Package mdp :: Package nodes :: Class EtaComputerNode

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 method get_eta to access them.

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. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

_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. If the training phase has not been completed yet, call stop_training.

stop_training(self)
Stop the training phase.

train(self, data)
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)

_check_train_args(self, x, *args, **kwargs)

_execute(self, x)

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

_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=None)
Return a deep copy of the node.

execute(self, x, *args, **kwargs)
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 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. If filename is None, return a string.

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]

Inherited from Node

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

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

[hide private]

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, 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)

_init_internals(self)

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.

Parameters:

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).

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, data)

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

Class EtaComputerNode

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

_init_internals(self)

_set_input_dim(self, n)

_stop_training(self)

_train(self, data)

get_eta(self, t=1)

stop_training(self)

train(self, data)

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