Upgrade to Eigen 3.2.4 from 3.2.2 - Some patches still applied
Chris Beall
parent
9e96f2513b
commit
74289d550d
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@ -1,4 +1,4 @@
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repo: 8a21fd850624c931e448cbcfb38168cb2717c790
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node: ffa86ffb557094721ca71dcea6aed2651b9fd610
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node: 10219c95fe653d4962aa9db4946f6fbea96dd740
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branch: 3.2
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tag: 3.2.0
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tag: 3.2.4
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@ -23,3 +23,7 @@ bf4cb8c934fa3a79f45f1e629610f0225e93e493 3.1.0-rc2
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da195914abcc1d739027cbee7c52077aab30b336 3.2-beta1
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4b687cad1d23066f66863f4f87298447298443df 3.2-rc1
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1eeda7b1258bcd306018c0738e2b6a8543661141 3.2-rc2
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ffa86ffb557094721ca71dcea6aed2651b9fd610 3.2.0
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6b38706d90a9fe182e66ab88477b3dbde34b9f66 3.2.1
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1306d75b4a21891e59ff9bd96678882cf831e39f 3.2.2
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36fd1ba04c120cfdd90f3e4cede47f43b21d19ad 3.2.3
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@ -442,6 +442,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
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m_transpositions.resize(size);
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m_isInitialized = false;
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m_temporary.resize(size);
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m_sign = internal::ZeroSign;
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internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign);
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@ -502,7 +503,6 @@ struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs>
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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 typename Diagonal<const MatrixType>::RealReturnType vectorD(dec().vectorD());
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// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
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@ -29,6 +29,11 @@ struct traits<ArrayWrapper<ExpressionType> >
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: public traits<typename remove_all<typename ExpressionType::Nested>::type >
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{
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typedef ArrayXpr XprKind;
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// Let's remove NestByRefBit
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enum {
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Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
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Flags = Flags0 & ~NestByRefBit
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};
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};
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}
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@ -149,6 +154,11 @@ struct traits<MatrixWrapper<ExpressionType> >
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: public traits<typename remove_all<typename ExpressionType::Nested>::type >
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{
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typedef MatrixXpr XprKind;
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// Let's remove NestByRefBit
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enum {
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Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
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Flags = Flags0 & ~NestByRefBit
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};
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};
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}
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@ -462,8 +462,10 @@ template<typename Derived> class DenseBase
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template<int p> RealScalar lpNorm() const;
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template<int RowFactor, int ColFactor>
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const Replicate<Derived,RowFactor,ColFactor> replicate() const;
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const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
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inline const Replicate<Derived,RowFactor,ColFactor> replicate() const;
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typedef Replicate<Derived,Dynamic,Dynamic> ReplicateReturnType;
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inline const ReplicateReturnType replicate(Index rowFacor,Index colFactor) const;
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typedef Reverse<Derived, BothDirections> ReverseReturnType;
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typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
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@ -190,18 +190,18 @@ MatrixBase<Derived>::diagonal() const
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*
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* \sa MatrixBase::diagonal(), class Diagonal */
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template<typename Derived>
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inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<DynamicIndex>::Type
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inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
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MatrixBase<Derived>::diagonal(Index index)
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{
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return typename DiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
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return DiagonalDynamicIndexReturnType(derived(), index);
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}
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/** This is the const version of diagonal(Index). */
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template<typename Derived>
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inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<DynamicIndex>::Type
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inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
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MatrixBase<Derived>::diagonal(Index index) const
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{
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return typename ConstDiagonalIndexReturnType<DynamicIndex>::Type(derived(), index);
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return ConstDiagonalDynamicIndexReturnType(derived(), index);
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}
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/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
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@ -232,7 +232,7 @@ EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest&
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// FIXME not very good if rhs is real and lhs complex while alpha is real too
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const Index cols = dest.cols();
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for (Index j=0; j<cols; ++j)
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func(dest.col(j), prod.rhs().coeff(j) * prod.lhs());
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func(dest.col(j), prod.rhs().coeff(0,j) * prod.lhs());
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}
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// Row major
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@ -243,7 +243,7 @@ EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest&
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// FIXME not very good if lhs is real and rhs complex while alpha is real too
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const Index rows = dest.rows();
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for (Index i=0; i<rows; ++i)
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func(dest.row(i), prod.lhs().coeff(i) * prod.rhs());
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func(dest.row(i), prod.lhs().coeff(i,0) * prod.rhs());
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}
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template<typename Lhs, typename Rhs>
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@ -168,6 +168,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
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template<typename Derived> class MapBase<Derived, WriteAccessors>
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: public MapBase<Derived, ReadOnlyAccessors>
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{
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typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
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public:
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typedef MapBase<Derived, ReadOnlyAccessors> Base;
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@ -230,11 +231,13 @@ template<typename Derived> class MapBase<Derived, WriteAccessors>
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Derived& operator=(const MapBase& other)
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{
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Base::Base::operator=(other);
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ReadOnlyMapBase::Base::operator=(other);
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return derived();
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}
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using Base::Base::operator=;
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// In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
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// see bugs 821 and 920.
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using ReadOnlyMapBase::Base::operator=;
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};
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#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
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@ -215,7 +215,7 @@ template<typename Derived> class MatrixBase
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typedef Diagonal<Derived> DiagonalReturnType;
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DiagonalReturnType diagonal();
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typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
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typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
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ConstDiagonalReturnType diagonal() const;
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template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
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@ -223,16 +223,12 @@ template<typename Derived> class MatrixBase
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template<int Index> typename DiagonalIndexReturnType<Index>::Type diagonal();
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template<int Index> typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
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typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
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typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
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// Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations.
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// On the other hand they confuse MSVC8...
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#if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later
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typename MatrixBase::template DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
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typename MatrixBase::template ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
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#else
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typename DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index);
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typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const;
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#endif
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DiagonalDynamicIndexReturnType diagonal(Index index);
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ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
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#ifdef EIGEN2_SUPPORT
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template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part();
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@ -555,7 +555,10 @@ struct permut_matrix_product_retval
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const Index n = Side==OnTheLeft ? rows() : cols();
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// FIXME we need an is_same for expression that is not sensitive to constness. For instance
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// is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
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if(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix))
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if( is_same<MatrixTypeNestedCleaned,Dest>::value
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&& blas_traits<MatrixTypeNestedCleaned>::HasUsableDirectAccess
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&& blas_traits<Dest>::HasUsableDirectAccess
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&& extract_data(dst) == extract_data(m_matrix))
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{
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// apply the permutation inplace
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Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size());
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@ -85,7 +85,14 @@ class ProductBase : public MatrixBase<Derived>
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public:
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#ifndef EIGEN_NO_MALLOC
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typedef typename Base::PlainObject BasePlainObject;
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typedef Matrix<Scalar,RowsAtCompileTime==1?1:Dynamic,ColsAtCompileTime==1?1:Dynamic,BasePlainObject::Options> DynPlainObject;
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typedef typename internal::conditional<(BasePlainObject::SizeAtCompileTime==Dynamic) || (BasePlainObject::SizeAtCompileTime*int(sizeof(Scalar)) < int(EIGEN_STACK_ALLOCATION_LIMIT)),
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BasePlainObject, DynPlainObject>::type PlainObject;
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#else
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typedef typename Base::PlainObject PlainObject;
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#endif
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ProductBase(const Lhs& a_lhs, const Rhs& a_rhs)
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: m_lhs(a_lhs), m_rhs(a_rhs)
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@ -180,7 +187,12 @@ namespace internal {
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template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
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struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
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{
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typedef PlainObject const& type;
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typedef typename GeneralProduct<Lhs,Rhs,Mode>::PlainObject const& type;
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};
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template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject>
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struct nested<const GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject>
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{
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typedef typename GeneralProduct<Lhs,Rhs,Mode>::PlainObject const& type;
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};
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}
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@ -188,6 +188,8 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
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: public RefBase<Ref<PlainObjectType, Options, StrideType> >
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{
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typedef internal::traits<Ref> Traits;
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template<typename Derived>
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inline Ref(const PlainObjectBase<Derived>& expr);
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public:
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typedef RefBase<Ref> Base;
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@ -196,20 +198,21 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
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#ifndef EIGEN_PARSED_BY_DOXYGEN
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template<typename Derived>
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inline Ref(PlainObjectBase<Derived>& expr,
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typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0)
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inline Ref(PlainObjectBase<Derived>& expr)
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{
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Base::construct(expr);
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EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
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Base::construct(expr.derived());
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}
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template<typename Derived>
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inline Ref(const DenseBase<Derived>& expr,
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typename internal::enable_if<bool(internal::is_lvalue<Derived>::value&&bool(Traits::template match<Derived>::MatchAtCompileTime)),Derived>::type* = 0,
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int = Derived::ThisConstantIsPrivateInPlainObjectBase)
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inline Ref(const DenseBase<Derived>& expr)
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#else
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template<typename Derived>
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inline Ref(DenseBase<Derived>& expr)
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#endif
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{
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EIGEN_STATIC_ASSERT(static_cast<bool>(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
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EIGEN_STATIC_ASSERT(static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
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enum { THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY = Derived::ThisConstantIsPrivateInPlainObjectBase};
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Base::construct(expr.const_cast_derived());
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}
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@ -135,7 +135,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
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*/
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template<typename Derived>
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template<int RowFactor, int ColFactor>
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inline const Replicate<Derived,RowFactor,ColFactor>
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const Replicate<Derived,RowFactor,ColFactor>
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DenseBase<Derived>::replicate() const
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{
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return Replicate<Derived,RowFactor,ColFactor>(derived());
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@ -150,7 +150,7 @@ DenseBase<Derived>::replicate() const
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* \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
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*/
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template<typename Derived>
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inline const Replicate<Derived,Dynamic,Dynamic>
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const typename DenseBase<Derived>::ReplicateReturnType
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DenseBase<Derived>::replicate(Index rowFactor,Index colFactor) const
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{
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return Replicate<Derived,Dynamic,Dynamic>(derived(),rowFactor,colFactor);
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@ -380,19 +380,19 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
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EIGEN_STRONG_INLINE TriangularView& operator=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
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{
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setZero();
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return assignProduct(other,1);
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return assignProduct(other.derived(),1);
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}
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template<typename ProductDerived, typename Lhs, typename Rhs>
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EIGEN_STRONG_INLINE TriangularView& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
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{
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return assignProduct(other,1);
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return assignProduct(other.derived(),1);
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}
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template<typename ProductDerived, typename Lhs, typename Rhs>
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EIGEN_STRONG_INLINE TriangularView& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other)
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{
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return assignProduct(other,-1);
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return assignProduct(other.derived(),-1);
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}
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@ -400,25 +400,34 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
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EIGEN_STRONG_INLINE TriangularView& operator=(const ScaledProduct<ProductDerived>& other)
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{
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setZero();
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return assignProduct(other,other.alpha());
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return assignProduct(other.derived(),other.alpha());
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}
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template<typename ProductDerived>
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EIGEN_STRONG_INLINE TriangularView& operator+=(const ScaledProduct<ProductDerived>& other)
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{
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return assignProduct(other,other.alpha());
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return assignProduct(other.derived(),other.alpha());
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}
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template<typename ProductDerived>
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EIGEN_STRONG_INLINE TriangularView& operator-=(const ScaledProduct<ProductDerived>& other)
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{
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return assignProduct(other,-other.alpha());
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return assignProduct(other.derived(),-other.alpha());
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}
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protected:
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template<typename ProductDerived, typename Lhs, typename Rhs>
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EIGEN_STRONG_INLINE TriangularView& assignProduct(const ProductBase<ProductDerived, Lhs,Rhs>& prod, const Scalar& alpha);
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template<int Mode, bool LhsIsTriangular,
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typename Lhs, bool LhsIsVector,
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typename Rhs, bool RhsIsVector>
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EIGEN_STRONG_INLINE TriangularView& assignProduct(const TriangularProduct<Mode, LhsIsTriangular, Lhs, LhsIsVector, Rhs, RhsIsVector>& prod, const Scalar& alpha)
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{
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lazyAssign(alpha*prod.eval());
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return *this;
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}
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MatrixTypeNested m_matrix;
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};
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@ -110,7 +110,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<
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template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
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template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
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template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { __pld((float *)addr); }
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template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { EIGEN_ARM_PREFETCH((float *)addr); }
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template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
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{
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@ -48,9 +48,18 @@ typedef uint32x4_t Packet4ui;
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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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// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
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// which available on LLVM and GCC (at least)
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#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || defined(__GNUC__)
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#define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
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#elif defined __pld
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#define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
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#elif !defined(__aarch64__)
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#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ( " pld [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
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#else
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// by default no explicit prefetching
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#define EIGEN_ARM_PREFETCH(ADDR)
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#endif
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template<> struct packet_traits<float> : default_packet_traits
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@ -209,8 +218,8 @@ template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& f
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template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to, from); }
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template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to, from); }
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template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { __pld(addr); }
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template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { __pld(addr); }
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template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_ARM_PREFETCH(addr); }
|
||||
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { EIGEN_ARM_PREFETCH(addr); }
|
||||
|
||||
// FIXME only store the 2 first elements ?
|
||||
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vst1q_f32(x, a); return x[0]; }
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
|
|||
|
||||
Packet4i emm0;
|
||||
|
||||
Packet4f invalid_mask = _mm_cmplt_ps(x, _mm_setzero_ps());
|
||||
Packet4f invalid_mask = _mm_cmpnge_ps(x, _mm_setzero_ps()); // not greater equal is true if x is NaN
|
||||
Packet4f iszero_mask = _mm_cmpeq_ps(x, _mm_setzero_ps());
|
||||
|
||||
x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */
|
||||
|
|
@ -166,7 +166,7 @@ Packet4f pexp<Packet4f>(const Packet4f& _x)
|
|||
emm0 = _mm_cvttps_epi32(fx);
|
||||
emm0 = _mm_add_epi32(emm0, p4i_0x7f);
|
||||
emm0 = _mm_slli_epi32(emm0, 23);
|
||||
return pmul(y, _mm_castsi128_ps(emm0));
|
||||
return pmax(pmul(y, Packet4f(_mm_castsi128_ps(emm0))), _x);
|
||||
}
|
||||
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
|
||||
Packet2d pexp<Packet2d>(const Packet2d& _x)
|
||||
|
|
@ -239,7 +239,7 @@ Packet2d pexp<Packet2d>(const Packet2d& _x)
|
|||
emm0 = _mm_add_epi32(emm0, p4i_1023_0);
|
||||
emm0 = _mm_slli_epi32(emm0, 20);
|
||||
emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(1,2,0,3));
|
||||
return pmul(x, _mm_castsi128_pd(emm0));
|
||||
return pmax(pmul(x, Packet2d(_mm_castsi128_pd(emm0))), _x);
|
||||
}
|
||||
|
||||
/* evaluation of 4 sines at onces, using SSE2 intrinsics.
