficients of \c *this.
* \warning the result is undefined if \c *this contains NaN.
*/
template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar

256
eigenlib/Eigen/src/Core/Ref.h Normal file
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@ -0,0 +1,256 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 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_REF_H
#define EIGEN_REF_H
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>
struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
: public traits<Map<_PlainObjectType, _Options, _StrideType> >
{
typedef _PlainObjectType PlainObjectType;
typedef _StrideType StrideType;
enum {
Options = _Options,
Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit
};
template<typename Derived> struct match {
enum {
HasDirectAccess = internal::has_direct_access<Derived>::ret,
StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic)
|| int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime)
|| (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),
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch
};
typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type;
};
};
template<typename Derived>
struct traits<RefBase<Derived> > : public traits<Derived> {};
}
template<typename Derived> class RefBase
: public MapBase<Derived>
{
typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
typedef typename internal::traits<Derived>::StrideType StrideType;
public:
typedef MapBase<Derived> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
inline Index innerStride() const
{
return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
}
inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
}
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,
StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime)
{}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
protected:
typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
template<typename Expression>
void construct(Expression& expr)
{
if(PlainObjectType::RowsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);
::new (static_cast<Base*>(this)) Base(expr.data(), 1, expr.size());
}
else if(PlainObjectType::ColsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);
::new (static_cast<Base*>(this)) Base(expr.data(), expr.size(), 1);
}
else
::new (static_cast<Base*>(this)) Base(expr.data(), expr.rows(), expr.cols());
::new (&m_stride) StrideBase(StrideType::OuterStrideAtCompileTime==0?0:expr.outerStride(),
StrideType::InnerStrideAtCompileTime==0?0:expr.innerStride());
}
StrideBase m_stride;
};
template<typename PlainObjectType, int Options, typename StrideType> class Ref
: public RefBase<Ref<PlainObjectType, Options, StrideType> >
{
typedef internal::traits<Ref> Traits;
public:
typedef RefBase<Ref> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(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)
{
Base::construct(expr);
}
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr,
typename internal::enable_if<bool(internal::is_lvalue<Derived>::value&&bool(Traits::template match<Derived>::MatchAtCompileTime)),Derived>::type* = 0,
int = Derived::ThisConstantIsPrivateInPlainObjectBase)
#else
template<typename Derived>
inline Ref(DenseBase<Derived>& expr)
#endif
{
Base::construct(expr.const_cast_derived());
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
};
// this is the const ref version
template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType>
: public RefBase<Ref<const TPlainObjectType, Options, StrideType> >
{
typedef internal::traits<Ref> Traits;
public:
typedef RefBase<Ref> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
template<typename Derived>
inline Ref(const DenseBase<Derived>& expr)
{
// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
// std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
construct(expr.derived(), typename Traits::template match<Derived>::type());
}
protected:
template<typename Expression>
void construct(const Expression& expr,internal::true_type)
{
Base::construct(expr);
}
template<typename Expression>
void construct(const Expression& expr, internal::false_type)
{
m_object.lazyAssign(expr);
Base::construct(m_object);
}
protected:
TPlainObjectType m_object;
};
} // end namespace Eigen
#endif // EIGEN_REF_H

28
eigenlib/Eigen/src/Core/Replicate.h
View File

@ -70,8 +70,8 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
template<typename OriginalMatrixType>
inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
inline explicit Replicate(const OriginalMatrixType& a_matrix)
: m_matrix(a_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)
@ -79,8 +79,8 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
}
template<typename OriginalMatrixType>
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
inline Replicate(const OriginalMatrixType& a_matrix, Index rowFactor, Index colFactor)
: m_matrix(a_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)
@ -89,27 +89,27 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
inline Scalar coeff(Index row, Index col) const
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 ? row
: row%m_matrix.rows();
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.coeff(actual_row, actual_col);
}
template<int LoadMode>
inline PacketScalar packet(Index row, Index col) const
inline PacketScalar packet(Index rowId, Index colId) const
{
const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0
: RowFactor==1 ? row
: row%m_matrix.rows();
: RowFactor==1 ? rowId
: rowId%m_matrix.rows();
const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0
: ColFactor==1 ? col
: col%m_matrix.cols();
: ColFactor==1 ? colId
: colId%m_matrix.cols();
return m_matrix.template packet<LoadMode>(actual_row, actual_col);
}

2
eigenlib/Eigen/src/Core/ReturnByValue.h
View File

@ -48,7 +48,7 @@ struct nested<ReturnByValue<Derived>, n, PlainObject>
} // end namespace internal
template<typename Derived> class ReturnByValue
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type
: internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;

14
eigenlib/Eigen/src/Core/Select.h
View File

@ -60,10 +60,10 @@ class Select : internal::no_assignment_operator,
typedef typename internal::dense_xpr_base<Select>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Select)
Select(const ConditionMatrixType& conditionMatrix,
const ThenMatrixType& thenMatrix,
const ElseMatrixType& elseMatrix)
: m_condition(conditionMatrix), m_then(thenMatrix), m_else(elseMatrix)
Select(const ConditionMatrixType& a_conditionMatrix,
const ThenMatrixType& a_thenMatrix,
const ElseMatrixType& a_elseMatrix)
: m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix)
{
eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
@ -136,7 +136,7 @@ template<typename Derived>
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
typename ThenDerived::Scalar elseScalar) const
const typename ThenDerived::Scalar& elseScalar) const
{
return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>(
derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar));
@ -150,8 +150,8 @@ DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
template<typename Derived>
template<typename ElseDerived>
inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
DenseBase<Derived>::select(typename ElseDerived::Scalar thenScalar,
const DenseBase<ElseDerived>& elseMatrix) const
DenseBase<Derived>::select(const typename ElseDerived::Scalar& thenScalar,
const DenseBase<ElseDerived>& elseMatrix) const
{
return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>(
derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived());

16
eigenlib/Eigen/src/Core/SelfAdjointView.h
View File

@ -132,7 +132,7 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
*/
template<typename DerivedU, typename DerivedV>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, Scalar alpha = Scalar(1));
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:
* \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
@ -145,7 +145,7 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
* \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
*/
template<typename DerivedU>
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
/////////// Cholesky module ///////////
@ -214,9 +214,9 @@ struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), U
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
@ -239,9 +239,9 @@ struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), U
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
@ -262,7 +262,7 @@ struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, Dyn
for(Index i = 0; i < j; ++i)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(j, j, src);
}
@ -280,7 +280,7 @@ struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dyn
for(Index j = 0; j < i; ++j)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(i, i, src);
}

5
eigenlib/Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@ -185,7 +185,10 @@ inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
internal::scalar_product_op<Scalar> >::type BinOp;
typedef typename Derived::PlainObject PlainObject;
SelfCwiseBinaryOp<BinOp, Derived, typename PlainObject::ConstantReturnType> tmp(derived());
tmp = PlainObject::Constant(rows(),cols(), NumTraits<Scalar>::IsInteger ? other : Scalar(1)/other);
Scalar actual_other;
if(NumTraits<Scalar>::IsInteger) actual_other = other;
else actual_other = Scalar(1)/other;
tmp = PlainObject::Constant(rows(),cols(), actual_other);
return derived();
}

213
eigenlib/Eigen/src/Core/StableNorm.h
View File

@ -13,22 +13,131 @@
namespace Eigen {
namespace internal {
template<typename ExpressionType, typename Scalar>
inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
{
Scalar max = bl.cwiseAbs().maxCoeff();
if (max>scale)
using std::max;
Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
if (maxCoeff>scale)
{
ssq = ssq * abs2(scale/max);
scale = max;
invScale = Scalar(1)/scale;
ssq = ssq * numext::abs2(scale/maxCoeff);
Scalar tmp = Scalar(1)/maxCoeff;
if(tmp > NumTraits<Scalar>::highest())
{
invScale = NumTraits<Scalar>::highest();
scale = Scalar(1)/invScale;
}
else
{
scale = maxCoeff;
invScale = tmp;
}
}
// TODO if the max is much much smaller than the current scale,
// TODO if the maxCoeff is much much smaller than the current scale,
// then we can neglect this sub vector
ssq += (bl*invScale).squaredNorm();
if(scale>Scalar(0)) // if scale==0, then bl is 0
ssq += (bl*invScale).squaredNorm();
}
template<typename Derived>
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;
if(!initialized)
{
int ibeta, it, iemin, iemax, iexp;
RealScalar eps;
// This program calculates the machine-dependent constants
// bl, b2, slm, s2m, relerr overfl
// from the "basic" machine-dependent numbers
// nbig, ibeta, it, iemin, iemax, rbig.
// The following define the basic machine-dependent constants.
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
iexp = (iemax + 1 - it)/2;
b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange
iexp = (2-iemin)/2;
s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range
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;
}
Index n = vec.size();
RealScalar ab2 = b2 / RealScalar(n);
RealScalar asml = RealScalar(0);
RealScalar amed = RealScalar(0);
RealScalar abig = RealScalar(0);
for(typename Derived::InnerIterator it(vec, 0); it; ++it)
{
RealScalar ax = abs(it.value());
if(ax > ab2) abig += numext::abs2(ax*s2m);
else if(ax < b1) asml += numext::abs2(ax*s1m);
else amed += numext::abs2(ax);
}
if(abig > RealScalar(0))
{
abig = sqrt(abig);
if(abig > overfl)
{
return rbig;
}
if(amed > RealScalar(0))
{
abig = abig/s2m;
amed = sqrt(amed);
}
else
return abig/s2m;
}
else if(asml > RealScalar(0))
{
if (amed > RealScalar(0))
{
abig = sqrt(amed);
amed = sqrt(asml) / s1m;
}
else
return sqrt(asml)/s1m;
}
else
return sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
if(asml <= abig*relerr)
return abig;
else
return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
}
} // end namespace internal
/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
* This version use a blockwise two passes algorithm:
* 1 - find the absolute largest coefficient \c s
@ -44,6 +153,7 @@ inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::stableNorm() const
{
using std::min;
using std::sqrt;
const Index blockSize = 4096;
RealScalar scale(0);
RealScalar invScale(1);
@ -57,7 +167,7 @@ MatrixBase<Derived>::stableNorm() const
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
for (; bi<n; bi+=blockSize)
internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
return scale * internal::sqrt(ssq);
return scale * sqrt(ssq);
}
/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
@ -73,92 +183,7 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
MatrixBase<Derived>::blueNorm() const
{
using std::pow;
using std::min;
using std::max;
static Index nmax = -1;
static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr;
if(nmax <= 0)
{
int nbig, ibeta, it, iemin, iemax, iexp;
RealScalar abig, eps;
// This program calculates the machine-dependent constants
// bl, b2, slm, s2m, relerr overfl, nmax
// from the "basic" machine-dependent numbers
// nbig, ibeta, it, iemin, iemax, rbig.
// The following define the basic machine-dependent constants.
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
nbig = (std::numeric_limits<Index>::max)(); // largest integer
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
iexp = (iemax + 1 - it)/2;
b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange
iexp = (2-iemin)/2;
s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range
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 = internal::sqrt(eps); // tolerance for neglecting asml
abig = RealScalar(1.0/eps - 1.0);
if (RealScalar(nbig)>abig) nmax = int(abig); // largest safe n
else nmax = nbig;
}
Index n = size();
RealScalar ab2 = b2 / RealScalar(n);
RealScalar asml = RealScalar(0);
RealScalar amed = RealScalar(0);
RealScalar abig = RealScalar(0);
for(Index j=0; j<n; ++j)
{
RealScalar ax = internal::abs(coeff(j));
if(ax > ab2) abig += internal::abs2(ax*s2m);
else if(ax < b1) asml += internal::abs2(ax*s1m);
else amed += internal::abs2(ax);
}
if(abig > RealScalar(0))
{
abig = internal::sqrt(abig);
if(abig > overfl)
{
return rbig;
}
if(amed > RealScalar(0))
{
abig = abig/s2m;
amed = internal::sqrt(amed);
}
else
return abig/s2m;
}
else if(asml > RealScalar(0))
{
if (amed > RealScalar(0))
{
abig = internal::sqrt(amed);
amed = internal::sqrt(asml) / s1m;
}
else
return internal::sqrt(asml)/s1m;
}
else
return internal::sqrt(amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
if(asml <= abig*relerr)
return abig;
else
return abig * internal::sqrt(RealScalar(1) + internal::abs2(asml/abig));
return internal::blueNorm_impl(*this);
}
/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.

