Class AffinityPropagationScikitsLearnNode

Perform Affinity Propagation Clustering of data.

This node has been automatically generated by wrapping the ``sklearn.cluster.affinity_propagation_.AffinityPropagation`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <affinity_propagation>`.

**Parameters**

damping : float, optional, default: 0.5
    Damping factor between 0.5 and 1.

convergence_iter : int, optional, default: 15
    Number of iterations with no change in the number
    of estimated clusters that stops the convergence.

max_iter : int, optional, default: 200
    Maximum number of iterations.

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`` and
    ``euclidean`` are supported. ``euclidean`` uses the
    negative squared euclidean distance between points.

verbose : boolean, optional, default: False
    Whether to be verbose.


**Attributes**

``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.

**Notes**

See examples/cluster/plot_affinity_propagation.py for an example.

The algorithmic complexity of affinity propagation is quadratic
in the number of points.

**References**


Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

Overrides: object.__init__

Predict the closest cluster each sample in X belongs to.

This node has been automatically generated by wrapping the sklearn.cluster.affinity_propagation_.AffinityPropagation class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

New data to predict.

Returns

labels : array, shape (n_samples,)

Index of the cluster each sample belongs to.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Create affinity matrix from negative euclidean distances, then apply affinity propagation clustering.

This node has been automatically generated by wrapping the sklearn.cluster.affinity_propagation_.AffinityPropagation class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X: array-like, shape (n_samples, n_features) or (n_samples, n_samples)

Data matrix or, if affinity is precomputed, matrix of similarities / affinities.

Overrides: Node.stop_training