From 4ae7e98b255bef3522e76b2c321b6938378d8dc7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jun 2019 19:11:21 +0200 Subject: debut doc --- docs/source/howto.rst | 25 +++++++++++++++++++++++++ docs/source/index.rst | 1 + 2 files changed, 26 insertions(+) create mode 100644 docs/source/howto.rst (limited to 'docs') diff --git a/docs/source/howto.rst b/docs/source/howto.rst new file mode 100644 index 0000000..48b1532 --- /dev/null +++ b/docs/source/howto.rst @@ -0,0 +1,25 @@ + +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 + + diff --git a/docs/source/index.rst b/docs/source/index.rst index b8eabcb..d92f50f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,6 +13,7 @@ Contents :maxdepth: 3 self + howto all auto_examples/index -- cgit v1.2.3