34
eigenlib/Eigen/src/Core/Swap.h
View File

@ -49,9 +49,9 @@ template<typename ExpressionType> class SwapWrapper
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline Scalar& coeffRef(Index row, Index col)
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(row, col);
return m_expression.const_cast_derived().coeffRef(rowId, colId);
}
inline Scalar& coeffRef(Index index)
@ -59,9 +59,9 @@ template<typename ExpressionType> class SwapWrapper
return m_expression.const_cast_derived().coeffRef(index);
}
inline Scalar& coeffRef(Index row, Index col) const
inline Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.coeffRef(row, col);
return m_expression.coeffRef(rowId, colId);
}
inline Scalar& coeffRef(Index index) const
@ -70,14 +70,14 @@ template<typename ExpressionType> class SwapWrapper
}
template<typename OtherDerived>
void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other)
void copyCoeff(Index rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Scalar tmp = m_expression.coeff(row, col);
m_expression.coeffRef(row, col) = _other.coeff(row, col);
_other.coeffRef(row, col) = tmp;
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>
@ -91,16 +91,16 @@ template<typename ExpressionType> class SwapWrapper
}
template<typename OtherDerived, int StoreMode, int LoadMode>
void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other)
void copyPacket(Index rowId, Index colId, const DenseBase<OtherDerived>& other)
{
OtherDerived& _other = other.const_cast_derived();
eigen_internal_assert(row >= 0 && row < rows()
&& col >= 0 && col < cols());
Packet tmp = m_expression.template packet<StoreMode>(row, col);
m_expression.template writePacket<StoreMode>(row, col,
_other.template packet<LoadMode>(row, col)
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>(row, col, tmp);
_other.template writePacket<LoadMode>(rowId, colId, tmp);
}
template<typename OtherDerived, int StoreMode, int LoadMode>

43
eigenlib/Eigen/src/Core/Transpose.h
View File

@ -62,7 +62,7 @@ template<typename MatrixType> class Transpose
typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
inline Transpose(MatrixType& matrix) : m_matrix(matrix) {}
inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
@ -104,6 +104,7 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
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(); }
@ -117,10 +118,10 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
inline const Scalar* data() const { return derived().nestedExpression().data(); }
inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
inline ScalarWithConstIfNotLvalue& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return derived().nestedExpression().const_cast_derived().coeffRef(col, row);
return derived().nestedExpression().const_cast_derived().coeffRef(colId, rowId);
}
inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
@ -129,9 +130,9 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
return derived().nestedExpression().const_cast_derived().coeffRef(index);
}
inline const Scalar& coeffRef(Index row, Index col) const
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return derived().nestedExpression().coeffRef(col, row);
return derived().nestedExpression().coeffRef(colId, rowId);
}
inline const Scalar& coeffRef(Index index) const
@ -139,9 +140,9 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
return derived().nestedExpression().coeffRef(index);
}
inline CoeffReturnType coeff(Index row, Index col) const
inline CoeffReturnType coeff(Index rowId, Index colId) const
{
return derived().nestedExpression().coeff(col, row);
return derived().nestedExpression().coeff(colId, rowId);
}
inline CoeffReturnType coeff(Index index) const
@ -150,15 +151,15 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
}
template<int LoadMode>
inline const PacketScalar packet(Index row, Index col) const
inline const PacketScalar packet(Index rowId, Index colId) const
{
return derived().nestedExpression().template packet<LoadMode>(col, row);
return derived().nestedExpression().template packet<LoadMode>(colId, rowId);
}
template<int LoadMode>
inline void writePacket(Index row, Index col, const PacketScalar& x)
inline void writePacket(Index rowId, Index colId, const PacketScalar& x)
{
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(col, row, x);
derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(colId, rowId, x);
}
template<int LoadMode>
@ -206,7 +207,7 @@ DenseBase<Derived>::transpose()
*
* \sa transposeInPlace(), adjoint() */
template<typename Derived>
inline const typename DenseBase<Derived>::ConstTransposeReturnType
inline typename DenseBase<Derived>::ConstTransposeReturnType
DenseBase<Derived>::transpose() const
{
return ConstTransposeReturnType(derived());
@ -252,7 +253,7 @@ struct inplace_transpose_selector;
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,true> { // square matrix
static void run(MatrixType& m) {
m.template triangularView<StrictlyUpper>().swap(m.transpose());
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
}
};
@ -260,7 +261,7 @@ template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,false> { // non square matrix
static void run(MatrixType& m) {
if (m.rows()==m.cols())
m.template triangularView<StrictlyUpper>().swap(m.transpose());
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
else
m = m.transpose().eval();
}
@ -278,17 +279,20 @@ struct inplace_transpose_selector<MatrixType,false> { // non square matrix
* m = m.transpose().eval();
* \endcode
* and is faster and also safer because in the latter line of code, forgetting the eval() results
* in a bug caused by aliasing.
* in a bug caused by \ref TopicAliasing "aliasing".
*
* Notice however that this method is only useful if you want to replace a matrix by its own transpose.
* If you just need the transpose of a matrix, use transpose().
*
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* This excludes (non-square) fixed-size matrices, block-expressions and maps.
*
* \sa transpose(), adjoint(), adjointInPlace() */
template<typename Derived>
inline void DenseBase<Derived>::transposeInPlace()
{
eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic))
&& "transposeInPlace() called on a non-square non-resizable matrix");
internal::inplace_transpose_selector<Derived>::run(derived());
}
@ -312,6 +316,7 @@ inline void DenseBase<Derived>::transposeInPlace()
* If you just need the adjoint of a matrix, use adjoint().
*
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* This excludes (non-square) fixed-size matrices, block-expressions and maps.
*
* \sa transpose(), adjoint(), transposeInPlace() */
template<typename Derived>
@ -362,8 +367,8 @@ struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseB
{
static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src)
{
return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.lhs())))
|| ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.rhs())));
return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs())))
|| ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs())));
}
};
@ -385,7 +390,7 @@ struct checkTransposeAliasing_impl
eigen_assert((!check_transpose_aliasing_run_time_selector
<typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived>
::run(extract_data(dst), other))
&& "aliasing detected during tranposition, use transposeInPlace() "
&& "aliasing detected during transposition, use transposeInPlace() "
"or evaluate the rhs into a temporary using .eval()");
}

18
eigenlib/Eigen/src/Core/Transpositions.h
View File

@ -99,9 +99,9 @@ class TranspositionsBase
IndicesType& indices() { return derived().indices(); }
/** Resizes to given size. */
inline void resize(int size)
inline void resize(int newSize)
{
indices().resize(size);
indices().resize(newSize);
}
/** Sets \c *this to represents an identity transformation */
@ -177,7 +177,7 @@ class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTim
/** Generic constructor from expression of the transposition indices. */
template<typename Other>
explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
explicit inline Transpositions(const MatrixBase<Other>& a_indices) : m_indices(a_indices)
{}
/** Copies the \a other transpositions into \c *this */
@ -234,12 +234,12 @@ class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,Packe
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
inline Map(const Index* indices)
: m_indices(indices)
inline Map(const Index* indicesPtr)
: m_indices(indicesPtr)
{}
inline Map(const Index* indices, Index size)
: m_indices(indices,size)
inline Map(const Index* indicesPtr, Index size)
: m_indices(indicesPtr,size)
{}
/** Copies the \a other transpositions into \c *this */
@ -291,8 +291,8 @@ class TranspositionsWrapper
typedef typename Traits::IndicesType IndicesType;
typedef typename IndicesType::Scalar Index;
inline TranspositionsWrapper(IndicesType& indices)
: m_indices(indices)
inline TranspositionsWrapper(IndicesType& a_indices)
: m_indices(a_indices)
{}
/** Copies the \a other transpositions into \c *this */

14
eigenlib/Eigen/src/Core/TriangularMatrix.h
View File

@ -779,22 +779,23 @@ MatrixBase<Derived>::triangularView() const
* \sa isLowerTriangular()
*/
template<typename Derived>
bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const
{
using std::abs;
RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
Index maxi = (std::min)(j, rows()-1);
for(Index i = 0; i <= maxi; ++i)
{
RealScalar absValue = internal::abs(coeff(i,j));
RealScalar absValue = abs(coeff(i,j));
if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
}
}
RealScalar threshold = maxAbsOnUpperPart * prec;
for(Index j = 0; j < cols(); ++j)
for(Index i = j+1; i < rows(); ++i)
if(internal::abs(coeff(i, j)) > threshold) return false;
if(abs(coeff(i, j)) > threshold) return false;
return true;
}
@ -804,13 +805,14 @@ bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
* \sa isUpperTriangular()
*/
template<typename Derived>
bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const
{
using std::abs;
RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
for(Index i = j; i < rows(); ++i)
{
RealScalar absValue = internal::abs(coeff(i,j));
RealScalar absValue = abs(coeff(i,j));
if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
}
RealScalar threshold = maxAbsOnLowerPart * prec;
@ -818,7 +820,7 @@ bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
{
Index maxi = (std::min)(j, rows()-1);
for(Index i = 0; i < maxi; ++i)
if(internal::abs(coeff(i, j)) > threshold) return false;
if(abs(coeff(i, j)) > threshold) return false;
}
return true;
}

