mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-11-27 06:30:28 +08:00
Code Issues Packages Projects Releases Wiki Activity

[PATCH 1/2] Misc. typos

Browse Source 
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
CMakeLists.txt
View File

@ -327,7 +327,7 @@ else(NOT MSVC)
# because we are oftentimes returning objects that have a destructor or may
# throw exceptions - in particular in the unit tests we are throwing extra many
# exceptions to cover indexing errors.
# C4505 - unreferenced local function has been removed (impossible to deactive selectively)
# C4505 - unreferenced local function has been removed (impossible to deactivate selectively)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc /wd4127 /wd4505 /wd4714")
# replace all /Wx by /W4

2
Eigen/src/Core/GenericPacketMath.h
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)

2
Eigen/src/Core/PlainObjectBase.h
View File

@ -705,7 +705,7 @@ 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

2
Eigen/src/Jacobi/Jacobi.h
View File

@ -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()

2
Eigen/src/SparseLU/SparseLU.h
View File

@ -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.

2
bench/bench_norm.cpp
View File

@ -134,7 +134,7 @@ EIGEN_DONT_INLINE typename T::Scalar pblueNorm(const T& v)
iexp = - ((iemax+it)/2);
s2m = std::pow(ibeta,iexp); // scaling factor for upper range
overfl = rbig*s2m; // overfow boundary for abig
overfl = rbig*s2m; // overflow boundary for abig
eps = std::pow(ibeta, 1-it);
relerr = std::sqrt(eps); // tolerance for neglecting asml
abig = 1.0/eps - 1.0;

2
doc/HiPerformance.dox
View File

@ -105,7 +105,7 @@ m1.noalias() += m2 * m3; \endcode</td>
<td>First of all, here the .noalias() in the first expression is useless because
m2*m3 will be evaluated anyway. However, note how this expression can be rewritten
so that no temporary is required. (tip: for very small fixed size matrix
it is slighlty better to rewrite it like this: m1.noalias() = m2 * m3; m1 += m4;</td>
it is slightly better to rewrite it like this: m1.noalias() = m2 * m3; m1 += m4;</td>
</tr>
<tr class="alt">
<td>\code

2
doc/QuickStartGuide.dox
View File

@ -66,7 +66,7 @@ The output is as follows:
\section GettingStartedExplanation2 Explanation of the second example
The second example starts by declaring a 3-by-3 matrix \c m which is initialized using the \link DenseBase::Random(Index,Index) Random() \endlink method with random values between -1 and 1. The next line applies a linear mapping such that the values are between 10 and 110. The function call \link DenseBase::Constant(Index,Index,const Scalar&) MatrixXd::Constant\endlink(3,3,1.2) returns a 3-by-3 matrix expression having all coefficients equal to 1.2. The rest is standard arithmetics.
The second example starts by declaring a 3-by-3 matrix \c m which is initialized using the \link DenseBase::Random(Index,Index) Random() \endlink method with random values between -1 and 1. The next line applies a linear mapping such that the values are between 10 and 110. The function call \link DenseBase::Constant(Index,Index,const Scalar&) MatrixXd::Constant\endlink(3,3,1.2) returns a 3-by-3 matrix expression having all coefficients equal to 1.2. The rest is standard arithmetic.
The next line of the \c main function introduces a new type: \c VectorXd. This represents a (column) vector of arbitrary size. Here, the vector \c v is created to contain \c 3 coefficients which are left uninitialized. The one but last line uses the so-called comma-initializer, explained in \ref TutorialAdvancedInitialization, to set all coefficients of the vector \c v to be as follows:

6
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
View File

@ -244,7 +244,7 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
return rslt;
} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
// m_stride is always greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
return m_impl.template packet<LoadMode>(index + m_inputOffset);
} else {
@ -377,7 +377,7 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
inputIndex = index * m_inputStride + m_inputOffset;
} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer
// m_stride is always greater than index, so let's avoid the integer
// division.
eigen_assert(m_stride > index);
inputIndex = index + m_inputOffset;
@ -462,7 +462,7 @@ struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
}
} else if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == NumInputDims-1) ||
(static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
// m_stride is always greater than index, so let's avoid the integer division.
eigen_assert(this->m_stride > index);
this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
} else {

2
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
View File

@ -12,7 +12,7 @@
// This header file container defines fo gpu* macros which will resolve to
// their equivalent hip* or cuda* versions depending on the compiler in use
// A separte header (included at the end of this file) will undefine all
// A separate header (included at the end of this file) will undefine all
#include "TensorGpuHipCudaDefines.h"
namespace Eigen {

4
unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
View File

@ -25,9 +25,9 @@ template<typename T> struct MakePointer {
};
// The PointerType class is a container of the device specefic pointer
// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
// used for referring to a Pointer on TensorEvaluator class. While the TensorExpression
// is a device-agnostic type and need MakePointer class for type conversion,
// the TensorEvaluator calss can be specialized for a device, hence it is possible
// the TensorEvaluator calls can be specialized for a device, hence it is possible
// to construct different types of temproray storage memory in TensorEvaluator
// for different devices by specializing the following PointerType class.
template<typename T, typename Device> struct PointerType : MakePointer<T>{};

2
unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
View File

@ -16,7 +16,7 @@
// for some reason gets sent to the gcc/host compiler instead of the gpu/nvcc/hipcc compiler
// When compiling such files, gcc will end up trying to pick up the CUDA headers by
// default (see the code within "unsupported/Eigen/CXX11/Tensor" that is guarded by EIGEN_USE_GPU)
// This will obsviously not work when trying to compile tensorflow on a sytem with no CUDA
// This will obsviously not work when trying to compile tensorflow on a system with no CUDA
// To work around this issue for HIP systems (and leave the default behaviour intact), the
// HIP tensorflow build defines EIGEN_USE_HIP when compiling all source files, and
// "unsupported/Eigen/CXX11/Tensor" has been updated to use HIP header when EIGEN_USE_HIP is

2
unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
View File

@ -965,7 +965,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>,
}
}
// Intialize output coefficient reducers.
// Initialize output coefficient reducers.
for (int i = 0; i < num_reducers; ++i) {
new (&reducers[i]) BlockReducer(m_reducer);
}

2
unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
View File

@ -771,7 +771,7 @@ struct OuterReducer<Self, Op, GpuDevice> {
// terminate called after throwing an instance of 'std::runtime_error'
// what(): No device code available for function: _ZN5Eigen8internal20OuterReductionKernelIL...
//
// dont know why this happens (and why is it a runtime error instead of a compile time errror)
// don't know why this happens (and why is it a runtime error instead of a compile time error)
//
// this will be fixed by HIP PR#457
EIGEN_DEVICE_FUNC

2
unsupported/test/cxx11_tensor_concatenation.cpp
View File

@ -50,7 +50,7 @@ static void test_static_dimension_failure()
.reshape(Tensor<int, 3>::Dimensions(2, 3, 1))
.concatenate(right, 0);
Tensor<int, 2, DataLayout> alternative = left
// Clang compiler break with {{{}}} with an ambigous error on copy constructor
// Clang compiler break with {{{}}} with an ambiguous error on copy constructor
// the variadic DSize constructor added for #ifndef EIGEN_EMULATE_CXX11_META_H.
// Solution:
// either the code should change to

2
unsupported/test/cxx11_tensor_executor.cpp
View File

@ -433,7 +433,7 @@ static void test_execute_slice_lvalue(Device d)
Tensor<T, NumDims, Options, Index> slice(slice_size);
slice.setRandom();
// Asign a slice using default executor.
// Assign a slice using default executor.
Tensor<T, NumDims, Options, Index> golden = src;
golden.slice(slice_start, slice_size) = slice;