Package mdp :: Package nodes :: Class EtaComputerNode
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Class EtaComputerNode


Compute the eta values of the normalized training data.

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.

EtaComputerNode normalizes the training data to have unit
variance, such that it is possible to compare the temporal
variation of two signals independently from their scaling.

Reference: Wiskott, L. and Sejnowski, T.J. (2002).
Slow Feature Analysis: Unsupervised Learning of Invariances,
Neural Computation, 14(4):715-770.

Important: if a data chunk is tlen data points long, this node is
going to consider only the first tlen-1 points together with their
derivatives. This means in particular that the variance of the
signal is not computed on all data points. This behavior is
compatible with that of SFANode.

This is an analysis node, i.e. the data is analyzed during training
and the results are stored internally.  Use the functions
'get_eta' to access them.

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, 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.
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_init_internals(self)
 
_set_input_dim(self, n)
 
_stop_training(self)
 
_train(self, data)
 
get_eta(self, t=1)
Return the eta values of the data received during the training phase.

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)
 
_execute(self, x)
 
_get_train_seq(self)
 
_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_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 [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, 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)

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node.

Overrides: Node._get_supported_dtypes

_init_internals(self)

 

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, data)

 
Overrides: Node._train

get_eta(self, t=1)

 
Return the eta values of the data received during the training
phase. If the training phase has not been completed yet, call
stop_training.

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