189
eigenlib/Eigen/src/Core/VectorBlock.h
View File

@ -90,195 +90,6 @@ template<typename VectorType, int Size> class VectorBlock
};
/** \returns a dynamic-size expression of a segment (i.e. a vector block) in *this.
*
* \only_for_vectors
*
* \param start the first coefficient in the segment
* \param size the number of coefficients in the segment
*
* Example: \include MatrixBase_segment_int_int.cpp
* Output: \verbinclude MatrixBase_segment_int_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, segment(Index)
*/
template<typename Derived>
inline typename DenseBase<Derived>::SegmentReturnType
DenseBase<Derived>::segment(Index start, Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return SegmentReturnType(derived(), start, size);
}
/** This is the const version of segment(Index,Index).*/
template<typename Derived>
inline typename DenseBase<Derived>::ConstSegmentReturnType
DenseBase<Derived>::segment(Index start, Index size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return ConstSegmentReturnType(derived(), start, size);
}
/** \returns a dynamic-size expression of the first coefficients of *this.
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
*
* Example: \include MatrixBase_start_int.cpp
* Output: \verbinclude MatrixBase_start_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(Index,Index)
*/
template<typename Derived>
inline typename DenseBase<Derived>::SegmentReturnType
DenseBase<Derived>::head(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return SegmentReturnType(derived(), 0, size);
}
/** This is the const version of head(Index).*/
template<typename Derived>
inline typename DenseBase<Derived>::ConstSegmentReturnType
DenseBase<Derived>::head(Index size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return ConstSegmentReturnType(derived(), 0, size);
}
/** \returns a dynamic-size expression of the last coefficients of *this.
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
*
* Example: \include MatrixBase_end_int.cpp
* Output: \verbinclude MatrixBase_end_int.out
*
* \note Even though the returned expression has dynamic size, in the case
* when it is applied to a fixed-size vector, it inherits a fixed maximal size,
* which means that evaluating it does not cause a dynamic memory allocation.
*
* \sa class Block, block(Index,Index)
*/
template<typename Derived>
inline typename DenseBase<Derived>::SegmentReturnType
DenseBase<Derived>::tail(Index size)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return SegmentReturnType(derived(), this->size() - size, size);
}
/** This is the const version of tail(Index).*/
template<typename Derived>
inline typename DenseBase<Derived>::ConstSegmentReturnType
DenseBase<Derived>::tail(Index size) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return ConstSegmentReturnType(derived(), this->size() - size, size);
}
/** \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* \param start the index of the first element of the sub-vector
*
* Example: \include MatrixBase_template_int_segment.cpp
* Output: \verbinclude MatrixBase_template_int_segment.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template FixedSegmentReturnType<Size>::Type
DenseBase<Derived>::segment(Index start)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename FixedSegmentReturnType<Size>::Type(derived(), start);
}
/** This is the const version of segment<int>(Index).*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template ConstFixedSegmentReturnType<Size>::Type
DenseBase<Derived>::segment(Index start) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename ConstFixedSegmentReturnType<Size>::Type(derived(), start);
}
/** \returns a fixed-size expression of the first coefficients of *this.
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* Example: \include MatrixBase_template_int_start.cpp
* Output: \verbinclude MatrixBase_template_int_start.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template FixedSegmentReturnType<Size>::Type
DenseBase<Derived>::head()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename FixedSegmentReturnType<Size>::Type(derived(), 0);
}
/** This is the const version of head<int>().*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template ConstFixedSegmentReturnType<Size>::Type
DenseBase<Derived>::head() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename ConstFixedSegmentReturnType<Size>::Type(derived(), 0);
}
/** \returns a fixed-size expression of the last coefficients of *this.
*
* \only_for_vectors
*
* The template parameter \a Size is the number of coefficients in the block
*
* Example: \include MatrixBase_template_int_end.cpp
* Output: \verbinclude MatrixBase_template_int_end.out
*
* \sa class Block
*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template FixedSegmentReturnType<Size>::Type
DenseBase<Derived>::tail()
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename FixedSegmentReturnType<Size>::Type(derived(), size() - Size);
}
/** This is the const version of tail<int>.*/
template<typename Derived>
template<int Size>
inline typename DenseBase<Derived>::template ConstFixedSegmentReturnType<Size>::Type
DenseBase<Derived>::tail() const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return typename ConstFixedSegmentReturnType<Size>::Type(derived(), size() - Size);
}
} // end namespace Eigen
#endif // EIGEN_VECTORBLOCK_H

52
eigenlib/Eigen/src/Core/VectorwiseOp.h
View File

@ -50,7 +50,7 @@ struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits,
Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0),
TraversalSize = Direction==Vertical ? RowsAtCompileTime : ColsAtCompileTime
TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime
};
#if EIGEN_GNUC_AT_LEAST(3,4)
typedef typename MemberOp::template Cost<InputScalar,int(TraversalSize)> CostOpType;
@ -58,7 +58,8 @@ struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
typedef typename MemberOp::template Cost<InputScalar,TraversalSize> CostOpType;
#endif
enum {
CoeffReadCost = TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value)
CoeffReadCost = TraversalSize==Dynamic ? Dynamic
: TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value)
};
};
}
@ -103,8 +104,8 @@ class PartialReduxExpr : internal::no_assignment_operator,
#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
template <typename ResultType> \
struct member_##MEMBER { \
EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \
struct member_##MEMBER { \
EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \
typedef ResultType result_type; \
template<typename Scalar, int Size> struct Cost \
{ enum { value = COST }; }; \
@ -233,6 +234,28 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
Direction==Vertical ? 1 : m_matrix.rows(),
Direction==Horizontal ? 1 : m_matrix.cols());
}
template<typename OtherDerived> struct OppositeExtendedType {
typedef Replicate<OtherDerived,
Direction==Horizontal ? 1 : ExpressionType::RowsAtCompileTime,
Direction==Vertical ? 1 : ExpressionType::ColsAtCompileTime> Type;
};
/** \internal
* Replicates a vector in the opposite direction to match the size of \c *this */
template<typename OtherDerived>
typename OppositeExtendedType<OtherDerived>::Type
extendedToOpposite(const DenseBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxColsAtCompileTime==1),
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, 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());
}
public:
@ -255,6 +278,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** \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
@ -265,6 +290,8 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** \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
@ -504,6 +531,23 @@ 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.
* The referenced matrix is \b not modified.
* \sa MatrixBase::normalized(), normalize()
*/
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() {
m_matrix = this->normalized();
}
/////////// Geometry module ///////////

28
eigenlib/Eigen/src/Core/Visitor.h
View File

@ -164,25 +164,25 @@ 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.
/** \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()
*/
template<typename Derived>
template<typename IndexType>
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff(IndexType* row, IndexType* col) const
DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
{
internal::min_coeff_visitor<Derived> minVisitor;
this->visit(minVisitor);
*row = minVisitor.row;
if (col) *col = minVisitor.col;
*rowId = minVisitor.row;
if (colId) *colId = minVisitor.col;
return minVisitor.res;
}
/** \returns the minimum of all coefficients of *this
* and puts in *index its location.
/** \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()
*/
@ -198,25 +198,25 @@ DenseBase<Derived>::minCoeff(IndexType* index) const
return minVisitor.res;
}
/** \returns the maximum of all coefficients of *this
* and puts in *row and *col its location.
/** \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()
*/
template<typename Derived>
template<typename IndexType>
typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff(IndexType* row, IndexType* col) const
DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
{
internal::max_coeff_visitor<Derived> maxVisitor;
this->visit(maxVisitor);
*row = maxVisitor.row;
if (col) *col = maxVisitor.col;
*rowPtr = maxVisitor.row;
if (colPtr) *colPtr = maxVisitor.col;
return maxVisitor.res;
}
/** \returns the maximum of all coefficients of *this
* and puts in *index its location.
/** \returns the maximum of all coefficients of *this and puts in *index its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
*/

3
eigenlib/Eigen/src/Core/arch/AltiVec/PacketMath.h
View File

@ -173,6 +173,9 @@ template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return psub<Packet4f>(p4f_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return psub<Packet4i>(p4i_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b,p4f_ZERO); }
/* Commented out: it's actually slower than processing it scalar
*

10
eigenlib/Eigen/src/Core/arch/NEON/Complex.h
View File

@ -68,7 +68,6 @@ template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
Packet4f v1, v2;
float32x2_t a_lo, a_hi;
// Get the real values of a | a1_re | a1_re | a2_re | a2_re |
v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
@ -81,9 +80,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
// Conjugate v2
v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR));
// Swap real/imag elements in v2.
a_lo = vrev64_f32(vget_low_f32(v2));
a_hi = vrev64_f32(vget_high_f32(v2));
v2 = vcombine_f32(a_lo, a_hi);
v2 = vrev64q_f32(v2);
// Add and return the result
return Packet2cf(vaddq_f32(v1, v2));
}
@ -241,13 +238,10 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
// TODO optimize it for AltiVec
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s, rev_s;
float32x2_t a_lo, a_hi;
// this computes the norm
s = vmulq_f32(b.v, b.v);
a_lo = vrev64_f32(vget_low_f32(s));
a_hi = vrev64_f32(vget_high_f32(s));
rev_s = vcombine_f32(a_lo, a_hi);
rev_s = vrev64q_f32(s);
return Packet2cf(pdiv(res.v, vaddq_f32(s,rev_s)));
}

50
eigenlib/Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -115,6 +115,9 @@ template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); }
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); }
@ -188,15 +191,15 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) { EI
template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
{
float32x2_t lo, hi;
lo = vdup_n_f32(*from);
hi = vdup_n_f32(*(from+1));
lo = vld1_dup_f32(from);
hi = vld1_dup_f32(from+1);
return vcombine_f32(lo, hi);
}
template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from)
{
int32x2_t lo, hi;
lo = vdup_n_s32(*from);
hi = vdup_n_s32(*(from+1));
lo = vld1_dup_s32(from);
hi = vld1_dup_s32(from+1);
return vcombine_s32(lo, hi);
}
@ -237,15 +240,12 @@ template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s
template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
{
float32x2_t a_lo, a_hi, sum;
float s[2];
a_lo = vget_low_f32(a);
a_hi = vget_high_f32(a);
sum = vpadd_f32(a_lo, a_hi);
sum = vpadd_f32(sum, sum);
vst1_f32(s, sum);
return s[0];
return vget_lane_f32(sum, 0);
}
template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs)
@ -271,15 +271,12 @@ template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs)
template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
{
int32x2_t a_lo, a_hi, sum;
int32_t s[2];
a_lo = vget_low_s32(a);
a_hi = vget_high_s32(a);
sum = vpadd_s32(a_lo, a_hi);
sum = vpadd_s32(sum, sum);
vst1_s32(s, sum);
return s[0];
return vget_lane_s32(sum, 0);
}
template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs)
@ -307,7 +304,6 @@ template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs)
template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
{
float32x2_t a_lo, a_hi, prod;
float s[2];
// Get a_lo = |a1|a2| and a_hi = |a3|a4|
a_lo = vget_low_f32(a);
@ -316,14 +312,12 @@ template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
prod = vmul_f32(a_lo, a_hi);
// Multiply prod with its swapped value |a2*a4|a1*a3|
prod = vmul_f32(prod, vrev64_f32(prod));
vst1_f32(s, prod);
return s[0];
return vget_lane_f32(prod, 0);
}
template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
{
int32x2_t a_lo, a_hi, prod;
int32_t s[2];
// Get a_lo = |a1|a2| and a_hi = |a3|a4|
a_lo = vget_low_s32(a);
@ -332,65 +326,57 @@ template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
prod = vmul_s32(a_lo, a_hi);
// Multiply prod with its swapped value |a2*a4|a1*a3|
prod = vmul_s32(prod, vrev64_s32(prod));
vst1_s32(s, prod);
return s[0];
return vget_lane_s32(prod, 0);
}
// min
template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
{
float32x2_t a_lo, a_hi, min;
float s[2];
a_lo = vget_low_f32(a);
a_hi = vget_high_f32(a);
min = vpmin_f32(a_lo, a_hi);
min = vpmin_f32(min, min);
vst1_f32(s, min);
return s[0];
return vget_lane_f32(min, 0);
}
template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
{
int32x2_t a_lo, a_hi, min;
int32_t s[2];
a_lo = vget_low_s32(a);
a_hi = vget_high_s32(a);
min = vpmin_s32(a_lo, a_hi);
min = vpmin_s32(min, min);
vst1_s32(s, min);
return s[0];
return vget_lane_s32(min, 0);
}
// max
template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
{
float32x2_t a_lo, a_hi, max;
float s[2];
a_lo = vget_low_f32(a);
a_hi = vget_high_f32(a);
max = vpmax_f32(a_lo, a_hi);
max = vpmax_f32(max, max);
vst1_f32(s, max);
return s[0];
return vget_lane_f32(max, 0);
}
template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
{
int32x2_t a_lo, a_hi, max;
int32_t s[2];
a_lo = vget_low_s32(a);
a_hi = vget_high_s32(a);
max = vpmax_s32(a_lo, a_hi);
max = vpmax_s32(max, max);
vst1_s32(s, max);
return s[0];
return vget_lane_s32(max, 0);
}
// this PALIGN_NEON business is to work around a bug in LLVM Clang 3.0 causing incorrect compilation errors,

22
eigenlib/Eigen/src/Core/arch/SSE/Complex.h
View File

@ -81,25 +81,31 @@ template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a,
template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
{
Packet2cf res;
#if EIGEN_GNUC_AT_MOST(4,2)
// workaround annoying "may be used uninitialized in this function" warning with gcc 4.2
#if EIGEN_GNUC_AT_MOST(4,2)
// Workaround annoying "may be used uninitialized in this function" warning with gcc 4.2
res.v = _mm_loadl_pi(_mm_set1_ps(0.0f), reinterpret_cast<const __m64*>(&from));
#else
#elif EIGEN_GNUC_AT_LEAST(4,6)
// Suppress annoying "may be used uninitialized in this function" warning with gcc >= 4.6
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wuninitialized"
res.v = _mm_loadl_pi(res.v, (const __m64*)&from);
#endif
#pragma GCC diagnostic pop
#else
res.v = _mm_loadl_pi(res.v, (const __m64*)&from);
#endif
return Packet2cf(_mm_movelh_ps(res.v,res.v));
}
template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& 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 Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }

