Slow Feature Analysis

Slow Feature Analysis.

This module provides functionality for slow feature analysis. A helpful article is hosted at scholarpedia.

class breze.learn.sfa.SlowFeatureAnalysis(n_components=None)

Class for performing Slow feature analysis.

Attributes

n_components

(integer) Number of components to keep.

Methods

fit(X)	Fit the parameters of the model.
transform(X)	Transform data according to the model.

__init__(n_components=None)

Create a SlowFeatureAnalysis object.

Parameters:

n_components : integer

Amount of components to keep.

fit(X)

Fit the parameters of the model.

The data should be centered (that is, its mean subtracted rowwise) and white (e.g. via pca.Pca) before using this method.

Parameters:

X : list of array_like

A list of sequences. Each entry is expected to be an array of shape (*, d) where * is the number of data points and may vary from item to item in the list. d is the input dimensionality and has to be consistent.

Returns:

F : list of array_like

List of sequences. Each item in the list is an array which corresponds to the sequence in X. It is of the same shape, except that d is replaced by n_components.

transform(X)

Transform data according to the model.

Parameters:

X : array_like

An array of shape (n, d) where n is the number of time steps and d the input dimensionality.

Returns:

F : array_like

An array of shape (n, c) where n is the number of time steps and c is the number of components kept.