|
||||
|
|
|
|||
|
|
@ -90,6 +90,7 @@ struct traits<CoeffBasedProduct<LhsNested,RhsNested,NestingFlags> >
|
|||
| (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0),
|
||||
|
||||
CoeffReadCost = InnerSize == Dynamic ? Dynamic
|
||||
: InnerSize == 0 ? 0
|
||||
: InnerSize * (NumTraits<Scalar>::MulCost + LhsCoeffReadCost + RhsCoeffReadCost)
|
||||
+ (InnerSize - 1) * NumTraits<Scalar>::AddCost,
|
||||
|
||||
|
|
@ -133,7 +134,7 @@ class CoeffBasedProduct
|
|||
};
|
||||
|
||||
typedef internal::product_coeff_impl<CanVectorizeInner ? InnerVectorizedTraversal : DefaultTraversal,
|
||||
Unroll ? InnerSize-1 : Dynamic,
|
||||
Unroll ? (InnerSize==0 ? 0 : InnerSize-1) : Dynamic,
|
||||
_LhsNested, _RhsNested, Scalar> ScalarCoeffImpl;
|
||||
|
||||
typedef CoeffBasedProduct<LhsNested,RhsNested,NestByRefBit> LazyCoeffBasedProductType;
|
||||
|
|
@ -184,7 +185,7 @@ class CoeffBasedProduct
|
|||
{
|
||||
PacketScalar res;
|
||||
internal::product_packet_impl<Flags&RowMajorBit ? RowMajor : ColMajor,
|
||||
Unroll ? InnerSize-1 : Dynamic,
|
||||
Unroll ? (InnerSize==0 ? 0 : InnerSize-1) : Dynamic,
|
||||
_LhsNested, _RhsNested, PacketScalar, LoadMode>
|
||||
::run(row, col, m_lhs, m_rhs, res);
|
||||
return res;
|
||||
|
|
@ -262,10 +263,7 @@ struct product_coeff_impl<DefaultTraversal, Dynamic, Lhs, Rhs, RetScalar>
|
|||
typedef typename Lhs::Index Index;
|
||||
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar& res)
|
||||
{
|
||||
eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix");
|
||||
res = lhs.coeff(row, 0) * rhs.coeff(0, col);
|
||||
for(Index i = 1; i < lhs.cols(); ++i)
|
||||
res += lhs.coeff(row, i) * rhs.coeff(i, col);
|
||||
res = (lhs.row(row).transpose().cwiseProduct( rhs.col(col) )).sum();
|
||||
}
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
|
||||
#define EIGEN_WORLD_VERSION 3
|
||||
#define EIGEN_MAJOR_VERSION 2
|
||||
#define EIGEN_MINOR_VERSION 2
|
||||
#define EIGEN_MINOR_VERSION 4
|
||||
|
||||
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
|
||||
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
|
||||
|
|
@ -96,6 +96,13 @@
|
|||
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
|
||||
#endif
|
||||
|
||||
// Cross compiler wrapper around LLVM's __has_builtin
|
||||
#ifdef __has_builtin
|
||||
# define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
|
||||
#else
|
||||
# define EIGEN_HAS_BUILTIN(x) 0
|
||||
#endif
|
||||
|
||||
/** Allows to disable some optimizations which might affect the accuracy of the result.
|
||||
* Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them.
|
||||
* They currently include:
|
||||
|
|
@ -247,7 +254,7 @@ namespace Eigen {
|
|||
|
||||
#if !defined(EIGEN_ASM_COMMENT)
|
||||
#if (defined __GNUC__) && ( defined(__i386__) || defined(__x86_64__) )
|
||||
#define EIGEN_ASM_COMMENT(X) asm("#" X)
|
||||
#define EIGEN_ASM_COMMENT(X) __asm__("#" X)
|
||||
#else
|
||||
#define EIGEN_ASM_COMMENT(X)
|
||||
#endif
|
||||
|
|
@ -306,7 +313,7 @@ namespace Eigen {
|
|||
// just an empty macro !
|
||||
#define EIGEN_EMPTY
|
||||
|
||||
#if defined(_MSC_VER) && (!defined(__INTEL_COMPILER))
|
||||
#if defined(_MSC_VER) && (_MSC_VER < 1900) && (!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)
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@
|
|||
// 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)
|
||||
#if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || (defined __PGI) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) && (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0)
|
||||
#define EIGEN_HAS_POSIX_MEMALIGN 1
|
||||
#endif
|
||||
#endif
|
||||
|
|
@ -417,6 +417,8 @@ 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)
|
||||
{
|
||||
if(size==0)
|
||||
return 0; // short-cut. Also fixes Bug 884
|
||||
check_size_for_overflow<T>(size);
|
||||
T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
|
||||
if(NumTraits<T>::RequireInitialization)
|
||||
|
|
@ -464,9 +466,8 @@ 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)
|
||||
{
|
||||
enum { PacketSize = packet_traits<Scalar>::size,
|
||||
PacketAlignedMask = PacketSize-1
|
||||
};
|
||||
static const Index PacketSize = packet_traits<Scalar>::size;
|
||||
static const Index PacketAlignedMask = PacketSize-1;
|
||||
|
||||
if(PacketSize==1)
|
||||
{
|
||||
|
|
@ -612,7 +613,6 @@ template<typename T> class aligned_stack_memory_handler
|
|||
void* operator new(size_t size, const std::nothrow_t&) throw() { \
|
||||
try { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); } \
|
||||
catch (...) { return 0; } \
|
||||
return 0; \
|
||||
}
|
||||
#else
|
||||
#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
|
||||
|
|
|
|||
|
|
@ -90,7 +90,9 @@
|
|||
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,
|
||||
OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG
|
||||
OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG,
|
||||
IMPLICIT_CONVERSION_TO_SCALAR_IS_FOR_INNER_PRODUCT_ONLY,
|
||||
STORAGE_LAYOUT_DOES_NOT_MATCH
|
||||
};
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -341,7 +341,7 @@ template<typename T, int n=1, typename PlainObject = typename eval<T>::type> str
|
|||
};
|
||||
|
||||
template<typename T>
|
||||
T* const_cast_ptr(const T* ptr)
|
||||
inline T* const_cast_ptr(const T* ptr)
|
||||
{
|
||||
return const_cast<T*>(ptr);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -147,7 +147,6 @@ void fitHyperplane(int numPoints,
|
|||
|
||||
// compute the covariance matrix
|
||||
CovMatrixType covMat = CovMatrixType::Zero(size, size);
|
||||
VectorType remean = VectorType::Zero(size);
|
||||
for(int i = 0; i < numPoints; ++i)
|
||||
{
|
||||
VectorType diff = (*(points[i]) - mean).conjugate();
|
||||
|
|
|
|||
|
|
@ -313,7 +313,7 @@ namespace Eigen {
|
|||
using std::abs;
|
||||
using std::sqrt;
|
||||
const Index dim=m_S.cols();
|
||||
if (abs(m_S.coeff(i+1,i)==Scalar(0)))
|
||||
if (abs(m_S.coeff(i+1,i))==Scalar(0))
|
||||
return;
|
||||
Index z = findSmallDiagEntry(i,i+1);
|
||||
if (z==i-1)
|
||||
|
|
|
|||
|
|
@ -563,7 +563,6 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
|
|||
if(computeEigenvectors)
|
||||
{
|
||||
Scalar safeNorm2 = Eigen::NumTraits<Scalar>::epsilon();
|
||||
safeNorm2 *= safeNorm2;
|
||||
if((eivals(2)-eivals(0))<=Eigen::NumTraits<Scalar>::epsilon())
|
||||
{
|
||||
eivecs.setIdentity();
|
||||
|
|
@ -577,7 +576,7 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
|
|||
Scalar d0 = eivals(2) - eivals(1);
|
||||
Scalar d1 = eivals(1) - eivals(0);
|
||||
int k = d0 > d1 ? 2 : 0;
|
||||
d0 = d0 > d1 ? d1 : d0;
|
||||
d0 = d0 > d1 ? d0 : d1;
|
||||
|
||||
tmp.diagonal().array () -= eivals(k);
|
||||
VectorType cross;
|
||||
|
|
@ -585,19 +584,25 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
|
|||
n = (cross = tmp.row(0).cross(tmp.row(1))).squaredNorm();
|
||||
|
||||
if(n>safeNorm2)
|
||||
{
|
||||
eivecs.col(k) = cross / sqrt(n);
|
||||
}
|
||||
else
|
||||
{
|
||||
n = (cross = tmp.row(0).cross(tmp.row(2))).squaredNorm();
|
||||
|
||||
if(n>safeNorm2)
|
||||
{
|
||||
eivecs.col(k) = cross / sqrt(n);
|
||||
}
|
||||
else
|
||||
{
|
||||
n = (cross = tmp.row(1).cross(tmp.row(2))).squaredNorm();
|
||||
|
||||
if(n>safeNorm2)
|
||||
{
|
||||
eivecs.col(k) = cross / sqrt(n);
|
||||
}
|
||||
else
|
||||
{
|
||||
// the input matrix and/or the eigenvaues probably contains some inf/NaN,
|
||||
|
|
@ -617,12 +622,16 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
|
|||
tmp.diagonal().array() -= eivals(1);
|
||||
|
||||
if(d0<=Eigen::NumTraits<Scalar>::epsilon())
|
||||
{
|
||||
eivecs.col(1) = eivecs.col(k).unitOrthogonal();
|
||||
}
|
||||
else
|
||||
{
|
||||
n = (cross = eivecs.col(k).cross(tmp.row(0).normalized())).squaredNorm();
|
||||
n = (cross = eivecs.col(k).cross(tmp.row(0))).squaredNorm();
|
||||
if(n>safeNorm2)
|
||||
{
|
||||
eivecs.col(1) = cross / sqrt(n);
|
||||
}
|
||||
else
|
||||
{
|
||||
n = (cross = eivecs.col(k).cross(tmp.row(1))).squaredNorm();
|
||||
|
|
@ -636,13 +645,14 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3
|
|||
else
|
||||
{
|
||||
// we should never reach this point,
|
||||
// if so the last two eigenvalues are likely to ve very closed to each other
|
||||
// if so the last two eigenvalues are likely to be very close to each other
|
||||
eivecs.col(1) = eivecs.col(k).unitOrthogonal();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// make sure that eivecs[1] is orthogonal to eivecs[2]
|
||||
// FIXME: this step should not be needed
|
||||
Scalar d = eivecs.col(1).dot(eivecs.col(k));
|
||||
eivecs.col(1) = (eivecs.col(1) - d * eivecs.col(k)).normalized();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -100,7 +100,17 @@ public:
|
|||
{
|
||||
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
|
||||
Hyperplane result(p0.size());
|
||||
result.normal() = (p2 - p0).cross(p1 - p0).normalized();
|
||||
VectorType v0(p2 - p0), v1(p1 - p0);
|
||||
result.normal() = v0.cross(v1);
|
||||
RealScalar norm = result.normal().norm();
|
||||
if(norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon())
|
||||
{
|
||||
Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
|
||||
JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
|
||||
result.normal() = svd.matrixV().col(2);
|
||||
}
|
||||
else
|
||||
result.normal() /= norm;
|
||||
result.offset() = -p0.dot(result.normal());
|
||||
return result;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -60,6 +60,9 @@ public:
|
|||
|
||||
/** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
|
||||
inline Rotation2D(const Scalar& a) : m_angle(a) {}
|
||||
|
||||
/** Default constructor wihtout initialization. The represented rotation is undefined. */
|
||||
Rotation2D() {}
|
||||
|
||||
/** \returns the rotation angle */
|
||||
inline Scalar angle() const { return m_angle; }
|
||||
|
|
@ -81,10 +84,10 @@ public:
|
|||
/** Applies the rotation to a 2D vector */
|
||||
Vector2 operator* (const Vector2& vec) const
|
||||
{ return toRotationMatrix() * vec; }
|
||||
|
||||
|
||||
template<typename Derived>
|
||||
Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
|
||||
Matrix2 toRotationMatrix(void) const;
|
||||
Matrix2 toRotationMatrix() const;
|
||||
|
||||
/** \returns the spherical interpolation between \c *this and \a other using
|
||||
* parameter \a t. It is in fact equivalent to a linear interpolation.
