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Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class GrowingNeuralGasExpansionNode
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Class GrowingNeuralGasExpansionNode

Perform a trainable radial basis expansion, where the centers and sizes of the basis functions are learned through a growing neural gas.

The positions of RBFs correspond to position of the nodes of the neural gas The sizes of the RBFs correspond to mean distance to the neighbouring nodes.


Note: Adjust the maximum number of nodes to control the dimension of the expansion.

Reference

More information on this expansion type can be found in: B. Fritzke. Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Networks 7, p. 1441--1460 (1994).

Instance Methods [hide private]
 
__init__(self, start_poss=None, eps_b=0.2, eps_n=0.006, max_age=50, lambda_=100, alpha=0.5, d=0.995, max_nodes=100, input_dim=None, dtype=None)
Initializes an object of type 'GrowingNeuralGasExpansionNode'.
 
_execute(self, x)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
_stop_training(self)
 
execute(self, x)
Process the data contained in x.
 
stop_training(self)
Stop the training phase.

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 GrowingNeuralGasNode
 
_add_edge(self, from_, to_)
 
_add_node(self, pos)
 
_get_nearest_nodes(self, x)
Return the two nodes in the graph that are nearest to x and their squared distances.
 
_insert_new_node(self)
Insert a new node in the graph where it is more necessary (i.e. where the error is the largest).
 
_move_node(self, node, x, eps)
Move a node by eps in the direction x.
 
_remove_old_edges(self, edges)
Remove all edges older than the maximal age.
 
_train(self, input)
 
get_nodes_position(self)
tuple
nearest_neighbor(self, input)
Assign each point in the input data to the nearest node in the graph. Return the list of the nearest node instances, and the list of distances.
 
train(self, input)
Update the internal structures according to the input data x.
    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_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_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)
 
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.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Instance Variables [hide private]
    Inherited from GrowingNeuralGasNode
  graph
The corresponding mdp.graph.Graph object.
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, start_poss=None, eps_b=0.2, eps_n=0.006, max_age=50, lambda_=100, alpha=0.5, d=0.995, max_nodes=100, input_dim=None, dtype=None)
(Constructor)

  
Initializes an object of type 'GrowingNeuralGasExpansionNode'.

:param start_poss: Sequence of two arrays containing the position of the
    first two nodes in the GNG graph. If unspecified, the initial nodes
    are chosen with a random position generated from a gaussian 
    distribution with zero mean and unit variance.
:type start_poss: tuple or array

:param eps_b: Coefficient of movement of the nearest node to a new data
    point. Typical values are 0 < eps_b << 1 .
:type eps_b: float

:param eps_n: coefficient of movement of the neighbours of the nearest
    node to a new data point. Typical values are 0 < eps_n << eps_b .
:type eps_n: float

:param max_age: Remove an edge after `max_age` updates. Typical values are
    10 < max_age < lambda.
:type max_age: int

:param lambda_: Insert a new node after `lambda_` steps. Typical values are O(100).
:type lambda_: int

:param alpha: When a new node is inserted, multiply the error of the
    nodes from which it generated by 0<alpha<1. A typical value is 0.5.
:type alpha: float

:param d: Each step the error of the nodes are multiplied by 0<d<1.
    Typical values are close to 1.
:type d: float

:param max_nodes: Maximum number of nodes in the neural gas, therefore an
    upper bound to the output dimension of the expansion.
:type max_nodes: int

:param input_dim: The input dimensionality.
:type input_dim: int

:param dtype: The datatype.
:type dtype: numpy.dtype or str
Overrides: object.__init__

_execute(self, x)

 
Overrides: Node._execute

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_set_output_dim(self, n)

 
Overrides: Node._set_output_dim

_stop_training(self)

 
Overrides: Node._stop_training

execute(self, x)

 

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

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
(inherited documentation)

stop_training(self)

 

Stop the training phase.

By default, subclasses should overwrite _stop_training to implement this functionality. The docstring of the _stop_training method overwrites this docstring.

Overrides: Node.stop_training

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Modular toolkit for Data Processing MDP
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