101
eigenlib/Eigen/src/Core/arch/SSE/MathFunctions.h
View File

@ -31,7 +31,8 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
/* the smallest non denormalized float number */
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000);//-1.f/0.f);
/* natural logarithm computed for 4 simultaneous float
return NaN for x <= 0
*/
@ -51,7 +52,8 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
Packet4i emm0;
Packet4f invalid_mask = _mm_cmple_ps(x, _mm_setzero_ps());
Packet4f invalid_mask = _mm_cmplt_ps(x, _mm_setzero_ps());
Packet4f iszero_mask = _mm_cmpeq_ps(x, _mm_setzero_ps());
x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */
emm0 = _mm_srli_epi32(_mm_castps_si128(x), 23);
@ -96,7 +98,9 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
y2 = pmul(e, p4f_cephes_log_q2);
x = padd(x, y);
x = padd(x, y2);
return _mm_or_ps(x, invalid_mask); // negative arg will be NAN
// negative arg will be NAN, 0 will be -INF
return _mm_or_ps(_mm_andnot_ps(iszero_mask, _mm_or_ps(x, invalid_mask)),
_mm_and_ps(iszero_mask, p4f_minus_inf));
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
@ -131,13 +135,16 @@ Packet4f pexp<Packet4f>(const Packet4f& _x)
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
/* how to perform a floorf with SSE: just below */
#ifdef EIGEN_VECTORIZE_SSE4_1
fx = _mm_floor_ps(fx);
#else
emm0 = _mm_cvttps_epi32(fx);
tmp = _mm_cvtepi32_ps(emm0);
/* if greater, substract 1 */
Packet4f mask = _mm_cmpgt_ps(tmp, fx);
mask = _mm_and_ps(mask, p4f_1);
fx = psub(tmp, mask);
#endif
tmp = pmul(fx, p4f_cephes_exp_C1);
Packet4f z = pmul(fx, p4f_cephes_exp_C2);
@ -161,6 +168,79 @@ Packet4f pexp<Packet4f>(const Packet4f& _x)
emm0 = _mm_slli_epi32(emm0, 23);
return pmul(y, _mm_castsi128_ps(emm0));
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d pexp<Packet2d>(const Packet2d& _x)
{
Packet2d x = _x;
_EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
_EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
_EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
_EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437);
_EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
_EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
static const __m128i p4i_1023_0 = _mm_setr_epi32(1023, 1023, 0, 0);
Packet2d tmp = _mm_setzero_pd(), fx;
Packet4i emm0;
// clamp x
x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
#ifdef EIGEN_VECTORIZE_SSE4_1
fx = _mm_floor_pd(fx);
#else
emm0 = _mm_cvttpd_epi32(fx);
tmp = _mm_cvtepi32_pd(emm0);
/* if greater, substract 1 */
Packet2d mask = _mm_cmpgt_pd(tmp, fx);
mask = _mm_and_pd(mask, p2d_1);
fx = psub(tmp, mask);
#endif
tmp = pmul(fx, p2d_cephes_exp_C1);
Packet2d z = pmul(fx, p2d_cephes_exp_C2);
x = psub(x, tmp);
x = psub(x, z);
Packet2d x2 = pmul(x,x);
Packet2d px = p2d_cephes_exp_p0;
px = pmadd(px, x2, p2d_cephes_exp_p1);
px = pmadd(px, x2, p2d_cephes_exp_p2);
px = pmul (px, x);
Packet2d qx = p2d_cephes_exp_q0;
qx = pmadd(qx, x2, p2d_cephes_exp_q1);
qx = pmadd(qx, x2, p2d_cephes_exp_q2);
qx = pmadd(qx, x2, p2d_cephes_exp_q3);
x = pdiv(px,psub(qx,px));
x = pmadd(p2d_2,x,p2d_1);
// build 2^n
emm0 = _mm_cvttpd_epi32(fx);
emm0 = _mm_add_epi32(emm0, p4i_1023_0);
emm0 = _mm_slli_epi32(emm0, 20);
emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(1,2,0,3));
return pmul(x, _mm_castsi128_pd(emm0));
}
/* evaluation of 4 sines at onces, using SSE2 intrinsics.
@ -362,21 +442,32 @@ Packet4f pcos<Packet4f>(const Packet4f& _x)
return _mm_xor_ps(y, sign_bit);
}
#if EIGEN_FAST_MATH
// This is based on Quake3's fast inverse square root.
// For detail see here: http://www.beyond3d.com/content/articles/8/
// It lacks 1 (or 2 bits in some rare cases) of precision, and does not handle negative, +inf, or denormalized numbers correctly.
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f psqrt<Packet4f>(const Packet4f& _x)
{
Packet4f half = pmul(_x, pset1<Packet4f>(.5f));
/* select only the inverse sqrt of non-zero inputs */
Packet4f non_zero_mask = _mm_cmpgt_ps(_x, pset1<Packet4f>(std::numeric_limits<float>::epsilon()));
Packet4f non_zero_mask = _mm_cmpge_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)()));
Packet4f x = _mm_and_ps(non_zero_mask, _mm_rsqrt_ps(_x));
x = pmul(x, psub(pset1<Packet4f>(1.5f), pmul(half, pmul(x,x))));
return pmul(_x,x);
}
#else
template<> EIGEN_STRONG_INLINE Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
#endif
template<> EIGEN_STRONG_INLINE Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); }
} // end namespace internal
} // end namespace Eigen

29
eigenlib/Eigen/src/Core/arch/SSE/PacketMath.h
View File

@ -48,6 +48,9 @@ template<> struct is_arithmetic<__m128d> { enum { value = true }; };
#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
const Packet4f p4f_##NAME = pset1<Packet4f>(X)
#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
const Packet2d p2d_##NAME = pset1<Packet2d>(X)
#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
const Packet4f p4f_##NAME = _mm_castsi128_ps(pset1<Packet4i>(X))
@ -63,7 +66,7 @@ template<> struct packet_traits<float> : default_packet_traits
AlignedOnScalar = 1,
size=4,
HasDiv = 1,
HasDiv = 1,
HasSin = EIGEN_FAST_MATH,
HasCos = EIGEN_FAST_MATH,
HasLog = 1,
@ -79,7 +82,9 @@ template<> struct packet_traits<double> : default_packet_traits
AlignedOnScalar = 1,
size=2,
HasDiv = 1
HasDiv = 1,
HasExp = 1,
HasSqrt = 1
};
};
template<> struct packet_traits<int> : default_packet_traits
@ -137,6 +142,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
return psub(_mm_setr_epi32(0,0,0,0), a);
}
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)
@ -169,18 +178,26 @@ template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_min_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b)
{
#ifdef EIGEN_VECTORIZE_SSE4_1
return _mm_min_epi32(a,b);
#else
// after some bench, this version *is* faster than a scalar implementation
Packet4i mask = _mm_cmplt_epi32(a,b);
return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
#endif
}
template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_max_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_max_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b)
{
#ifdef EIGEN_VECTORIZE_SSE4_1
return _mm_max_epi32(a,b);
#else
// after some bench, this version *is* faster than a scalar implementation
Packet4i mask = _mm_cmpgt_epi32(a,b);
return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
#endif
}
template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_and_ps(a,b); }
@ -491,8 +508,8 @@ template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
// for GCC (eg., it does not like using std::min after the pstore !!)
EIGEN_ALIGN16 int aux[4];
pstore(aux, a);
register int aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
register int aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
int aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
int aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
return aux0<aux2 ? aux0 : aux2;
}
@ -512,8 +529,8 @@ template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
// for GCC (eg., it does not like using std::min after the pstore !!)
EIGEN_ALIGN16 int aux[4];
pstore(aux, a);
register int aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
register int aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
int aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
int aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
return aux0>aux2 ? aux0 : aux2;
}

2
eigenlib/Eigen/src/Core/products/CoeffBasedProduct.h
View File

@ -150,7 +150,7 @@ class CoeffBasedProduct
{
// we don't allow taking products of matrices of different real types, as that wouldn't be vectorizable.
// We still allow to mix T and complex<T>.
EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
EIGEN_STATIC_ASSERT((internal::scalar_product_traits<typename Lhs::RealScalar, typename Rhs::RealScalar>::Defined),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
eigen_assert(lhs.cols() == rhs.rows()
&& "invalid matrix product"

