Updated Eigen - From 3.02 to 3.05. Hopefully it should be harmless.
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@ -167,7 +167,7 @@
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#include <intrin.h>
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#endif
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#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_NO_EXCEPTIONS)
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#if defined(_CPPUNWIND) || defined(__EXCEPTIONS)
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#define EIGEN_EXCEPTIONS
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#endif
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@ -13,9 +13,9 @@ namespace Eigen {
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*
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*
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*
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* This module provides SVD decomposition for (currently) real matrices.
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* This module provides SVD decomposition for matrices (both real and complex).
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* This decomposition is accessible via the following MatrixBase method:
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* - MatrixBase::svd()
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* - MatrixBase::jacobiSvd()
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*
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* \code
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* #include <Eigen/SVD>
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@ -158,10 +158,19 @@ template<typename _MatrixType, int _UpLo> class LDLT
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}
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/** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
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*
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* This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
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*
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* \note_about_checking_solutions
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*
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* \sa solveInPlace(), MatrixBase::ldlt()
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* More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
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* by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
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* \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
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* \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
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* least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
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* computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular.
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*
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* \sa MatrixBase::ldlt()
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*/
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template<typename Rhs>
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inline const internal::solve_retval<LDLT, Rhs>
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@ -376,7 +385,21 @@ struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
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dec().matrixL().solveInPlace(dst);
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// dst = D^-1 (L^-1 P b)
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dst = dec().vectorD().asDiagonal().inverse() * dst;
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// more precisely, use pseudo-inverse of D (see bug 241)
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using std::abs;
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using std::max;
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typedef typename LDLTType::MatrixType MatrixType;
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typedef typename LDLTType::Scalar Scalar;
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typedef typename LDLTType::RealScalar RealScalar;
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const Diagonal<const MatrixType> vectorD = dec().vectorD();
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RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(),
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RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS
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for (Index i = 0; i < vectorD.size(); ++i) {
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if(abs(vectorD(i)) > tolerance)
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dst.row(i) /= vectorD(i);
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else
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dst.row(i).setZero();
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}
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// dst = L^-T (D^-1 L^-1 P b)
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dec().matrixU().solveInPlace(dst);
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@ -68,10 +68,8 @@ class Array
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friend struct internal::conservative_resize_like_impl;
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using Base::m_storage;
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public:
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enum { NeedsToAlign = (!(Options&DontAlign))
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&& SizeAtCompileTime!=Dynamic && ((static_cast<int>(sizeof(Scalar))*SizeAtCompileTime)%16)==0 };
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
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using Base::base;
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using Base::coeff;
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@ -94,7 +94,7 @@ struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel, HasDirectAccess>
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MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
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&& (InnerStrideAtCompileTime == 1)
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? PacketAccessBit : 0,
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MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && ((OuterStrideAtCompileTime % packet_traits<Scalar>::size) == 0)) ? AlignedBit : 0,
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MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
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FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
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FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
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FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
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@ -94,7 +94,7 @@ struct isMuchSmallerThan_scalar_selector<Derived, true>
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*
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* \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
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* are considered to be approximately equal within precision \f$ p \f$ if
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* \f[ \Vert v - w \Vert \leqslant p\,\(min)(\Vert v\Vert, \Vert w\Vert). \f]
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* \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
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* For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
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* L2 norm).
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*
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@ -34,7 +34,7 @@
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* \tparam PlainObjectType the equivalent matrix type of the mapped data
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* \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned.
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* The default is \c #Unaligned.
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* \tparam StrideType optionnally specifies strides. By default, Map assumes the memory layout
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* \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
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* of an ordinary, contiguous array. This can be overridden by specifying strides.
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* The type passed here must be a specialization of the Stride template, see examples below.
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*
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@ -72,9 +72,9 @@
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* Example: \include Map_placement_new.cpp
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* Output: \verbinclude Map_placement_new.out
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*
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* This class is the return type of Matrix::Map() but can also be used directly.
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* This class is the return type of PlainObjectBase::Map() but can also be used directly.
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*
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* \sa Matrix::Map(), \ref TopicStorageOrders
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* \sa PlainObjectBase::Map(), \ref TopicStorageOrders
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*/
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namespace internal {
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@ -102,7 +102,7 @@ struct traits<Map<PlainObjectType, MapOptions, StrideType> >
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|| HasNoOuterStride
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|| ( OuterStrideAtCompileTime!=Dynamic
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&& ((static_cast<int>(sizeof(Scalar))*OuterStrideAtCompileTime)%16)==0 ) ),
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Flags0 = TraitsBase::Flags,
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Flags0 = TraitsBase::Flags & (~NestByRefBit),
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Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit),
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Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime))
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? int(Flags1) : int(Flags1 & ~LinearAccessBit),
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@ -120,7 +120,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
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public:
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typedef MapBase<Map> Base;
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EIGEN_DENSE_PUBLIC_INTERFACE(Map)
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typedef typename Base::PointerType PointerType;
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@ -181,7 +180,6 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
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PlainObjectType::Base::_check_template_params();
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}
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EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
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protected:
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@ -170,8 +170,8 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
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EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit,
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internal::inner_stride_at_compile_time<Derived>::ret==1),
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PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
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eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % (sizeof(Scalar)*internal::packet_traits<Scalar>::size)) == 0)
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&& "data is not aligned");
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eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % 16) == 0)
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&& "data is not aligned");
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}
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PointerType m_data;
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@ -153,10 +153,6 @@ class Matrix
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typedef typename Base::PlainObject PlainObject;
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enum { NeedsToAlign = (!(Options&DontAlign))
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&& SizeAtCompileTime!=Dynamic && ((static_cast<int>(sizeof(Scalar))*SizeAtCompileTime)%16)==0 };
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
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using Base::base;
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using Base::coeffRef;
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@ -250,7 +250,8 @@ template<typename Derived> class MatrixBase
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// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
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// of an integer constant. Solution: overload the part() method template wrt template parameters list.
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template<template<typename T, int n> class U>
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// Note: replacing next line by "template<template<typename T, int n> class U>" produces a mysterious error C2082 in MSVC.
