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rSFA (version 1.04)

Slow Feature Analysis in R

Description

Slow Feature Analysis in R, ported to R based on the matlab versions SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen for matlab.

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Version

Install

install.packages('rSFA')

Monthly Downloads

168

Version

1.04

License

GPL (>= 2)

Maintainer

Martin Zaefferer

Last Published

December 17th, 2014

Functions in rSFA (1.04)

gaussClassifier

Classifier for SFA demos

Helper Function of SFA.

Update a step of the SFA algorithm.

Custom Repeater Function

Save a GAUSS object.

Create structured list for expanded SFA

Create structured list for linear SFA

The SFA2 algorithm, SFA with degree 2 expansion.

A step in the SFA1 algorithm.

Create an Gaussian classifier object

Custom Nonlinear Dimension Calculation

Fix a covariance object

Perform non-linear regression

Parametric Bootstrap

Save a SFA object.

Create a new covariance object.

sfaClassPredict

Predict Class for SFA classification

Degree 2 Dimension Calculation

Custom repmat Function

sfaCheckCondition

Check Condition of a matrix for SFA

Backslash operator.

Computes the eta value of a signal (slowness)

The SFA1 algorithm, linear SFA.

Transform a covariance object

Load a SFA object.

Custom Size Function.

Improved Principal Component Analysis on a covariance object

Degree 2 Expansion

Calculates the first derivative of signal data

Execute learned function for input data

Predict Class for SFA classification

Load a GAUSS object.

Return a SFA function as a quadratic form.

Expand a signal in the for Nonlinear Expansion demo

Preprocessing for SFA classification

Add noisy copies for parametric bootstrap

Update a covariance object

A step in the SFA2 algorithm.

Slow Feature Analysis in R

Principal Component Analysis on a covariance object