|
||||
|
|
|
|||
|
|
@ -62,6 +62,8 @@ struct transform_construct_from_matrix;
|
|||
|
||||
template<typename TransformType> struct transform_take_affine_part;
|
||||
|
||||
template<int Mode> struct transform_make_affine;
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
/** \geometry_module \ingroup Geometry_Module
|
||||
|
|
@ -230,8 +232,7 @@ public:
|
|||
inline Transform()
|
||||
{
|
||||
check_template_params();
|
||||
if (int(Mode)==Affine)
|
||||
makeAffine();
|
||||
internal::transform_make_affine<(int(Mode)==Affine) ? Affine : AffineCompact>::run(m_matrix);
|
||||
}
|
||||
|
||||
inline Transform(const Transform& other)
|
||||
|
|
@ -591,11 +592,7 @@ public:
|
|||
*/
|
||||
void makeAffine()
|
||||
{
|
||||
if(int(Mode)!=int(AffineCompact))
|
||||
{
|
||||
matrix().template block<1,Dim>(Dim,0).setZero();
|
||||
matrix().coeffRef(Dim,Dim) = Scalar(1);
|
||||
}
|
||||
internal::transform_make_affine<int(Mode)>::run(m_matrix);
|
||||
}
|
||||
|
||||
/** \internal
|
||||
|
|
@ -1079,6 +1076,24 @@ Transform<Scalar,Dim,Mode,Options>::fromPositionOrientationScale(const MatrixBas
|
|||
|
||||
namespace internal {
|
||||
|
||||
template<int Mode>
|
||||
struct transform_make_affine
|
||||
{
|
||||
template<typename MatrixType>
|
||||
static void run(MatrixType &mat)
|
||||
{
|
||||
static const int Dim = MatrixType::ColsAtCompileTime-1;
|
||||
mat.template block<1,Dim>(Dim,0).setZero();
|
||||
mat.coeffRef(Dim,Dim) = typename MatrixType::Scalar(1);
|
||||
}
|
||||
};
|
||||
|
||||
template<>
|
||||
struct transform_make_affine<AffineCompact>
|
||||
{
|
||||
template<typename MatrixType> static void run(MatrixType &) { }
|
||||
};
|
||||
|
||||
// selector needed to avoid taking the inverse of a 3x4 matrix
|
||||
template<typename TransformType, int Mode=TransformType::Mode>
|
||||
struct projective_transform_inverse
|
||||
|
|
|
|||
|
|
@ -39,7 +39,6 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
|
|||
int maxIters = iters;
|
||||
|
||||
int n = mat.cols();
|
||||
x = precond.solve(x);
|
||||
VectorType r = rhs - mat * x;
|
||||
VectorType r0 = r;
|
||||
|
||||
|
|
@ -143,7 +142,7 @@ struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
|
|||
* SparseMatrix<double> A(n,n);
|
||||
* // fill A and b
|
||||
* BiCGSTAB<SparseMatrix<double> > solver;
|
||||
* solver(A);
|
||||
* solver.compute(A);
|
||||
* x = solver.solve(b);
|
||||
* std::cout << "#iterations: " << solver.iterations() << std::endl;
|
||||
* std::cout << "estimated error: " << solver.error() << std::endl;
|
||||
|
|
|
|||
|
|
@ -219,7 +219,7 @@ class PardisoImpl
|
|||
void pardisoInit(int type)
|
||||
{
|
||||
m_type = type;
|
||||
bool symmetric = abs(m_type) < 10;
|
||||
bool symmetric = std::abs(m_type) < 10;
|
||||
m_iparm[0] = 1; // No solver default
|
||||
m_iparm[1] = 3; // use Metis for the ordering
|
||||
m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS
|
||||
|
|
|
|||
|
|
@ -762,6 +762,7 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
|
|||
|
||||
internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols;
|
||||
internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows;
|
||||
MatrixType m_scaledMatrix;
|
||||
};
|
||||
|
||||
template<typename MatrixType, int QRPreconditioner>
|
||||
|
|
@ -808,8 +809,9 @@ void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, u
|
|||
: 0);
|
||||
m_workMatrix.resize(m_diagSize, m_diagSize);
|
||||
|
||||
if(m_cols>m_rows) m_qr_precond_morecols.allocate(*this);
|
||||
if(m_rows>m_cols) m_qr_precond_morerows.allocate(*this);
|
||||
if(m_cols>m_rows) m_qr_precond_morecols.allocate(*this);
|
||||
if(m_rows>m_cols) m_qr_precond_morerows.allocate(*this);
|
||||
if(m_cols!=m_cols) m_scaledMatrix.resize(rows,cols);
|
||||
}
|
||||
|
||||
template<typename MatrixType, int QRPreconditioner>
|
||||
|
|
@ -826,21 +828,26 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
|
|||
// 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();
|
||||
|
||||
// Scaling factor to reduce over/under-flows
|
||||
RealScalar scale = matrix.cwiseAbs().maxCoeff();
|
||||
if(scale==RealScalar(0)) scale = RealScalar(1);
|
||||
|
||||
/*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
|
||||
|
||||
if(!m_qr_precond_morecols.run(*this, matrix) && !m_qr_precond_morerows.run(*this, matrix))
|
||||
if(m_rows!=m_cols)
|
||||
{
|
||||
m_workMatrix = matrix.block(0,0,m_diagSize,m_diagSize);
|
||||
m_scaledMatrix = matrix / scale;
|
||||
m_qr_precond_morecols.run(*this, m_scaledMatrix);
|
||||
m_qr_precond_morerows.run(*this, m_scaledMatrix);
|
||||
}
|
||||
else
|
||||
{
|
||||
m_workMatrix = matrix.block(0,0,m_diagSize,m_diagSize) / scale;
|
||||
if(m_computeFullU) m_matrixU.setIdentity(m_rows,m_rows);
|
||||
if(m_computeThinU) m_matrixU.setIdentity(m_rows,m_diagSize);
|
||||
if(m_computeFullV) m_matrixV.setIdentity(m_cols,m_cols);
|
||||
if(m_computeThinV) m_matrixV.setIdentity(m_cols, m_diagSize);
|
||||
}
|
||||
|
||||
// Scaling factor to reduce over/under-flows
|
||||
RealScalar scale = m_workMatrix.cwiseAbs().maxCoeff();
|
||||
if(scale==RealScalar(0)) scale = RealScalar(1);
|
||||
m_workMatrix /= scale;
|
||||
|
||||
/*** step 2. The main Jacobi SVD iteration. ***/
|
||||
|
||||
|
|
@ -861,7 +868,8 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
|
|||
using std::max;
|
||||
RealScalar threshold = (max)(considerAsZero, precision * (max)(abs(m_workMatrix.coeff(p,p)),
|
||||
abs(m_workMatrix.coeff(q,q))));
|
||||
if((max)(abs(m_workMatrix.coeff(p,q)),abs(m_workMatrix.coeff(q,p))) > threshold)
|
||||
// We compare both values to threshold instead of calling max to be robust to NaN (See bug 791)
|
||||
if(abs(m_workMatrix.coeff(p,q))>threshold || abs(m_workMatrix.coeff(q,p)) > threshold)
|
||||
{
|
||||
finished = false;
|
||||
|
||||
|
|
|
|||
|
|
@ -69,7 +69,7 @@ class AmbiVector
|
|||
delete[] m_buffer;
|
||||
if (size<1000)
|
||||
{
|
||||
Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
|
||||
Index allocSize = (size * sizeof(ListEl) + sizeof(Scalar) - 1)/sizeof(Scalar);
|
||||
m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
|
||||
m_buffer = new Scalar[allocSize];
|
||||
}
|
||||
|
|
@ -88,7 +88,7 @@ class AmbiVector
|
|||
Index copyElements = m_allocatedElements;
|
||||
m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
|
||||
Index allocSize = m_allocatedElements * sizeof(ListEl);
|
||||
allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
|
||||
allocSize = (allocSize + sizeof(Scalar) - 1)/sizeof(Scalar);
|
||||
Scalar* newBuffer = new Scalar[allocSize];
|
||||
memcpy(newBuffer, m_buffer, copyElements * sizeof(ListEl));
|
||||
delete[] m_buffer;
|
||||
|
|
|
|||
|
|
@ -68,6 +68,8 @@ public:
|
|||
const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
|
||||
|
||||
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
|
||||
private:
|
||||
Index nonZeros() const;
|
||||
};
|
||||
|
||||
|
||||
|
|
@ -82,6 +84,7 @@ class BlockImpl<SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols,true
|
|||
typedef SparseMatrix<_Scalar, _Options, _Index> SparseMatrixType;
|
||||
typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _MatrixTypeNested;
|
||||
typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType;
|
||||
typedef Block<const SparseMatrixType, BlockRows, BlockCols, true> ConstBlockType;
|
||||
public:
|
||||
enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
|
||||
EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
|
||||
|
|
@ -245,6 +248,93 @@ public:
|
|||
|
||||
};
|
||||
|
||||
|
||||
template<typename _Scalar, int _Options, typename _Index, int BlockRows, int BlockCols>
|
||||
class BlockImpl<const SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols,true,Sparse>
|
||||
: public SparseMatrixBase<Block<const SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols,true> >
|
||||
{
|
||||
typedef SparseMatrix<_Scalar, _Options, _Index> SparseMatrixType;
|
||||
typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _MatrixTypeNested;
|
||||
typedef Block<const SparseMatrixType, BlockRows, BlockCols, true> BlockType;
|
||||
public:
|
||||
enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
|
||||
EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
|
||||
protected:
|
||||
enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
|
||||
public:
|
||||
|
||||
class InnerIterator: public SparseMatrixType::InnerIterator
|
||||
{
|
||||
public:
|
||||
inline InnerIterator(const BlockType& xpr, Index outer)
|
||||
: SparseMatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
|
||||
{}
|
||||
inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
|
||||
inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
|
||||
protected:
|
||||
Index m_outer;
|
||||
};
|
||||
class ReverseInnerIterator: public SparseMatrixType::ReverseInnerIterator
|
||||
{
|
||||
public:
|
||||
inline ReverseInnerIterator(const BlockType& xpr, Index outer)
|
||||
: SparseMatrixType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
|
||||
{}
|
||||
inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
|
||||
inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
|
||||
protected:
|
||||
Index m_outer;
|
||||
};
|
||||
|
||||
inline BlockImpl(const SparseMatrixType& xpr, int i)
|
||||
: m_matrix(xpr), m_outerStart(i), m_outerSize(OuterSize)
|
||||
{}
|
||||
|
||||
inline BlockImpl(const SparseMatrixType& xpr, int startRow, int startCol, int blockRows, int blockCols)
|
||||
: m_matrix(xpr), m_outerStart(IsRowMajor ? startRow : startCol), m_outerSize(IsRowMajor ? blockRows : blockCols)
|
||||
{}
|
||||
|
||||
inline const Scalar* valuePtr() const
|
||||
{ return m_matrix.valuePtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
|
||||
|
||||
inline const Index* innerIndexPtr() const
|
||||
{ return m_matrix.innerIndexPtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
|
||||
|
||||
inline const Index* outerIndexPtr() const
|
||||
{ return m_matrix.outerIndexPtr() + m_outerStart; }
|
||||
|
||||
Index nonZeros() const
|
||||
{
|
||||
if(m_matrix.isCompressed())
|
||||
return std::size_t(m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()])
|
||||
- std::size_t(m_matrix.outerIndexPtr()[m_outerStart]);
|
||||
else if(m_outerSize.value()==0)
|
||||
return 0;
|
||||
else
|
||||
return Map<const Matrix<Index,OuterSize,1> >(m_matrix.innerNonZeroPtr()+m_outerStart, m_outerSize.value()).sum();
|
||||
}
|
||||
|
||||
const Scalar& lastCoeff() const
|
||||
{
|
||||
EIGEN_STATIC_ASSERT_VECTOR_ONLY(BlockImpl);
|
||||
eigen_assert(nonZeros()>0);
|
||||
if(m_matrix.isCompressed())
|
||||
return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1];
|
||||
else
|
||||
return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1];
|
||||
}
|
||||
|
||||
EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
|
||||
EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
|
||||
|
||||
protected:
|
||||
|
||||
typename SparseMatrixType::Nested m_matrix;
|
||||
Index m_outerStart;
|
||||
const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
|
||||
|
||||
};
|
||||
|
||||
//----------
|
||||
|
||||
/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
|
||||
|
|
|
|||
|
|
@ -306,15 +306,6 @@ class DenseTimeSparseProduct
|
|||
DenseTimeSparseProduct& operator=(const DenseTimeSparseProduct&);
|
||||
};
|
||||
|
||||
// sparse * dense
|
||||
template<typename Derived>
|
||||
template<typename OtherDerived>
|
||||
inline const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
|
||||
SparseMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
|
||||
{
|
||||
return typename SparseDenseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
|
||||
}
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SPARSEDENSEPRODUCT_H
|
||||
|
|
|
|||
|
|
@ -358,7 +358,8 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
|
|||
/** sparse * dense (returns a dense object unless it is an outer product) */
|
||||
template<typename OtherDerived>
|
||||
const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
|
||||
operator*(const MatrixBase<OtherDerived> &other) const;
|
||||
operator*(const MatrixBase<OtherDerived> &other) const
|
||||
{ return typename SparseDenseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); }
|
||||
|
||||
/** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
|
||||
SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ struct permut_sparsematrix_product_retval
|
|||
for(Index j=0; j<m_matrix.outerSize(); ++j)
|
||||
{
|
||||
Index jp = m_permutation.indices().coeff(j);
|
||||
sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).size();
|
||||
sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).nonZeros();
|
||||
}
|
||||
tmp.reserve(sizes);
|
||||
for(Index j=0; j<m_matrix.outerSize(); ++j)
|
||||
|
|
|
|||
|
|
@ -260,14 +260,13 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
|
|||
eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
|
||||
// Initialize with the determinant of the row matrix
|
||||
Scalar det = Scalar(1.);
|
||||
//Note that the diagonal blocks of U are stored in supernodes,
|
||||
// Note that the diagonal blocks of U are stored in supernodes,
|
||||
// which are available in the L part :)
|
||||
for (Index j = 0; j < this->cols(); ++j)
|
||||
{
|
||||
for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
|
||||
{
|
||||
if(it.row() < j) continue;
|
||||
if(it.row() == j)
|
||||
if(it.index() == j)
|
||||
{
|
||||
det *= (std::abs)(it.value());
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -189,8 +189,8 @@ class MappedSuperNodalMatrix<Scalar,Index>::InnerIterator
|
|||
m_idval(mat.colIndexPtr()[outer]),
|
||||
m_startidval(m_idval),
|
||||
m_endidval(mat.colIndexPtr()[outer+1]),
|
||||
m_idrow(mat.rowIndexPtr()[outer]),
|
||||
m_endidrow(mat.rowIndexPtr()[outer+1])
|
||||
m_idrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]]),
|
||||
m_endidrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]+1])
|
||||
{}
|
||||
inline InnerIterator& operator++()
|
||||
{
|
||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@ class SparseQR
|
|||
typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
|
||||
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
|
||||
public:
|
||||
SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
|
||||
SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
|
||||
{ }
|
||||
|
||||
/** Construct a QR factorization of the matrix \a mat.