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template<template<typename, int> class U>
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const DiagonalWrapper<ConstDiagonalReturnType> part() const
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{ return diagonal().asDiagonal(); }
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#endif // EIGEN2_SUPPORT
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@ -34,6 +34,19 @@
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namespace internal {
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template<typename Index>
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EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols)
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{
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// http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242
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// we assume Index is signed
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Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed
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bool error = (rows < 0 || cols < 0) ? true
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: (rows == 0 || cols == 0) ? false
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: (rows > max_index / cols);
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if (error)
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throw_std_bad_alloc();
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}
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template <typename Derived, typename OtherDerived = Derived, bool IsVector = static_cast<bool>(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
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template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
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@ -84,14 +97,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
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template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, Aligned, StrideType> type; };
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template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, Aligned, StrideType> type; };
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protected:
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DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
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public:
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enum { NeedsToAlign = (!(Options&DontAlign))
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&& SizeAtCompileTime!=Dynamic && ((static_cast<int>(sizeof(Scalar))*SizeAtCompileTime)%16)==0 };
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enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 };
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
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Base& base() { return *static_cast<Base*>(this); }
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@ -200,11 +211,13 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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EIGEN_STRONG_INLINE void resize(Index rows, Index cols)
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{
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#ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO
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internal::check_rows_cols_for_overflow(rows, cols);
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Index size = rows*cols;
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bool size_changed = size != this->size();
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m_storage.resize(size, rows, cols);
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if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
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#else
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internal::check_rows_cols_for_overflow(rows, cols);
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m_storage.resize(rows*cols, rows, cols);
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#endif
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}
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@ -273,6 +286,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
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{
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const OtherDerived& other = _other.derived();
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internal::check_rows_cols_for_overflow(other.rows(), other.cols());
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const Index othersize = other.rows()*other.cols();
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if(RowsAtCompileTime == 1)
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{
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@ -417,6 +431,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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: m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols())
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{
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_check_template_params();
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internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols());
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Base::operator=(other.derived());
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}
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@ -425,9 +440,6 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
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* \a data pointers.
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*
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* These methods do not allow to specify strides. If you need to specify strides, you have to
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* use the Map class directly.
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*
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* \see class Map
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*/
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//@{
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@ -584,6 +596,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
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{
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eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
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&& cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
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internal::check_rows_cols_for_overflow(rows, cols);
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m_storage.resize(rows*cols,rows,cols);
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EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED
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}
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@ -641,6 +654,7 @@ struct internal::conservative_resize_like_impl
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if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows
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(!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns
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{
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internal::check_rows_cols_for_overflow(rows, cols);
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_this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
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}
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else
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@ -256,16 +256,16 @@ class ScaledProduct
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: Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {}
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template<typename Dest>
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inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,m_alpha); }
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inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); }
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template<typename Dest>
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inline void addTo(Dest& dst) const { scaleAndAddTo(dst,m_alpha); }
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inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); }
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template<typename Dest>
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inline void subTo(Dest& dst) const { scaleAndAddTo(dst,-m_alpha); }
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inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); }
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template<typename Dest>
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inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha); }
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inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); }
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const Scalar& alpha() const { return m_alpha; }
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@ -180,7 +180,7 @@ void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived
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eigen_assert(cols() == rows());
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eigen_assert( (Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols()) );