342
eigenlib/Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@ -69,8 +69,8 @@ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdi
* - the number of scalars that fit into a packet (when vectorization is enabled).
*
* \sa setCpuCacheSizes */
template<typename LhsScalar, typename RhsScalar, int KcFactor>
void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType>
void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
{
EIGEN_UNUSED_VARIABLE(n);
// Explanations:
@ -91,13 +91,13 @@ void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrd
};
manage_caching_sizes(GetAction, &l1, &l2);
k = std::min<std::ptrdiff_t>(k, l1/kdiv);
std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
k = std::min<SizeType>(k, l1/kdiv);
SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
if(_m<m) m = _m & mr_mask;
}
template<typename LhsScalar, typename RhsScalar>
inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
template<typename LhsScalar, typename RhsScalar, typename SizeType>
inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
{
computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n);
}
@ -527,9 +527,16 @@ struct gebp_kernel
ResPacketSize = Traits::ResPacketSize
};
EIGEN_DONT_INLINE EIGEN_FLATTEN_ATTRIB
EIGEN_DONT_INLINE
void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB = 0)
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB=0);
};
template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void gebp_kernel<LhsScalar,RhsScalar,Index,mr,nr,ConjugateLhs,ConjugateRhs>
::operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
Index strideA, Index strideB, Index offsetA, Index offsetB, RhsScalar* unpackedB)
{
Traits traits;
@ -1089,7 +1096,7 @@ EIGEN_ASM_COMMENT("mybegin4");
}
}
}
};
#undef CJMADD
@ -1110,81 +1117,86 @@ EIGEN_ASM_COMMENT("mybegin4");
template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
struct gemm_pack_lhs
{
EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows,
Index stride=0, Index offset=0)
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
Index count = 0;
Index peeled_mc = (rows/Pack1)*Pack1;
for(Index i=0; i<peeled_mc; i+=Pack1)
{
if(PanelMode) count += Pack1 * offset;
if(StorageOrder==ColMajor)
{
for(Index k=0; k<depth; k++)
{
Packet A, B, C, D;
if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
}
}
else
{
for(Index k=0; k<depth; k++)
{
// TODO add a vectorized transpose here
Index w=0;
for(; w<Pack1-3; w+=4)
{
Scalar a(cj(lhs(i+w+0, k))),
b(cj(lhs(i+w+1, k))),
c(cj(lhs(i+w+2, k))),
d(cj(lhs(i+w+3, k)));
blockA[count++] = a;
blockA[count++] = b;
blockA[count++] = c;
blockA[count++] = d;
}
if(Pack1%4)
for(;w<Pack1;++w)
blockA[count++] = cj(lhs(i+w, k));
}
}
if(PanelMode) count += Pack1 * (stride-offset-depth);
}
if(rows-peeled_mc>=Pack2)
{
if(PanelMode) count += Pack2*offset;
for(Index k=0; k<depth; k++)
for(Index w=0; w<Pack2; w++)
blockA[count++] = cj(lhs(peeled_mc+w, k));
if(PanelMode) count += Pack2 * (stride-offset-depth);
peeled_mc += Pack2;
}
for(Index i=peeled_mc; i<rows; i++)
{
if(PanelMode) count += offset;
for(Index k=0; k<depth; k++)
blockA[count++] = cj(lhs(i, k));
if(PanelMode) count += (stride-offset-depth);
}
}
EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, Pack1, Pack2, StorageOrder, Conjugate, PanelMode>
::operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride, Index offset)
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
Index count = 0;
Index peeled_mc = (rows/Pack1)*Pack1;
for(Index i=0; i<peeled_mc; i+=Pack1)
{
if(PanelMode) count += Pack1 * offset;
if(StorageOrder==ColMajor)
{
for(Index k=0; k<depth; k++)
{
Packet A, B, C, D;
if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
}
}
else
{
for(Index k=0; k<depth; k++)
{
// TODO add a vectorized transpose here
Index w=0;
for(; w<Pack1-3; w+=4)
{
Scalar a(cj(lhs(i+w+0, k))),
b(cj(lhs(i+w+1, k))),
c(cj(lhs(i+w+2, k))),
d(cj(lhs(i+w+3, k)));
blockA[count++] = a;
blockA[count++] = b;
blockA[count++] = c;
blockA[count++] = d;
}
if(Pack1%4)
for(;w<Pack1;++w)
blockA[count++] = cj(lhs(i+w, k));
}
}
if(PanelMode) count += Pack1 * (stride-offset-depth);
}
if(rows-peeled_mc>=Pack2)
{
if(PanelMode) count += Pack2*offset;
for(Index k=0; k<depth; k++)
for(Index w=0; w<Pack2; w++)
blockA[count++] = cj(lhs(peeled_mc+w, k));
if(PanelMode) count += Pack2 * (stride-offset-depth);
peeled_mc += Pack2;
}
for(Index i=peeled_mc; i<rows; i++)
{
if(PanelMode) count += offset;
for(Index k=0; k<depth; k++)
blockA[count++] = cj(lhs(i, k));
if(PanelMode) count += (stride-offset-depth);
}
}
// copy a complete panel of the rhs
// this version is optimized for column major matrices
// The traversal order is as follow: (nr==4):
@ -1197,93 +1209,103 @@ struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols,
Index stride=0, Index offset=0)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
const Scalar* b1 = &rhs[(j2+1)*rhsStride];
const Scalar* b2 = &rhs[(j2+2)*rhsStride];
const Scalar* b3 = &rhs[(j2+3)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count+0] = cj(b0[k]);
blockB[count+1] = cj(b1[k]);
if(nr==4) blockB[count+2] = cj(b2[k]);
if(nr==4) blockB[count+3] = cj(b3[k]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k]);
count += 1;
}
if(PanelMode) count += (stride-offset-depth);
}
}
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
const Scalar* b1 = &rhs[(j2+1)*rhsStride];
const Scalar* b2 = &rhs[(j2+2)*rhsStride];
const Scalar* b3 = &rhs[(j2+3)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count+0] = cj(b0[k]);
blockB[count+1] = cj(b1[k]);
if(nr==4) blockB[count+2] = cj(b2[k]);
if(nr==4) blockB[count+3] = cj(b3[k]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k]);
count += 1;
}
if(PanelMode) count += (stride-offset-depth);
}
}
// this version is optimized for row major matrices
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
{
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols,
Index stride=0, Index offset=0)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
for(Index k=0; k<depth; k++)
{
const Scalar* b0 = &rhs[k*rhsStride + j2];
blockB[count+0] = cj(b0[0]);
blockB[count+1] = cj(b0[1]);
if(nr==4) blockB[count+2] = cj(b0[2]);
if(nr==4) blockB[count+3] = cj(b0[3]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[j2];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k*rhsStride]);
count += 1;
}
if(PanelMode) count += stride-offset-depth;
}
}
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
for(Index k=0; k<depth; k++)
{
const Scalar* b0 = &rhs[k*rhsStride + j2];
blockB[count+0] = cj(b0[0]);
blockB[count+1] = cj(b0[1]);
if(nr==4) blockB[count+2] = cj(b0[2]);
if(nr==4) blockB[count+3] = cj(b0[3]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[j2];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k*rhsStride]);
count += 1;
}
if(PanelMode) count += stride-offset-depth;
}
}
} // end namespace internal
/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.

7
eigenlib/Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -50,6 +50,7 @@ template<
typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs>
struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static void run(Index rows, Index cols, Index depth,
const LhsScalar* _lhs, Index lhsStride,
@ -169,7 +170,6 @@ static void run(Index rows, Index cols, Index depth,
// vertical panel which is, in practice, a very low number.
pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, cols);
// For each mc x kc block of the lhs's vertical panel...
// (==GEPP_VAR1)
for(Index i2=0; i2<rows; i2+=mc)
@ -183,7 +183,6 @@ static void run(Index rows, Index cols, Index depth,
// Everything is packed, we can now call the block * panel kernel:
gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, cols, alpha, -1, -1, 0, 0, blockW);
}
}
}
@ -204,7 +203,7 @@ struct traits<GeneralProduct<Lhs,Rhs,GemmProduct> >
template<typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest, typename BlockingType>
struct gemm_functor
{
gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, Scalar actualAlpha,
gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha,
BlockingType& blocking)
: m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking)
{}
@ -395,7 +394,7 @@ class GeneralProduct<Lhs, Rhs, GemmProduct>
EIGEN_CHECK_BINARY_COMPATIBILIY(BinOp,LhsScalar,RhsScalar);
}
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());

112
eigenlib/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -12,6 +12,9 @@
namespace Eigen {
template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
struct selfadjoint_rank1_update;
namespace internal {
/**********************************************************************
@ -39,7 +42,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha)
const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, const ResScalar& alpha)
{
general_matrix_matrix_triangular_product<Index,
RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
@ -55,7 +58,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha)
const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, const ResScalar& alpha)
{
const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> lhs(_lhs,lhsStride);
const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> rhs(_rhs,rhsStride);
@ -133,7 +136,7 @@ struct tribb_kernel
enum {
BlockSize = EIGEN_PLAIN_ENUM_MAX(mr,nr)
};
void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, ResScalar alpha, RhsScalar* workspace)
void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha, RhsScalar* workspace)
{
gebp_kernel<LhsScalar, RhsScalar, Index, mr, nr, ConjLhs, ConjRhs> gebp_kernel;
Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer;
@ -180,31 +183,92 @@ struct tribb_kernel
// high level API
template<typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
struct general_product_to_triangular_selector;
template<typename MatrixType, typename ProductType, int UpLo>
struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
{
static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
enum {
StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
UseLhsDirectly = _ActualLhs::InnerStrideAtCompileTime==1,
UseRhsDirectly = _ActualRhs::InnerStrideAtCompileTime==1
};
internal::gemv_static_vector_if<Scalar,Lhs::SizeAtCompileTime,Lhs::MaxSizeAtCompileTime,!UseLhsDirectly> static_lhs;
ei_declare_aligned_stack_constructed_variable(Scalar, actualLhsPtr, actualLhs.size(),
(UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
if(!UseLhsDirectly) Map<typename _ActualLhs::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;
internal::gemv_static_vector_if<Scalar,Rhs::SizeAtCompileTime,Rhs::MaxSizeAtCompileTime,!UseRhsDirectly> static_rhs;
ei_declare_aligned_stack_constructed_variable(Scalar, actualRhsPtr, actualRhs.size(),
(UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
if(!UseRhsDirectly) Map<typename _ActualRhs::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>
::run(actualLhs.size(), mat.data(), mat.outerStride(), actualLhsPtr, actualRhsPtr, actualAlpha);
}
};
template<typename MatrixType, typename ProductType, int UpLo>
struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
{
static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha)
{
typedef typename MatrixType::Index Index;
typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
internal::general_matrix_matrix_triangular_product<Index,
typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo>
::run(mat.cols(), actualLhs.cols(),
&actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(),
mat.data(), mat.outerStride(), actualAlpha);
}
};
template<typename MatrixType, unsigned int UpLo>
template<typename ProductDerived, typename _Lhs, typename _Rhs>
TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha)
{
typedef typename internal::remove_all<typename ProductDerived::LhsNested>::type Lhs;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
typedef typename internal::remove_all<typename ProductDerived::RhsNested>::type Rhs;
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
typename ProductDerived::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
internal::general_matrix_matrix_triangular_product<Index,
typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo>
::run(m_matrix.cols(), actualLhs.cols(),
&actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(),
const_cast<Scalar*>(m_matrix.data()), m_matrix.outerStride(), actualAlpha);
general_product_to_triangular_selector<MatrixType, ProductDerived, UpLo, (_Lhs::ColsAtCompileTime==1) || (_Rhs::RowsAtCompileTime==1)>::run(m_matrix.const_cast_derived(), prod.derived(), alpha);
return *this;
}

32
eigenlib/Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -52,12 +52,17 @@ EIGEN_DONT_INLINE static void run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index
#ifdef EIGEN_INTERNAL_DEBUGGING
resIncr
#endif
, RhsScalar alpha)
ResScalar* res, Index resIncr, RhsScalar alpha);
};
template<typename Index, typename LhsScalar, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs, int Version>
EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,ColMajor,ConjugateLhs,RhsScalar,ConjugateRhs,Version>::run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index resIncr, RhsScalar alpha)
{
EIGEN_UNUSED_VARIABLE(resIncr)
eigen_internal_assert(resIncr==1);
#ifdef _EIGEN_ACCUMULATE_PACKETS
#error _EIGEN_ACCUMULATE_PACKETS has already been defined
@ -74,13 +79,14 @@ EIGEN_DONT_INLINE static void run(
conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
if(ConjugateRhs)
alpha = conj(alpha);
alpha = numext::conj(alpha);
enum { AllAligned = 0, EvenAligned, FirstAligned, NoneAligned };
const Index columnsAtOnce = 4;
const Index peels = 2;
const Index LhsPacketAlignedMask = LhsPacketSize-1;
const Index ResPacketAlignedMask = ResPacketSize-1;
// const Index PeelAlignedMask = ResPacketSize*peels-1;
const Index size = rows;
// How many coeffs of the result do we have to skip to be aligned.
@ -252,7 +258,7 @@ EIGEN_DONT_INLINE static void run(
// process aligned result's coeffs
if ((size_t(lhs0+alignedStart)%sizeof(LhsPacket))==0)
for (Index i = alignedStart;i<alignedSize;i+=ResPacketSize)
pstore(&res[i], pcj.pmadd(ploadu<LhsPacket>(&lhs0[i]), ptmp0, pload<ResPacket>(&res[i])));
pstore(&res[i], pcj.pmadd(pload<LhsPacket>(&lhs0[i]), ptmp0, pload<ResPacket>(&res[i])));
else
for (Index i = alignedStart;i<alignedSize;i+=ResPacketSize)
pstore(&res[i], pcj.pmadd(ploadu<LhsPacket>(&lhs0[i]), ptmp0, pload<ResPacket>(&res[i])));
@ -273,7 +279,6 @@ EIGEN_DONT_INLINE static void run(
} while(Vectorizable);
#undef _EIGEN_ACCUMULATE_PACKETS
}
};
/* Optimized row-major matrix * vector product:
* This algorithm processes 4 rows at onces that allows to both reduce
@ -307,6 +312,15 @@ typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
EIGEN_DONT_INLINE static void run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index resIncr,
ResScalar alpha);
};
template<typename Index, typename LhsScalar, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs, int Version>
EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,RowMajor,ConjugateLhs,RhsScalar,ConjugateRhs,Version>::run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
@ -334,6 +348,7 @@ EIGEN_DONT_INLINE static void run(
const Index peels = 2;
const Index RhsPacketAlignedMask = RhsPacketSize-1;
const Index LhsPacketAlignedMask = LhsPacketSize-1;
// const Index PeelAlignedMask = RhsPacketSize*peels-1;
const Index depth = cols;
// How many coeffs of the result do we have to skip to be aligned.
@ -543,7 +558,6 @@ EIGEN_DONT_INLINE static void run(
#undef _EIGEN_ACCUMULATE_PACKETS
}
};
} // end namespace internal

