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.

Note: - 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.

Reference

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

Instance Methods

[hide private]

__init__(self, input_dim=None, dtype=None)
Initializes an object of type 'EtaComputerNode'.

_init_internals(self)

_set_input_dim(self, n)

_stop_training(self)

_train(self, data)

numpy.ndarray

get_eta(self, t=1)
Return the eta values of the data received during the training phase.

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 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. 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)

Initializes an object of type 'EtaComputerNode'.

Parameters:

input_dim (int) - The input dimensionality.
dtype (numpy.dtype or str) - The datatype.

Overrides: object.__init__

_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 (int) - 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).

Returns: numpy.ndarray

The eta values of the data received.

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)