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eigen_assert(!(Mode & ZeroDiag));
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eigen_assert(Mode & (Upper|Lower));
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eigen_assert((Mode & (Upper|Lower)) != 0);
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enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
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typedef typename internal::conditional<copy,
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@ -27,8 +27,8 @@
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namespace internal {
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static uint32x4_t p4ui_CONJ_XOR = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
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static uint32x2_t p2ui_CONJ_XOR = { 0x00000000, 0x80000000 };
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static uint32x4_t p4ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET4(0x00000000, 0x80000000, 0x00000000, 0x80000000);
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static uint32x2_t p2ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x00000000, 0x80000000);
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//---------- float ----------
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struct Packet2cf
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@ -52,6 +52,16 @@ typedef uint32x4_t Packet4ui;
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#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
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const Packet4i p4i_##NAME = pset1<Packet4i>(X)
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#if defined(__llvm__) && !defined(__clang__)
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//Special treatment for Apple's llvm-gcc, its NEON packet types are unions
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#define EIGEN_INIT_NEON_PACKET2(X, Y) {{X, Y}}
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#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {{X, Y, Z, W}}
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#else
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//Default initializer for packets
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#define EIGEN_INIT_NEON_PACKET2(X, Y) {X, Y}
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#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {X, Y, Z, W}
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#endif
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#ifndef __pld
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#define __pld(x) asm volatile ( " pld [%[addr]]\n" :: [addr] "r" (x) : "cc" );
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#endif
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@ -84,7 +94,7 @@ template<> struct packet_traits<int> : default_packet_traits
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};
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};
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#if EIGEN_GNUC_AT_MOST(4,4)
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#if EIGEN_GNUC_AT_MOST(4,4) && !defined(__llvm__)
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// workaround gcc 4.2, 4.3 and 4.4 compilatin issue
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EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); }
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EIGEN_STRONG_INLINE float32x2_t vld1_f32 (const float* x) { return ::vld1_f32 ((const float32_t*)x); }
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@ -100,12 +110,12 @@ template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
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template<> EIGEN_STRONG_INLINE Packet4f plset<float>(const float& a)
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{
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Packet4f countdown = { 0, 1, 2, 3 };
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Packet4f countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
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return vaddq_f32(pset1<Packet4f>(a), countdown);
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}
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template<> EIGEN_STRONG_INLINE Packet4i plset<int>(const int& a)
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{
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Packet4i countdown = { 0, 1, 2, 3 };
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Packet4i countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
|
||||
return vaddq_s32(pset1<Packet4i>(a), countdown);
|
||||
}
|
||||
|
||||
|
|
@ -395,25 +405,29 @@ template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
|
|||
return s[0];
|
||||
}
|
||||
|
||||
template<int Offset>
|
||||
struct palign_impl<Offset,Packet4f>
|
||||
{
|
||||
EIGEN_STRONG_INLINE static void run(Packet4f& first, const Packet4f& second)
|
||||
{
|
||||
if (Offset!=0)
|
||||
first = vextq_f32(first, second, Offset);
|
||||
}
|
||||
};
|
||||
// this PALIGN_NEON business is to work around a bug in LLVM Clang 3.0 causing incorrect compilation errors,
|
||||
// see bug 347 and this LLVM bug: http://llvm.org/bugs/show_bug.cgi?id=11074
|
||||
#define PALIGN_NEON(Offset,Type,Command) \
|
||||
template<>\
|
||||
struct palign_impl<Offset,Type>\
|
||||
{\
|
||||
EIGEN_STRONG_INLINE static void run(Type& first, const Type& second)\
|
||||
{\
|
||||
if (Offset!=0)\
|
||||
first = Command(first, second, Offset);\
|
||||
}\
|
||||
};\
|
||||
|
||||
template<int Offset>
|
||||
struct palign_impl<Offset,Packet4i>
|
||||
{
|
||||
EIGEN_STRONG_INLINE static void run(Packet4i& first, const Packet4i& second)
|
||||
{
|
||||
if (Offset!=0)
|
||||
first = vextq_s32(first, second, Offset);
|
||||
}
|
||||
};
|
||||
PALIGN_NEON(0,Packet4f,vextq_f32)
|
||||
PALIGN_NEON(1,Packet4f,vextq_f32)
|
||||
PALIGN_NEON(2,Packet4f,vextq_f32)
|
||||
PALIGN_NEON(3,Packet4f,vextq_f32)
|
||||
PALIGN_NEON(0,Packet4i,vextq_s32)
|
||||
PALIGN_NEON(1,Packet4i,vextq_s32)
|
||||
PALIGN_NEON(2,Packet4i,vextq_s32)
|
||||
PALIGN_NEON(3,Packet4i,vextq_s32)
|
||||
|
||||
#undef PALIGN_NEON
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
|
|
|
|||
|
|
@ -30,19 +30,16 @@ namespace internal {
|
|||
template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false>
|
||||
class gebp_traits;
|
||||
|
||||
inline std::ptrdiff_t manage_caching_sizes_second_if_negative(std::ptrdiff_t a, std::ptrdiff_t b)
|
||||
{
|
||||
return a<=0 ? b : a;
|
||||
}
|
||||
|
||||
/** \internal */
|
||||
inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0)
|
||||
{
|
||||
static std::ptrdiff_t m_l1CacheSize = 0;
|
||||
static std::ptrdiff_t m_l2CacheSize = 0;
|
||||
if(m_l1CacheSize==0)
|
||||
{
|
||||
m_l1CacheSize = queryL1CacheSize();
|
||||
m_l2CacheSize = queryTopLevelCacheSize();
|
||||
|
||||
if(m_l1CacheSize<=0) m_l1CacheSize = 8 * 1024;
|
||||
if(m_l2CacheSize<=0) m_l2CacheSize = 1 * 1024 * 1024;
|
||||
}
|
||||
static std::ptrdiff_t m_l1CacheSize = manage_caching_sizes_second_if_negative(queryL1CacheSize(),8 * 1024);
|
||||
static std::ptrdiff_t m_l2CacheSize = manage_caching_sizes_second_if_negative(queryTopLevelCacheSize(),1*1024*1024);
|
||||
|
||||
if(action==SetAction)
|
||||
{
|
||||
|
|
@ -118,14 +115,14 @@ inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, st
|
|||
// FIXME (a bit overkill maybe ?)
|
||||
|
||||
template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector {
|
||||
EIGEN_STRONG_INLINE EIGEN_ALWAYS_INLINE_ATTRIB static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/)
|
||||
EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/)
|
||||
{
|
||||
c = cj.pmadd(a,b,c);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> {
|
||||
EIGEN_STRONG_INLINE EIGEN_ALWAYS_INLINE_ATTRIB static void run(const CJ& cj, T& a, T& b, T& c, T& t)
|
||||
EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t)
|
||||
{
|
||||
t = b; t = cj.pmul(a,t); c = padd(c,t);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
|
|
@ -28,7 +29,7 @@
|
|||
|
||||
#define EIGEN_WORLD_VERSION 3
|
||||
#define EIGEN_MAJOR_VERSION 0
|
||||
#define EIGEN_MINOR_VERSION 2
|
||||
#define EIGEN_MINOR_VERSION 5
|
||||
|
||||
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
|
||||
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
|
||||
|
|
@ -45,7 +46,7 @@
|
|||
#define EIGEN_GNUC_AT_MOST(x,y) 0
|
||||
#endif
|
||||
|
||||
#if EIGEN_GNUC_AT_MOST(4,3)
|
||||
#if EIGEN_GNUC_AT_MOST(4,3) && !defined(__clang__)
|
||||
// see bug 89
|
||||
#define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 0
|
||||
#else
|
||||
|
|
@ -130,31 +131,34 @@
|
|||
#define EIGEN_MAKESTRING2(a) #a
|
||||
#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
|
||||
|
||||
// EIGEN_ALWAYS_INLINE_ATTRIB should be use in the declaration of function
|
||||
// which should be inlined even in debug mode.
|
||||
// FIXME with the always_inline attribute,
|
||||
// gcc 3.4.x reports the following compilation error:
|
||||
// Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
|
||||
// : function body not available
|
||||
#if EIGEN_GNUC_AT_LEAST(4,0)
|
||||
#define EIGEN_ALWAYS_INLINE_ATTRIB __attribute__((always_inline))
|
||||
#else
|
||||
#define EIGEN_ALWAYS_INLINE_ATTRIB
|
||||
#endif
|
||||
|
||||
#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_FORCE_INLINE means "inline as much as possible"
|
||||
// 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.