6
eigenlib/Eigen/src/Core/products/GeneralMatrixVector_MKL.h
View File

@ -53,7 +53,7 @@ struct general_matrix_vector_product_gemv :
#define EIGEN_MKL_GEMV_SPECIALIZE(Scalar) \
template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
struct general_matrix_vector_product<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const Scalar* lhs, Index lhsStride, \
const Scalar* rhs, Index rhsIncr, \
@ -70,7 +70,7 @@ static EIGEN_DONT_INLINE void run( \
}; \
template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
struct general_matrix_vector_product<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const Scalar* lhs, Index lhsStride, \
const Scalar* rhs, Index rhsIncr, \
@ -92,7 +92,7 @@ struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,Conjugat
{ \
typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const EIGTYPE* lhs, Index lhsStride, \
const EIGTYPE* rhs, Index rhsIncr, \

66
eigenlib/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
View File

@ -30,9 +30,9 @@ struct symm_pack_lhs
for(Index k=i; k<i+BlockRows; k++)
{
for(Index w=0; w<h; w++)
blockA[count++] = conj(lhs(k, i+w)); // transposed
blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
blockA[count++] = real(lhs(k,k)); // real (diagonal)
blockA[count++] = numext::real(lhs(k,k)); // real (diagonal)
for(Index w=h+1; w<BlockRows; w++)
blockA[count++] = lhs(i+w, k); // normal
@ -41,7 +41,7 @@ struct symm_pack_lhs
// transposed copy
for(Index k=i+BlockRows; k<cols; k++)
for(Index w=0; w<BlockRows; w++)
blockA[count++] = conj(lhs(k, i+w)); // transposed
blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
}
void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
{
@ -65,10 +65,10 @@ struct symm_pack_lhs
for(Index k=0; k<i; k++)
blockA[count++] = lhs(i, k); // normal
blockA[count++] = real(lhs(i, i)); // real (diagonal)
blockA[count++] = numext::real(lhs(i, i)); // real (diagonal)
for(Index k=i+1; k<cols; k++)
blockA[count++] = conj(lhs(k, i)); // transposed
blockA[count++] = numext::conj(lhs(k, i)); // transposed
}
}
};
@ -107,12 +107,12 @@ struct symm_pack_rhs
// transpose
for(Index k=k2; k<j2; k++)
{
blockB[count+0] = conj(rhs(j2+0,k));
blockB[count+1] = conj(rhs(j2+1,k));
blockB[count+0] = numext::conj(rhs(j2+0,k));
blockB[count+1] = numext::conj(rhs(j2+1,k));
if (nr==4)
{
blockB[count+2] = conj(rhs(j2+2,k));
blockB[count+3] = conj(rhs(j2+3,k));
blockB[count+2] = numext::conj(rhs(j2+2,k));
blockB[count+3] = numext::conj(rhs(j2+3,k));
}
count += nr;
}
@ -124,11 +124,11 @@ struct symm_pack_rhs
for (Index w=0 ; w<h; ++w)
blockB[count+w] = rhs(k,j2+w);
blockB[count+h] = real(rhs(k,k));
blockB[count+h] = numext::real(rhs(k,k));
// transpose
for (Index w=h+1 ; w<nr; ++w)
blockB[count+w] = conj(rhs(j2+w,k));
blockB[count+w] = numext::conj(rhs(j2+w,k));
count += nr;
++h;
}
@ -151,12 +151,12 @@ struct symm_pack_rhs
{
for(Index k=k2; k<end_k; k++)
{
blockB[count+0] = conj(rhs(j2+0,k));
blockB[count+1] = conj(rhs(j2+1,k));
blockB[count+0] = numext::conj(rhs(j2+0,k));
blockB[count+1] = numext::conj(rhs(j2+1,k));
if (nr==4)
{
blockB[count+2] = conj(rhs(j2+2,k));
blockB[count+3] = conj(rhs(j2+3,k));
blockB[count+2] = numext::conj(rhs(j2+2,k));
blockB[count+3] = numext::conj(rhs(j2+3,k));
}
count += nr;
}
@ -169,13 +169,13 @@ struct symm_pack_rhs
Index half = (std::min)(end_k,j2);
for(Index k=k2; k<half; k++)
{
blockB[count] = conj(rhs(j2,k));
blockB[count] = numext::conj(rhs(j2,k));
count += 1;
}
if(half==j2 && half<k2+rows)
{
blockB[count] = real(rhs(j2,j2));
blockB[count] = numext::real(rhs(j2,j2));
count += 1;
}
else
@ -211,7 +211,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,Co
const Scalar* lhs, Index lhsStride,
const Scalar* rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha)
const Scalar& alpha)
{
product_selfadjoint_matrix<Scalar, Index,
EIGEN_LOGICAL_XOR(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
@ -234,7 +234,18 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha)
const Scalar& alpha);
};
template <typename Scalar, typename Index,
int LhsStorageOrder, bool ConjugateLhs,
int RhsStorageOrder, bool ConjugateRhs>
EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor>::run(
Index rows, Index cols,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha)
{
Index size = rows;
@ -301,7 +312,6 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
}
}
}
};
// matrix * selfadjoint product
template <typename Scalar, typename Index,
@ -315,7 +325,18 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha)
const Scalar& alpha);
};
template <typename Scalar, typename Index,
int LhsStorageOrder, bool ConjugateLhs,
int RhsStorageOrder, bool ConjugateRhs>
EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor>::run(
Index rows, Index cols,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha)
{
Index size = cols;
@ -353,7 +374,6 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
}
}
}
};
} // end namespace internal
@ -383,7 +403,7 @@ struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>
RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint
};
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());

10
eigenlib/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h
View File

@ -23,7 +23,7 @@
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 Intel(R) MKL
* Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
@ -47,7 +47,7 @@ template <typename Index, \
struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \
{\
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
@ -98,7 +98,7 @@ template <typename Index, \
int RhsStorageOrder, bool ConjugateRhs> \
struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \
{\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
@ -174,7 +174,7 @@ template <typename Index, \
struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \
{\
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
@ -224,7 +224,7 @@ template <typename Index, \
int RhsStorageOrder, bool ConjugateRhs> \
struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \
{\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index rows, Index cols, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \

33
eigenlib/Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -28,6 +28,15 @@ struct selfadjoint_matrix_vector_product
{
static EIGEN_DONT_INLINE void run(
Index size,
const Scalar* lhs, Index lhsStride,
const Scalar* _rhs, Index rhsIncr,
Scalar* res,
Scalar alpha);
};
template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run(
Index size,
const Scalar* lhs, Index lhsStride,
const Scalar* _rhs, Index rhsIncr,
@ -35,7 +44,6 @@ static EIGEN_DONT_INLINE void run(
Scalar alpha)
{
typedef typename packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
const Index PacketSize = sizeof(Packet)/sizeof(Scalar);
enum {
@ -51,7 +59,7 @@ static EIGEN_DONT_INLINE void run(
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0;
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;
Scalar cjAlpha = ConjugateRhs ? conj(alpha) : alpha;
Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
// FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed.
// if the rhs is not sequentially stored in memory we copy it to a temporary buffer,
@ -71,8 +79,8 @@ static EIGEN_DONT_INLINE void run(
for (Index j=FirstTriangular ? bound : 0;
j<(FirstTriangular ? size : bound);j+=2)
{
register const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
register const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;
const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;
Scalar t0 = cjAlpha * rhs[j];
Packet ptmp0 = pset1<Packet>(t0);
@ -90,8 +98,8 @@ static EIGEN_DONT_INLINE void run(
size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed
res[j] += cjd.pmul(internal::real(A0[j]), t0);
res[j+1] += cjd.pmul(internal::real(A1[j+1]), t1);
res[j] += cjd.pmul(numext::real(A0[j]), t0);
res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);
if(FirstTriangular)
{
res[j] += cj0.pmul(A1[j], t1);
@ -106,8 +114,8 @@ static EIGEN_DONT_INLINE void run(
for (size_t i=starti; i<alignedStart; ++i)
{
res[i] += t0 * A0[i] + t1 * A1[i];
t2 += conj(A0[i]) * rhs[i];
t3 += conj(A1[i]) * rhs[i];
t2 += numext::conj(A0[i]) * rhs[i];
t3 += numext::conj(A1[i]) * rhs[i];
}
// Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
// gcc 4.2 does this optimization automatically.
@ -139,12 +147,12 @@ static EIGEN_DONT_INLINE void run(
}
for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++)
{
register const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
Scalar t1 = cjAlpha * rhs[j];
Scalar t2(0);
// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed
res[j] += cjd.pmul(internal::real(A0[j]), t1);
res[j] += cjd.pmul(numext::real(A0[j]), t1);
for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)
{
res[i] += cj0.pmul(A0[i], t1);
@ -153,7 +161,6 @@ static EIGEN_DONT_INLINE void run(
res[j] += alpha * t2;
}
}
};
} // end namespace internal
@ -180,7 +187,7 @@ struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>
SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
typedef typename Dest::Scalar ResScalar;
typedef typename Base::RhsScalar RhsScalar;
@ -260,7 +267,7 @@ struct SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>
SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const
{
// let's simply transpose the product
Transpose<Dest> destT(dest);

4
eigenlib/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h
View File

@ -50,7 +50,7 @@ struct selfadjoint_matrix_vector_product_symv :
#define EIGEN_MKL_SYMV_SPECIALIZE(Scalar) \
template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \
static EIGEN_DONT_INLINE void run( \
static void run( \
Index size, const Scalar* lhs, Index lhsStride, \
const Scalar* _rhs, Index rhsIncr, Scalar* res, Scalar alpha) { \
enum {\
@ -77,7 +77,7 @@ struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,Co
{ \
typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index size, const EIGTYPE* lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* res, EIGTYPE alpha) \
{ \

20
eigenlib/Eigen/src/Core/products/SelfadjointProduct.h
View File

@ -18,21 +18,19 @@
namespace Eigen {
template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
struct selfadjoint_rank1_update;
template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs>
{
static void run(Index size, Scalar* mat, Index stride, const Scalar* vec, Scalar alpha)
static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
{
internal::conj_if<ConjRhs> cj;
typedef Map<const Matrix<Scalar,Dynamic,1> > OtherMap;
typedef typename internal::conditional<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&>::type ConjRhsType;
typedef typename internal::conditional<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&>::type ConjLhsType;
for (Index i=0; i<size; ++i)
{
Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1)))
+= (alpha * cj(vec[i])) * ConjRhsType(OtherMap(vec+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1)));
+= (alpha * cj(vecY[i])) * ConjLhsType(OtherMap(vecX+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1)));
}
}
};
@ -40,9 +38,9 @@ struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs>
template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
struct selfadjoint_rank1_update<Scalar,Index,RowMajor,UpLo,ConjLhs,ConjRhs>
{
static void run(Index size, Scalar* mat, Index stride, const Scalar* vec, Scalar alpha)
static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
{
selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vec,alpha);
selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vecY,vecX,alpha);
}
};
@ -52,7 +50,7 @@ struct selfadjoint_product_selector;
template<typename MatrixType, typename OtherType, int UpLo>
struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true>
{
static void run(MatrixType& mat, const OtherType& other, typename MatrixType::Scalar alpha)
static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
@ -78,14 +76,14 @@ struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true>
selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
(!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex>
::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualAlpha);
::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualOtherPtr, actualAlpha);
}
};
template<typename MatrixType, typename OtherType, int UpLo>
struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
{
static void run(MatrixType& mat, const OtherType& other, typename MatrixType::Scalar alpha)
static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
@ -113,7 +111,7 @@ struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
template<typename MatrixType, unsigned int UpLo>
template<typename DerivedU>
SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
::rankUpdate(const MatrixBase<DerivedU>& u, Scalar alpha)
::rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha)
{
selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);

