Package mdp :: Package nodes :: Class SFA2Node
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Class SFA2Node


Get an input signal, expand it in the space of
inhomogeneous polynomials of degree 2 and extract its slowly varying
components. The 'get_quadratic_form' method returns the input-output
function of one of the learned unit as a QuadraticForm object.
See the documentation of mdp.utils.QuadraticForm for additional
information.

More information about Slow Feature Analysis can be found in
Wiskott, L. and Sejnowski, T.J., Slow Feature Analysis: Unsupervised
Learning of Invariances, Neural Computation, 14(4):715-770 (2002).

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, input_dim=None, output_dim=None, dtype=None)
If the input dimension and the output dimension are unspecified, they will be set when the 'train' or 'execute' method is called for the first time.
 
_execute(self, x, range=None)
Compute the output of the slowest functions.
 
_set_input_dim(self, n)
 
_set_range(self)
 
_stop_training(self, debug=False)
 
_train(self, x)
 
execute(self, x, *args, **kargs)
Compute the output of the slowest functions.
 
get_quadratic_form(self, nr)
Return the matrix H, the vector f and the constant c of the quadratic form 1/2 x'Hx + f'x + c that defines the output of the component 'nr' of the SFA node.
 
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 SFANode
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_inverse(self, y)
 
get_eta_values(self, t=1)
Return the eta values of the slow components learned during the training phase.
 
time_derivative(self, x)
Compute the linear approximation of the time derivative.
    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.
 
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]

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, input_dim=None, output_dim=None, dtype=None)
(Constructor)

 
If the input dimension and the output dimension are
unspecified, they will be set when the 'train' or 'execute'
method is called for the first time.
If dtype is unspecified, it will be inherited from the data
it receives at the first call of 'train' or 'execute'.

Every subclass must take care of up- or down-casting the internal
structures to match this argument (use _refcast private
method when possible).

Overrides: object.__init__
(inherited documentation)

_execute(self, x, range=None)

 
Compute the output of the slowest functions.
if 'range' is a number, then use the first 'range' functions.
if 'range' is the interval=(i,j), then use all functions
           between i and j.

Overrides: Node._execute

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_set_range(self)

 
Overrides: SFANode._set_range

_stop_training(self, debug=False)

 
Overrides: Node._stop_training

_train(self, x)

 
Overrides: Node._train

execute(self, x, *args, **kargs)

 
Compute the output of the slowest functions.
if 'range' is a number, then use the first 'range' functions.
if 'range' is the interval=(i,j), then use all functions
           between i and j.

Overrides: Node.execute

get_quadratic_form(self, nr)

 

Return the matrix H, the vector f and the constant c of the
quadratic form 1/2 x'Hx + f'x + c that defines the output
of the component 'nr' of the SFA node.

is_invertible(self)

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

Overrides: Node.is_invertible