|
||||
#if (defined _MSC_VER) || (defined __INTEL_COMPILER)
|
||||
#define EIGEN_STRONG_INLINE __forceinline
|
||||
#else
|
||||
#define EIGEN_STRONG_INLINE inline
|
||||
#endif
|
||||
|
||||
// EIGEN_ALWAYS_INLINE is the stronget, it has the effect of making the function inline and adding every possible
|
||||
// attribute to maximize inlining. This should only be used when really necessary: in particular,
|
||||
// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
|
||||
// FIXME with the always_inline attribute,
|
||||
// gcc 3.4.x reports the following compilation error:
|
||||
// Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const'
|
||||
// : function body not available
|
||||
#if EIGEN_GNUC_AT_LEAST(4,0)
|
||||
#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
|
||||
#else
|
||||
#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE
|
||||
#endif
|
||||
|
||||
#if (defined __GNUC__)
|
||||
#define EIGEN_DONT_INLINE __attribute__((noinline))
|
||||
#elif (defined _MSC_VER)
|
||||
|
|
@ -249,7 +253,7 @@
|
|||
#define EIGEN_UNUSED_VARIABLE(var) (void)var;
|
||||
|
||||
#if (defined __GNUC__)
|
||||
#define EIGEN_ASM_COMMENT(X) asm("#"X)
|
||||
#define EIGEN_ASM_COMMENT(X) asm("#" X)
|
||||
#else
|
||||
#define EIGEN_ASM_COMMENT(X)
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -82,6 +82,16 @@
|
|||
|
||||
namespace internal {
|
||||
|
||||
inline void throw_std_bad_alloc()
|
||||
{
|
||||
#ifdef EIGEN_EXCEPTIONS
|
||||
throw std::bad_alloc();
|
||||
#else
|
||||
std::size_t huge = -1;
|
||||
new int[huge];
|
||||
#endif
|
||||
}
|
||||
|
||||
/*****************************************************************************
|
||||
*** Implementation of handmade aligned functions ***
|
||||
*****************************************************************************/
|
||||
|
|
@ -192,7 +202,7 @@ inline void check_that_malloc_is_allowed()
|
|||
#endif
|
||||
|
||||
/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 bytes alignment.
|
||||
* On allocation error, the returned pointer is null, and if exceptions are enabled then a std::bad_alloc is thrown.
|
||||
* On allocation error, the returned pointer is null, and std::bad_alloc is thrown.
|
||||
*/
|
||||
inline void* aligned_malloc(size_t size)
|
||||
{
|
||||
|
|
@ -213,10 +223,9 @@ inline void* aligned_malloc(size_t size)
|
|||
result = handmade_aligned_malloc(size);
|
||||
#endif
|
||||
|
||||
#ifdef EIGEN_EXCEPTIONS
|
||||
if(result == 0)
|
||||
throw std::bad_alloc();
|
||||
#endif
|
||||
if(!result && size)
|
||||
throw_std_bad_alloc();
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -241,7 +250,7 @@ inline void aligned_free(void *ptr)
|
|||
/**
|
||||
* \internal
|
||||
* \brief Reallocates an aligned block of memory.
|
||||
* \throws std::bad_alloc if EIGEN_EXCEPTIONS are defined.
|
||||
* \throws std::bad_alloc on allocation failure
|
||||
**/
|
||||
inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size)
|
||||
{
|
||||
|
|
@ -269,10 +278,9 @@ inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size)
|
|||
result = handmade_aligned_realloc(ptr,new_size,old_size);
|
||||
#endif
|
||||
|
||||
#ifdef EIGEN_EXCEPTIONS
|
||||
if (result==0 && new_size!=0)
|
||||
throw std::bad_alloc();
|
||||
#endif
|
||||
if (!result && new_size)
|
||||
throw_std_bad_alloc();
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -281,7 +289,7 @@ inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size)
|
|||
*****************************************************************************/
|
||||
|
||||
/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned.
|
||||
* On allocation error, the returned pointer is null, and if exceptions are enabled then a std::bad_alloc is thrown.
|
||||
* On allocation error, the returned pointer is null, and a std::bad_alloc is thrown.
|
||||
*/
|
||||
template<bool Align> inline void* conditional_aligned_malloc(size_t size)
|
||||
{
|
||||
|
|
@ -293,9 +301,8 @@ template<> inline void* conditional_aligned_malloc<false>(size_t size)
|
|||
check_that_malloc_is_allowed();
|
||||
|
||||
void *result = std::malloc(size);
|
||||
#ifdef EIGEN_EXCEPTIONS
|
||||
if(!result) throw std::bad_alloc();
|
||||
#endif
|
||||
if(!result && size)
|
||||
throw_std_bad_alloc();
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -347,18 +354,27 @@ template<typename T> inline void destruct_elements_of_array(T *ptr, size_t size)
|
|||
*** Implementation of aligned new/delete-like functions ***
|
||||
*****************************************************************************/
|
||||
|
||||
template<typename T>
|
||||
EIGEN_ALWAYS_INLINE void check_size_for_overflow(size_t size)
|
||||
{
|
||||
if(size > size_t(-1) / sizeof(T))
|
||||
throw_std_bad_alloc();
|
||||
}
|
||||
|
||||
/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment.
|
||||
* On allocation error, the returned pointer is undefined, but if exceptions are enabled then a std::bad_alloc is thrown.
|
||||
* On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown.
|
||||
* The default constructor of T is called.
