diff options
Diffstat (limited to 'src/python/gudhi/clustering/tomato.py')
| -rw-r--r-- | src/python/gudhi/clustering/tomato.py | 7 |
1 files changed, 2 insertions, 5 deletions
diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py index e3eaa300..a3e304dc 100644 --- a/src/python/gudhi/clustering/tomato.py +++ b/src/python/gudhi/clustering/tomato.py @@ -9,11 +9,9 @@ from ._tomato import * class Tomato: """ - Clustering - This clustering algorithm needs a neighborhood graph on the points, and an estimation of the density at each point. A few possible graph constructions and density estimators are provided for convenience, but it is perfectly natural - to provide your own. In particular, we do not provide anything specific to cluster pixels on images yet. + to provide your own. Attributes ---------- @@ -92,10 +90,9 @@ class Tomato: raise ValueError("Cannot specify both a merge threshold and a number of clusters") def fit(self, X, y=None, weights=None): - # FIXME: Iterable -> Sequence? """ Args: - X ((n,d)-array of float|(n,n)-array of float|Iterable[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". + 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". weights (ndarray of shape (n_samples)): if density_type is 'manual', a density estimate at each point """ # TODO: First detect if this is a new call with the same data (only threshold changed?) |