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merge default and evaluator branches

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This commit is contained in:
Gael Guennebaud 2014-03-12 16:24:25 +01:00
commit 74b1d79d77
34 changed files with 235 additions and 99 deletions
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8
CTestConfig.cmake
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@ -4,10 +4,14 @@
## # The following are required to uses Dart and the Cdash dashboard
## ENABLE_TESTING()
## INCLUDE(CTest)
set(CTEST_PROJECT_NAME "Eigen3.2")
set(CTEST_PROJECT_NAME "Eigen")
set(CTEST_NIGHTLY_START_TIME "00:00:00 UTC")
set(CTEST_DROP_METHOD "http")
set(CTEST_DROP_SITE "manao.inria.fr")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen3.2")
set(CTEST_DROP_LOCATION "/CDash/submit.php?project=Eigen")
set(CTEST_DROP_SITE_CDASH TRUE)
set(CTEST_PROJECT_SUBPROJECTS
Official
Unsupported
)

8
Eigen/src/Cholesky/LDLT.h
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@ -309,13 +309,6 @@ template<> struct ldlt_inplace<Lower>
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
}
// Finish early if the matrix is not full rank.
if(biggest_in_corner < cutoff)
{
for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
break;
}
transpositions.coeffRef(k) = index_of_biggest_in_corner;
if(k != index_of_biggest_in_corner)
{
@ -351,6 +344,7 @@ template<> struct ldlt_inplace<Lower>
if(rs>0)
A21.noalias() -= A20 * temp.head(k);
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);

2
Eigen/src/Core/ArrayWrapper.h
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@ -49,7 +49,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
typedef typename internal::nested<ExpressionType>::type NestedExpressionType;
EIGEN_DEVICE_FUNC
inline ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_expression.rows(); }

14
Eigen/src/Core/CommaInitializer.h
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@ -45,6 +45,18 @@ struct CommaInitializer
m_xpr.block(0, 0, other.rows(), other.cols()) = other;
}
/* Copy/Move constructor which transfers ownership. This is crucial in
* absence of return value optimization to avoid assertions during destruction. */
// FIXME in C++11 mode this could be replaced by a proper RValue constructor
EIGEN_DEVICE_FUNC
inline CommaInitializer(const CommaInitializer& o)
: m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
// Mark original object as finished. In absence of R-value references we need to const_cast:
const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
}
/* inserts a scalar value in the target matrix */
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const Scalar& s)
@ -110,7 +122,7 @@ struct CommaInitializer
EIGEN_DEVICE_FUNC
inline XprType& finished() { return m_xpr; }
XprType& m_xpr; // target expression
XprType& m_xpr; // target expression
Index m_row; // current row id
Index m_col; // current col id
Index m_currentBlockRows; // current block height

8
Eigen/src/Core/MatrixBase.h
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@ -378,8 +378,6 @@ template<typename Derived> class MatrixBase
Scalar trace() const;
/////////// Array module ///////////
template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
@ -387,8 +385,10 @@ template<typename Derived> class MatrixBase
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC ArrayWrapper<Derived> array() { return derived(); }
EIGEN_DEVICE_FUNC const ArrayWrapper<const Derived> array() const { return derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return derived(); }
/** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return derived(); }
/////////// LU module ///////////

4
Eigen/src/Core/Transpose.h
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@ -300,7 +300,8 @@ struct inplace_transpose_selector<MatrixType,false> { // non square matrix
* Notice however that this method is only useful if you want to replace a matrix by its own transpose.
* If you just need the transpose of a matrix, use transpose().
*
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* This excludes (non-square) fixed-size matrices, block-expressions and maps.
*
* \sa transpose(), adjoint(), adjointInPlace() */
template<typename Derived>
@ -331,6 +332,7 @@ inline void DenseBase<Derived>::transposeInPlace()
* If you just need the adjoint of a matrix, use adjoint().
*
* \note if the matrix is not square, then \c *this must be a resizable matrix.
* This excludes (non-square) fixed-size matrices, block-expressions and maps.
*
* \sa transpose(), adjoint(), transposeInPlace() */
template<typename Derived>