|
||||
*/
|
||||
template<typename T> inline T* aligned_new(size_t size)
|
||||
{
|
||||
check_size_for_overflow<T>(size);
|
||||
T *result = reinterpret_cast<T*>(aligned_malloc(sizeof(T)*size));
|
||||
return construct_elements_of_array(result, size);
|
||||
}
|
||||
|
||||
template<typename T, bool Align> inline T* conditional_aligned_new(size_t size)
|
||||
{
|
||||
check_size_for_overflow<T>(size);
|
||||
T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
|
||||
return construct_elements_of_array(result, size);
|
||||
}
|
||||
|
|
@ -383,6 +399,8 @@ template<typename T, bool Align> inline void conditional_aligned_delete(T *ptr,
|
|||
|
||||
template<typename T, bool Align> inline T* conditional_aligned_realloc_new(T* pts, size_t new_size, size_t old_size)
|
||||
{
|
||||
check_size_for_overflow<T>(new_size);
|
||||
check_size_for_overflow<T>(old_size);
|
||||
if(new_size < old_size)
|
||||
destruct_elements_of_array(pts+new_size, old_size-new_size);
|
||||
T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
|
||||
|
|
@ -394,6 +412,7 @@ template<typename T, bool Align> inline T* conditional_aligned_realloc_new(T* pt
|
|||
|
||||
template<typename T, bool Align> inline T* conditional_aligned_new_auto(size_t size)
|
||||
{
|
||||
check_size_for_overflow<T>(size);
|
||||
T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
|
||||
if(NumTraits<T>::RequireInitialization)
|
||||
construct_elements_of_array(result, size);
|
||||
|
|
@ -402,6 +421,8 @@ template<typename T, bool Align> inline T* conditional_aligned_new_auto(size_t s
|
|||
|
||||
template<typename T, bool Align> inline T* conditional_aligned_realloc_new_auto(T* pts, size_t new_size, size_t old_size)
|
||||
{
|
||||
check_size_for_overflow<T>(new_size);
|
||||
check_size_for_overflow<T>(old_size);
|
||||
if(NumTraits<T>::RequireInitialization && (new_size < old_size))
|
||||
destruct_elements_of_array(pts+new_size, old_size-new_size);
|
||||
T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
|
||||
|
|
@ -536,6 +557,7 @@ template<typename T> class aligned_stack_memory_handler
|
|||
#endif
|
||||
|
||||
#define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
|
||||
Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
|
||||
TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \
|
||||
: reinterpret_cast<TYPE*>( \
|
||||
(sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \
|
||||
|
|
@ -545,6 +567,7 @@ template<typename T> class aligned_stack_memory_handler
|
|||
#else
|
||||
|
||||
#define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
|
||||
Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
|
||||
TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE)); \
|
||||
Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true)
|
||||
|
||||
|
|
@ -669,6 +692,7 @@ public:
|
|||
pointer allocate( size_type num, const void* hint = 0 )
|
||||
{
|
||||
EIGEN_UNUSED_VARIABLE(hint);
|
||||
internal::check_size_for_overflow<T>(num);
|
||||
return static_cast<pointer>( internal::aligned_malloc( num * sizeof(T) ) );
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -125,10 +125,9 @@ class compute_matrix_flags
|
|||
aligned_bit =
|
||||
(
|
||||
((Options&DontAlign)==0)
|
||||
&& packet_traits<Scalar>::Vectorizable
|
||||
&& (
|
||||
#if EIGEN_ALIGN_STATICALLY
|
||||
((!is_dynamic_size_storage) && (((MaxCols*MaxRows) % packet_traits<Scalar>::size) == 0))
|
||||
((!is_dynamic_size_storage) && (((MaxCols*MaxRows*int(sizeof(Scalar))) % 16) == 0))
|
||||
#else
|
||||
0
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -51,19 +51,19 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
|
|||
{ if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
|
||||
|
||||
/** Constructs a null box with \a _dim the dimension of the ambient space. */
|
||||
inline explicit AlignedBox(int _dim) : m_(min)(_dim), m_(max)(_dim)
|
||||
inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim)
|
||||
{ setNull(); }
|
||||
|
||||
/** Constructs a box with extremities \a _min and \a _max. */
|
||||
inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_(min)(_min), m_(max)(_max) {}
|
||||
inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {}
|
||||
|
||||
/** Constructs a box containing a single point \a p. */
|
||||
inline explicit AlignedBox(const VectorType& p) : m_(min)(p), m_(max)(p) {}
|
||||
inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {}
|
||||
|
||||
~AlignedBox() {}
|
||||
|
||||
/** \returns the dimension in which the box holds */
|
||||
inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : AmbientDimAtCompileTime; }
|
||||
inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : int(AmbientDimAtCompileTime); }
|
||||
|
||||
/** \returns true if the box is null, i.e, empty. */
|
||||
inline bool isNull() const { return (m_min.cwise() > m_max).any(); }
|
||||
|
|
@ -71,8 +71,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
|
|||
/** Makes \c *this a null/empty box. */
|
||||
inline void setNull()
|
||||
{
|
||||
m_min.setConstant( std::numeric_limits<Scalar>::(max)());
|
||||
m_max.setConstant(-std::numeric_limits<Scalar>::(max)());
|
||||
m_min.setConstant( (std::numeric_limits<Scalar>::max)());
|
||||
m_max.setConstant(-(std::numeric_limits<Scalar>::max)());
|
||||
}
|
||||
|
||||
/** \returns the minimal corner */
|
||||
|
|
@ -90,19 +90,19 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
|
|||
|
||||
/** \returns true if the box \a b is entirely inside the box \c *this. */
|
||||
inline bool contains(const AlignedBox& b) const
|
||||
{ return (m_min.cwise()<=b.(min)()).all() && (b.(max)().cwise()<=m_max).all(); }
|
||||
{ return (m_min.cwise()<=(b.min)()).all() && ((b.max)().cwise()<=m_max).all(); }
|
||||
|
||||
/** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
|
||||
inline AlignedBox& extend(const VectorType& p)
|
||||
{ m_min = m_min.cwise().(min)(p); m_max = m_max.cwise().(max)(p); return *this; }
|
||||
{ m_min = (m_min.cwise().min)(p); m_max = (m_max.cwise().max)(p); return *this; }
|
||||
|
||||
/** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */
|
||||
inline AlignedBox& extend(const AlignedBox& b)
|
||||
{ m_min = m_min.cwise().(min)(b.m_min); m_max = m_max.cwise().(max)(b.m_max); return *this; }
|
||||
{ m_min = (m_min.cwise().min)(b.m_min); m_max = (m_max.cwise().max)(b.m_max); return *this; }
|
||||
|
||||
/** Clamps \c *this by the box \a b and returns a reference to \c *this. */
|
||||
inline AlignedBox& clamp(const AlignedBox& b)
|
||||
{ m_min = m_min.cwise().(max)(b.m_min); m_max = m_max.cwise().(min)(b.m_max); return *this; }
|
||||
{ m_min = (m_min.cwise().max)(b.m_min); m_max = (m_max.cwise().min)(b.m_max); return *this; }
|
||||
|
||||
/** Translate \c *this by the vector \a t and returns a reference to \c *this. */
|
||||
inline AlignedBox& translate(const VectorType& t)
|
||||
|
|
@ -138,8 +138,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
|
|||
template<typename OtherScalarType>
|
||||
inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
|
||||
{
|
||||
m_min = other.(min)().template cast<Scalar>();
|
||||
m_max = other.(max)().template cast<Scalar>();
|
||||
m_min = (other.min)().template cast<Scalar>();
|
||||
m_max = (other.max)().template cast<Scalar>();
|
||||
}
|
||||
|
||||
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
|
||||
|
|
|
|||
|
|
@ -291,7 +291,7 @@ template<typename _MatrixType> class EigenSolver
|
|||
|
||||
ComputationInfo info() const
|
||||
{
|
||||
eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
|
||||
eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
|
||||
return m_realSchur.info();
|
||||
}
|
||||
|
||||
|
|
@ -339,7 +339,7 @@ typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eige
|
|||
EigenvectorsType matV(n,n);
|
||||
for (Index j=0; j<n; ++j)
|
||||
{
|
||||
if (internal::isMuchSmallerThan(internal::imag(m_eivalues.coeff(j)), internal::real(m_eivalues.coeff(j))))
|
||||
if (internal::isMuchSmallerThan(internal::imag(m_eivalues.coeff(j)), internal::real(m_eivalues.coeff(j))) || j+1==n)
|
||||
{
|
||||
// we have a real eigen value
|
||||
matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
|
||||
|
|
@ -570,10 +570,13 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
|
|||
|
||||
}
|
||||
}
|
||||
|
||||
// We handled a pair of complex conjugate eigenvalues, so need to skip them both
|
||||
n--;
|
||||
}
|
||||
else
|
||||
{
|
||||
eigen_assert("Internal bug in EigenSolver"); // this should not happen
|
||||
eigen_assert(0 && "Internal bug in EigenSolver"); // this should not happen
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -307,7 +307,8 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
|
|||
|
||||
/** \brief Maximum number of iterations.
