Package mdp :: Package nodes :: Class WardAgglomerationScikitsLearnNode

Class WardAgglomerationScikitsLearnNode

Feature agglomeration based on Ward hierarchical clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

n_clusters : int or ndarray: The number of clusters.
connectivity : sparse matrix: connectivity matrix. Defines for each feature the neigbhoring features following a given structure of the data. Defaut is None, i.e, the hiearchical agglomeration algorithm is unstructured.
memory : Instance of joblib.Memory or string: Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
copy : bool: Copy the connectivity matrix or work inplace.
n_components : int (optional): The number of connected components in the graph defined by the connectivity matrix. If not set, it is estimated.

Methods

fit:

Compute the clustering of features

Attributes

children_ : array-like, shape = [n_nodes, 2]: List of the children of each nodes. Leaves of the tree do not appear.
labels_ : array [n_points]: cluster labels for each point
n_leaves_ : int: Number of leaves in the hiearchical tree.

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Feature agglomeration based on Ward hierarchical clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

_execute(self, x)

list

_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)
Transform a new matrix using the built clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

stop_training(self, **kwargs)
Fit the hierarchical clustering on the data This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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 numpy.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.

bool

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)

n_clusters : int or ndarray: The number of clusters.
connectivity : sparse matrix: connectivity matrix. Defines for each feature the neigbhoring features following a given structure of the data. Defaut is None, i.e, the hiearchical agglomeration algorithm is unstructured.
memory : Instance of joblib.Memory or string: Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.
copy : bool: Copy the connectivity matrix or work inplace.
n_components : int (optional): The number of connected components in the graph defined by the connectivity matrix. If not set, it is estimated.

Methods

fit:

Compute the clustering of features

Attributes

children_ : array-like, shape = [n_nodes, 2]: List of the children of each nodes. Leaves of the tree do not appear.
labels_ : array [n_points]: cluster labels for each point
n_leaves_ : int: Number of leaves in the hiearchical tree.

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.

Returns: list: The list of dtypes supported by this node.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

Transform a new matrix using the built clustering This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration 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]: A M by N array of M observations in N dimensions or a length M array of M one-dimensional observations.
pooling_func : a function that takes an array of shape = [M, N] and: return an array of value of size M. Defaut is np.mean

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.

Returns: bool: A boolean indication whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Fit the hierarchical clustering on the data This node has been automatically generated by wrapping the scikits.learn.cluster.hierarchical.WardAgglomeration 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]: The data

Returns

self

Overrides: Node.stop_training

Class WardAgglomerationScikitsLearnNode

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

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

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

is_invertible()
Static Method

is_trainable()
Static Method