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[MRG] DOC fixed AffinityPropagation default values #15777

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Merged
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Dec 10, 2019
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[MRG] DOC fixed AffinityPropagation default values #15777

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659f294
fixed default values
Dec 4, 2019
76a808e
fixed datatype of attributes
Dec 4, 2019
6ecb477
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glemaitre Dec 10, 2019
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26 changes: 13 additions & 13 deletions sklearn/cluster/_affinity_propagation.py
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Expand Up @@ -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
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