18
eigenlib/Eigen/src/Core/products/SelfadjointRank2Update.h
View File

@ -24,14 +24,14 @@ struct selfadjoint_rank2_update_selector;
template<typename Scalar, typename Index, typename UType, typename VType>
struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
{
static void run(Scalar* mat, Index stride, const UType& u, const VType& v, Scalar alpha)
static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
{
const Index size = u.size();
for (Index i=0; i<size; ++i)
{
Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) +=
(conj(alpha) * conj(u.coeff(i))) * v.tail(size-i)
+ (alpha * conj(v.coeff(i))) * u.tail(size-i);
(numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i)
+ (alpha * numext::conj(v.coeff(i))) * u.tail(size-i);
}
}
};
@ -39,13 +39,13 @@ struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
template<typename Scalar, typename Index, typename UType, typename VType>
struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper>
{
static void run(Scalar* mat, Index stride, const UType& u, const VType& v, Scalar alpha)
static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
{
const Index size = u.size();
for (Index i=0; i<size; ++i)
Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) +=
(conj(alpha) * conj(u.coeff(i))) * v.head(i+1)
+ (alpha * conj(v.coeff(i))) * u.head(i+1);
(numext::conj(alpha) * numext::conj(u.coeff(i))) * v.head(i+1)
+ (alpha * numext::conj(v.coeff(i))) * u.head(i+1);
}
};
@ -58,7 +58,7 @@ template<bool Cond, typename T> struct conj_expr_if
template<typename MatrixType, unsigned int UpLo>
template<typename DerivedU, typename DerivedV>
SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, Scalar alpha)
::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha)
{
typedef internal::blas_traits<DerivedU> UBlasTraits;
typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
@ -75,9 +75,9 @@ SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived())
* internal::conj(VBlasTraits::extractScalarFactor(v.derived()));
* numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
if (IsRowMajor)
actualAlpha = internal::conj(actualAlpha);
actualAlpha = numext::conj(actualAlpha);
internal::selfadjoint_rank2_update_selector<Scalar, Index,
typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ UBlasTraits::NeedToConjugate,_ActualUType>::type>::type,

40
eigenlib/Eigen/src/Core/products/TriangularMatrixMatrix.h
View File

@ -61,7 +61,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,LhsIsTriangular,
const Scalar* lhs, Index lhsStride,
const Scalar* rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha, level3_blocking<Scalar,Scalar>& blocking)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
product_triangular_matrix_matrix<Scalar, Index,
(Mode&(UnitDiag|ZeroDiag)) | ((Mode&Upper) ? Lower : Upper),
@ -96,7 +96,20 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha, level3_blocking<Scalar,Scalar>& blocking)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index, int Mode,
int LhsStorageOrder, bool ConjugateLhs,
int RhsStorageOrder, bool ConjugateRhs, int Version>
EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
LhsStorageOrder,ConjugateLhs,
RhsStorageOrder,ConjugateRhs,ColMajor,Version>::run(
Index _rows, Index _cols, Index _depth,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
// strip zeros
Index diagSize = (std::min)(_rows,_depth);
@ -203,15 +216,14 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
}
}
}
};
// implements col-major += alpha * op(general) * op(triangular)
template <typename Scalar, typename Index, int Mode,
int LhsStorageOrder, bool ConjugateLhs,
int RhsStorageOrder, bool ConjugateRhs, int Version>
struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
LhsStorageOrder,ConjugateLhs,
RhsStorageOrder,ConjugateRhs,ColMajor,Version>
LhsStorageOrder,ConjugateLhs,
RhsStorageOrder,ConjugateRhs,ColMajor,Version>
{
typedef gebp_traits<Scalar,Scalar> Traits;
enum {
@ -225,7 +237,20 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
Scalar alpha, level3_blocking<Scalar,Scalar>& blocking)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index, int Mode,
int LhsStorageOrder, bool ConjugateLhs,
int RhsStorageOrder, bool ConjugateRhs, int Version>
EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
LhsStorageOrder,ConjugateLhs,
RhsStorageOrder,ConjugateRhs,ColMajor,Version>::run(
Index _rows, Index _cols, Index _depth,
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
// strip zeros
Index diagSize = (std::min)(_cols,_depth);
@ -343,7 +368,6 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
}
}
}
};
/***************************************************************************
* Wrapper to product_triangular_matrix_matrix
@ -364,7 +388,7 @@ struct TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs);

4
eigenlib/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h
View File

@ -91,7 +91,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \
}; \
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index _rows, Index _cols, Index _depth, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
@ -205,7 +205,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \
}; \
\
static EIGEN_DONT_INLINE void run( \
static void run( \
Index _rows, Index _cols, Index _depth, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \

32
eigenlib/Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -27,7 +27,13 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
};
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, ResScalar alpha)
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
};
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
{
static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
Index size = (std::min)(_rows,_cols);
@ -78,7 +84,6 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
_res, resIncr, alpha);
}
}
};
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
@ -89,8 +94,14 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
};
static void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, ResScalar alpha)
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
};
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
{
static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
Index diagSize = (std::min)(_rows,_cols);
@ -141,7 +152,6 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
&res.coeffRef(diagSize), resIncr, alpha);
}
}
};
/***************************************************************************
* Wrapper to product_triangular_vector
@ -171,7 +181,7 @@ struct TriangularProduct<Mode,true,Lhs,false,Rhs,true>
TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());
@ -187,7 +197,7 @@ struct TriangularProduct<Mode,false,Lhs,true,Rhs,false>
TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {}
template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const
{
eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols());
@ -205,7 +215,7 @@ namespace internal {
template<> struct trmv_selector<ColMajor>
{
template<int Mode, typename Lhs, typename Rhs, typename Dest>
static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar alpha)
static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, const typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar& alpha)
{
typedef TriangularProduct<Mode,true,Lhs,false,Rhs,true> ProductType;
typedef typename ProductType::Index Index;
@ -235,7 +245,7 @@ template<> struct trmv_selector<ColMajor>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
@ -246,7 +256,7 @@ template<> struct trmv_selector<ColMajor>
if(!evalToDest)
{
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
int size = dest.size();
Index size = dest.size();
EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
if(!alphaIsCompatible)
@ -281,7 +291,7 @@ template<> struct trmv_selector<ColMajor>
template<> struct trmv_selector<RowMajor>
{
template<int Mode, typename Lhs, typename Rhs, typename Dest>
static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar alpha)
static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, const typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar& alpha)
{
typedef TriangularProduct<Mode,true,Lhs,false,Rhs,true> ProductType;
typedef typename ProductType::LhsScalar LhsScalar;

12
eigenlib/Eigen/src/Core/products/TriangularMatrixVector_MKL.h
View File

@ -50,7 +50,7 @@ struct triangular_matrix_vector_product_trmv :
#define EIGEN_MKL_TRMV_SPECIALIZE(Scalar) \
template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
@ -58,7 +58,7 @@ struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs
}; \
template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
@ -81,8 +81,8 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \
LowUp = IsLower ? Lower : Upper \
}; \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
{ \
if (ConjLhs || IsZeroDiag) { \
triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
@ -166,8 +166,8 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \
LowUp = IsLower ? Lower : Upper \
}; \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
{ \
if (IsZeroDiag) { \
triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \

18
eigenlib/Eigen/src/Core/products/TriangularSolverMatrix.h
View File

@ -18,7 +18,7 @@ namespace internal {
template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder>
struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor>
{
static EIGEN_DONT_INLINE void run(
static void run(
Index size, Index cols,
const Scalar* tri, Index triStride,
Scalar* _other, Index otherStride,
@ -39,6 +39,13 @@ template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStor
struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor>
{
static EIGEN_DONT_INLINE void run(
Index size, Index otherSize,
const Scalar* _tri, Index triStride,
Scalar* _other, Index otherStride,
level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor>::run(
Index size, Index otherSize,
const Scalar* _tri, Index triStride,
Scalar* _other, Index otherStride,
@ -173,7 +180,6 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO
}
}
}
};
/* Optimized triangular solver with multiple left hand sides and the trinagular matrix on the right
*/
@ -181,6 +187,13 @@ template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStor
struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>
{
static EIGEN_DONT_INLINE void run(
Index size, Index otherSize,
const Scalar* _tri, Index triStride,
Scalar* _other, Index otherStride,
level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder>
EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>::run(
Index size, Index otherSize,
const Scalar* _tri, Index triStride,
Scalar* _other, Index otherStride,
@ -308,7 +321,6 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage
}
}
}
};
} // end namespace internal

4
eigenlib/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h
View File

@ -48,7 +48,7 @@ struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorage
IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \
conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
}; \
static EIGEN_DONT_INLINE void run( \
static void run( \
Index size, Index otherSize, \
const EIGTYPE* _tri, Index triStride, \
EIGTYPE* _other, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
@ -103,7 +103,7 @@ struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorag
IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \
conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
}; \
static EIGEN_DONT_INLINE void run( \
static void run( \
Index size, Index otherSize, \
const EIGTYPE* _tri, Index triStride, \
EIGTYPE* _other, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \

10
eigenlib/Eigen/src/Core/util/BlasUtil.h
View File

@ -42,7 +42,7 @@ template<bool Conjugate> struct conj_if;
template<> struct conj_if<true> {
template<typename T>
inline T operator()(const T& x) { return conj(x); }
inline T operator()(const T& x) { return numext::conj(x); }
template<typename T>
inline T pconj(const T& x) { return internal::pconj(x); }
};
@ -67,7 +67,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) + imag(x)*imag(y), imag(x)*real(y) - real(x)*imag(y)); }
{ return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::imag(x)*numext::real(y) - numext::real(x)*numext::imag(y)); }
};
template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,false>
@ -77,7 +77,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) + imag(x)*imag(y), real(x)*imag(y) - imag(x)*real(y)); }
{ return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); }
};
template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,true>
@ -87,7 +87,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) - imag(x)*imag(y), - real(x)*imag(y) - imag(x)*real(y)); }
{ return Scalar(numext::real(x)*numext::real(y) - numext::imag(x)*numext::imag(y), - numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); }
};
template<typename RealScalar,bool Conj> struct conj_helper<std::complex<RealScalar>, RealScalar, Conj,false>
@ -113,7 +113,7 @@ template<typename From,typename To> struct get_factor {
};
template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return real(x); }
static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); }
};
// Lightweight helper class to access matrix coefficients.

