Package mdp :: Package nodes :: Class ICANode
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Class ICANode



ICANode is a general class to handle different batch-mode algorithm for
Independent Component Analysis. More information about ICA can be found
among others in
Hyvarinen A., Karhunen J., Oja E. (2001). Independent Component Analysis,
Wiley.

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]
 
__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.
 
core(self, data)
This is the core routine of the ICANode.
 
stop_training(self, *args, **kwargs)
Whiten data if needed and call the 'core' routine to perform ICA.

Inherited from unreachable.ProjectMatrixMixin: get_projmatrix, get_recmatrix

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

    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]

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

 

Input arguments:

whitened -- Set whitened is True if input data are already whitened.
            Otherwise the node will whiten the data itself.

white_comp -- If whitened is 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.

telescope -- If telescope == True, use Telescope mode: Instead of
  using all input data in a single batch try larger and larger chunks
  of the input data until convergence is achieved. This should lead to
  significantly faster convergence for stationary statistics. This mode
  has not been thoroughly tested and must be considered beta.

Overrides: object.__init__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node.

Overrides: Node._get_supported_dtypes

_inverse(self, y)

 
Overrides: Node._inverse

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_stop_training(self)

 
Whiten data if needed and call the 'core' routine to perform ICA.
Take care of telescope-mode if needed.

Overrides: Node._stop_training

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.

stop_training(self, *args, **kwargs)

 
Whiten data if needed and call the 'core' routine to perform ICA.
Take care of telescope-mode if needed.

Overrides: Node.stop_training