Package mdp :: Package nodes :: Class AffinityPropagationScikitsLearnNode
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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

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Perform Affinity Propagation Clustering of data.
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
 
_stop_training(self, **kwargs)
Concatenate the collected data in a single array.
 
execute(self, x)
Predict the closest cluster each sample in X belongs to.
 
stop_training(self, **kwargs)
Create affinity matrix from negative euclidean distances, then apply affinity propagation clustering.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

    Inherited from Cumulator
 
_train(self, *args)
Collect all input data in a list.
 
train(self, *args)
Collect all input data in a list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_check_train_args(self, x, *args, **kwargs)
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_pre_execution_checks(self, x)
This method contains all pre-execution checks.
 
_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.
 
_refcast(self, x)
Helper function to cast arrays to the internal dtype.
 
_set_dtype(self, t)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
copy(self, protocol=None)
Return a deep copy of the node.
 
get_current_train_phase(self)
Return the index of the current training phase.
 
get_dtype(self)
Return dtype.
 
get_input_dim(self)
Return input dimensions.
 
get_output_dim(self)
Return output dimensions.
 
get_remaining_train_phase(self)
Return the number of training phases still to accomplish.
 
get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert y.
 
is_training(self)
Return True if the node is in the training phase, False otherwise.
 
save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples:
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

 

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__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node._stop_training

execute(self, x)

 

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

is_invertible()
Static Method

 
Return True if the node can be inverted, False otherwise.
Overrides: Node.is_invertible
(inherited documentation)

is_trainable()
Static Method

 
Return True if the node can be trained, False otherwise.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

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