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| author | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-24 17:13:33 +0200 |
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| committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-24 17:13:33 +0200 |
| commit | 4e2f6b45662fe206414652ccc8f715c420f3b9cd (patch) | |
| tree | e3b16e092590986db9a32014f9b1a53d2903b3e6 /docs/source/howto.rst | |
| parent | 8c935200558a1071d8df33bc752afed60e2f49f7 (diff) | |
first shot part OT Wass
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| -rw-r--r-- | docs/source/howto.rst | 25 |
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diff --git a/docs/source/howto.rst b/docs/source/howto.rst deleted file mode 100644 index 48b1532..0000000 --- a/docs/source/howto.rst +++ /dev/null @@ -1,25 +0,0 @@ - -How to ? -======== - -In the following we provide some pointers about which functions and classes -to use for different problems related to optimal transport (OTs). - -1. **How to solve a discrete optimal transport problem ?** - - The solver for discrete is the function :py:mod:`ot.emd` that returns - the OT transport matrix. If you want to solve a regularized OT you can - use :py:mod:`ot.sinkhorn`. - - More detailed examples can be seen on this :ref:`auto_examples/plot_OT_2D_samples` - - Here is a simple use case: - - .. code:: python - - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - T=ot.emd(a,b,M) # exact linear program - T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - - |