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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-06-19 15:38:41 +0200 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2020-06-19 15:38:41 +0200 |
| commit | 8723a3a88b35ee4b08115b07fb0f5fe813567a87 (patch) | |
| tree | 2e1fc9b4b24d163d7255189d3a77f7999e525f8c /src/python/gudhi | |
| parent | f2ebd5d52eb2005d84c4abb631aaae6caf1f0157 (diff) | |
Mention the 2 billion limit in the doc
Diffstat (limited to 'src/python/gudhi')
| -rw-r--r-- | src/python/gudhi/clustering/tomato.py | 1 |
1 files changed, 1 insertions, 0 deletions
diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py index 133754b4..fbba3cc8 100644 --- a/src/python/gudhi/clustering/tomato.py +++ b/src/python/gudhi/clustering/tomato.py @@ -107,6 +107,7 @@ class Tomato: X ((n,d)-array of float|(n,n)-array of float|Sequence[Iterable[int]]): coordinates of the points, or distance matrix (full, not just a triangle) if metric is "precomputed", or list of neighbors for each point (points are represented by their index, starting from 0) if graph_type is "manual". + The number of points is currently limited to about 2 billion. weights (ndarray of shape (n_samples)): if density_type is 'manual', a density estimate at each point y: Not used, present here for API consistency with scikit-learn by convention. """ |