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| author | Marc Glisse <marc.glisse@inria.fr> | 2019-11-26 17:51:50 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2019-11-26 17:51:50 +0100 |
| commit | 177e80b653d60119acb4455feaba02615083532b (patch) | |
| tree | 75ea273ba3a82af59b2c94f69c6d740bb7d63acc | |
| parent | da22cbc891b2b7a9b326e3840533b4d3b49a26d3 (diff) | |
Fix link.
| -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 a137a035..c870f834 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 `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 |