25
Eigen/src/Core/products/GeneralBlockPanelKernel.h
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@ -1128,6 +1128,8 @@ EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, Pack1, Pack2, StorageOrder,
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
EIGEN_UNUSED_VARIABLE(stride);
EIGEN_UNUSED_VARIABLE(offset);
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
@ -1215,6 +1217,8 @@ EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, Pan
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
EIGEN_UNUSED_VARIABLE(stride);
EIGEN_UNUSED_VARIABLE(offset);
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
@ -1257,6 +1261,7 @@ EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, Pan
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
};
@ -1266,6 +1271,8 @@ EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, Pan
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
EIGEN_UNUSED_VARIABLE(stride);
EIGEN_UNUSED_VARIABLE(offset);
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
@ -1276,12 +1283,18 @@ EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, Pan
if(PanelMode) count += nr * offset;
for(Index k=0; k<depth; k++)
{
const Scalar* b0 = &rhs[k*rhsStride + j2];
blockB[count+0] = cj(b0[0]);
blockB[count+1] = cj(b0[1]);
if(nr==4) blockB[count+2] = cj(b0[2]);
if(nr==4) blockB[count+3] = cj(b0[3]);
count += nr;
if (nr == PacketSize) {
Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]);
pstoreu(blockB+count, cj.pconj(A));
count += PacketSize;
} else {
const Scalar* b0 = &rhs[k*rhsStride + j2];
blockB[count+0] = cj(b0[0]);
blockB[count+1] = cj(b0[1]);
if(nr==4) blockB[count+2] = cj(b0[2]);
if(nr==4) blockB[count+3] = cj(b0[3]);
count += nr;
}
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);

18
Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -80,11 +80,8 @@ EIGEN_DONT_INLINE static void run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index
#ifdef EIGEN_INTERNAL_DEBUGGING
resIncr
#endif
, RhsScalar alpha);
ResScalar* res, Index resIncr,
RhsScalar alpha);
};
template<typename Index, typename LhsScalar, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs, int Version>
@ -92,12 +89,10 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,ColMajor,Co
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index
#ifdef EIGEN_INTERNAL_DEBUGGING
resIncr
#endif
, RhsScalar alpha)
ResScalar* res, Index resIncr,
RhsScalar alpha)
{
EIGEN_UNUSED_VARIABLE(resIncr);
eigen_internal_assert(resIncr==1);
#ifdef _EIGEN_ACCUMULATE_PACKETS
#error _EIGEN_ACCUMULATE_PACKETS has already been defined
@ -350,7 +345,7 @@ EIGEN_DONT_INLINE static void run(
Index rows, Index cols,
const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsIncr,
ResScalar* res, Index resIncr,
ResScalar* res, Index resIncr,
ResScalar alpha);
};
@ -364,6 +359,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,RowMajor,Co
{
EIGEN_UNUSED_VARIABLE(rhsIncr);
eigen_internal_assert(rhsIncr==1);
#ifdef _EIGEN_ACCUMULATE_PACKETS
#error _EIGEN_ACCUMULATE_PACKETS has already been defined
#endif

6
Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -113,9 +113,9 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
for (size_t i=starti; i<alignedStart; ++i)
{
res[i] += t0 * A0[i] + t1 * A1[i];
t2 += numext::conj(A0[i]) * rhs[i];
t3 += numext::conj(A1[i]) * rhs[i];
res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
t2 += cj1.pmul(A0[i], rhs[i]);
t3 += cj1.pmul(A1[i], rhs[i]);
}
// Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
// gcc 4.2 does this optimization automatically.

7
Eigen/src/Core/util/Macros.h
View File

@ -252,7 +252,12 @@
#endif
// Suppresses 'unused variable' warnings.
#define EIGEN_UNUSED_VARIABLE(var) (void)var;
namespace Eigen {
namespace internal {
template<typename T> void ignore_unused_variable(const T&) {}
}
}
#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
#if !defined(EIGEN_ASM_COMMENT)
#if (defined __GNUC__) && ( defined(__i386__) || defined(__x86_64__) )

