Package mdp :: Package nodes :: Class SFANode

Class SFANode

Extract the slowly varying components from the input data.
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).

Internal variables of interest:
self.avg -- Mean of the input data (available after training)
self.sf -- Matrix of the SFA filters (available after training)
self.d -- Delta values corresponding to the SFA components
          (generalized eigenvalues).
          (See the docs of the 'get_eta_values' method for
          more information)

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

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__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.

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_inverse(self, y)

_set_range(self)

_stop_training(self, debug=False)

_train(self, x)

execute(self, x, *args, **kargs)
Compute the output of the slowest functions.

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 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)

_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_input_dim(self, n)

_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_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.

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

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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

_get_supported_dtypes(self)

Return the list of dtypes supported by this node.
The types can be specified in any format allowed by numpy.dtype.

Overrides: Node._get_supported_dtypes: (inherited documentation)

_inverse(self, y)

Overrides: Node._inverse

_set_range(self)

_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_eta_values(self, t=1)

Return the eta values of the slow components learned during
the training phase. If the training phase has not been completed
yet, call stop_training.

The delta value of a signal is a measure of its temporal
variation, and is defined as the mean of the derivative squared,
i.e. delta(x) = mean(dx/dt(t)^2).  delta(x) is zero if
x is a constant signal, and increases if the temporal variation
of the signal is bigger.

The eta value is a more intuitive measure of temporal variation,
defined as
    eta(x) = t/(2*pi) * sqrt(delta(x))
If x is a signal of length 't' which consists of a sine function
that accomplishes exactly N oscillations, then eta(x)=N.

Input arguments:
t -- Sampling frequency in Hz
     The original definition in (Wiskott and Sejnowski, 2002)
     is obtained for t = number of training data points, while
     for t=1 (default), this corresponds to the beta-value defined in
     (Berkes and Wiskott, 2005).

time_derivative(self, x)

Compute the linear approximation of the time derivative.

Class SFANode

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

_execute(self, x, range=None)

_get_supported_dtypes(self)

_inverse(self, y)

_set_range(self)

_stop_training(self, debug=False)

_train(self, x)

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

get_eta_values(self, t=1)

time_derivative(self, x)

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