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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):715770.
Important: if a data chunk is tlen data points long, this node is going to consider only the first tlen1 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.


















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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

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 downcasting the internal structures to match this argument (use _refcast private method when possible).






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.

Update the internal structures according to the input data
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 docstring. Having consistent signatures is a requirement to use the node in a flow.

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