|
||||
*
|
||||
* Maximum number of iterations allowed for an eigenvalue to converge.
|
||||
* The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n
|
||||
* denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK).
|
||||
*/
|
||||
static const int m_maxIterations = 30;
|
||||
|
||||
|
|
@ -407,7 +408,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
|
|||
|
||||
Index end = n-1;
|
||||
Index start = 0;
|
||||
Index iter = 0; // number of iterations we are working on one element
|
||||
Index iter = 0; // total number of iterations
|
||||
|
||||
while (end>0)
|
||||
{
|
||||
|
|
@ -418,15 +419,14 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
|
|||
// find the largest unreduced block
|
||||
while (end>0 && m_subdiag[end-1]==0)
|
||||
{
|
||||
iter = 0;
|
||||
end--;
|
||||
}
|
||||
if (end<=0)
|
||||
break;
|
||||
|
||||
// if we spent too many iterations on the current element, we give up
|
||||
// if we spent too many iterations, we give up
|
||||
iter++;
|
||||
if(iter > m_maxIterations) break;
|
||||
if(iter > m_maxIterations * n) break;
|
||||
|
||||
start = end - 1;
|
||||
while (start>0 && m_subdiag[start-1]!=0)
|
||||
|
|
@ -435,7 +435,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
|
|||
internal::tridiagonal_qr_step<MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor>(diag.data(), m_subdiag.data(), start, end, computeEigenvectors ? m_eivec.data() : (Scalar*)0, n);
|
||||
}
|
||||
|
||||
if (iter <= m_maxIterations)
|
||||
if (iter <= m_maxIterations * n)
|
||||
m_info = Success;
|
||||
else
|
||||
m_info = NoConvergence;
|
||||
|
|
|
|||
|
|
@ -225,7 +225,7 @@ public:
|
|||
normal() = mat * normal();
|
||||
else
|
||||
{
|
||||
eigen_assert("invalid traits value in Hyperplane::transform()");
|
||||
eigen_assert(0 && "invalid traits value in Hyperplane::transform()");
|
||||
}
|
||||
return *this;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -182,10 +182,9 @@ public:
|
|||
template<typename NewScalarType>
|
||||
inline typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type cast() const
|
||||
{
|
||||
return typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type(
|
||||
coeffs().template cast<NewScalarType>());
|
||||
return typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type(derived());
|
||||
}
|
||||
|
||||
|
||||
#ifdef EIGEN_QUATERNIONBASE_PLUGIN
|
||||
# include EIGEN_QUATERNIONBASE_PLUGIN
|
||||
#endif
|
||||
|
|
@ -225,22 +224,25 @@ struct traits<Quaternion<_Scalar,_Options> >
|
|||
typedef _Scalar Scalar;
|
||||
typedef Matrix<_Scalar,4,1,_Options> Coefficients;
|
||||
enum{
|
||||
IsAligned = bool(EIGEN_ALIGN) && ((int(_Options)&Aligned)==Aligned),
|
||||
IsAligned = internal::traits<Coefficients>::Flags & AlignedBit,
|
||||
Flags = IsAligned ? (AlignedBit | LvalueBit) : LvalueBit
|
||||
};
|
||||
};
|
||||
}
|
||||
|
||||
template<typename _Scalar, int _Options>
|
||||
class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> >{
|
||||
class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> >
|
||||
{
|
||||
typedef QuaternionBase<Quaternion<_Scalar,_Options> > Base;
|
||||
enum { IsAligned = internal::traits<Quaternion>::IsAligned };
|
||||
|
||||
public:
|
||||
typedef _Scalar Scalar;
|
||||
|
||||
EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Quaternion)
|
||||
using Base::operator*=;
|
||||
|
||||
typedef typename internal::traits<Quaternion<Scalar,_Options> >::Coefficients Coefficients;
|
||||
typedef typename internal::traits<Quaternion>::Coefficients Coefficients;
|
||||
typedef typename Base::AngleAxisType AngleAxisType;
|
||||
|
||||
/** Default constructor leaving the quaternion uninitialized. */
|
||||
|
|
@ -271,9 +273,16 @@ public:
|
|||
template<typename Derived>
|
||||
explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; }
|
||||
|
||||
/** Explicit copy constructor with scalar conversion */
|
||||
template<typename OtherScalar, int OtherOptions>
|
||||
explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other)
|
||||
{ m_coeffs = other.coeffs().template cast<Scalar>(); }
|
||||
|
||||
inline Coefficients& coeffs() { return m_coeffs;}
|
||||
inline const Coefficients& coeffs() const { return m_coeffs;}
|
||||
|
||||
EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(IsAligned)
|
||||
|
||||
protected:
|
||||
Coefficients m_coeffs;
|
||||
|
||||
|
|
@ -673,7 +682,7 @@ QuaternionBase<Derived>::slerp(Scalar t, const QuaternionBase<OtherDerived>& oth
|
|||
Scalar scale0;
|
||||
Scalar scale1;
|
||||
|
||||
if (absD>=one)
|
||||
if(absD>=one)
|
||||
{
|
||||
scale0 = Scalar(1) - t;
|
||||
scale1 = t;
|
||||
|
|
@ -686,9 +695,8 @@ QuaternionBase<Derived>::slerp(Scalar t, const QuaternionBase<OtherDerived>& oth
|
|||
|
||||
scale0 = internal::sin( ( Scalar(1) - t ) * theta) / sinTheta;
|
||||
scale1 = internal::sin( ( t * theta) ) / sinTheta;
|
||||
if (d<0)
|
||||
scale1 = -scale1;
|
||||
}
|
||||
if(d<0) scale1 = -scale1;
|
||||
|
||||
return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
|
||||
}
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ public:
|
|||
|
||||
/** Concatenates two rotations */
|
||||
inline Rotation2D& operator*=(const Rotation2D& other)
|
||||
{ return m_angle += other.m_angle; return *this; }
|
||||
{ m_angle += other.m_angle; return *this; }
|
||||
|
||||
/** Applies the rotation to a 2D vector */
|
||||
Vector2 operator* (const Vector2& vec) const
|
||||
|
|
|
|||
|
|
@ -443,7 +443,6 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
|
|||
|
||||
m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
|
||||
m_maxpivot = RealScalar(0);
|
||||
RealScalar cutoff(0);
|
||||
|
||||
for(Index k = 0; k < size; ++k)
|
||||
{
|
||||
|
|
@ -458,14 +457,7 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
|
|||
row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner,
|
||||
col_of_biggest_in_corner += k; // need to add k to them.
