Package mdp :: Package nodes :: Class CuBICANode
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Class CuBICANode



Perform Independent Component Analysis using the CuBICA algorithm.
Note that CuBICA is a batch-algorithm, which 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.

As an alternative to this batch mode you might consider the telescope
mode (see the docs of the __init__ function).

Reference:
Blaschke, T. and Wiskott, L. (2003).
CuBICA: Independent Component Analysis by Simultaneous Third- and
Fourth-Order Cumulant Diagonalization.
IEEE Transactions on Signal Processing, 52(5), pp. 1250-1256.


Internal variables of interest:

  self.white       -- the whitening node used for preprocessing.
  self.filters     -- the ICA filters matrix (this is the transposed of the
                    projection matrix after whitening).
  self.convergence -- the value of the convergence threshold.

Nested Classes [hide private]
    Inherited from Node
  __metaclass__
This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc...
Instance Methods [hide private]
 
core(self, data)
This is the core routine of the ICANode.

Inherited from unreachable.ProjectMatrixMixin: get_projmatrix, get_recmatrix

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

    Inherited from ICANode
 
__init__(self, limit=0.001, telescope=False, verbose=False, whitened=False, white_comp=None, white_parm=None, input_dim=None, dtype=None)
Input arguments:
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_inverse(self, y)
 
_set_input_dim(self, n)
 
_stop_training(self)
Whiten data if needed and call the 'core' routine to perform ICA.
 
stop_training(self, *args, **kwargs)
Whiten data if needed and call the 'core' routine to perform ICA.
    Inherited from Cumulator
 
_train(self, x)
Cumulate all input data in a one dimensional list.
 
train(self, x, *args, **kwargs)
Cumulate all input data in a one dimensional list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kargs)
Calling an instance of Node is equivalent to call 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)
 
_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=-1)
Return a deep copy of the node.
 
execute(self, x, *args, **kargs)
Process the data contained in 'x'.
 
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.
 
inverse(self, y, *args, **kargs)
Invert 'y'.
 
is_invertible(self)
Return True if the node can be inverted, False otherwise.
 
is_trainable(self)
Return True if the node can be trained, False otherwise.
 
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'.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ...
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

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)