The sum accuracy test currently uses the default test precision for
the given scalar type. However, scalars are generated via a normal
distribution, and given a large enough count and strong enough random
generator, the expected sum is zero. This causes the test to
periodically fail.
Here we estimate an upper-bound for the error as `sqrt(N) * prec` for
summing N values, with each having an approximate epsilon of `prec`.
Also fixed a few warnings generated by MSVC when compiling the
reduction test.
The original swap approach leads to potential undefined behavior (reading
uninitialized memory) and results in unnecessary copying of data for static
storage.
Here we pass down the move assignment to the underlying storage. Static
storage does a one-way copy, dynamic storage does a swap.
Modified the tests to no longer read from the moved-from matrix/tensor,
since that can lead to UB. Added a test to ensure we do not access
uninitialized memory in a move.
Fixes: #2119
Only compile the custom index code when EIGEN_HAS_SFINAE is defined. For the time beeing, EIGEN_HAS_SFINAE is a synonym for EIGEN_HAS_VARIADIC_TEMPLATES, but this might evolve in the future.
Moved some code around.
using Sfinae and is_base_of to select correct template which converts to array<Index,NumIndices>
user: Gabriel Nützi <gnuetzi@gmx.ch>
branch 'default'
added unsupported/Eigen/CXX11/src/Tensor/TensorMetaMacros.h
added unsupported/test/cxx11_tensor_customIndex.cpp
changed unsupported/Eigen/CXX11/Tensor
changed unsupported/Eigen/CXX11/src/Tensor/Tensor.h
changed unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
changed unsupported/test/CMakeLists.txt
* The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class
* The assignment itself is now a regular node on the expression tree
* The expression evaluators start by recursively evaluating all their subexpressions if needed
Remove the symCoeff() method of the the Tensor module and move the
functionality into a new operator() of the symmetry classes. This makes
the Tensor module now completely self-contained without symmetry
support (even though previously it was only a forward declaration and a
otherwise harmless trivial templated method) and also removes the
inconsistency with the rest of eigen w.r.t. the method's naming scheme.
Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
* Added ability to map a region of the memory to a tensor
* Added basic support for unary and binary coefficient wise expressions, such as addition or square root
* Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.