|
||||
|
||||
// when k==0, biggest_in_corner is the biggest coeff absolute value in the original matrix
|
||||
if(k == 0) cutoff = biggest_in_corner * NumTraits<Scalar>::epsilon();
|
||||
|
||||
// if the pivot (hence the corner) is "zero", terminate to avoid generating nan/inf values.
|
||||
// Notice that using an exact comparison (biggest_in_corner==0) here, as Golub-van Loan do in
|
||||
// their pseudo-code, results in numerical instability! The cutoff here has been validated
|
||||
// by running the unit test 'lu' with many repetitions.
|
||||
if(biggest_in_corner < cutoff)
|
||||
if(biggest_in_corner==RealScalar(0))
|
||||
{
|
||||
// before exiting, make sure to initialize the still uninitialized transpositions
|
||||
// in a sane state without destroying what we already have.
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ struct compute_inverse_size4<Architecture::SSE, float, MatrixType, ResultType>
|
|||
|
||||
static void run(const MatrixType& matrix, ResultType& result)
|
||||
{
|
||||
EIGEN_ALIGN16 const int _Sign_PNNP[4] = { 0x00000000, 0x80000000, 0x80000000, 0x00000000 };
|
||||
EIGEN_ALIGN16 const unsigned int _Sign_PNNP[4] = { 0x00000000, 0x80000000, 0x80000000, 0x00000000 };
|
||||
|
||||
// Load the full matrix into registers
|
||||
__m128 _L1 = matrix.template packet<MatrixAlignment>( 0);
|
||||
|
|
|
|||
|
|
@ -590,6 +590,9 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
|
|||
// only worsening the precision of U and V as we accumulate more rotations
|
||||
const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
|
||||
|
||||
// limit for very small denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
|
||||
const RealScalar considerAsZero = RealScalar(2) * std::numeric_limits<RealScalar>::denorm_min();
|
||||
|
||||
/*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
|
||||
|
||||
if(!internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows>::run(*this, matrix)
|
||||
|
|
@ -617,10 +620,11 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
|
|||
{
|
||||
// if this 2x2 sub-matrix is not diagonal already...
|
||||
// notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
|
||||
// keep us iterating forever.
|
||||
// keep us iterating forever. Similarly, small denormal numbers are considered zero.
|
||||
using std::max;
|
||||
if((max)(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
|
||||
> (max)(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
|
||||
RealScalar threshold = (max)(considerAsZero, precision * (max)(internal::abs(m_workMatrix.coeff(p,p)),
|
||||
internal::abs(m_workMatrix.coeff(q,q))));
|
||||
if((max)(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p))) > threshold)
|
||||
{
|
||||
finished = false;
|
||||
|
||||
|
|
@ -704,6 +708,13 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
|
|||
};
|
||||
} // end namespace internal
|
||||
|
||||
/** \svd_module
|
||||
*
|
||||
* \return the singular value decomposition of \c *this computed by two-sided
|
||||
* Jacobi transformations.
|
||||
*
|
||||
* \sa class JacobiSVD
|
||||
*/
|
||||
template<typename Derived>
|
||||
JacobiSVD<typename MatrixBase<Derived>::PlainObject>
|
||||
MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
|
||||
|
|
|
|||
|
|
@ -171,7 +171,7 @@ void SparseTriangularView<ExpressionType,Mode>::solveInPlace(MatrixBase<OtherDer
|
|||
eigen_assert(m_matrix.cols() == m_matrix.rows());
|
||||
eigen_assert(m_matrix.cols() == other.rows());
|
||||
eigen_assert(!(Mode & ZeroDiag));
|
||||
eigen_assert(Mode & (Upper|Lower));
|
||||
eigen_assert((Mode & (Upper|Lower)) != 0);
|
||||
|
||||
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
|
||||
|
||||
|
|
@ -298,7 +298,7 @@ void SparseTriangularView<ExpressionType,Mode>::solveInPlace(SparseMatrixBase<Ot
|
|||
eigen_assert(m_matrix.cols() == m_matrix.rows());
|
||||
eigen_assert(m_matrix.cols() == other.rows());
|
||||
eigen_assert(!(Mode & ZeroDiag));
|
||||
eigen_assert(Mode & (Upper|Lower));
|
||||
eigen_assert((Mode & (Upper|Lower)) != 0);
|
||||
|
||||
// enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,7 @@
|
|||
Current Eigen Version 3.05 (10.02.2012)
|
||||
To update the lib:
|
||||
- download Eigen
|
||||
- unzip it somewhere
|
||||
- copy the two folders 'Eigen' and 'unsupported' here
|
||||
- update this file
|
||||
- commit
|
||||
|
|
@ -331,7 +331,7 @@ class FFT
|
|||
// if the vector is strided, then we need to copy it to a packed temporary
|
||||
Matrix<src_type,1,Dynamic> tmp;
|
||||
if ( resize_input ) {
|
||||
size_t ncopy = std::min(src.size(),src.size() + resize_input);
|
||||
size_t ncopy = (std::min)(src.size(),src.size() + resize_input);
|
||||
tmp.setZero(src.size() + resize_input);
|
||||
if ( realfft && HasFlag(HalfSpectrum) ) {
|
||||
// pad at the Nyquist bin
|
||||
|
|
|
|||
|
|
@ -35,7 +35,8 @@
|
|||
|
||||
namespace Eigen {
|
||||
|
||||
/** \defgroup MPRealSupport_Module MPFRC++ Support module
|
||||
/** \ingroup Unsupported_modules
|
||||
* \defgroup MPRealSupport_Module MPFRC++ Support module
|
||||
*
|
||||
* \code
|
||||
* #include <Eigen/MPRealSupport>
|
||||
|
|
|
|||
|
|
@ -231,7 +231,7 @@ private:
|
|||
template<typename BVH, typename Minimizer>
|
||||
typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer)
|
||||
{
|
||||
return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), std::numeric_limits<typename Minimizer::Scalar>::max());
|
||||
return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), (std::numeric_limits<typename Minimizer::Scalar>::max)());
|
||||
}
|
||||
|
||||
/** Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer.