8
Eigen/src/Core/util/Memory.h
View File

@ -274,12 +274,12 @@ inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size)
// The defined(_mm_free) is just here to verify that this MSVC version
// implements _mm_malloc/_mm_free based on the corresponding _aligned_
// functions. This may not always be the case and we just try to be safe.
#if defined(_MSC_VER) && defined(_mm_free)
#if defined(_MSC_VER) && (!defined(_WIN32_WCE)) && defined(_mm_free)
result = _aligned_realloc(ptr,new_size,16);
#else
result = generic_aligned_realloc(ptr,new_size,old_size);
#endif
#elif defined(_MSC_VER)
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
result = _aligned_realloc(ptr,new_size,16);
#else
result = handmade_aligned_realloc(ptr,new_size,old_size);
@ -464,7 +464,7 @@ template<typename T, bool Align> inline void conditional_aligned_delete_auto(T *
* There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h.
*/
template<typename Scalar, typename Index>
static inline Index first_aligned(const Scalar* array, Index size)
inline Index first_aligned(const Scalar* array, Index size)
{
enum { PacketSize = packet_traits<Scalar>::size,
PacketAlignedMask = PacketSize-1
@ -492,7 +492,7 @@ static inline Index first_aligned(const Scalar* array, Index size)
/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
*/
template<typename Index>
inline static Index first_multiple(Index size, Index base)
inline Index first_multiple(Index size, Index base)
{
return ((size+base-1)/base)*base;
}

21
Eigen/src/Geometry/Quaternion.h
View File

@ -34,8 +34,9 @@ struct quaternionbase_assign_impl;
template<class Derived>
class QuaternionBase : public RotationBase<Derived, 3>
{
public:
typedef RotationBase<Derived, 3> Base;
public:
using Base::operator*;
using Base::derived;
@ -203,6 +204,8 @@ public:
* \li \c Quaternionf for \c float
* \li \c Quaterniond for \c double
*
* \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not normalized.
*
* \sa class AngleAxis, class Transform
*/
@ -223,10 +226,10 @@ struct traits<Quaternion<_Scalar,_Options> >
template<typename _Scalar, int _Options>
class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> >
{
public:
typedef QuaternionBase<Quaternion<_Scalar,_Options> > Base;
enum { IsAligned = internal::traits<Quaternion>::IsAligned };
public:
typedef _Scalar Scalar;
EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Quaternion)
@ -334,9 +337,9 @@ template<typename _Scalar, int _Options>
class Map<const Quaternion<_Scalar>, _Options >
: public QuaternionBase<Map<const Quaternion<_Scalar>, _Options> >
{
public:
typedef QuaternionBase<Map<const Quaternion<_Scalar>, _Options> > Base;
public:
typedef _Scalar Scalar;
typedef typename internal::traits<Map>::Coefficients Coefficients;
EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)
@ -344,7 +347,7 @@ class Map<const Quaternion<_Scalar>, _Options >
/** Constructs a Mapped Quaternion object from the pointer \a coeffs
*
* The pointer \a coeffs must reference the four coeffecients of Quaternion in the following order:
* The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
* \code *coeffs == {x, y, z, w} \endcode
*
* If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */
@ -371,9 +374,9 @@ template<typename _Scalar, int _Options>
class Map<Quaternion<_Scalar>, _Options >
: public QuaternionBase<Map<Quaternion<_Scalar>, _Options> >
{
public:
typedef QuaternionBase<Map<Quaternion<_Scalar>, _Options> > Base;
public:
typedef _Scalar Scalar;
typedef typename internal::traits<Map>::Coefficients Coefficients;
EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)
@ -464,7 +467,7 @@ QuaternionBase<Derived>::_transformVector(Vector3 v) const
// Note that this algorithm comes from the optimization by hand
// of the conversion to a Matrix followed by a Matrix/Vector product.
// It appears to be much faster than the common algorithm found
// in the litterature (30 versus 39 flops). It also requires two
// in the literature (30 versus 39 flops). It also requires two
// Vector3 as temporaries.
Vector3 uv = this->vec().cross(v);
uv += uv;
@ -667,10 +670,10 @@ QuaternionBase<Derived>::angularDistance(const QuaternionBase<OtherDerived>& oth
{
using std::acos;
using std::abs;
double d = abs(this->dot(other));
if (d>=1.0)
Scalar d = abs(this->dot(other));
if (d>=Scalar(1))
return Scalar(0);
return static_cast<Scalar>(2 * acos(d));
return Scalar(2) * acos(d);
}