|
||||
|
|
@ -84,7 +84,7 @@ class SparseQR
|
|||
*
|
||||
* \sa compute()
|
||||
*/
|
||||
SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
|
||||
SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
|
||||
{
|
||||
compute(mat);
|
||||
}
|
||||
|
|
@ -262,6 +262,7 @@ class SparseQR
|
|||
IndexVector m_etree; // Column elimination tree
|
||||
IndexVector m_firstRowElt; // First element in each row
|
||||
bool m_isQSorted; // whether Q is sorted or not
|
||||
bool m_isEtreeOk; // whether the elimination tree match the initial input matrix
|
||||
|
||||
template <typename, typename > friend struct SparseQR_QProduct;
|
||||
template <typename > friend struct SparseQRMatrixQReturnType;
|
||||
|
|
@ -281,9 +282,11 @@ template <typename MatrixType, typename OrderingType>
|
|||
void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
|
||||
{
|
||||
eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
|
||||
// Copy to a column major matrix if the input is rowmajor
|
||||
typename internal::conditional<MatrixType::IsRowMajor,QRMatrixType,const MatrixType&>::type matCpy(mat);
|
||||
// Compute the column fill reducing ordering
|
||||
OrderingType ord;
|
||||
ord(mat, m_perm_c);
|
||||
ord(matCpy, m_perm_c);
|
||||
Index n = mat.cols();
|
||||
Index m = mat.rows();
|
||||
Index diagSize = (std::min)(m,n);
|
||||
|
|
@ -296,7 +299,8 @@ void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
|
|||
|
||||
// Compute the column elimination tree of the permuted matrix
|
||||
m_outputPerm_c = m_perm_c.inverse();
|
||||
internal::coletree(mat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
|
||||
internal::coletree(matCpy, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
|
||||
m_isEtreeOk = true;
|
||||
|
||||
m_R.resize(m, n);
|
||||
m_Q.resize(m, diagSize);
|
||||
|
|
@ -330,15 +334,38 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
Index nzcolR, nzcolQ; // Number of nonzero for the current column of R and Q
|
||||
ScalarVector tval(m); // The dense vector used to compute the current column
|
||||
RealScalar pivotThreshold = m_threshold;
|
||||
|
||||
|
||||
m_R.setZero();
|
||||
m_Q.setZero();
|
||||
m_pmat = mat;
|
||||
m_pmat.uncompress(); // To have the innerNonZeroPtr allocated
|
||||
// Apply the fill-in reducing permutation lazily:
|
||||
for (int i = 0; i < n; i++)
|
||||
if(!m_isEtreeOk)
|
||||
{
|
||||
Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
|
||||
m_pmat.outerIndexPtr()[p] = mat.outerIndexPtr()[i];
|
||||
m_pmat.innerNonZeroPtr()[p] = mat.outerIndexPtr()[i+1] - mat.outerIndexPtr()[i];
|
||||
m_outputPerm_c = m_perm_c.inverse();
|
||||
internal::coletree(m_pmat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
|
||||
m_isEtreeOk = true;
|
||||
}
|
||||
|
||||
m_pmat.uncompress(); // To have the innerNonZeroPtr allocated
|
||||
|
||||
// Apply the fill-in reducing permutation lazily:
|
||||
{
|
||||
// If the input is row major, copy the original column indices,
|
||||
// otherwise directly use the input matrix
|
||||
//
|
||||
IndexVector originalOuterIndicesCpy;
|
||||
const Index *originalOuterIndices = mat.outerIndexPtr();
|
||||
if(MatrixType::IsRowMajor)
|
||||
{
|
||||
originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(),n+1);
|
||||
originalOuterIndices = originalOuterIndicesCpy.data();
|
||||
}
|
||||
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
|
||||
m_pmat.outerIndexPtr()[p] = originalOuterIndices[i];
|
||||
m_pmat.innerNonZeroPtr()[p] = originalOuterIndices[i+1] - originalOuterIndices[i];
|
||||
}
|
||||
}
|
||||
|
||||
/* Compute the default threshold as in MatLab, see:
|
||||
|
|
@ -349,6 +376,8 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
{
|
||||
RealScalar max2Norm = 0.0;
|
||||
for (int j = 0; j < n; j++) max2Norm = (max)(max2Norm, m_pmat.col(j).norm());
|
||||
if(max2Norm==RealScalar(0))
|
||||
max2Norm = RealScalar(1);
|
||||
pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
|
||||
}
|
||||
|
||||
|
|
@ -357,7 +386,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
|
||||
Index nonzeroCol = 0; // Record the number of valid pivots
|
||||
m_Q.startVec(0);
|
||||
|
||||
|
||||
// Left looking rank-revealing QR factorization: compute a column of R and Q at a time
|
||||
for (Index col = 0; col < n; ++col)
|
||||
{
|
||||
|
|
@ -373,7 +402,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
// all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node k.
|
||||
// Note: if the diagonal entry does not exist, then its contribution must be explicitly added,
|
||||
// thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found.
|
||||
for (typename MatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp)
|
||||
for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp)
|
||||
{
|
||||
Index curIdx = nonzeroCol;
|
||||
if(itp) curIdx = itp.row();
|
||||
|
|
@ -447,7 +476,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
}
|
||||
} // End update current column
|
||||
|
||||
Scalar tau;
|
||||
Scalar tau = 0;
|
||||
RealScalar beta = 0;
|
||||
|
||||
if(nonzeroCol < diagSize)
|
||||
|
|
@ -461,7 +490,6 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
|
||||
if(sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0))
|
||||
{
|
||||
tau = RealScalar(0);
|
||||
beta = numext::real(c0);
|
||||
tval(Qidx(0)) = 1;
|
||||
}
|
||||
|
|
@ -514,6 +542,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
|
||||
// Recompute the column elimination tree
|
||||
internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data());
|
||||
m_isEtreeOk = false;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -525,13 +554,13 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
|
|||
m_R.finalize();
|
||||
m_R.makeCompressed();
|
||||
m_isQSorted = false;
|
||||
|
||||
|
||||
m_nonzeropivots = nonzeroCol;
|
||||
|
||||
if(nonzeroCol<n)
|
||||
{
|
||||
// Permute the triangular factor to put the 'dead' columns to the end
|
||||
MatrixType tempR(m_R);
|
||||
QRMatrixType tempR(m_R);
|
||||
m_R = tempR * m_pivotperm;
|
||||
|
||||
// Update the column permutation
|
||||
|
|
|
|||
|
|
@ -107,6 +107,16 @@ inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *N
|
|||
return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
|
||||
}
|
||||
|
||||
namespace internal {
|
||||
template<typename T> struct umfpack_helper_is_sparse_plain : false_type {};
|
||||
template<typename Scalar, int Options, typename StorageIndex>
|
||||
struct umfpack_helper_is_sparse_plain<SparseMatrix<Scalar,Options,StorageIndex> >
|
||||
: true_type {};
|
||||
template<typename Scalar, int Options, typename StorageIndex>
|
||||
struct umfpack_helper_is_sparse_plain<MappedSparseMatrix<Scalar,Options,StorageIndex> >
|
||||
: true_type {};
|
||||
}
|
||||
|
||||
/** \ingroup UmfPackSupport_Module
|
||||
* \brief A sparse LU factorization and solver based on UmfPack
|
||||
*
|
||||
|
|
@ -192,10 +202,14 @@ class UmfPackLU : internal::noncopyable
|
|||
* Note that the matrix should be column-major, and in compressed format for best performance.
|
||||
* \sa SparseMatrix::makeCompressed().
|
||||
*/
|
||||
void compute(const MatrixType& matrix)
|
||||
template<typename InputMatrixType>
|
||||
void compute(const InputMatrixType& matrix)
|
||||
{
|
||||
analyzePattern(matrix);
|
||||
factorize(matrix);
|
||||
if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
|
||||
if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
|
||||
grapInput(matrix.derived());
|
||||
analyzePattern_impl();
|
||||
factorize_impl();
|
||||
}
|
||||
|
||||
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
|
||||
|
|
@ -230,23 +244,15 @@ class UmfPackLU : internal::noncopyable
|
|||
*
|
||||
* \sa factorize(), compute()
|
||||
*/
|
||||
void analyzePattern(const MatrixType& matrix)
|
||||
template<typename InputMatrixType>
|
||||
void analyzePattern(const InputMatrixType& matrix)
|
||||
{
|
||||
if(m_symbolic)
|
||||
umfpack_free_symbolic(&m_symbolic,Scalar());
|
||||
if(m_numeric)
|
||||
umfpack_free_numeric(&m_numeric,Scalar());
|
||||
if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
|
||||
if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
|
||||
|
||||
grapInput(matrix);
|
||||
grapInput(matrix.derived());
|
||||
|
||||
int errorCode = 0;
|
||||
errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
|
||||
&m_symbolic, 0, 0);
|
||||
|
||||
m_isInitialized = true;
|
||||
m_info = errorCode ? InvalidInput : Success;
|
||||
m_analysisIsOk = true;
|
||||
m_factorizationIsOk = false;
|
||||
analyzePattern_impl();
|
||||
}
|
||||
|
||||
/** Performs a numeric decomposition of \a matrix
|
||||
|
|
@ -255,20 +261,16 @@ class UmfPackLU : internal::noncopyable
|
|||
*
|
||||
* \sa analyzePattern(), compute()
|
||||
*/
|
||||
void factorize(const MatrixType& matrix)
|
||||
template<typename InputMatrixType>
|
||||
void factorize(const InputMatrixType& matrix)
|
||||
{
|
||||
eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
|
||||
if(m_numeric)
|
||||
umfpack_free_numeric(&m_numeric,Scalar());
|
||||
|
||||
grapInput(matrix);
|
||||
|
||||
int errorCode;
|
||||
errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
|
||||
m_symbolic, &m_numeric, 0, 0);
|
||||
|
||||
m_info = errorCode ? NumericalIssue : Success;
|
||||
m_factorizationIsOk = true;
|
||||
grapInput(matrix.derived());
|
||||
|
||||
factorize_impl();
|
||||
}
|
||||
|
||||
#ifndef EIGEN_PARSED_BY_DOXYGEN
|
||||
|
|
@ -283,19 +285,20 @@ class UmfPackLU : internal::noncopyable
|
|||
|
||||
protected:
|
||||
|
||||
|
||||
void init()
|
||||
{
|
||||
m_info = InvalidInput;
|
||||
m_isInitialized = false;
|
||||
m_numeric = 0;
|
||||
m_symbolic = 0;
|
||||
m_outerIndexPtr = 0;
|
||||
m_innerIndexPtr = 0;
|
||||
m_valuePtr = 0;
|
||||
m_info = InvalidInput;
|
||||
m_isInitialized = false;
|
||||
m_numeric = 0;
|
||||
m_symbolic = 0;
|
||||
m_outerIndexPtr = 0;
|
||||
m_innerIndexPtr = 0;
|
||||
m_valuePtr = 0;
|
||||
m_extractedDataAreDirty = true;
|
||||
}
|
||||
|
||||
void grapInput(const MatrixType& mat)
|
||||
template<typename InputMatrixType>
|
||||
void grapInput_impl(const InputMatrixType& mat, internal::true_type)
|
||||
{
|
||||
m_copyMatrix.resize(mat.rows(), mat.cols());
|
||||
if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() )
|
||||
|
|
@ -313,6 +316,45 @@ class UmfPackLU : internal::noncopyable
|
|||
m_valuePtr = mat.valuePtr();
|
||||
}
|
||||
}
|
||||
|
||||
template<typename InputMatrixType>
|
||||
void grapInput_impl(const InputMatrixType& mat, internal::false_type)
|
||||
{
|
||||
m_copyMatrix = mat;
|
||||
m_outerIndexPtr = m_copyMatrix.outerIndexPtr();
|
||||
m_innerIndexPtr = m_copyMatrix.innerIndexPtr();
|
||||
m_valuePtr = m_copyMatrix.valuePtr();
|
||||
}
|
||||
|
||||
template<typename InputMatrixType>
|
||||
void grapInput(const InputMatrixType& mat)
|
||||
{
|
||||
grapInput_impl(mat, internal::umfpack_helper_is_sparse_plain<InputMatrixType>());
|
||||
}
|
||||
|
||||
void analyzePattern_impl()
|
||||
{
|
||||
int errorCode = 0;
|
||||
errorCode = umfpack_symbolic(m_copyMatrix.rows(), m_copyMatrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
|
||||
&m_symbolic, 0, 0);
|
||||
|
||||
m_isInitialized = true;
|
||||
m_info = errorCode ? InvalidInput : Success;
|
||||
m_analysisIsOk = true;
|
||||
m_factorizationIsOk = false;
|
||||
m_extractedDataAreDirty = true;
|
||||
}
|
||||
|
||||
void factorize_impl()
|
||||
{
|
||||
int errorCode;
|
||||
errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
|
||||
m_symbolic, &m_numeric, 0, 0);
|
||||
|
||||
m_info = errorCode ? NumericalIssue : Success;
|
||||
m_factorizationIsOk = true;
|
||||
m_extractedDataAreDirty = true;
|
||||
}
|
||||
|
||||
// cached data to reduce reallocation, etc.
|
||||
mutable LUMatrixType m_l;
|
||||
|
|
|
|||
|
|
@ -452,20 +452,12 @@ macro(ei_set_build_string)
|
|||
endmacro(ei_set_build_string)
|
||||
|
||||
macro(ei_is_64bit_env VAR)
|
||||
|
||||
file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/is64.cpp"
|
||||
"int main() { return (sizeof(int*) == 8 ? 1 : 0); }
|
||||
")
|
||||
try_run(run_res compile_res
|
||||
${CMAKE_CURRENT_BINARY_DIR} "${CMAKE_CURRENT_BINARY_DIR}/is64.cpp"
|
||||
RUN_OUTPUT_VARIABLE run_output)
|
||||
|
||||
if(compile_res AND run_res)
|
||||
set(${VAR} ${run_res})
|
||||
elseif(CMAKE_CL_64)
|
||||
set(${VAR} 1)
|
||||
elseif("$ENV{Platform}" STREQUAL "X64") # nmake 64 bit
|
||||
if(CMAKE_SIZEOF_VOID_P EQUAL 8)
|
||||
set(${VAR} 1)
|
||||
elseif(CMAKE_SIZEOF_VOID_P EQUAL 4)
|
||||
set(${VAR} 0)
|
||||
else()
|
||||
message(WARNING "Unsupported pointer size. Please contact the authors.")
|
||||
endif()
|
||||
endmacro(ei_is_64bit_env)
|
||||
|
||||
|
|
|
|||
|
|
@ -86,4 +86,4 @@ include(FindPackageHandleStandardArgs)
|
|||
find_package_handle_standard_args(CHOLMOD DEFAULT_MSG
|
||||
CHOLMOD_INCLUDES CHOLMOD_LIBRARIES)
|
||||
|
||||
mark_as_advanced(CHOLMOD_INCLUDES CHOLMOD_LIBRARIES AMD_LIBRARY COLAMD_LIBRARY SUITESPARSE_LIBRARY)
|
||||
mark_as_advanced(CHOLMOD_INCLUDES CHOLMOD_LIBRARIES AMD_LIBRARY COLAMD_LIBRARY SUITESPARSE_LIBRARY CAMD_LIBRARY CCOLAMD_LIBRARY CHOLMOD_METIS_LIBRARY)
|
||||
|
|
|
|||
|
|
@ -115,5 +115,5 @@ include(FindPackageHandleStandardArgs)
|
|||
find_package_handle_standard_args(FFTW DEFAULT_MSG
|
||||
FFTW_INCLUDES FFTW_LIBRARIES)
|
||||
|
||||
mark_as_advanced(FFTW_INCLUDES FFTW_LIBRARIES)
|
||||
mark_as_advanced(FFTW_INCLUDES FFTW_LIBRARIES FFTW_LIB FFTWF_LIB FFTWL_LIB)
|
||||
|
||||
|
|
|
|||
|
|
@ -10,16 +10,50 @@ find_path(METIS_INCLUDES
|
|||
PATHS
|
||||
$ENV{METISDIR}
|
||||
${INCLUDE_INSTALL_DIR}
|
||||
PATH_SUFFIXES
|
||||
PATH_SUFFIXES
|
||||
.
