Package mdp :: Package nodes :: Class JADENode

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.

Main references:

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:

May 2005 version 1.8 for MATLAB released by Jean-Francois Cardoso
Dec 2007 MATLAB version 1.8 ported to Python/NumPy by Gabriel Beckers
Feb 15 2008 Python/NumPy version adapted for MDP by Gabriel Beckers

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)
Input arguments:

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 that 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.

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. Take care of telescope-mode if needed.

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. Take care of telescope-mode if needed.

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 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)

Input arguments:

General:

whitened -- Set whitened == True if input data are already whitened.: Otherwise the node will whiten the data itself
white_comp -- 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 -- a dictionary with additional parameters for whitening.: It is passed directly to the WhiteningNode constructor. Ex: white_parm = { 'svd' : True }

limit -- convergence threshold.

Specific for JADE:

max_it -- maximum number of iterations

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 that 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.

Overrides: ICANode.core: (inherited documentation)

Class JADENode

__init__(self, limit=0.001, max_it=1000, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None) (Constructor)

core(self, data)

init(self, limit=0.001, max_it=1000, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
(Constructor)