Package mdp :: Package nodes :: 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 BiMDP. For example::

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

this will run training and execution with bimdp 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. 
http://cogprints.org/7056/1/SprekelerZitoWiskott-Cogprints-2010.pdf

Instance Methods [hide private]
 
__init__(self, basic_exp=None, intern_exp=None, svd=False, verbose=False, input_dim=None, output_dim=None, dtype=None)
:Keywords: basic_exp a tuple ``(node, args, kwargs)`` defining the node used for the basic nonlinear expansion.
 
_check_train_args(self, x)
 
_execute(self, x)
 
_get_source_extractor(self, dim, nsources)
 
_get_train_seq(self)
 
_initialize_internal_flow(self)
 
_set_input_dim(self, n)
 
_stop_training(self)
 
_train(self, x)
 
execute(self, x)
Process the data contained in `x`.
 
stop_training(self)
Stop the training phase.
 
train(self, x)
Update the internal structures according to the input data `x`.

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 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)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_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=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 :numpy:`dtype` objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert `y`.
 
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.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
    Inherited from Node
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]
  flow
Read-only internal flow property.

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

 

:Keywords:
   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.

     Default: False

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_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

execute(self, x)

 
Process the data contained in `x`.

If the object is still in the training phase, the function
`stop_training` will be called.
`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_execute` to implement
their execution phase. The docstring of the `_execute` method
overwrites this docstring.

Overrides: Node.execute

is_invertible()
Static Method

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

Overrides: Node.is_invertible
(inherited documentation)

stop_training(self)

 
Stop the training phase.

By default, subclasses should overwrite `_stop_training` to implement
this functionality. The docstring of the `_stop_training` method
overwrites this docstring.

Overrides: Node.stop_training

train(self, x)

 
Update the internal structures according to the input data `x`.

`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_train` to implement their
training phase. The docstring of the `_train` method overwrites this
docstring.

Note: a subclass supporting multiple training phases should implement
the *same* signature for all the training phases and document the
meaning of the arguments in the `_train` method doc-string. Having
consistent signatures is a requirement to use the node in a flow.

Overrides: Node.train

Property Details [hide private]

flow

Read-only internal flow property.

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