|
||||
metis
|
||||
include
|
||||
)
|
||||
|
||||
macro(_metis_check_version)
|
||||
file(READ "${METIS_INCLUDES}/metis.h" _metis_version_header)
|
||||
|
||||
string(REGEX MATCH "define[ \t]+METIS_VER_MAJOR[ \t]+([0-9]+)" _metis_major_version_match "${_metis_version_header}")
|
||||
set(METIS_MAJOR_VERSION "${CMAKE_MATCH_1}")
|
||||
string(REGEX MATCH "define[ \t]+METIS_VER_MINOR[ \t]+([0-9]+)" _metis_minor_version_match "${_metis_version_header}")
|
||||
set(METIS_MINOR_VERSION "${CMAKE_MATCH_1}")
|
||||
string(REGEX MATCH "define[ \t]+METIS_VER_SUBMINOR[ \t]+([0-9]+)" _metis_subminor_version_match "${_metis_version_header}")
|
||||
set(METIS_SUBMINOR_VERSION "${CMAKE_MATCH_1}")
|
||||
if(NOT METIS_MAJOR_VERSION)
|
||||
message(WARNING "Could not determine Metis version. Assuming version 4.0.0")
|
||||
set(METIS_VERSION 4.0.0)
|
||||
else()
|
||||
set(METIS_VERSION ${METIS_MAJOR_VERSION}.${METIS_MINOR_VERSION}.${METIS_SUBMINOR_VERSION})
|
||||
endif()
|
||||
if(${METIS_VERSION} VERSION_LESS ${Metis_FIND_VERSION})
|
||||
set(METIS_VERSION_OK FALSE)
|
||||
else()
|
||||
set(METIS_VERSION_OK TRUE)
|
||||
endif()
|
||||
|
||||
if(NOT METIS_VERSION_OK)
|
||||
message(STATUS "Metis version ${METIS_VERSION} found in ${METIS_INCLUDES}, "
|
||||
"but at least version ${Metis_FIND_VERSION} is required")
|
||||
endif(NOT METIS_VERSION_OK)
|
||||
endmacro(_metis_check_version)
|
||||
|
||||
if(METIS_INCLUDES AND Metis_FIND_VERSION)
|
||||
_metis_check_version()
|
||||
else()
|
||||
set(METIS_VERSION_OK TRUE)
|
||||
endif()
|
||||
|
||||
|
||||
find_library(METIS_LIBRARIES metis PATHS $ENV{METISDIR} ${LIB_INSTALL_DIR} PATH_SUFFIXES lib)
|
||||
|
||||
include(FindPackageHandleStandardArgs)
|
||||
find_package_handle_standard_args(METIS DEFAULT_MSG
|
||||
METIS_INCLUDES METIS_LIBRARIES)
|
||||
METIS_INCLUDES METIS_LIBRARIES METIS_VERSION_OK)
|
||||
|
||||
mark_as_advanced(METIS_INCLUDES METIS_LIBRARIES)
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ function(workaround_9220 language language_works)
|
|||
file(WRITE ${CMAKE_BINARY_DIR}/language_tests/${language}/CMakeLists.txt
|
||||
${text})
|
||||
execute_process(
|
||||
COMMAND ${CMAKE_COMMAND} .
|
||||
COMMAND ${CMAKE_COMMAND} . -G "${CMAKE_GENERATOR}"
|
||||
WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/language_tests/${language}
|
||||
RESULT_VARIABLE return_code
|
||||
OUTPUT_QUIET
|
||||
|
|
|
|||
|
|
@ -67,10 +67,10 @@ P.rightCols<cols>() // P(:, end-cols+1:end)
|
|||
P.rightCols(cols) // P(:, end-cols+1:end)
|
||||
P.topRows<rows>() // P(1:rows, :)
|
||||
P.topRows(rows) // P(1:rows, :)
|
||||
P.middleRows<rows>(i) // P(:, i+1:i+rows)
|
||||
P.middleRows(i, rows) // P(:, i+1:i+rows)
|
||||
P.bottomRows<rows>() // P(:, end-rows+1:end)
|
||||
P.bottomRows(rows) // P(:, end-rows+1:end)
|
||||
P.middleRows<rows>(i) // P(i+1:i+rows, :)
|
||||
P.middleRows(i, rows) // P(i+1:i+rows, :)
|
||||
P.bottomRows<rows>() // P(end-rows+1:end, :)
|
||||
P.bottomRows(rows) // P(end-rows+1:end, :)
|
||||
P.topLeftCorner(rows, cols) // P(1:rows, 1:cols)
|
||||
P.topRightCorner(rows, cols) // P(1:rows, end-cols+1:end)
|
||||
P.bottomLeftCorner(rows, cols) // P(end-rows+1:end, 1:cols)
|
||||
|
|
|
|||
|
|
@ -71,11 +71,10 @@ i.e either row major or column major. The default is column major. Most arithmet
|
|||
<td> Constant or Random Insertion</td>
|
||||
<td>
|
||||
\code
|
||||
sm1.setZero(); // Set the matrix with zero elements
|
||||
sm1.setConstant(val); //Replace all the nonzero values with val
|
||||
sm1.setZero();
|
||||
\endcode
|
||||
</td>
|
||||
<td> The matrix sm1 should have been created before ???</td>
|
||||
<td>Remove all non-zero coefficients</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
|
|
|
|||
|
|
@ -6,12 +6,10 @@ foreach(example_src ${examples_SRCS})
|
|||
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
|
||||
target_link_libraries(${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
|
||||
endif()
|
||||
get_target_property(example_executable
|
||||
${example} LOCATION)
|
||||
add_custom_command(
|
||||
TARGET ${example}
|
||||
POST_BUILD
|
||||
COMMAND ${example_executable}
|
||||
COMMAND ${example}
|
||||
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
|
||||
)
|
||||
add_dependencies(all_examples ${example})
|
||||
|
|
|
|||
|
|
@ -14,12 +14,10 @@ foreach(snippet_src ${snippets_SRCS})
|
|||
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
|
||||
target_link_libraries(${compile_snippet_target} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
|
||||
endif()
|
||||
get_target_property(compile_snippet_executable
|
||||
${compile_snippet_target} LOCATION)
|
||||
add_custom_command(
|
||||
TARGET ${compile_snippet_target}
|
||||
POST_BUILD
|
||||
COMMAND ${compile_snippet_executable}
|
||||
COMMAND ${compile_snippet_target}
|
||||
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${snippet}.out
|
||||
)
|
||||
add_dependencies(all_snippets ${compile_snippet_target})
|
||||
|
|
@ -27,4 +25,4 @@ foreach(snippet_src ${snippets_SRCS})
|
|||
PROPERTIES OBJECT_DEPENDS ${snippet_src})
|
||||
endforeach(snippet_src)
|
||||
|
||||
ei_add_target_property(compile_tut_arithmetic_transpose_aliasing COMPILE_FLAGS -DEIGEN_NO_DEBUG)
|
||||
ei_add_target_property(compile_tut_arithmetic_transpose_aliasing COMPILE_FLAGS -DEIGEN_NO_DEBUG)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
|
||||
if(NOT EIGEN_TEST_NOQT)
|
||||
find_package(Qt4)
|
||||
if(QT4_FOUND)
|
||||
|
|
@ -6,16 +5,16 @@ if(NOT EIGEN_TEST_NOQT)
|
|||
endif()
|
||||
endif(NOT EIGEN_TEST_NOQT)
|
||||
|
||||
|
||||
if(QT4_FOUND)
|
||||
add_executable(Tutorial_sparse_example Tutorial_sparse_example.cpp Tutorial_sparse_example_details.cpp)
|
||||
target_link_libraries(Tutorial_sparse_example ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO} ${QT_QTCORE_LIBRARY} ${QT_QTGUI_LIBRARY})
|
||||
|
||||
|
||||
add_custom_command(
|
||||
TARGET Tutorial_sparse_example
|
||||
POST_BUILD
|
||||
COMMAND Tutorial_sparse_example ARGS ${CMAKE_CURRENT_BINARY_DIR}/../html/Tutorial_sparse_example.jpeg
|
||||
)
|
||||
|
||||
|
||||
add_dependencies(all_examples Tutorial_sparse_example)
|
||||
endif(QT4_FOUND)
|
||||
|
||||
|
|
|
|||
|
|
@ -26,6 +26,12 @@ ei_add_failtest("block_on_const_type_actually_const_1")
|
|||
ei_add_failtest("transpose_on_const_type_actually_const")
|
||||
ei_add_failtest("diagonal_on_const_type_actually_const")
|
||||
|
||||
ei_add_failtest("ref_1")
|
||||
ei_add_failtest("ref_2")
|
||||
ei_add_failtest("ref_3")
|
||||
ei_add_failtest("ref_4")
|
||||
ei_add_failtest("ref_5")
|
||||
|
||||
if (EIGEN_FAILTEST_FAILURE_COUNT)
|
||||
message(FATAL_ERROR
|
||||
"${EIGEN_FAILTEST_FAILURE_COUNT} out of ${EIGEN_FAILTEST_COUNT} failtests FAILED. "
|
||||
|
|
|
|||
|
|
@ -66,7 +66,7 @@ endif()
|
|||
|
||||
find_package(Pastix)
|
||||
find_package(Scotch)
|
||||
find_package(Metis)
|
||||
find_package(Metis 5.0 REQUIRED)
|
||||
if(PASTIX_FOUND AND BLAS_FOUND)
|
||||
add_definitions("-DEIGEN_PASTIX_SUPPORT")
|
||||
include_directories(${PASTIX_INCLUDES})
|
||||
|
|
@ -279,6 +279,7 @@ ei_add_property(EIGEN_TESTING_SUMMARY "CXX_FLAGS: ${CMAKE_CXX_FLAGS}\n")
|
|||
ei_add_property(EIGEN_TESTING_SUMMARY "Sparse lib flags: ${SPARSE_LIBS}\n")
|
||||
|
||||
option(EIGEN_TEST_EIGEN2 "Run whole Eigen2 test suite against EIGEN2_SUPPORT" OFF)
|
||||
mark_as_advanced(EIGEN_TEST_EIGEN2)
|
||||
if(EIGEN_TEST_EIGEN2)
|
||||
add_subdirectory(eigen2)
|
||||
endif()
|
||||
|
|
|
|||
|
|
@ -320,33 +320,35 @@ template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
|
|||
{
|
||||
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
||||
MatrixType mat;
|
||||
LDLT<MatrixType> ldlt(2);
|
||||
|
||||
{
|
||||
mat << 1, 0, 0, -1;
|
||||
LDLT<MatrixType> ldlt(mat);
|
||||
ldlt.compute(mat);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
}
|
||||
{
|
||||
mat << 1, 2, 2, 1;
|
||||
LDLT<MatrixType> ldlt(mat);
|
||||
ldlt.compute(mat);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
}
|
||||
{
|
||||
mat << 0, 0, 0, 0;
|
||||
LDLT<MatrixType> ldlt(mat);
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.isNegative());
|
||||
VERIFY(ldlt.isPositive());
|
||||
}
|
||||
{
|
||||
mat << 0, 0, 0, 1;
|
||||
LDLT<MatrixType> ldlt(mat);
|
||||
ldlt.compute(mat);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(ldlt.isPositive());
|
||||
}
|
||||
{
|
||||
mat << -1, 0, 0, 0;
|
||||
LDLT<MatrixType> ldlt(mat);
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
}
|
||||
|
|
|
|||
|
|
@ -9,6 +9,8 @@
|
|||
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
#define EIGEN2_SUPPORT
|
||||
#define EIGEN_NO_EIGEN2_DEPRECATED_WARNING
|
||||
|
||||
#define EIGEN_NO_STATIC_ASSERT
|
||||
#include "main.h"
|
||||
#include <functional>
|
||||
|
|
|
|||
|
|
@ -87,32 +87,6 @@ template<typename T> void check_dynaligned()
|
|||
delete obj;
|
||||
}
|
||||
|
||||
template<typename T> void check_custom_new_delete()
|
||||
{
|
||||
{
|
||||
T* t = new T;
|
||||
delete t;
|
||||
}
|
||||
|
||||
{
|
||||
std::size_t N = internal::random<std::size_t>(1,10);
|
||||
T* t = new T[N];
|
||||
delete[] t;
|
||||
}
|
||||
|
||||
#ifdef EIGEN_ALIGN
|
||||
{
|
||||
T* t = static_cast<T *>((T::operator new)(sizeof(T)));
|
||||
(T::operator delete)(t, sizeof(T));
|
||||
}
|
||||
|
||||
{
|
||||
T* t = static_cast<T *>((T::operator new)(sizeof(T)));
|
||||
(T::operator delete)(t);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
void test_dynalloc()
|
||||
{
|
||||
// low level dynamic memory allocation
|
||||
|
|
@ -128,12 +102,6 @@ void test_dynalloc()
|
|||
CALL_SUBTEST(check_dynaligned<Matrix4f>() );
|
||||
CALL_SUBTEST(check_dynaligned<Vector4d>() );
|
||||
CALL_SUBTEST(check_dynaligned<Vector4i>() );
|
||||
CALL_SUBTEST(check_dynaligned<Vector8f>() );
|
||||
|
||||
CALL_SUBTEST( check_custom_new_delete<Vector4f>() );
|
||||
CALL_SUBTEST( check_custom_new_delete<Vector2f>() );
|
||||
CALL_SUBTEST( check_custom_new_delete<Matrix4f>() );
|
||||
CALL_SUBTEST( check_custom_new_delete<MatrixXi>() );
|
||||
}
|
||||
|
||||
// check static allocation, who knows ?