8
Eigen/src/Geometry/Scaling.h
View File

@ -62,10 +62,10 @@ public:
template<int Dim, int Mode, int Options>
inline Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> operator* (const Transform<Scalar,Dim, Mode, Options>& t) const
{
Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> res = t;
res.prescale(factor());
return res;
}
Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> res = t;
res.prescale(factor());
return res;
}
/** Concatenates a uniform scaling and a linear transformation matrix */
// TODO returns an expression

4
Eigen/src/Geometry/Transform.h
View File

@ -530,9 +530,9 @@ public:
inline Transform& operator=(const UniformScaling<Scalar>& t);
inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); }
inline Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> operator*(const UniformScaling<Scalar>& s) const
inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const
{
Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode),Options> res = *this;
TransformTimeDiagonalReturnType res = *this;
res.scale(s.factor());
return res;
}

12
Eigen/src/PaStiXSupport/PaStiXSupport.h
View File

@ -12,6 +12,14 @@
namespace Eigen {
#if defined(DCOMPLEX)
#define PASTIX_COMPLEX COMPLEX
#define PASTIX_DCOMPLEX DCOMPLEX
#else
#define PASTIX_COMPLEX std::complex<float>
#define PASTIX_DCOMPLEX std::complex<double>
#endif
/** \ingroup PaStiXSupport_Module
* \brief Interface to the PaStix solver
*
@ -74,14 +82,14 @@ namespace internal
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}
c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<COMPLEX*>(vals), perm, invp, reinterpret_cast<COMPLEX*>(x), nbrhs, iparm, dparm);
c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm);
}
void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}
z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<DCOMPLEX*>(vals), perm, invp, reinterpret_cast<DCOMPLEX*>(x), nbrhs, iparm, dparm);
z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm);
}
// Convert the matrix to Fortran-style Numbering

2
Eigen/src/QR/ColPivHouseholderQR.h
View File

@ -378,7 +378,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
return m_usePrescribedThreshold ? m_prescribedThreshold
// this formula comes from experimenting (see "LU precision tuning" thread on the list)
// and turns out to be identical to Higham's formula used already in LDLt.
: NumTraits<Scalar>::epsilon() * m_qr.diagonalSize();
: NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
}
/** \returns the number of nonzero pivots in the QR decomposition.

4
Eigen/src/QR/FullPivHouseholderQR.h
View File

@ -372,7 +372,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
return m_usePrescribedThreshold ? m_prescribedThreshold
// this formula comes from experimenting (see "LU precision tuning" thread on the list)
// and turns out to be identical to Higham's formula used already in LDLt.
: NumTraits<Scalar>::epsilon() * m_qr.diagonalSize();
: NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
}
/** \returns the number of nonzero pivots in the QR decomposition.
@ -449,7 +449,7 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
m_temp.resize(cols);
m_precision = NumTraits<Scalar>::epsilon() * size;
m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
m_rows_transpositions.resize(size);
m_cols_transpositions.resize(size);

9
Eigen/src/SparseCore/SparseMatrix.h
View File

@ -223,7 +223,7 @@ class SparseMatrix
if(isCompressed())
{
reserve(VectorXi::Constant(outerSize(), 2));
reserve(Matrix<Index,Dynamic,1>::Constant(outerSize(), 2));
}
return insertUncompressed(row,col);
}
@ -939,12 +939,13 @@ void set_from_triplets(const InputIterator& begin, const InputIterator& end, Spa
EIGEN_UNUSED_VARIABLE(Options);
enum { IsRowMajor = SparseMatrixType::IsRowMajor };
typedef typename SparseMatrixType::Scalar Scalar;
typedef typename SparseMatrixType::Index Index;
SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor> trMat(mat.rows(),mat.cols());
if(begin!=end)
{
// pass 1: count the nnz per inner-vector
VectorXi wi(trMat.outerSize());
Matrix<Index,Dynamic,1> wi(trMat.outerSize());
wi.setZero();
for(InputIterator it(begin); it!=end; ++it)
{
@ -1018,7 +1019,7 @@ void SparseMatrix<Scalar,_Options,_Index>::sumupDuplicates()
{
eigen_assert(!isCompressed());
// TODO, in practice we should be able to use m_innerNonZeros for that task
VectorXi wi(innerSize());
Matrix<Index,Dynamic,1> wi(innerSize());
wi.fill(-1);
Index count = 0;
// for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
@ -1081,7 +1082,7 @@ EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_Index>& SparseMatrix<Scalar,_Opt
// prefix sum
Index count = 0;
VectorXi positions(dest.outerSize());
Matrix<Index,Dynamic,1> positions(dest.outerSize());
for (Index j=0; j<dest.outerSize(); ++j)
{
Index tmp = dest.m_outerIndex[j];

