AffinityPropagation
¶
-
class
ibex.sklearn.cluster.
AffinityPropagation
(damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False)¶ Bases:
sklearn.cluster.affinity_propagation_.AffinityPropagation
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Perform Affinity Propagation Clustering of data.
Read more in the User Guide.
- damping : float, optional, 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
- Maximum number of iterations.
- convergence_iter : int, optional, default: 15
- Number of iterations with no change in the number of estimated clusters that stops the convergence.
- copy : boolean, optional, default: True
- Make a copy of input data.
- preference : array-like, shape (n_samples,) or float, optional
- 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
andeuclidean
are supported.euclidean
uses the negative squared euclidean distance between points. - verbose : boolean, optional, default: False
- Whether to be verbose.
- cluster_centers_indices_ : array, shape (n_clusters,)
- Indices of cluster centers
- cluster_centers_ : array, shape (n_clusters, n_features)
- Cluster centers (if affinity !=
precomputed
). - labels_ : array, shape (n_samples,)
- Labels of each point
- affinity_matrix_ : array, shape (n_samples, n_samples)
- Stores the affinity matrix used in
fit
. - n_iter_ : int
- Number of iterations taken to converge.
For an example, see examples/cluster/plot_affinity_propagation.py.
The algorithmic complexity of affinity propagation is quadratic in the number of points.
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
-
fit
(X, y=None)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
- Create affinity matrix from negative euclidean distances, then
apply affinity propagation clustering.
- X : array-like, shape (n_samples, n_features) or (n_samples, n_samples)
- Data matrix or, if affinity is
precomputed
, matrix of similarities / affinities.
y : Ignored
- A parameter
-
fit_predict
(X, y=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Performs clustering on X and returns cluster labels.
- X : ndarray, shape (n_samples, n_features)
- Input data.
- y : ndarray, shape (n_samples,)
- cluster labels
- A parameter
-
predict
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Predict the closest cluster each sample in X belongs to.
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- New data to predict.
- labels : array, shape (n_samples,)
- Index of the cluster each sample belongs to.
- A parameter
- A parameter