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ add_dependencies(eigen2_check eigen2_buildtests)
|
|||
add_dependencies(buildtests eigen2_buildtests)
|
||||
|
||||
add_definitions("-DEIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API")
|
||||
add_definitions("-DEIGEN_NO_EIGEN2_DEPRECATED_WARNING")
|
||||
|
||||
ei_add_test(eigen2_meta)
|
||||
ei_add_test(eigen2_sizeof)
|
||||
|
|
|
|||
|
|
@ -29,8 +29,6 @@ template<typename MatrixType> void adjoint(const MatrixType& m)
|
|||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
identity = SquareMatrixType::Identity(rows, rows),
|
||||
square = SquareMatrixType::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
|
|
|
|||
|
|
@ -23,11 +23,8 @@ template<typename MatrixType> void basicStuff(const MatrixType& m)
|
|||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Identity(rows, rows),
|
||||
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
vzero = VectorType::Zero(rows);
|
||||
|
||||
Scalar x = ei_random<Scalar>();
|
||||
|
|
|
|||
|
|
@ -35,11 +35,8 @@ template<typename MatrixType> void cwiseops(const MatrixType& m)
|
|||
mzero = MatrixType::Zero(rows, cols),
|
||||
mones = MatrixType::Ones(rows, cols),
|
||||
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Identity(rows, rows),
|
||||
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
vzero = VectorType::Zero(rows),
|
||||
::Identity(rows, rows);
|
||||
VectorType vzero = VectorType::Zero(rows),
|
||||
vones = VectorType::Ones(rows),
|
||||
v3(rows);
|
||||
|
||||
|
|
|
|||
|
|
@ -392,6 +392,7 @@ template<typename Scalar> void geometry(void)
|
|||
#define VERIFY_EULER(I,J,K, X,Y,Z) { \
|
||||
Vector3 ea = m.eulerAngles(I,J,K); \
|
||||
Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \
|
||||
VERIFY_IS_APPROX(m, m1); \
|
||||
VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \
|
||||
}
|
||||
VERIFY_EULER(0,1,2, X,Y,Z);
|
||||
|
|
|
|||
|
|
@ -394,6 +394,7 @@ template<typename Scalar> void geometry(void)
|
|||
#define VERIFY_EULER(I,J,K, X,Y,Z) { \
|
||||
Vector3 ea = m.eulerAngles(I,J,K); \
|
||||
Matrix3 m1 = Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z())); \
|
||||
VERIFY_IS_APPROX(m, m1); \
|
||||
VERIFY_IS_APPROX(m, Matrix3(AngleAxisx(ea[0], Vector3::Unit##X()) * AngleAxisx(ea[1], Vector3::Unit##Y()) * AngleAxisx(ea[2], Vector3::Unit##Z()))); \
|
||||
}
|
||||
VERIFY_EULER(0,1,2, X,Y,Z);
|
||||
|
|
|
|||
|
|
@ -25,7 +25,6 @@ template<typename MatrixType> void inverse(const MatrixType& m)
|
|||
|
||||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
identity = MatrixType::Identity(rows, rows);
|
||||
|
||||
while(ei_abs(m1.determinant()) < RealScalar(0.1) && rows <= 8)
|
||||
|
|
|
|||
|
|
@ -25,8 +25,7 @@ template<typename MatrixType> void linearStructure(const MatrixType& m)
|
|||
// to test it, hence I consider that we will have tested Random.h
|
||||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols);
|
||||
m3(rows, cols);
|
||||
|
||||
Scalar s1 = ei_random<Scalar>();
|
||||
while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>();
|
||||
|
|
|
|||
|
|
@ -25,22 +25,12 @@ template<typename MatrixType> void nomalloc(const MatrixType& m)
|
|||
*/
|
||||
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
||||
|
||||
int rows = m.rows();
|
||||
int cols = m.cols();
|
||||
|
||||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Identity(rows, rows),
|
||||
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
vzero = VectorType::Zero(rows);
|
||||
m2 = MatrixType::Random(rows, cols);
|
||||
|
||||
Scalar s1 = ei_random<Scalar>();
|
||||
|
||||
|
|
|
|||
|
|
@ -51,16 +51,10 @@ template<typename MatrixType> void submatrices(const MatrixType& m)
|
|||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
ones = MatrixType::Ones(rows, cols),
|
||||
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Identity(rows, rows),
|
||||
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
v3 = VectorType::Random(rows),
|
||||
vzero = VectorType::Zero(rows);
|
||||
VectorType v1 = VectorType::Random(rows);
|
||||
|
||||
Scalar s1 = ei_random<Scalar>();
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,6 @@ template<typename MatrixType> void triangular(const MatrixType& m)
|
|||
{
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
typedef typename NumTraits<Scalar>::Real RealScalar;
|
||||
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
||||
|
||||
RealScalar largerEps = 10*test_precision<RealScalar>();
|
||||
|
||||
|
|
@ -25,16 +24,7 @@ template<typename MatrixType> void triangular(const MatrixType& m)
|
|||
m3(rows, cols),
|
||||
m4(rows, cols),
|
||||
r1(rows, cols),
|
||||
r2(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols),
|
||||
mones = MatrixType::Ones(rows, cols),
|
||||
identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Identity(rows, rows),
|
||||
square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
|
||||
::Random(rows, rows);
|
||||
VectorType v1 = VectorType::Random(rows),
|
||||
v2 = VectorType::Random(rows),
|
||||
vzero = VectorType::Zero(rows);
|
||||
r2(rows, cols);
|
||||
|
||||
MatrixType m1up = m1.template part<Eigen::UpperTriangular>();
|
||||
MatrixType m2up = m2.template part<Eigen::UpperTriangular>();
|
||||
|
|
|
|||
|
|
@ -40,8 +40,7 @@ template<typename MatrixType> void product(const MatrixType& m)
|
|||
// to test it, hence I consider that we will have tested Random.h
|
||||
MatrixType m1 = MatrixType::Random(rows, cols),
|
||||
m2 = MatrixType::Random(rows, cols),
|
||||
m3(rows, cols),
|
||||
mzero = MatrixType::Zero(rows, cols);
|
||||
m3(rows, cols);
|
||||
RowSquareMatrixType
|
||||
identity = RowSquareMatrixType::Identity(rows, rows),
|
||||
square = RowSquareMatrixType::Random(rows, rows),
|
||||
|
|
@ -49,9 +48,7 @@ template<typename MatrixType> void product(const MatrixType& m)
|
|||
ColSquareMatrixType
|
||||
square2 = ColSquareMatrixType::Random(cols, cols),
|
||||
res2 = ColSquareMatrixType::Random(cols, cols);
|
||||
RowVectorType v1 = RowVectorType::Random(rows),
|
||||
v2 = RowVectorType::Random(rows),
|
||||
vzero = RowVectorType::Zero(rows);
|
||||
RowVectorType v1 = RowVectorType::Random(rows);
|
||||
ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
|
||||
OtherMajorMatrixType tm1 = m1;
|
||||
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@
|
|||
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
||||
|
||||
#define EIGEN2_SUPPORT
|
||||
#define EIGEN_NO_EIGEN2_DEPRECATED_WARNING
|
||||
|
||||
#include "main.h"
|
||||
|
||||
|
|
|
|||
|
|
@ -29,7 +29,21 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
|
|||
MatrixType a = MatrixType::Random(rows,cols);
|
||||
MatrixType a1 = MatrixType::Random(rows,cols);
|
||||
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
|
||||
MatrixType symmC = symmA;
|
||||
|
||||
// randomly nullify some rows/columns
|
||||
{
|
||||
Index count = 1;//internal::random<Index>(-cols,cols);
|
||||
for(Index k=0; k<count; ++k)
|
||||
{
|
||||
Index i = internal::random<Index>(0,cols-1);
|
||||
symmA.row(i).setZero();
|
||||
symmA.col(i).setZero();
|
||||
}
|
||||
}
|
||||
|
||||
symmA.template triangularView<StrictlyUpper>().setZero();
|
||||
symmC.template triangularView<StrictlyUpper>().setZero();
|
||||
|
||||
MatrixType b = MatrixType::Random(rows,cols);
|
||||
MatrixType b1 = MatrixType::Random(rows,cols);
|
||||
|
|
@ -40,7 +54,7 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
|
|||
SelfAdjointEigenSolver<MatrixType> eiDirect;
|
||||
eiDirect.computeDirect(symmA);
|
||||
// generalized eigen pb
|
||||
GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmA, symmB);
|
||||
GeneralizedSelfAdjointEigenSolver<MatrixType> eiSymmGen(symmC, symmB);
|
||||
|
||||
VERIFY_IS_EQUAL(eiSymm.info(), Success);
|
||||
VERIFY((symmA.template selfadjointView<Lower>() * eiSymm.eigenvectors()).isApprox(
|
||||
|
|
@ -57,27 +71,28 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
|
|||
VERIFY_IS_APPROX(eiSymm.eigenvalues(), eiSymmNoEivecs.eigenvalues());
|
||||
|
||||
// generalized eigen problem Ax = lBx
|
||||
eiSymmGen.compute(symmA, symmB,Ax_lBx);
|
||||
eiSymmGen.compute(symmC, symmB,Ax_lBx);
|
||||
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
|
||||
VERIFY((symmA.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox(
|
||||
VERIFY((symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors()).isApprox(
|
||||
symmB.template selfadjointView<Lower>() * (eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
|
||||
|
||||
// generalized eigen problem BAx = lx
|
||||
eiSymmGen.compute(symmA, symmB,BAx_lx);
|
||||
eiSymmGen.compute(symmC, symmB,BAx_lx);
|
||||
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
|
||||
VERIFY((symmB.template selfadjointView<Lower>() * (symmA.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
|
||||
VERIFY((symmB.template selfadjointView<Lower>() * (symmC.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
|
||||
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
|
||||
|
||||
// generalized eigen problem ABx = lx
|
||||
eiSymmGen.compute(symmA, symmB,ABx_lx);
|
||||
eiSymmGen.compute(symmC, symmB,ABx_lx);
|
||||
VERIFY_IS_EQUAL(eiSymmGen.info(), Success);
|
||||
VERIFY((symmA.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
|
||||
VERIFY((symmC.template selfadjointView<Lower>() * (symmB.template selfadjointView<Lower>() * eiSymmGen.eigenvectors())).isApprox(
|
||||
(eiSymmGen.eigenvectors() * eiSymmGen.eigenvalues().asDiagonal()), largerEps));
|
||||
|
||||
|
||||
eiSymm.compute(symmC);
|
||||
MatrixType sqrtSymmA = eiSymm.operatorSqrt();
|
||||
VERIFY_IS_APPROX(MatrixType(symmA.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA);
|
||||
VERIFY_IS_APPROX(sqrtSymmA, symmA.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt());
|
||||
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), sqrtSymmA*sqrtSymmA);
|
||||
VERIFY_IS_APPROX(sqrtSymmA, symmC.template selfadjointView<Lower>()*eiSymm.operatorInverseSqrt());
|
||||
|
||||
MatrixType id = MatrixType::Identity(rows, cols);
|
||||
VERIFY_IS_APPROX(id.template selfadjointView<Lower>().operatorNorm(), RealScalar(1));
|
||||
|
|
@ -95,15 +110,15 @@ template<typename MatrixType> void selfadjointeigensolver(const MatrixType& m)
|
|||
VERIFY_RAISES_ASSERT(eiSymmUninitialized.operatorInverseSqrt());
|
||||
|
||||
// test Tridiagonalization's methods
|
||||
Tridiagonalization<MatrixType> tridiag(symmA);
|
||||
Tridiagonalization<MatrixType> tridiag(symmC);
|
||||
// FIXME tridiag.matrixQ().adjoint() does not work
|
||||
VERIFY_IS_APPROX(MatrixType(symmA.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
|
||||
VERIFY_IS_APPROX(MatrixType(symmC.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
|
||||
|
||||
if (rows > 1)
|
||||
{
|
||||
// Test matrix with NaN
|
||||
symmA(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
|
||||
SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmA);
|
||||
symmC(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
|
||||
SelfAdjointEigenSolver<MatrixType> eiSymmNaN(symmC);
|
||||
VERIFY_IS_EQUAL(eiSymmNaN.info(), NoConvergence);
|
||||
}
|
||||
}
|
||||
|
|
@ -113,8 +128,10 @@ void test_eigensolver_selfadjoint()
|
|||
int s = 0;
|
||||
for(int i = 0; i < g_repeat; i++) {
|
||||
// very important to test 3x3 and 2x2 matrices since we provide special paths for them
|
||||
CALL_SUBTEST_1( selfadjointeigensolver(Matrix2f()) );
|
||||
CALL_SUBTEST_1( selfadjointeigensolver(Matrix2d()) );
|
||||
CALL_SUBTEST_1( selfadjointeigensolver(Matrix3f()) );
|
||||
CALL_SUBTEST_1( selfadjointeigensolver(Matrix3d()) );
|
||||
CALL_SUBTEST_2( selfadjointeigensolver(Matrix4d()) );
|
||||
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
|
||||
