Class FastICANode

   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 FastICA:

   approach  -- Approach to use. Possible values are:
                                     'defl' --> deflation
                                     'symm' --> symmetric

          g  -- Nonlinearity to use. Possible values are:
                                     'pow3' --> x^3
                                     'tanh' --> tanh(fine_tanh*x)
                                     'gaus' --> x*exp(-fine_gaus*x^2/2)
                                     'skew' --> x^2 (for skewed signals)

      fine_g -- Nonlinearity for fine tuning. Possible values
                are the same as for 'g'. Set it to None to disable fine
                tuning.

          mu -- Step size. If mu != 1, a stabilization procedure is used:
                the value of mu can momentarily be halved if the algorithm
                is stuck between two points (this is called a stroke).
                Also if there is no convergence before half of the maximum
                number of iterations has been reached then mu will be halved
                for the rest of the rounds.

 sample_size -- Percentage of samples used in one iteration. If
                sample_size < 1, samples are chosen in random order.

coarse_limit -- initial convergence threshold, to switch to
                fine_g function (i.e. linear to non-linear) even
                before reaching the limit and final tuning. Set
                it to a value higher than limit to be in effect.

   fine_tanh -- parameter for 'tanh' nonlinearity
   fine_gaus -- parameter for 'gaus' nonlinearity

       guess -- initial guess for the mixing matrix (ignored if None)

      max_it -- maximum number of iterations

 max_it_fine -- maximum number of iterations for fine tuning

    failures -- maximum number of failures to allow in deflation mode

   

Overrides: object.__init__

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)