From 177e80b653d60119acb4455feaba02615083532b Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Tue, 26 Nov 2019 17:51:50 +0100 Subject: Fix link. --- src/python/doc/representations.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'src/python/doc/representations.rst') 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 -- cgit v1.2.3