DOC fix docstring of AffinityPropagation based on sklearn guideline
@@ -242,51 +242,51 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
Parameters
----------
- damping : float, optional, default: 0.5
+ damping : float, default=0.5
Damping factor (between 0.5 and 1) is the extent to
which the current value is maintained relative to
incoming values (weighted 1 - damping). This in order
to avoid numerical oscillations when updating these
values (messages).
- max_iter : int, optional, default: 200
+ max_iter : int, default=200
Maximum number of iterations.
- convergence_iter : int, optional, default: 15
+ convergence_iter : int, default=15
Number of iterations with no change in the number
of estimated clusters that stops the convergence.
- copy : boolean, optional, default: True
+ copy : bool, default=True
Make a copy of input data.
- preference : array-like, shape (n_samples,) or float, optional
+ preference : array-like of shape (n_samples,) or float, default=None
Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number
of exemplars, ie of clusters, is influenced by the input
preferences value. If the preferences are not passed as arguments,
they will be set to the median of the input similarities.
- affinity : string, optional, default=``euclidean``
- Which affinity to use. At the moment ``precomputed`` and
- ``euclidean`` are supported. ``euclidean`` uses the
+ affinity : {'euclidean', 'precomputed'}, default='euclidean'
+ Which affinity to use. At the moment 'precomputed' and
+ ``euclidean`` are supported. 'euclidean' uses the
negative squared euclidean distance between points.
- verbose : boolean, optional, default: False
+ verbose : bool, default=False
Whether to be verbose.
Attributes
----------
- cluster_centers_indices_ : array, shape (n_clusters,)
+ cluster_centers_indices_ : ndarray of shape (n_clusters,)
Indices of cluster centers
- cluster_centers_ : array, shape (n_clusters, n_features)
+ cluster_centers_ : ndarray of shape (n_clusters, n_features)
Cluster centers (if affinity != ``precomputed``).
- labels_ : array, shape (n_samples,)
+ labels_ : ndarray of shape (n_samples,)
Labels of each point
- affinity_matrix_ : array, shape (n_samples, n_samples)
+ affinity_matrix_ : ndarray of shape (n_samples, n_samples)
Stores the affinity matrix used in ``fit``.
n_iter_ : int