4
Eigen/src/SparseCore/SparsePermutation.h
View File

@ -57,7 +57,7 @@ struct permut_sparsematrix_product_retval
if(MoveOuter)
{
SparseMatrix<Scalar,SrcStorageOrder,Index> tmp(m_matrix.rows(), m_matrix.cols());
VectorXi sizes(m_matrix.outerSize());
Matrix<Index,Dynamic,1> sizes(m_matrix.outerSize());
for(Index j=0; j<m_matrix.outerSize(); ++j)
{
Index jp = m_permutation.indices().coeff(j);
@ -77,7 +77,7 @@ struct permut_sparsematrix_product_retval
else
{
SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> tmp(m_matrix.rows(), m_matrix.cols());
VectorXi sizes(tmp.outerSize());
Matrix<Index,Dynamic,1> sizes(tmp.outerSize());
sizes.setZero();
PermutationMatrix<Dynamic,Dynamic,Index> perm;
if((Side==OnTheLeft) ^ Transposed)

8
bench/btl/CMakeLists.txt
View File

@ -48,6 +48,12 @@ include_directories(
# set(DEFAULT_LIBRARIES ${MKL_LIBRARIES})
# endif (MKL_FOUND)
find_library(EIGEN_BTL_RT_LIBRARY rt)
# if we cannot find it easily, then we don't need it!
if(NOT EIGEN_BTL_RT_LIBRARY)
set(EIGEN_BTL_RT_LIBRARY "")
endif()
MACRO(BTL_ADD_BENCH targetname)
foreach(_current_var ${ARGN})
@ -70,7 +76,7 @@ MACRO(BTL_ADD_BENCH targetname)
IF(BUILD_${targetname})
ADD_EXECUTABLE(${targetname} ${_sources})
ADD_TEST(${targetname} "${targetname}")
target_link_libraries(${targetname} ${DEFAULT_LIBRARIES} rt)
target_link_libraries(${targetname} ${DEFAULT_LIBRARIES} ${EIGEN_BTL_RT_LIBRARY})
ENDIF(BUILD_${targetname})
ENDMACRO(BTL_ADD_BENCH)

4
bench/btl/generic_bench/bench.hh
View File

@ -102,8 +102,8 @@ BTL_DONT_INLINE void bench( int size_min, int size_max, int nb_point )
// merge the two data
std::vector<int> newSizes;
std::vector<double> newFlops;
int i=0;
int j=0;
unsigned int i=0;
unsigned int j=0;
while (i<tab_sizes.size() && j<oldSizes.size())
{
if (tab_sizes[i] == oldSizes[j])

2
bench/btl/generic_bench/btl.hh
View File

@ -46,7 +46,7 @@
#if (defined __GNUC__) && (!defined __INTEL_COMPILER) && !defined(__arm__) && !defined(__powerpc__)
#define BTL_DISABLE_SSE_EXCEPTIONS() { \
int aux; \
int aux = 0; \
asm( \
"stmxcsr %[aux] \n\t" \
"orl $32832, %[aux] \n\t" \