CALL_SUBTEST_3( selfadjointeigensolver(MatrixXf(s,s)) );
|
||||
|
|
|
|||
|
|
@ -114,6 +114,32 @@ template<typename Scalar> void lines()
|
|||
}
|
||||
}
|
||||
|
||||
template<typename Scalar> void planes()
|
||||
{
|
||||
using std::abs;
|
||||
typedef Hyperplane<Scalar, 3> Plane;
|
||||
typedef Matrix<Scalar,3,1> Vector;
|
||||
|
||||
for(int i = 0; i < 10; i++)
|
||||
{
|
||||
Vector v0 = Vector::Random();
|
||||
Vector v1(v0), v2(v0);
|
||||
if(internal::random<double>(0,1)>0.25)
|
||||
v1 += Vector::Random();
|
||||
if(internal::random<double>(0,1)>0.25)
|
||||
v2 += v1 * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16));
|
||||
if(internal::random<double>(0,1)>0.25)
|
||||
v2 += Vector::Random() * std::pow(internal::random<Scalar>(0,1),internal::random<int>(1,16));
|
||||
|
||||
Plane p0 = Plane::Through(v0, v1, v2);
|
||||
|
||||
VERIFY_IS_APPROX(p0.normal().norm(), Scalar(1));
|
||||
VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v0), Scalar(1));
|
||||
VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v1), Scalar(1));
|
||||
VERIFY_IS_MUCH_SMALLER_THAN(p0.absDistance(v2), Scalar(1));
|
||||
}
|
||||
}
|
||||
|
||||
template<typename Scalar> void hyperplane_alignment()
|
||||
{
|
||||
typedef Hyperplane<Scalar,3,AutoAlign> Plane3a;
|
||||
|
|
@ -153,5 +179,7 @@ void test_geo_hyperplane()
|
|||
CALL_SUBTEST_4( hyperplane(Hyperplane<std::complex<double>,5>()) );
|
||||
CALL_SUBTEST_1( lines<float>() );
|
||||
CALL_SUBTEST_3( lines<double>() );
|
||||
CALL_SUBTEST_2( planes<float>() );
|
||||
CALL_SUBTEST_5( planes<double>() );
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -98,11 +98,19 @@ template<typename Scalar, int Mode, int Options> void transformations()
|
|||
Matrix3 matrot1, m;
|
||||
|
||||
Scalar a = internal::random<Scalar>(-Scalar(M_PI), Scalar(M_PI));
|
||||
Scalar s0 = internal::random<Scalar>();
|
||||
Scalar s0 = internal::random<Scalar>(),
|
||||
s1 = internal::random<Scalar>();
|
||||
|
||||
while(v0.norm() < test_precision<Scalar>()) v0 = Vector3::Random();
|
||||
while(v1.norm() < test_precision<Scalar>()) v1 = Vector3::Random();
|
||||
|
||||
|
||||
VERIFY_IS_APPROX(v0, AngleAxisx(a, v0.normalized()) * v0);
|
||||
VERIFY_IS_APPROX(-v0, AngleAxisx(Scalar(M_PI), v0.unitOrthogonal()) * v0);
|
||||
VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
|
||||
if(abs(cos(a)) > test_precision<Scalar>())
|
||||
{
|
||||
VERIFY_IS_APPROX(cos(a)*v0.squaredNorm(), v0.dot(AngleAxisx(a, v0.unitOrthogonal()) * v0));
|
||||
}
|
||||
m = AngleAxisx(a, v0.normalized()).toRotationMatrix().adjoint();
|
||||
VERIFY_IS_APPROX(Matrix3::Identity(), m * AngleAxisx(a, v0.normalized()));
|
||||
VERIFY_IS_APPROX(Matrix3::Identity(), AngleAxisx(a, v0.normalized()) * m);
|
||||
|
|
@ -123,11 +131,18 @@ template<typename Scalar, int Mode, int Options> void transformations()
|
|||
// angle-axis conversion
|
||||
AngleAxisx aa = AngleAxisx(q1);
|
||||
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
|
||||
VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
|
||||
|
||||
if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision())
|
||||
{
|
||||
VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
|
||||
}
|
||||
|
||||
aa.fromRotationMatrix(aa.toRotationMatrix());
|
||||
VERIFY_IS_APPROX(q1 * v1, Quaternionx(aa) * v1);
|
||||
VERIFY_IS_NOT_APPROX(q1 * v1, Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1);
|
||||
if(abs(aa.angle()) > NumTraits<Scalar>::dummy_precision())
|
||||
{
|
||||
VERIFY( !(q1 * v1).isApprox(Quaternionx(AngleAxisx(aa.angle()*2,aa.axis())) * v1) );
|
||||
}
|
||||
|
||||
// AngleAxis
|
||||
VERIFY_IS_APPROX(AngleAxisx(a,v1.normalized()).toRotationMatrix(),
|
||||
|
|
@ -347,7 +362,9 @@ template<typename Scalar, int Mode, int Options> void transformations()
|
|||
// test transform inversion
|
||||
t0.setIdentity();
|
||||
t0.translate(v0);
|
||||
t0.linear().setRandom();
|
||||
do {
|
||||
t0.linear().setRandom();
|
||||
} while(t0.linear().jacobiSvd().singularValues()(2)<test_precision<Scalar>());
|
||||
Matrix4 t044 = Matrix4::Zero();
|
||||
t044(3,3) = 1;
|
||||
t044.block(0,0,t0.matrix().rows(),4) = t0.matrix();
|
||||
|
|
@ -397,6 +414,29 @@ template<typename Scalar, int Mode, int Options> void transformations()
|
|||
t20 = Translation2(v20) * (Rotation2D<Scalar>(s0) * Eigen::Scaling(s0));
|
||||
t21 = Translation2(v20) * Rotation2D<Scalar>(s0) * Eigen::Scaling(s0);
|
||||
VERIFY_IS_APPROX(t20,t21);
|
||||
|
||||
Rotation2D<Scalar> R0(s0), R1(s1);
|
||||
t20 = Translation2(v20) * (R0 * Eigen::Scaling(s0));
|
||||
t21 = Translation2(v20) * R0 * Eigen::Scaling(s0);
|
||||
VERIFY_IS_APPROX(t20,t21);
|
||||
|
||||
t20 = Translation2(v20) * (R0 * R0.inverse() * Eigen::Scaling(s0));
|
||||
t21 = Translation2(v20) * Eigen::Scaling(s0);
|
||||
VERIFY_IS_APPROX(t20,t21);
|
||||
|
||||
VERIFY_IS_APPROX(s0, (R0.slerp(0, R1)).angle());
|
||||
VERIFY_IS_APPROX(s1, (R0.slerp(1, R1)).angle());
|
||||
VERIFY_IS_APPROX(s0, (R0.slerp(0.5, R0)).angle());
|
||||
VERIFY_IS_APPROX(Scalar(0), (R0.slerp(0.5, R0.inverse())).angle());
|
||||
|
||||
// check basic features
|
||||
{
|
||||
Rotation2D<Scalar> r1; // default ctor
|
||||
r1 = Rotation2D<Scalar>(s0); // copy assignment
|
||||
VERIFY_IS_APPROX(r1.angle(),s0);
|
||||
Rotation2D<Scalar> r2(r1); // copy ctor
|
||||
VERIFY_IS_APPROX(r2.angle(),s0);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename Scalar> void transform_alignment()
|
||||
|
|
|
|||
|
|
@ -321,16 +321,23 @@ void jacobisvd_inf_nan()
|
|||
VERIFY(sub(some_inf, some_inf) != sub(some_inf, some_inf));
|
||||
svd.compute(MatrixType::Constant(10,10,some_inf), ComputeFullU | ComputeFullV);
|
||||
|
||||
Scalar some_nan = zero<Scalar>() / zero<Scalar>();
|
||||
VERIFY(some_nan != some_nan);
|
||||
svd.compute(MatrixType::Constant(10,10,some_nan), ComputeFullU | ComputeFullV);
|
||||
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
|
||||
VERIFY(nan != nan);
|
||||
svd.compute(MatrixType::Constant(10,10,nan), ComputeFullU | ComputeFullV);
|
||||
|
||||
MatrixType m = MatrixType::Zero(10,10);
|
||||
m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_inf;
|
||||
svd.compute(m, ComputeFullU | ComputeFullV);
|
||||
|
||||
m = MatrixType::Zero(10,10);
|
||||
m(internal::random<int>(0,9), internal::random<int>(0,9)) = some_nan;
|
||||
m(internal::random<int>(0,9), internal::random<int>(0,9)) = nan;
|
||||
svd.compute(m, ComputeFullU | ComputeFullV);
|
||||
|
||||
// regression test for bug 791
|
||||
m.resize(3,3);
|
||||
m << 0, 2*NumTraits<Scalar>::epsilon(), 0.5,
|
||||
0, -0.5, 0,
|
||||
nan, 0, 0;
|
||||
svd.compute(m, ComputeFullU | ComputeFullV);
|
||||
}
|
||||
|
||||
|
|
@ -434,6 +441,7 @@ void test_jacobisvd()
|
|||
|
||||
// Test on inf/nan matrix
|
||||
CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
|
||||
CALL_SUBTEST_10( jacobisvd_inf_nan<MatrixXd>() );
|
||||
}
|
||||
|
||||
CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
|
||||
|
|
|
|||
|
|
@ -17,13 +17,36 @@
|
|||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <typeinfo>
|
||||
|
||||
// The following includes of STL headers have to be done _before_ the
|
||||
// definition of macros min() and max(). The reason is that many STL
|
||||
// implementations will not work properly as the min and max symbols collide
|
||||
// with the STL functions std:min() and std::max(). The STL headers may check
|
||||
// for the macro definition of min/max and issue a warning or undefine the
|
||||
// macros.
|
||||
//
|
||||
// Still, Windows defines min() and max() in windef.h as part of the regular
|
||||
// Windows system interfaces and many other Windows APIs depend on these
|
||||
// macros being available. To prevent the macro expansion of min/max and to
|
||||
// make Eigen compatible with the Windows environment all function calls of
|
||||
// std::min() and std::max() have to be written with parenthesis around the
|
||||
// function name.
|
||||
//
|
||||
// All STL headers used by Eigen should be included here. Because main.h is
|
||||
// included before any Eigen header and because the STL headers are guarded
|
||||
// against multiple inclusions, no STL header will see our own min/max macro
|
||||
// definitions.
|
||||
#include <limits>
|
||||
#include <algorithm>
|
||||
#include <sstream>
|
||||
#include <complex>
|
||||
#include <deque>
|
||||
#include <queue>
|
||||
#include <list>
|
||||
|
||||
// To test that all calls from Eigen code to std::min() and std::max() are
|
||||
// protected by parenthesis against macro expansion, the min()/max() macros
|
||||
// are defined here and any not-parenthesized min/max call will cause a
|
||||
// compiler error.
|
||||
#define min(A,B) please_protect_your_min_with_parentheses
|
||||
#define max(A,B) please_protect_your_max_with_parentheses
|
||||
|
||||
|
|
@ -383,6 +406,26 @@ void randomPermutationVector(PermutationVectorType& v, typename PermutationVecto
|
|||
}
|
||||
}
|
||||
|
||||
template<typename T> bool isNotNaN(const T& x)
|
||||
{
|
||||
return x==x;
|
||||
}
|
||||
|
||||
template<typename T> bool isNaN(const T& x)
|
||||
{
|
||||
return x!=x;
|
||||
}
|
||||
|
||||
template<typename T> bool isInf(const T& x)
|
||||
{
|
||||
return x > NumTraits<T>::highest();
|
||||
}
|
||||
|
||||
template<typename T> bool isMinusInf(const T& x)
|
||||
{
|
||||
return x < NumTraits<T>::lowest();
|
||||
}
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
template<typename T> struct GetDifferentType;
|
||||
|
|
|
|||
|
|
@ -165,6 +165,38 @@ void ctms_decompositions()
|
|||
Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
|
||||
}
|
||||
|
||||
void test_zerosized() {
|
||||
// default constructors:
|
||||
Eigen::MatrixXd A;
|
||||
Eigen::VectorXd v;
|
||||
// explicit zero-sized:
|
||||
Eigen::ArrayXXd A0(0,0);
|
||||
Eigen::ArrayXd v0(std::ptrdiff_t(0)); // FIXME ArrayXd(0) is ambiguous
|
||||
|
||||
// assigning empty objects to each other:
|
||||
A=A0;
|
||||
v=v0;
|
||||
}
|
||||
|
||||
template<typename MatrixType> void test_reference(const MatrixType& m) {
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
enum { Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
|
||||
enum { TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor};
|
||||
typename MatrixType::Index rows = m.rows(), cols=m.cols();
|
||||
// Dynamic reference:
|
||||
typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag > > Ref;
|
||||
typedef Eigen::Ref<const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> > RefT;
|
||||
|
||||
Ref r1(m);
|
||||
Ref r2(m.block(rows/3, cols/4, rows/2, cols/2));
|
||||
RefT r3(m.transpose());
|
||||
RefT r4(m.topLeftCorner(rows/2, cols/2).transpose());
|
||||
|
||||
VERIFY_RAISES_ASSERT(RefT r5(m));
|
||||
VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
|
||||
VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));
|
||||
}
|
||||
|
||||
void test_nomalloc()
|
||||
{
|
||||
// check that our operator new is indeed called:
|
||||
|
|
@ -175,5 +207,6 @@ void test_nomalloc()
|
|||
|
||||
// Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
|
||||
CALL_SUBTEST_4(ctms_decompositions<float>());
|
||||
|
||||
CALL_SUBTEST_5(test_zerosized());
|
||||
CALL_SUBTEST_6(test_reference(Matrix<float,32,32>()));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -80,7 +80,9 @@ void testVectorType(const VectorType& base)
|
|||
Matrix<Scalar,1,Dynamic> col_vector(size);
|
||||
row_vector.setLinSpaced(size,low,high);
|
||||
col_vector.setLinSpaced(size,low,high);
|
||||
VERIFY( row_vector.isApprox(col_vector.transpose(), NumTraits<Scalar>::epsilon()));
|
||||
// when using the extended precision (e.g., FPU) the relative error might exceed 1 bit
|
||||
// when computing the squared sum in isApprox, thus the 2x factor.
|
||||
VERIFY( row_vector.isApprox(col_vector.transpose(), Scalar(2)*NumTraits<Scalar>::epsilon()));
|
||||
|
||||
Matrix<Scalar,Dynamic,1> size_changer(size+50);
|
||||
size_changer.setLinSpaced(size,low,high);
|
||||
|
|
|
|||
|
|
@ -239,6 +239,12 @@ template<typename Scalar> void packetmath_real()
|
|||
data2[i] = internal::random<Scalar>(-87,88);
|
||||
}
|
||||
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasExp, std::exp, internal::pexp);
|
||||
{
|
||||
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
|
||||
packet_helper<internal::packet_traits<Scalar>::HasExp,Packet> h;
|
||||
h.store(data2, internal::pexp(h.load(data1)));
|
||||
VERIFY(isNaN(data2[0]));
|
||||
}
|
||||
|
||||
for (int i=0; i<size; ++i)
|
||||
{
|
||||
|
|
@ -247,8 +253,22 @@ template<typename Scalar> void packetmath_real()
|
|||
}
|
||||
if(internal::random<float>(0,1)<0.1)
|
||||
data1[internal::random<int>(0, PacketSize)] = 0;
|
||||
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLog, std::log, internal::plog);
|
||||
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasSqrt, std::sqrt, internal::psqrt);
|
||||
CHECK_CWISE1_IF(internal::packet_traits<Scalar>::HasLog, std::log, internal::plog);
|
||||
{
|
||||
data1[0] = std::numeric_limits<Scalar>::quiet_NaN();
|
||||
packet_helper<internal::packet_traits<Scalar>::HasLog,Packet> h;
|
||||
h.store(data2, internal::plog(h.load(data1)));
|
||||
VERIFY(isNaN(data2[0]));
|
||||
data1[0] = -1.0f;
|
||||
h.store(data2, internal::plog(h.load(data1)));
|
||||
VERIFY(isNaN(data2[0]));
|
||||
#if !EIGEN_FAST_MATH
|
||||
h.store(data2, internal::psqrt(h.load(data1)));
|
||||
VERIFY(isNaN(data2[0]));
|
||||
VERIFY(isNaN(data2[1]));
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
template<typename Scalar> void packetmath_notcomplex()
|
||||
|
|
|
|||
|
|
@ -139,4 +139,12 @@ template<typename MatrixType> void product(const MatrixType& m)
|
|||
// inner product
|
||||
Scalar x = square2.row(c) * square2.col(c2);
|
||||
VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum());
|
||||
|
||||
// outer product
|
||||
VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
|
||||
VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose());
|
||||
VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
|
||||
VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
|
||||
VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols));
|
||||
VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -183,15 +183,15 @@ void call_ref()
|
|||
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(a,a), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(b,b.transpose()), 0);
|
||||
// call_ref_1(ac); // does not compile because ac is const
|
||||
// call_ref_1(ac,a<c); // does not compile because ac is const
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(ab,ab), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(a.head(4),a.head(4)), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(abc,abc), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_1(A.col(3),A.col(3)), 0);
|
||||
// call_ref_1(A.row(3)); // does not compile because innerstride!=1
|
||||
// call_ref_1(A.row(3),A.row(3)); // does not compile because innerstride!=1
|
||||
VERIFY_EVALUATION_COUNT( call_ref_3(A.row(3),A.row(3).transpose()), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_4(A.row(3),A.row(3).transpose()), 0);
|
||||
// call_ref_1(a+a); // does not compile for obvious reason
|
||||
// call_ref_1(a+a, a+a); // does not compile for obvious reason
|
||||
|
||||
MatrixXf tmp = A*A.col(1);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_2(A*A.col(1), tmp), 1); // evaluated into a temp
|
||||
|
|
@ -212,7 +212,7 @@ void call_ref()
|
|||
VERIFY_EVALUATION_COUNT( call_ref_5(a,a), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_5(a.head(3),a.head(3)), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_5(A,A), 0);
|
||||
// call_ref_5(A.transpose()); // does not compile
|
||||
// call_ref_5(A.transpose(),A.transpose()); // does not compile because storage order does not match
|
||||
VERIFY_EVALUATION_COUNT( call_ref_5(A.block(1,1,2,2),A.block(1,1,2,2)), 0);
|
||||
VERIFY_EVALUATION_COUNT( call_ref_5(b,b), 0); // storage order do not match, but this is a degenerate case that should work
|
||||
VERIFY_EVALUATION_COUNT( call_ref_5(a.row(3),a.row(3)), 0);
|
||||
|
|
|
|||
|
|
@ -124,7 +124,23 @@ void check_sparse_determinant(Solver& solver, const typename Solver::MatrixType&
|
|||
Scalar refDet = dA.determinant();
|
||||
VERIFY_IS_APPROX(refDet,solver.determinant());
|
||||
}
|
||||
template<typename Solver, typename DenseMat>
|
||||
void check_sparse_abs_determinant(Solver& solver, const typename Solver::MatrixType& A, const DenseMat& dA)
|
||||
{
|
||||
using std::abs;
|
||||
typedef typename Solver::MatrixType Mat;
|
||||
typedef typename Mat::Scalar Scalar;
|
||||
|
||||
solver.compute(A);
|
||||
if (solver.info() != Success)
|
||||
{
|
||||
std::cerr << "sparse solver testing: factorization failed (check_sparse_abs_determinant)\n";
|
||||
return;
|
||||
}
|
||||
|
||||
Scalar refDet = abs(dA.determinant());
|
||||
VERIFY_IS_APPROX(refDet,solver.absDeterminant());
|
||||
}
|
||||
|
||||
template<typename Solver, typename DenseMat>
|
||||
int generate_sparse_spd_problem(Solver& , typename Solver::MatrixType& A, typename Solver::MatrixType& halfA, DenseMat& dA, int maxSize = 300)
|
||||
|
|
@ -324,3 +340,20 @@ template<typename Solver> void check_sparse_square_determinant(Solver& solver)
|
|||
check_sparse_determinant(solver, A, dA);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename Solver> void check_sparse_square_abs_determinant(Solver& solver)
|
||||
{
|
||||
typedef typename Solver::MatrixType Mat;
|
||||
typedef typename Mat::Scalar Scalar;
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
||||
|
||||
// generate the problem
|
||||
Mat A;
|
||||
DenseMatrix dA;
|
||||
generate_sparse_square_problem(solver, A, dA, 30);
|
||||
A.makeCompressed();
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
check_sparse_abs_determinant(solver, A, dA);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -44,6 +44,9 @@ template<typename T> void test_sparselu_T()
|
|||
check_sparse_square_solving(sparselu_colamd);
|
||||
check_sparse_square_solving(sparselu_amd);
|
||||
check_sparse_square_solving(sparselu_natural);
|
||||
|
||||
check_sparse_square_abs_determinant(sparselu_colamd);
|
||||
check_sparse_square_abs_determinant(sparselu_amd);
|
||||
}
|
||||
|
||||
void test_sparselu()
|
||||
|
|
|
|||
|
|
@ -10,12 +10,11 @@
|
|||
#include <Eigen/SparseQR>
|
||||
|
||||
template<typename MatrixType,typename DenseMat>
|
||||
int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 150)
|
||||
int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300)
|
||||
{
|
||||
eigen_assert(maxRows >= maxCols);
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
int rows = internal::random<int>(1,maxRows);
|
||||
int cols = internal::random<int>(1,maxCols);
|
||||
int cols = internal::random<int>(1,rows);
|
||||
double density = (std::max)(8./(rows*cols), 0.01);
|
||||
|
||||
A.resize(rows,cols);
|
||||
|
|
@ -54,6 +53,8 @@ template<typename Scalar> void test_sparseqr_scalar()
|
|||
|
||||
b = dA * DenseVector::Random(A.cols());
|
||||
solver.compute(A);
|
||||
if(internal::random<float>(0,1)>0.5)
|
||||
solver.factorize(A); // this checks that calling analyzePattern is not needed if the pattern do not change.
|
||||
if (solver.info() != Success)
|
||||
{
|
||||
std::cerr << "sparse QR factorization failed\n";
|
||||
|
|
|
|||
|
|
@ -9,11 +9,6 @@
|
|||
|
||||
#include "main.h"
|
||||
|
||||
template<typename T> bool isNotNaN(const T& x)
|
||||
{
|
||||
return x==x;
|
||||
}
|
||||
|
||||
// workaround aggressive optimization in ICC
|
||||
template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; }
|
||||
|
||||
|
|
|
|||
|
|
@ -178,11 +178,11 @@ template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Affine>& t)
|
|||
template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Projective>& t) { glLoadMatrix(t.matrix()); }
|
||||
template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,AffineCompact>& t) { glLoadMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
|
||||
|
||||
static void glRotate(const Rotation2D<float>& rot)
|
||||
inline void glRotate(const Rotation2D<float>& rot)
|
||||
{
|
||||
glRotatef(rot.angle()*180.f/float(M_PI), 0.f, 0.f, 1.f);
|
||||
}
|
||||
static void glRotate(const Rotation2D<double>& rot)
|
||||
inline void glRotate(const Rotation2D<double>& rot)
|
||||
{
|
||||
glRotated(rot.angle()*180.0/M_PI, 0.0, 0.0, 1.0);
|
||||
}
|
||||
|
|
@ -246,18 +246,18 @@ EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,double, 4,4,Doublev)
|
|||
|
||||
#ifdef GL_VERSION_2_0
|
||||
|
||||
static void glUniform2fv_ei (GLint loc, const float* v) { glUniform2fv(loc,1,v); }
|
||||
static void glUniform2iv_ei (GLint loc, const int* v) { glUniform2iv(loc,1,v); }
|
||||
inline void glUniform2fv_ei (GLint loc, const float* v) { glUniform2fv(loc,1,v); }
|
||||
inline void glUniform2iv_ei (GLint loc, const int* v) { glUniform2iv(loc,1,v); }
|
||||
|
||||
static void glUniform3fv_ei (GLint loc, const float* v) { glUniform3fv(loc,1,v); }
|
||||
static void glUniform3iv_ei (GLint loc, const int* v) { glUniform3iv(loc,1,v); }
|
||||
inline void glUniform3fv_ei (GLint loc, const float* v) { glUniform3fv(loc,1,v); }
|
||||
inline void glUniform3iv_ei (GLint loc, const int* v) { glUniform3iv(loc,1,v); }
|
||||
|
||||
static void glUniform4fv_ei (GLint loc, const float* v) { glUniform4fv(loc,1,v); }
|
||||
static void glUniform4iv_ei (GLint loc, const int* v) { glUniform4iv(loc,1,v); }
|
||||
inline void glUniform4fv_ei (GLint loc, const float* v) { glUniform4fv(loc,1,v); }
|
||||
inline void glUniform4iv_ei (GLint loc, const int* v) { glUniform4iv(loc,1,v); }
|
||||
|
||||
static void glUniformMatrix2fv_ei (GLint loc, const float* v) { glUniformMatrix2fv(loc,1,false,v); }
|
||||
static void glUniformMatrix3fv_ei (GLint loc, const float* v) { glUniformMatrix3fv(loc,1,false,v); }
|
||||
static void glUniformMatrix4fv_ei (GLint loc, const float* v) { glUniformMatrix4fv(loc,1,false,v); }
|
||||
inline void glUniformMatrix2fv_ei (GLint loc, const float* v) { glUniformMatrix2fv(loc,1,false,v); }
|
||||
inline void glUniformMatrix3fv_ei (GLint loc, const float* v) { glUniformMatrix3fv(loc,1,false,v); }
|
||||
inline void glUniformMatrix4fv_ei (GLint loc, const float* v) { glUniformMatrix4fv(loc,1,false,v); }
|
||||
|
||||
|
||||
EIGEN_GL_FUNC1_DECLARATION (glUniform,GLint,const)
|
||||
|
|
@ -294,9 +294,9 @@ EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float, 4,3,Matrix
|
|||
|
||||
#ifdef GL_VERSION_3_0
|
||||
|
||||
static void glUniform2uiv_ei (GLint loc, const unsigned int* v) { glUniform2uiv(loc,1,v); }
|
||||
static void glUniform3uiv_ei (GLint loc, const unsigned int* v) { glUniform3uiv(loc,1,v); }
|
||||
static void glUniform4uiv_ei (GLint loc, const unsigned int* v) { glUniform4uiv(loc,1,v); }
|
||||
inline void glUniform2uiv_ei (GLint loc, const unsigned int* v) { glUniform2uiv(loc,1,v); }
|
||||
inline void glUniform3uiv_ei (GLint loc, const unsigned int* v) { glUniform3uiv(loc,1,v); }
|
||||
inline void glUniform4uiv_ei (GLint loc, const unsigned int* v) { glUniform4uiv(loc,1,v); }
|
||||
|
||||
EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 2,2uiv_ei)
|
||||
EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 3,3uiv_ei)
|
||||
|
|
@ -305,9 +305,9 @@ EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 4,4uiv_ei)
|
|||
#endif
|
||||
|
||||
#ifdef GL_ARB_gpu_shader_fp64
|
||||
static void glUniform2dv_ei (GLint loc, const double* v) { glUniform2dv(loc,1,v); }
|
||||
static void glUniform3dv_ei (GLint loc, const double* v) { glUniform3dv(loc,1,v); }
|
||||
static void glUniform4dv_ei (GLint loc, const double* v) { glUniform4dv(loc,1,v); }
|
||||
inline void glUniform2dv_ei (GLint loc, const double* v) { glUniform2dv(loc,1,v); }
|
||||
inline void glUniform3dv_ei (GLint loc, const double* v) { glUniform3dv(loc,1,v); }
|
||||
inline void glUniform4dv_ei (GLint loc, const double* v) { glUniform4dv(loc,1,v); }
|
||||
|
||||
EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double, 2,2dv_ei)
|
||||
EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double, 3,3dv_ei)
|
||||
|
|
|
|||
|
|
@ -110,7 +110,6 @@ void MatrixPowerAtomic<MatrixType>::compute2x2(MatrixType& res, RealScalar p) co
|
|||
using std::abs;
|
||||
using std::pow;
|
||||
|
||||
ArrayType logTdiag = m_A.diagonal().array().log();
|
||||
res.coeffRef(0,0) = pow(m_A.coeff(0,0), p);
|
||||
|
||||
for (Index i=1; i < m_A.cols(); ++i) {
|
||||
|
|
|
|||
|
|
@ -10,12 +10,10 @@ FOREACH(example_src ${examples_SRCS})
|
|||
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
|
||||
target_link_libraries(example_${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
|
||||
endif()
|
||||
GET_TARGET_PROPERTY(example_executable
|
||||
example_${example} LOCATION)
|
||||
ADD_CUSTOM_COMMAND(
|
||||
TARGET example_${example}
|
||||
POST_BUILD
|
||||
COMMAND ${example_executable}
|
||||
COMMAND example_${example}
|
||||
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
|
||||
)
|
||||
ADD_DEPENDENCIES(unsupported_examples example_${example})
|
||||
|
|
|
|||
|
|
@ -14,12 +14,10 @@ FOREACH(snippet_src ${snippets_SRCS})
|
|||
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
|
||||
target_link_libraries(${compile_snippet_target} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
|
||||
endif()
|
||||
GET_TARGET_PROPERTY(compile_snippet_executable
|
||||
${compile_snippet_target} LOCATION)
|
||||
ADD_CUSTOM_COMMAND(
|
||||
TARGET ${compile_snippet_target}
|
||||
POST_BUILD
|
||||
COMMAND ${compile_snippet_executable}
|
||||
COMMAND ${compile_snippet_target}
|
||||
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${snippet}.out
|
||||
)
|
||||
ADD_DEPENDENCIES(unsupported_snippets ${compile_snippet_target})
|
||||
|
|
|
|||
|
|
@ -29,11 +29,7 @@ ei_add_test(NonLinearOptimization)
|
|||
|
||||
ei_add_test(NumericalDiff)
|
||||
ei_add_test(autodiff)
|
||||
|
||||
if (NOT CMAKE_CXX_COMPILER MATCHES "clang\\+\\+$")
|
||||
ei_add_test(BVH)
|
||||
endif()
|
||||
|
||||
ei_add_test(matrix_exponential)
|
||||
ei_add_test(matrix_function)
|
||||
ei_add_test(matrix_power)
|
||||
|
|
@ -73,8 +69,9 @@ if(NOT EIGEN_TEST_NO_OPENGL)
|
|||
find_package(GLUT)
|
||||
find_package(GLEW)
|
||||
if(OPENGL_FOUND AND GLUT_FOUND AND GLEW_FOUND)
|
||||
include_directories(${OPENGL_INCLUDE_DIR} ${GLUT_INCLUDE_DIR} ${GLEW_INCLUDE_DIRS})
|
||||
ei_add_property(EIGEN_TESTED_BACKENDS "OpenGL, ")
|
||||
set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES})
|
||||
set(EIGEN_GL_LIB ${GLUT_LIBRARIES} ${GLEW_LIBRARIES} ${OPENGL_LIBRARIES})
|
||||
ei_add_test(openglsupport "" "${EIGEN_GL_LIB}" )
|
||||
else()
|
||||
ei_add_property(EIGEN_MISSING_BACKENDS "OpenGL, ")
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -104,9 +104,7 @@ void evalSolverSugarFunction( const POLYNOMIAL& pols, const ROOTS& roots, const
|
|||
// 1) the roots found are correct
|
||||
// 2) the roots have distinct moduli
|
||||
|
||||
typedef typename POLYNOMIAL::Scalar Scalar;
|
||||
typedef typename REAL_ROOTS::Scalar Real;
|
||||
typedef PolynomialSolver<Scalar, Deg > PolynomialSolverType;
|
||||
|
||||
//Test realRoots
|
||||
std::vector< Real > calc_realRoots;
|
||||
|
|
|
|||
Loading…
Reference in New Issue