|
||||
|
|
@ -264,7 +264,7 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
|
|||
ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
|
||||
std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
|
||||
|
||||
Scalar minimum = std::numeric_limits<Scalar>::max();
|
||||
Scalar minimum = (std::numeric_limits<Scalar>::max)();
|
||||
todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex())));
|
||||
|
||||
while(!todo.empty()) {
|
||||
|
|
|
|||
|
|
@ -259,7 +259,7 @@ void MatrixExponential<MatrixType>::computeUV(float)
|
|||
pade5(m_M);
|
||||
} else {
|
||||
const float maxnorm = 3.925724783138660f;
|
||||
m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm)));
|
||||
m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm)));
|
||||
MatrixType A = m_M / pow(Scalar(2), Scalar(static_cast<RealScalar>(m_squarings)));
|
||||
pade7(A);
|
||||
}
|
||||
|
|
@ -281,7 +281,7 @@ void MatrixExponential<MatrixType>::computeUV(double)
|
|||
pade9(m_M);
|
||||
} else {
|
||||
const double maxnorm = 5.371920351148152;
|
||||
m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm)));
|
||||
m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm)));
|
||||
MatrixType A = m_M / pow(Scalar(2), Scalar(m_squarings));
|
||||
pade13(A);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,13 +1,22 @@
|
|||
namespace Eigen {
|
||||
|
||||
o /** \mainpage Eigen's unsupported modules
|
||||
/** \mainpage Eigen's unsupported modules
|
||||
|
||||
This is the API documentation for Eigen's unsupported modules.
|
||||
|
||||
These modules are contributions from various users. They are provided "as is", without any support.
|
||||
|
||||
Click on the \e Modules tab at the top of this page to get a list of all unsupported modules.
|
||||
|
||||
Don't miss the <a href="..//index.html">official Eigen documentation</a>.
|
||||
|
||||
|
||||
\defgroup Unsupported_modules Unsupported modules
|
||||
|
||||
The unsupported modules are contributions from various users. They are
|
||||
provided "as is", without any support. Nevertheless, some of them are
|
||||
subject to be included in Eigen in the future.
|
||||
|
||||
*/
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -90,13 +90,13 @@ struct BallPointStuff //this class provides functions to be both an intersector
|
|||
}
|
||||
|
||||
double minimumOnVolume(const BoxType &r) { ++calls; return r.squaredExteriorDistance(p); }
|
||||
double minimumOnObject(const BallType &b) { ++calls; return std::max(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
|
||||
double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); }
|
||||
double minimumOnVolumeVolume(const BoxType &r1, const BoxType &r2) { ++calls; return r1.squaredExteriorDistance(r2); }
|
||||
double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); }
|
||||
double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); }
|
||||
double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR(std::max(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); }
|
||||
double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); }
|
||||
double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); }
|
||||
double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR((std::max)(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); }
|
||||
double minimumOnVolumeObject(const BoxType &r, const VectorType &v) { ++calls; return r.squaredExteriorDistance(v); }
|
||||
double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR(std::max(0., (b.center - v).norm() - b.radius)); }
|
||||
double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR((std::max)(0., (b.center - v).norm() - b.radius)); }
|
||||
|
||||
VectorType p;
|
||||
int calls;
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@ double binom(int n, int k)
|
|||
template <typename Derived, typename OtherDerived>
|
||||
double relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B)
|
||||
{
|
||||
return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
|
||||
return std::sqrt((A - B).cwiseAbs2().sum() / (std::min)(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ template<typename SparseMatrixType> void sparse_extra(const SparseMatrixType& re
|
|||
typedef typename SparseMatrixType::Scalar Scalar;
|
||||
enum { Flags = SparseMatrixType::Flags };
|
||||
|
||||
double density = std::max(8./(rows*cols), 0.01);
|
||||
double density = (std::max)(8./(rows*cols), 0.01);
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
||||
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
||||
Scalar eps = 1e-6;
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
|
|||
{
|
||||
static bool odd = true;
|
||||
odd = !odd;
|
||||
double density = std::max(8./(rows*cols), 0.01);
|
||||
double density = (std::max)(8./(rows*cols), 0.01);
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
||||
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
||||
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@
|
|||
|
||||
template<typename Scalar> void sparse_llt(int rows, int cols)
|
||||
{
|
||||
double density = std::max(8./(rows*cols), 0.01);
|
||||
double density = (std::max)(8./(rows*cols), 0.01);
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
||||
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
||||
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@
|
|||
|
||||
template<typename Scalar> void sparse_lu(int rows, int cols)
|
||||
{
|
||||
double density = std::max(8./(rows*cols), 0.01);
|
||||
double density = (std::max)(8./(rows*cols), 0.01);
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
||||
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue