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| author | Marc Glisse <marc.glisse@inria.fr> | 2019-11-26 23:08:53 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2019-11-29 10:52:10 +0100 |
| commit | fe3bbb9b3de5001ba943d3be7109712847ec44ef (patch) | |
| tree | 342d67fdc5a661d2238e2444f1dd778647c5c0b1 /src/python/doc/representations.rst | |
| parent | 8ebfb8c5de9c55a20e3dafebc8f506ccb698bb68 (diff) | |
Fix various links for sphinx
and some minor doc changes along the way.
(why were we documenting a hasse diagram that doesn't exist?)
Diffstat (limited to 'src/python/doc/representations.rst')
| -rw-r--r-- | src/python/doc/representations.rst | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst index c870f834..b3131a25 100644 --- a/src/python/doc/representations.rst +++ b/src/python/doc/representations.rst @@ -10,7 +10,7 @@ Representations manual This module, originally named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning tools, in particular scikit-learn. It provides tools, using the scikit-learn standard interface, to compute distances and kernels on diagrams, and to convert diagrams into vectors. -A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. +A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time. A small example is provided |