10
bench/btl/generic_bench/init/init_matrix.hh
View File

@ -29,7 +29,7 @@ BTL_DONT_INLINE void init_row(Vector & X, int size, int row){
X.resize(size);
for (int j=0;j<X.size();j++){
for (unsigned int j=0;j<X.size();j++){
X[j]=typename Vector::value_type(init_function(row,j));
}
}
@ -42,7 +42,7 @@ BTL_DONT_INLINE void init_row(Vector & X, int size, int row){
template<double init_function(int,int),class Vector>
BTL_DONT_INLINE void init_matrix(Vector & A, int size){
A.resize(size);
for (int row=0; row<A.size() ; row++){
for (unsigned int row=0; row<A.size() ; row++){
init_row<init_function>(A[row],size,row);
}
}
@ -50,11 +50,11 @@ BTL_DONT_INLINE void init_matrix(Vector & A, int size){
template<double init_function(int,int),class Matrix>
BTL_DONT_INLINE void init_matrix_symm(Matrix& A, int size){
A.resize(size);
for (int row=0; row<A.size() ; row++)
for (unsigned int row=0; row<A.size() ; row++)
A[row].resize(size);
for (int row=0; row<A.size() ; row++){
for (unsigned int row=0; row<A.size() ; row++){
A[row][row] = init_function(row,row);
for (int col=0; col<row ; col++){
for (unsigned int col=0; col<row ; col++){
double x = init_function(row,col);
A[row][col] = A[col][row] = x;
}

2
bench/btl/generic_bench/init/init_vector.hh
View File

@ -29,7 +29,7 @@ void init_vector(Vector & X, int size){
X.resize(size);
for (int i=0;i<X.size();i++){
for (unsigned int i=0;i<X.size();i++){
X[i]=typename Vector::value_type(init_function(i));
}
}

2
bench/btl/generic_bench/timers/portable_perf_analyzer.hh
View File

@ -78,7 +78,7 @@ public:
// time measurement
action.calculate();
_chronos.start();
for (int ii=0;ii<_nb_calc;ii++)
for (unsigned int ii=0;ii<_nb_calc;ii++)
{
action.calculate();
}

46
bench/btl/generic_bench/timers/portable_timer.hh
View File

@ -34,7 +34,7 @@
// timer -------------------------------------------------------------------//
// A timer object measures CPU time.
#ifdef _MSC_VER
#if defined(_MSC_VER)
#define NOMINMAX
#include <windows.h>
@ -87,6 +87,48 @@
}; // Portable_Timer
#elif defined(__APPLE__)
#include <CoreServices/CoreServices.h>
#include <mach/mach_time.h>
class Portable_Timer
{
public:
Portable_Timer()
{
}
void start()
{
m_start_time = double(mach_absolute_time())*1e-9;;
}
void stop()
{
m_stop_time = double(mach_absolute_time())*1e-9;;
}
double elapsed()
{
return user_time();
}
double user_time()
{
return m_stop_time - m_start_time;
}
private:
double m_stop_time, m_start_time;
}; // Portable_Timer (Apple)
#else
#include <sys/time.h>
@ -138,7 +180,7 @@ private:
int m_clkid;
double m_stop_time, m_start_time;
}; // Portable_Timer
}; // Portable_Timer (Linux)
#endif

8
bench/btl/libs/eigen3/eigen3_interface.hh
View File

@ -52,8 +52,8 @@ public :
static BTL_DONT_INLINE void matrix_from_stl(gene_matrix & A, stl_matrix & A_stl){
A.resize(A_stl[0].size(), A_stl.size());
for (int j=0; j<A_stl.size() ; j++){
for (int i=0; i<A_stl[j].size() ; i++){
for (unsigned int j=0; j<A_stl.size() ; j++){
for (unsigned int i=0; i<A_stl[j].size() ; i++){
A.coeffRef(i,j) = A_stl[j][i];
}
}
@ -62,13 +62,13 @@ public :
static BTL_DONT_INLINE void vector_from_stl(gene_vector & B, stl_vector & B_stl){
B.resize(B_stl.size(),1);
for (int i=0; i<B_stl.size() ; i++){
for (unsigned int i=0; i<B_stl.size() ; i++){
B.coeffRef(i) = B_stl[i];
}
}
static BTL_DONT_INLINE void vector_to_stl(gene_vector & B, stl_vector & B_stl){
for (int i=0; i<B_stl.size() ; i++){
for (unsigned int i=0; i<B_stl.size() ; i++){
B_stl[i] = B.coeff(i);
}
}

2
doc/TopicLinearAlgebraDecompositions.dox
View File

@ -249,7 +249,7 @@ For an introduction on linear solvers and decompositions, check this \link Tutor
<dt><b>Implicit Multi Threading (MT)</b></dt>
<dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product rountines.</dd>
<dt><b>Explicit Multi Threading (MT)</b></dt>
<dd>Means the algorithm is explicitely parallelized to take advantage of multicore processors via OpenMP.</dd>
<dd>Means the algorithm is explicitly parallelized to take advantage of multicore processors via OpenMP.</dd>
<dt><b>Meta-unroller</b></dt>
<dd>Means the algorithm is automatically and explicitly unrolled for very small fixed size matrices.</dd>
<dt><b></b></dt>

