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).

Instance 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]

Special arguments for constructor

include_last_sample

If False the train method discards the last sample in every chunk during training when calculating the covariance matrix. The last sample is in this case only used for calculating the covariance matrix of the derivatives. The switch should be set to False if you plan to train with several small chunks. For example we can split a sequence (index is time):

x_1 x_2 x_3 x_4

in smaller parts like this:

x_1 x_2
x_2 x_3
x_3 x_4

The SFANode will see 3 derivatives for the temporal covariance matrix, and the first 3 points for the spatial covariance matrix. Of course you will need to use a generator that connects the small chunks (the last sample needs to be sent again in the next chunk). If include_last_sample was True, depending on the generator you use, you would either get:

x_1 x_2
x_2 x_3
x_3 x_4

in which case the last sample of every chunk would be used twice when calculating the covariance matrix, or:

x_1 x_2
x_3 x_4

in which case you loose the derivative between x_3 and x_2.

If you plan to train with a single big chunk leave include_last_sample to the default value, i.e. True.

You can even change this behaviour during training. Just set the corresponding switch in the train method.