diff options
| author | Marc Glisse <marc.glisse@inria.fr> | 2019-11-15 23:09:29 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2019-11-15 23:09:29 +0100 |
| commit | 57b86b2665cd0e35d18b697577b00c604212e369 (patch) | |
| tree | a109db95b131d7855ce44103ac4ec9b8e81269d9 | |
| parent | 908679b72c215d1914d8e3956126fa44367b937f (diff) | |
Token documentation
| -rw-r--r-- | src/python/doc/representations.rst | 19 |
1 files changed, 16 insertions, 3 deletions
diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst index 3db1c95a..a137a035 100644 --- a/src/python/doc/representations.rst +++ b/src/python/doc/representations.rst @@ -2,9 +2,22 @@ .. To get rid of WARNING: document isn't included in any toctree -=================================== -Representations reference manual -=================================== +====================== +Representations manual +====================== + +.. include:: representations_sum.inc + +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 small example is provided + +.. only:: builder_html + + * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>` + Preprocessing ------------- |