2
doc/TopicMultithreading.dox
View File

@ -39,7 +39,7 @@ int main(int argc, char** argv)
}
\endcode
\warning note that all functions generating random matrices are \b not re-entrant nor thread-safe. Those include DenseBase::Random(), and DenseBase::setRandom() despite a call to Eigen::initParallel(). This is because these functions are based on std::rand which is not re-entrant. For thread-safe random generator, we recommend the use of boost::random of c++11 random feature.
\warning note that all functions generating random matrices are \b not re-entrant nor thread-safe. Those include DenseBase::Random(), and DenseBase::setRandom() despite a call to Eigen::initParallel(). This is because these functions are based on std::rand which is not re-entrant. For thread-safe random generator, we recommend the use of boost::random or c++11 random feature.
In the case your application is parallelized with OpenMP, you might want to disable Eigen's own parallization as detailed in the previous section.

4
doc/snippets/EigenSolver_eigenvectors.cpp
View File

@ -1,4 +1,4 @@
MatrixXd ones = MatrixXd::Ones(3,3);
EigenSolver<MatrixXd> es(ones);
cout << "The first eigenvector of the 3x3 matrix of ones is:"
<< endl << es.eigenvectors().col(1) << endl;
cout << "The first eigenvector of the 3x3 matrix of ones is:"
<< endl << es.eigenvectors().col(0) << endl;

13
test/array.cpp
View File

@ -173,21 +173,14 @@ template<typename ArrayType> void array_real(const ArrayType& m)
Scalar s1 = internal::random<Scalar>();
// these tests are mostly to check possible compilation issues.
// VERIFY_IS_APPROX(m1.sin(), std::sin(m1));
VERIFY_IS_APPROX(m1.sin(), sin(m1));
// VERIFY_IS_APPROX(m1.cos(), std::cos(m1));
VERIFY_IS_APPROX(m1.cos(), cos(m1));
// VERIFY_IS_APPROX(m1.asin(), std::asin(m1));
VERIFY_IS_APPROX(m1.asin(), asin(m1));
// VERIFY_IS_APPROX(m1.acos(), std::acos(m1));
VERIFY_IS_APPROX(m1.acos(), acos(m1));
// VERIFY_IS_APPROX(m1.tan(), std::tan(m1));
VERIFY_IS_APPROX(m1.tan(), tan(m1));
VERIFY_IS_APPROX(cos(m1+RealScalar(3)*m2), cos((m1+RealScalar(3)*m2).eval()));
// VERIFY_IS_APPROX(std::cos(m1+RealScalar(3)*m2), std::cos((m1+RealScalar(3)*m2).eval()));
// VERIFY_IS_APPROX(m1.abs().sqrt(), std::sqrt(std::abs(m1)));
VERIFY_IS_APPROX(m1.abs().sqrt(), sqrt(abs(m1)));
VERIFY_IS_APPROX(m1.abs(), sqrt(numext::abs2(m1)));
@ -196,9 +189,10 @@ template<typename ArrayType> void array_real(const ArrayType& m)
if(!NumTraits<Scalar>::IsComplex)
VERIFY_IS_APPROX(numext::real(m1), m1);
VERIFY_IS_APPROX(m1.abs().log() , log(abs(m1)));
// shift argument of logarithm so that it is not zero
Scalar smallNumber = NumTraits<Scalar>::dummy_precision();
VERIFY_IS_APPROX((m1.abs() + smallNumber).log() , log(abs(m1) + smallNumber));
// VERIFY_IS_APPROX(m1.exp(), std::exp(m1));
VERIFY_IS_APPROX(m1.exp() * m2.exp(), exp(m1+m2));
VERIFY_IS_APPROX(m1.exp(), exp(m1));
VERIFY_IS_APPROX(m1.exp() / m2.exp(),(m1-m2).exp());
@ -242,7 +236,6 @@ template<typename ArrayType> void array_complex(const ArrayType& m)
m2(i,j) = sqrt(m1(i,j));
VERIFY_IS_APPROX(m1.sqrt(), m2);
// VERIFY_IS_APPROX(m1.sqrt(), std::sqrt(m1));
VERIFY_IS_APPROX(m1.sqrt(), Eigen::sqrt(m1));
}

