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

Perform Independent Component Analysis using the JADE algorithm.

Note that JADE is a batch-algorithm. This means that it needs all input data before it can start and compute the ICs. The algorithm is here given as a Node for convenience, but it actually accumulates all inputs it receives. Remember that to avoid running out of memory when you have many components and many time samples.

JADE does not support the telescope mode.


Reference

Cardoso, Jean-Francois and Souloumiac, Antoine (1993). Blind beamforming for non Gaussian signals. Radar and Signal Processing, IEE Proceedings F, 140(6): 362-370.

Cardoso, Jean-Francois (1999). High-order contrasts for independent component analysis. Neural Computation, 11(1): 157-192.

Original code contributed by: Gabriel Beckers (2008).


History

Instance Methods [hide private]
 
__init__(self, limit=0.001, max_it=1000, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
Initializes an object of type 'JADENode'.
 
core(self, data)
This is the core routine of the ICANode.

Inherited from unreachable.ProjectMatrixMixin: get_projmatrix, get_recmatrix

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 ICANode
 
_execute(self, x)
 
_inverse(self, y)
 
_set_input_dim(self, n)
 
_stop_training(self)
Whiten data if needed and call the 'core' routine to perform ICA.
 
execute(self, x)
Process the data contained in x.
 
inverse(self, y)
Invert y.
 
stop_training(self)
Whiten data if needed and call the 'core' routine to perform ICA.
    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_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_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_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.
 
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]
    Inherited from Node
 
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, limit=0.001, max_it=1000, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
(Constructor)

 
Initializes an object of type 'JADENode'.
Parameters:
  • limit (float) - Convergence threshold.
  • max_it (int) - Maximum number of iterations
  • verbose (bool) - Idicates whether information is to be reported about the operation.
  • whitened (bool) - Set whitened == True if input data are already whitened. Otherwise the node will whiten the data itself.
  • white_comp (int) - If whitened == False, you can set 'white_comp' to the number of whitened components to keep during the calculation (i.e., the input dimensions are reduced to white_comp by keeping the components of largest variance).
  • white_parm (dict) - A dictionary with additional parameters for whitening. It is passed directly to the WhiteningNode constructor. Ex: white_parm = { 'svd' : True }
  • input_dim (int) - The input dimensionality.
  • dtype (numpy.dtype or str) - The datatype.
Overrides: object.__init__

core(self, data)

 

This is the core routine of the ICANode.

Each subclass must define this function to return the achieved convergence value. This function is also responsible for setting the ICA filters matrix self.filters.

Note: The matrix self.filters is applied to the right of the matrix containing input data. This is the transposed of the matrix defining the linear transformation.
Parameters:
  • data - The data you want to perform ICA on.
Returns:
The achieved convergence value.
Overrides: ICANode.core
(inherited documentation)

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Modular toolkit for Data Processing MDP
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