[PATCH 1/2] Misc. typos

From 68d431b4c14ad60a778ee93c1f59ecc4b931950e Mon Sep 17 00:00:00 2001
Found via `codespell -q 3 -I ../eigen-word-whitelist.txt` where the whitelists consists of:
```
als
ans
cas
dum
lastr
lowd
nd
overfl
pres
preverse
substraction
te
uint
whch
```
---
 CMakeLists.txt                                | 26 +++++++++----------
 Eigen/src/Core/GenericPacketMath.h            |  2 +-
 Eigen/src/SparseLU/SparseLU.h                 |  2 +-
 bench/bench_norm.cpp                          |  2 +-
 doc/HiPerformance.dox                         |  2 +-
 doc/QuickStartGuide.dox                       |  2 +-
 .../Eigen/CXX11/src/Tensor/TensorChipping.h   |  6 ++---
 .../Eigen/CXX11/src/Tensor/TensorDeviceGpu.h  |  2 +-
 .../src/Tensor/TensorForwardDeclarations.h    |  4 +--
 .../src/Tensor/TensorGpuHipCudaDefines.h      |  2 +-
 .../Eigen/CXX11/src/Tensor/TensorReduction.h  |  2 +-
 .../CXX11/src/Tensor/TensorReductionGpu.h     |  2 +-
 .../test/cxx11_tensor_concatenation.cpp       |  2 +-
 unsupported/test/cxx11_tensor_executor.cpp    |  2 +-
 14 files changed, 29 insertions(+), 29 deletions(-)
This commit is contained in:
luz.paz"
2018-09-18 04:15:01 -04:00
parent 77b447c24e
commit f67b19a884
16 changed files with 56 additions and 56 deletions

View File

@@ -450,7 +450,7 @@ Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/
/** \internal copy a packet with constant coeficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
template<typename Packet>
inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a)

View File

@@ -104,7 +104,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Derived DenseType;
@@ -358,7 +358,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
{
const OtherDerived& other = _other.derived();
@@ -383,7 +383,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
* conservativeResize(Index, NoChange_t).
*
* Matrices are resized relative to the top-left element. In case values need to be
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will be uninitialized.
*/
EIGEN_DEVICE_FUNC
@@ -440,7 +440,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
* conservativeResize(Index, NoChange_t).
*
* Matrices are resized relative to the top-left element. In case values need to be
* Matrices are resized relative to the top-left element. In case values need to be
* appended to the matrix they will copied from \c other.
*/
template<typename OtherDerived>
@@ -564,7 +564,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
{
_resize_to_match(other);
@@ -678,7 +678,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* remain row-vectors and vectors remain vectors.
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
{
#ifdef EIGEN_NO_AUTOMATIC_RESIZING
@@ -705,10 +705,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
*
* \internal
*/
// aliasing is dealt once in internall::call_assignment
// aliasing is dealt once in internal::call_assignment
// so at this stage we have to assume aliasing... and resising has to be done later.
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
{
internal::call_assignment(this->derived(), other.derived());
@@ -721,7 +721,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* \sa operator=(const MatrixBase<OtherDerived>&), _set()
*/
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
{
// I don't think we need this resize call since the lazyAssign will anyways resize
@@ -742,18 +742,18 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(rows,cols);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = Scalar(val0);
m_storage.data()[1] = Scalar(val1);
}
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
typename internal::enable_if< (!internal::is_same<Index,Scalar>::value)
&& (internal::is_same<T0,Index>::value)
@@ -779,7 +779,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
resize(size);
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
template<typename T>
EIGEN_DEVICE_FUNC
@@ -788,7 +788,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
m_storage.data()[0] = val0;
}
// We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
template<typename T>
EIGEN_DEVICE_FUNC
@@ -844,7 +844,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
this->derived() = r;
}
// For fixed-size Array<Scalar,...>
template<typename T>
EIGEN_DEVICE_FUNC
@@ -856,7 +856,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
Base::setConstant(val0);
}
// For fixed-size Array<Index,...>
template<typename T>
EIGEN_DEVICE_FUNC
@@ -870,12 +870,12 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
{
Base::setConstant(val0);
}
template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
friend struct internal::matrix_swap_impl;
public:
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** \internal
* \brief Override DenseBase::swap() since for dynamic-sized matrices
@@ -888,7 +888,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
}
/** \internal
* \brief const version forwarded to DenseBase::swap
*/
@@ -896,8 +896,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_DEVICE_FUNC
void swap(DenseBase<OtherDerived> const & other)
{ Base::swap(other.derived()); }
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void _check_template_params()
{
EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor)
@@ -994,7 +994,7 @@ struct conservative_resize_like_impl<Derived,OtherDerived,true>
typedef conservative_resize_like_impl<Derived,OtherDerived,false> Base;
using Base::run;
using Base::IsRelocatable;
static void run(DenseBase<Derived>& _this, Index size)
{
const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;

View File

@@ -11,7 +11,7 @@
#ifndef EIGEN_JACOBI_H
#define EIGEN_JACOBI_H
namespace Eigen {
namespace Eigen {
/** \ingroup Jacobi_Module
* \jacobi_module
@@ -173,7 +173,7 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
using std::sqrt;
using std::abs;
using numext::conj;
if(q==Scalar(0))
{
m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1);
@@ -273,7 +273,7 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
namespace internal {
/** \jacobi_module
* Applies the clock wise 2D rotation \a j to the set of 2D vectors of cordinates \a x and \a y:
* Applies the clock wise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
* \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right ) = J \left ( \begin{array}{cc} x \\ y \end{array} \right ) \f$
*
* \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
@@ -463,7 +463,7 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
OtherScalar c = j.c();
OtherScalar s = j.s();
if (c==OtherScalar(1) && s==OtherScalar(0))

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@@ -26,7 +26,7 @@ template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixURetu
* This class implements the supernodal LU factorization for general matrices.
* It uses the main techniques from the sequential SuperLU package
* (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real
* and complex arithmetics with single and double precision, depending on the
* and complex arithmetic with single and double precision, depending on the
* scalar type of your input matrix.
* The code has been optimized to provide BLAS-3 operations during supernode-panel updates.
* It benefits directly from the built-in high-performant Eigen BLAS routines.