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Fix all the doxygen warnings.

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This commit is contained in:
Antonio Sánchez
2025-02-01 00:00:31 +00:00
parent 9589cc4e7f
commit b1e74b1ccd
85 changed files with 829 additions and 2782 deletions
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3
unsupported/Eigen/CXX11/src/Tensor/README.md
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@@ -8,7 +8,6 @@ but more complex types such as strings are also supported.
You can manipulate a tensor with one of the following classes. They all are in
the namespace `::Eigen.`
### Class Tensor<data_type, rank>
This is the class to use to create a tensor and allocate memory for it. The
@@ -90,7 +89,7 @@ See Assigning to a `TensorRef` below.
## Accessing Tensor Elements
#### <data_type> tensor(index0, index1...)
#### data_type tensor(index0, index1...)
Return the element at position `(index0, index1...)` in tensor
`tensor`. You must pass as many parameters as the rank of `tensor`.

2
unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
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@@ -428,7 +428,7 @@ struct ThreadProperties {
* \tparam input_mapper_properties : determine if the input tensors are matrix. If they are matrix, special memory
access is used to guarantee that always the memory access are coalesced.
*
* \tptaram IsFinal : determine if this is the final kernel. If so, the result will be written in a final output.
* \tparam IsFinal : determine if this is the final kernel. If so, the result will be written in a final output.
Otherwise, the result of contraction will be written iin a temporary buffer. This is the case when Tall/Skinny
contraction is used. So in this case, a final reduction step is required to compute final output.

10
unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
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@@ -261,7 +261,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
// pos_j_base_powered[0] = ComplexScalar(1, 0);
// if (line_len > 1) {
// const ComplexScalar pos_j_base = ComplexScalar(
// numext::cos(M_PI / line_len), numext::sin(M_PI / line_len));
// numext::cos(EIGEN_PI / line_len), numext::sin(EIGEN_PI / line_len));
// pos_j_base_powered[1] = pos_j_base;
// if (line_len > 2) {
// const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base;
@@ -511,8 +511,8 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
template <int Dir>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge(ComplexScalar* data, Index n, Index n_power_of_2) {
// Original code:
// RealScalar wtemp = std::sin(M_PI/n);
// RealScalar wpi = -std::sin(2 * M_PI/n);
// RealScalar wtemp = std::sin(EIGEN_PI/n);
// RealScalar wpi = -std::sin(2 * EIGEN_PI/n);
const RealScalar wtemp = m_sin_PI_div_n_LUT[n_power_of_2];
const RealScalar wpi =
(Dir == FFT_FORWARD) ? m_minus_sin_2_PI_div_n_LUT[n_power_of_2] : -m_minus_sin_2_PI_div_n_LUT[n_power_of_2];
@@ -600,7 +600,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
const Device EIGEN_DEVICE_REF m_device;
// This will support a maximum FFT size of 2^32 for each dimension
// m_sin_PI_div_n_LUT[i] = (-2) * std::sin(M_PI / std::pow(2,i)) ^ 2;
// m_sin_PI_div_n_LUT[i] = (-2) * std::sin(EIGEN_PI / std::pow(2,i)) ^ 2;
const RealScalar m_sin_PI_div_n_LUT[32] = {RealScalar(0.0),
RealScalar(-2),
RealScalar(-0.999999999999999),
@@ -634,7 +634,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
RealScalar(-1.71210344531737e-17),
RealScalar(-4.28025861329343e-18)};
// m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * M_PI / std::pow(2,i));
// m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * EIGEN_PI / std::pow(2,i));
const RealScalar m_minus_sin_2_PI_div_n_LUT[32] = {RealScalar(0.0),
RealScalar(0.0),
RealScalar(-1.00000000000000e+00),