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| author | vishwakftw <cs15btech11043@iith.ac.in> | 2019-03-26 07:49:58 -0700 |
|---|---|---|
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | 2019-03-26 07:53:07 -0700 |
| commit | 5e462a3ed6ec88a022e1ab4d60f9e2282e96ec44 (patch) | |
| tree | 0e23d6442a8e7d39c3fd1f7b7dfc09d8303b894c /docs/source | |
| parent | 9080942afb0f53be24efd3e213a39f1c270b8e5c (diff) | |
| download | pytorch-5e462a3ed6ec88a022e1ab4d60f9e2282e96ec44.tar.gz pytorch-5e462a3ed6ec88a022e1ab4d60f9e2282e96ec44.tar.bz2 pytorch-5e462a3ed6ec88a022e1ab4d60f9e2282e96ec44.zip | |
Introduce SobolEngine (#10505)
Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.
Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.
This closes #9332 .
cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505
Reviewed By: zou3519
Differential Revision: D9330179
Pulled By: ezyang
fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
Diffstat (limited to 'docs/source')
| -rw-r--r-- | docs/source/torch.rst | 7 |
1 files changed, 7 insertions, 0 deletions
diff --git a/docs/source/torch.rst b/docs/source/torch.rst index 782f58ef31..101312817c 100644 --- a/docs/source/torch.rst +++ b/docs/source/torch.rst @@ -107,6 +107,13 @@ There are a few more in-place random sampling functions defined on Tensors as we - :func:`torch.Tensor.random_` - numbers sampled from the discrete uniform distribution - :func:`torch.Tensor.uniform_` - numbers sampled from the continuous uniform distribution +Quasi-random sampling +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: torch.quasirandom.SobolEngine + :members: + :exclude-members: MAXBIT, MAXDIM + :undoc-members: Serialization ---------------------------------- |