diff options
Diffstat (limited to 'src/python/doc')
-rw-r--r-- | src/python/doc/representations.rst | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst index b338f7f0..409e97da 100644 --- a/src/python/doc/representations.rst +++ b/src/python/doc/representations.rst @@ -10,7 +10,7 @@ Representations manual This module, originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning, by providing implementations of most of the vector representations for persistence diagrams in the literature, in a scikit-learn format. More specifically, it provides tools, using the scikit-learn standard interface, to compute distances and kernels on persistence diagrams, and to convert these diagrams into vectors in Euclidean space. -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 @@ -66,4 +66,4 @@ The output is: .. testoutput:: - [[0. 1.25707872 2.51415744 1.88561808 0.7856742 2.04275292 3.29983165 2.51415744 1.25707872 0. 0. 0. 0.31426968 0. 0.62853936 0. 0. 0.31426968 1.25707872 0. ]] + [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304 2.95699199 3.08555686 0. 0.64282435 0. 0. 0.51425948 0. 0. 0. ]] |