25
test/block.cpp
View File

@ -10,6 +10,26 @@
#define EIGEN_NO_STATIC_ASSERT // otherwise we fail at compile time on unused paths
#include "main.h"
template<typename MatrixType, typename Index, typename Scalar>
typename Eigen::internal::enable_if<!NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type
block_real_only(const MatrixType &m1, Index r1, Index r2, Index c1, Index c2, const Scalar& s1) {
// check cwise-Functions:
VERIFY_IS_APPROX(m1.row(r1).cwiseMax(s1), m1.cwiseMax(s1).row(r1));
VERIFY_IS_APPROX(m1.col(c1).cwiseMin(s1), m1.cwiseMin(s1).col(c1));
VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMin(s1), m1.cwiseMin(s1).block(r1,c1,r2-r1+1,c2-c1+1));
VERIFY_IS_APPROX(m1.block(r1,c1,r2-r1+1,c2-c1+1).cwiseMax(s1), m1.cwiseMax(s1).block(r1,c1,r2-r1+1,c2-c1+1));
return Scalar(0);
}
template<typename MatrixType, typename Index, typename Scalar>
typename Eigen::internal::enable_if<NumTraits<typename MatrixType::Scalar>::IsComplex,typename MatrixType::Scalar>::type
block_real_only(const MatrixType &, Index, Index, Index, Index, const Scalar&) {
return Scalar(0);
}
template<typename MatrixType> void block(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
@ -37,6 +57,8 @@ template<typename MatrixType> void block(const MatrixType& m)
Index c1 = internal::random<Index>(0,cols-1);
Index c2 = internal::random<Index>(c1,cols-1);
block_real_only(m1, r1, r2, c1, c1, s1);
//check row() and col()
VERIFY_IS_EQUAL(m1.col(c1).transpose(), m1.transpose().row(c1));
//check operator(), both constant and non-constant, on row() and col()
@ -51,7 +73,8 @@ template<typename MatrixType> void block(const MatrixType& m)
VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + s1 * m1_copy.col(c2));
m1.col(c1).col(0) += s1 * m1_copy.col(c2);
VERIFY_IS_APPROX(m1.col(c1), m1_copy.col(c1) + Scalar(2) * s1 * m1_copy.col(c2));
//check block()
Matrix<Scalar,Dynamic,Dynamic> b1(1,1); b1(0,0) = m1(r1,c1);

32
test/cholesky.cpp
View File

@ -179,6 +179,38 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
// restore
if(sign == -1)
symm = -symm;
// check matrices coming from linear constraints with Lagrange multipliers
if(rows>=3)
{
SquareMatrixType A = symm;
int c = internal::random<int>(0,rows-2);
A.bottomRightCorner(c,c).setZero();
// Make sure a solution exists:
vecX.setRandom();
vecB = A * vecX;
vecX.setZero();
ldltlo.compute(A);
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(A * vecX, vecB);
}
// check non-full rank matrices
if(rows>=3)
{
int r = internal::random<int>(1,rows-1);
Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r);
SquareMatrixType A = a * a.adjoint();
// Make sure a solution exists:
vecX.setRandom();
vecB = A * vecX;
vecX.setZero();
ldltlo.compute(A);
VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
vecX = ldltlo.solve(vecB);
VERIFY_IS_APPROX(A * vecX, vecB);
}
}
// update/downdate

2
test/sizeof.cpp
View File

@ -21,6 +21,8 @@ template<typename MatrixType> void verifySizeOf(const MatrixType&)
void test_sizeof()
{
CALL_SUBTEST(verifySizeOf(Matrix<float, 1, 1>()) );
CALL_SUBTEST(verifySizeOf(Vector2d()) );
CALL_SUBTEST(verifySizeOf(Vector4f()) );
CALL_SUBTEST(verifySizeOf(Matrix4d()) );
CALL_SUBTEST(verifySizeOf(Matrix<double, 4, 2>()) );
CALL_SUBTEST(verifySizeOf(Matrix<bool, 7, 5>()) );