Package mdp :: Package contrib :: Class XSFANode
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Class XSFANode


Perform Non-linear Blind Source Separation using Slow Feature Analysis.

This node is designed to iteratively extract statistically
independent sources from (in principle) arbitrary invertible
nonlinear mixtures. The method relies on temporal correlations in
the sources and consists of a combination of nonlinear SFA and a
projection algorithm. More details can be found in the reference
given below (once it's published).

The node has multiple training phases. The number of training
phases depends on the number of sources that must be
extracted. The recommended way of training this node is through a
container flow:

>>> flow = mdp.Flow([XSFANode()])
>>> flow.train(x)

doing so will automatically train all training phases. The argument
'x' to the flow.train method can be an array or a list of iterables
(see the section about Iterators in the MDP tutorial for more info).

If the number of training samples is large, you may run into
memory problems: use data iterators and chunk training to reduce
memory usage.

If you need to debug training and/or execution of this node, the
suggested approach is to use the capabilities of mdp.binet. For example:

>>> flow = mdp.Flow([XSFANode()])
>>> tr_filename = binet.show_training(flow=flow, data_iterators=x)
>>> ex_filename, out = binet.show_execution(flow, x=x)

this will run training and execution with binet inspection. Snapshots
of the internal flow state for each training phase and execution step
will be opened in a web brower and presented as a slideshow.

References:
Sprekeler, H., Zito, T., and Wiskott, L. (2009).
An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
Journal of Machine Learning Research, submitted
[pdf link follows]

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, basic_exp=None, intern_exp=None, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
Keyword arguments:
 
_check_train_args(self, x)
 
_execute(self, x)
 
_get_source_extractor(self, dim, nsources)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_get_train_seq(self)
 
_initialize_internal_flow(self)
 
_set_input_dim(self, n)
 
_stop_training(self)
 
_train(self, x)
 
is_invertible(self)
Return True if the node can be inverted, False otherwise.

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

    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)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_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_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.
 
stop_training(self, *args, **kwargs)
Stop the training phase.
 
train(self, x, *args, **kwargs)
Update the internal structures according to the input data 'x'.
Properties [hide private]
  flow
Read-only internal flow property.

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, basic_exp=None, intern_exp=None, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

 

Keyword arguments:

  basic_exp --  a tuple (node, args, kwargs) defining the node
                used for the basic nonlinear expansion.  It is
                assumed that the mixture is linearly invertible
                after this expansion. The higher the complexity
                of the nonlinearity, the higher are the chances
                of inverting the unknown mixture. On the other
                hand, high complexity of the nonlinear
                expansion increases the danger of numeric
                instabilities, which can cause singularities in
                the simulation or errors in the source
                estimation. The trade-off has to be evaluated
                carefully.  Default:
                (mdp.nodes.PolynomialExpansionNode, (2, ), {})

  intern_exp -- a tuple (node, args, kwargs) defining the node
                used for the internal nonlinear expansion of
                the estimated sources to be removed from the input space.
                The same trade-off as for basic_exp is valid here.
                Default:
                (mdp.nodes.PolynomialExpansionNode, (10, ), {})

         svd -- enable Singular Value Decomposition for normalization
                and regularization. Use it if the node complains about
                singular covariance matrices.

     verbose -- show some progress during training.

Overrides: object.__init__

_check_train_args(self, x)

 
Overrides: Node._check_train_args

_execute(self, x)

 
Overrides: Node._execute

_get_source_extractor(self, dim, nsources)

 

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node.

Overrides: Node._get_supported_dtypes

_get_train_seq(self)

 
Overrides: Node._get_train_seq

_initialize_internal_flow(self)

 

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, x)

 
Overrides: Node._train

is_invertible(self)

 
Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible
(inherited documentation)

Property Details [hide private]

flow

Read-only internal flow property.

Get Method:
unreachable.flow(self) - Read-only internal flow property.