15
eigenlib/Eigen/src/Core/util/Constants.h
View File

@ -13,13 +13,18 @@
namespace Eigen {
/** This value means that a quantity is not known at compile-time, and that instead the value is
/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is
* stored in some runtime variable.
*
* Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.
*/
const int Dynamic = -1;
/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value
* has to be specified at runtime.
*/
const int DynamicIndex = 0xffffff;
/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
* The value Infinity there means the L-infinity norm.
*/
@ -227,7 +232,9 @@ enum {
* scalar loops to handle the unaligned boundaries */
SliceVectorizedTraversal,
/** \internal Special case to properly handle incompatible scalar types or other defecting cases*/
InvalidTraversal
InvalidTraversal,
/** \internal Evaluate all entries at once */
AllAtOnceTraversal
};
/** \internal \ingroup enums
@ -257,9 +264,9 @@ enum {
ColMajor = 0,
/** Storage order is row major (see \ref TopicStorageOrders). */
RowMajor = 0x1, // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
/** \internal Align the matrix itself if it is vectorizable fixed-size */
/** Align the matrix itself if it is vectorizable fixed-size */
AutoAlign = 0,
/** \internal Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
/** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
DontAlign = 0x2
};

7
eigenlib/Eigen/src/Core/util/ForwardDeclarations.h
View File

@ -78,8 +78,7 @@ template<typename ExpressionType> class NestByValue;
template<typename ExpressionType> class ForceAlignedAccess;
template<typename ExpressionType> class SwapWrapper;
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class Block;
template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false> class Block;
template<typename MatrixType, int Size=Dynamic> class VectorBlock;
template<typename MatrixType> class Transpose;
@ -154,7 +153,6 @@ template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRh
template<typename Scalar> struct scalar_sum_op;
template<typename Scalar> struct scalar_difference_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op;
template<typename Scalar> struct scalar_quotient_op;
template<typename Scalar> struct scalar_opposite_op;
template<typename Scalar> struct scalar_conjugate_op;
template<typename Scalar> struct scalar_real_op;
@ -185,6 +183,7 @@ template<typename Scalar> struct scalar_identity_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_multiple2_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
} // end namespace internal
@ -271,6 +270,8 @@ template<typename Derived> struct MatrixExponentialReturnValue;
template<typename Derived> class MatrixFunctionReturnValue;
template<typename Derived> class MatrixSquareRootReturnValue;
template<typename Derived> class MatrixLogarithmReturnValue;
template<typename Derived> class MatrixPowerReturnValue;
template<typename Derived, typename Lhs, typename Rhs> class MatrixPowerProduct;
namespace internal {
template <typename Scalar>

39
eigenlib/Eigen/src/Core/util/Macros.h
View File

@ -12,8 +12,8 @@
#define EIGEN_MACROS_H
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 1
#define EIGEN_MINOR_VERSION 2
#define EIGEN_MAJOR_VERSION 2
#define EIGEN_MINOR_VERSION 1
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@ -115,12 +115,6 @@
#define EIGEN_MAKESTRING2(a) #a
#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
#if EIGEN_GNUC_AT_LEAST(4,1) && !defined(__clang__) && !defined(__INTEL_COMPILER)
#define EIGEN_FLATTEN_ATTRIB __attribute__((flatten))
#else
#define EIGEN_FLATTEN_ATTRIB
#endif
// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
// but GCC is still doing fine with just inline.
@ -151,6 +145,12 @@
#define EIGEN_DONT_INLINE
#endif
#if (defined __GNUC__)
#define EIGEN_PERMISSIVE_EXPR __extension__
#else
#define EIGEN_PERMISSIVE_EXPR
#endif
// this macro allows to get rid of linking errors about multiply defined functions.
// - static is not very good because it prevents definitions from different object files to be merged.
// So static causes the resulting linked executable to be bloated with multiple copies of the same function.
@ -238,12 +238,19 @@
#endif
// Suppresses 'unused variable' warnings.
#define EIGEN_UNUSED_VARIABLE(var) (void)var;
namespace Eigen {
namespace internal {
template<typename T> void ignore_unused_variable(const T&) {}
}
}
#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
#if !defined(EIGEN_ASM_COMMENT) && (defined __GNUC__)
#define EIGEN_ASM_COMMENT(X) asm("#" X)
#else
#define EIGEN_ASM_COMMENT(X)
#if !defined(EIGEN_ASM_COMMENT)
#if (defined __GNUC__) && ( defined(__i386__) || defined(__x86_64__) )
#define EIGEN_ASM_COMMENT(X) asm("#" X)
#else
#define EIGEN_ASM_COMMENT(X)
#endif
#endif
/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.
@ -301,6 +308,12 @@
#if defined(_MSC_VER) && (!defined(__INTEL_COMPILER))
#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
using Base::operator =;
#elif defined(__clang__) // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
using Base::operator =; \
EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
template <typename OtherDerived> \
EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }
#else
#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
using Base::operator =; \

72
eigenlib/Eigen/src/Core/util/Memory.h
View File

@ -19,6 +19,10 @@
#ifndef EIGEN_MEMORY_H
#define EIGEN_MEMORY_H
#ifndef EIGEN_MALLOC_ALREADY_ALIGNED
// Try to determine automatically if malloc is already aligned.
// On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see:
// http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html
// This is true at least since glibc 2.8.
@ -27,7 +31,7 @@
// page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed
// quite safe, at least within the context of glibc, to equate 64-bit with LP64.
#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 8) || __GLIBC__>2) \
&& defined(__LP64__)
&& defined(__LP64__) && ! defined( __SANITIZE_ADDRESS__ )
#define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1
#else
#define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0
@ -52,10 +56,19 @@
#define EIGEN_MALLOC_ALREADY_ALIGNED 0
#endif
#if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) \
&& (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0)
#define EIGEN_HAS_POSIX_MEMALIGN 1
#else
#endif
// See bug 554 (http://eigen.tuxfamily.org/bz/show_bug.cgi?id=554)
// It seems to be unsafe to check _POSIX_ADVISORY_INFO without including unistd.h first.
// Currently, let's include it only on unix systems:
#if defined(__unix__) || defined(__unix)
#include <unistd.h>
#if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) && (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0)
#define EIGEN_HAS_POSIX_MEMALIGN 1
#endif
#endif
#ifndef EIGEN_HAS_POSIX_MEMALIGN
#define EIGEN_HAS_POSIX_MEMALIGN 0
#endif
@ -88,11 +101,11 @@ inline void throw_std_bad_alloc()
/** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned.
* Fast, but wastes 16 additional bytes of memory. Does not throw any exception.
*/
inline void* handmade_aligned_malloc(size_t size)
inline void* handmade_aligned_malloc(std::size_t size)
{
void *original = std::malloc(size+16);
if (original == 0) return 0;
void *aligned = reinterpret_cast<void*>((reinterpret_cast<size_t>(original) & ~(size_t(15))) + 16);
void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(15))) + 16);
*(reinterpret_cast<void**>(aligned) - 1) = original;
return aligned;
}
@ -108,13 +121,18 @@ inline void handmade_aligned_free(void *ptr)
* Since we know that our handmade version is based on std::realloc
* we can use std::realloc to implement efficient reallocation.
*/
inline void* handmade_aligned_realloc(void* ptr, size_t size, size_t = 0)
inline void* handmade_aligned_realloc(void* ptr, std::size_t size, std::size_t = 0)
{
if (ptr == 0) return handmade_aligned_malloc(size);
void *original = *(reinterpret_cast<void**>(ptr) - 1);
std::ptrdiff_t previous_offset = static_cast<char *>(ptr)-static_cast<char *>(original);
original = std::realloc(original,size+16);
if (original == 0) return 0;
void *aligned = reinterpret_cast<void*>((reinterpret_cast<size_t>(original) & ~(size_t(15))) + 16);
void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(15))) + 16);
void *previous_aligned = static_cast<char *>(original)+previous_offset;
if(aligned!=previous_aligned)
std::memmove(aligned, previous_aligned, size);
*(reinterpret_cast<void**>(aligned) - 1) = original;
return aligned;
}
@ -123,7 +141,7 @@ inline void* handmade_aligned_realloc(void* ptr, size_t size, size_t = 0)
*** Implementation of generic aligned realloc (when no realloc can be used)***
*****************************************************************************/
void* aligned_malloc(size_t size);
void* aligned_malloc(std::size_t size);
void aligned_free(void *ptr);
/** \internal
@ -204,7 +222,7 @@ inline void* aligned_malloc(size_t size)
if(posix_memalign(&result, 16, size)) result = 0;
#elif EIGEN_HAS_MM_MALLOC
result = _mm_malloc(size, 16);
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
result = _aligned_malloc(size, 16);
#else
result = handmade_aligned_malloc(size);
@ -227,7 +245,7 @@ inline void aligned_free(void *ptr)
std::free(ptr);
#elif EIGEN_HAS_MM_MALLOC
_mm_free(ptr);
#elif defined(_MSC_VER)
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
_aligned_free(ptr);
#else
handmade_aligned_free(ptr);
@ -446,7 +464,6 @@ template<typename T, bool Align> inline void conditional_aligned_delete_auto(T *
template<typename Scalar, typename Index>
static inline Index first_aligned(const Scalar* array, Index size)
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size,
PacketAlignedMask = PacketSize-1
};
@ -470,6 +487,13 @@ static inline Index first_aligned(const Scalar* array, Index size)
}
}
/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
*/
template<typename Index>
inline static Index first_multiple(Index size, Index base)
{
return ((size+base-1)/base)*base;
}
// std::copy is much slower than memcpy, so let's introduce a smart_copy which
// use memcpy on trivial types, i.e., on types that does not require an initialization ctor.
@ -554,7 +578,7 @@ template<typename T> class aligned_stack_memory_handler
*/
#ifdef EIGEN_ALLOCA
#ifdef __arm__
#if defined(__arm__) || defined(_WIN32)
#define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast<void*>((reinterpret_cast<size_t>(EIGEN_ALLOCA(SIZE+16)) & ~(size_t(15))) + 16)
#else
#define EIGEN_ALIGNED_ALLOCA EIGEN_ALLOCA
@ -610,7 +634,9 @@ template<typename T> class aligned_stack_memory_handler
/* memory allocated we can safely let the default implementation handle */ \
/* this particular case. */ \
static void *operator new(size_t size, void *ptr) { return ::operator new(size,ptr); } \
static void *operator new[](size_t size, void* ptr) { return ::operator new[](size,ptr); } \
void operator delete(void * memory, void *ptr) throw() { return ::operator delete(memory,ptr); } \
void operator delete[](void * memory, void *ptr) throw() { return ::operator delete[](memory,ptr); } \
/* nothrow-new (returns zero instead of std::bad_alloc) */ \
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
void operator delete(void *ptr, const std::nothrow_t&) throw() { \
@ -705,15 +731,6 @@ public:
::new( p ) T( value );
}
// Support for c++11
#if (__cplusplus >= 201103L)
template<typename... Args>
void construct(pointer p, Args&&... args)
{
::new(p) T(std::forward<Args>(args)...);
}
#endif
void destroy( pointer p )
{
p->~T();
@ -738,11 +755,16 @@ public:
# if defined(__PIC__) && defined(__i386__)
// Case for x86 with PIC
# define EIGEN_CPUID(abcd,func,id) \
__asm__ __volatile__ ("xchgl %%ebx, %%esi;cpuid; xchgl %%ebx,%%esi": "=a" (abcd[0]), "=S" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id));
__asm__ __volatile__ ("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id));
# elif defined(__PIC__) && defined(__x86_64__)
// Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with the default small code model.
// However, we cannot detect which code model is used, and the xchg overhead is negligible anyway.
# define EIGEN_CPUID(abcd,func,id) \
__asm__ __volatile__ ("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id));
# else
// Case for x86_64 or x86 w/o PIC
# define EIGEN_CPUID(abcd,func,id) \
__asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id) );
__asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id) );
# endif
# elif defined(_MSC_VER)
# if (_MSC_VER > 1500) && ( defined(_M_IX86) || defined(_M_X64) )

20
eigenlib/Eigen/src/Core/util/Meta.h
View File

@ -186,23 +186,35 @@ template<int Y, int InfX, int SupX>
class meta_sqrt<Y, InfX, SupX, true> { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
/** \internal determines whether the product of two numeric types is allowed and what the return type is */
template<typename T, typename U> struct scalar_product_traits;
template<typename T, typename U> struct scalar_product_traits
{
enum { Defined = 0 };
};
template<typename T> struct scalar_product_traits<T,T>
{
//enum { Cost = NumTraits<T>::MulCost };
enum {
// Cost = NumTraits<T>::MulCost,
Defined = 1
};
typedef T ReturnType;
};
template<typename T> struct scalar_product_traits<T,std::complex<T> >
{
//enum { Cost = 2*NumTraits<T>::MulCost };
enum {
// Cost = 2*NumTraits<T>::MulCost,
Defined = 1
};
typedef std::complex<T> ReturnType;
};
template<typename T> struct scalar_product_traits<std::complex<T>, T>
{
//enum { Cost = 2*NumTraits<T>::MulCost };
enum {
// Cost = 2*NumTraits<T>::MulCost,
Defined = 1
};
typedef std::complex<T> ReturnType;
};

3
eigenlib/Eigen/src/Core/util/StaticAssert.h
View File

@ -89,7 +89,8 @@
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED,
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED,
THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE,
THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH
THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH,
OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG
};
};

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