rSFA: Slow Feature Analysis in R

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.

AuthorWolfgang Konen <wolfgang.konen@fh-koeln.de>, Martin Zaefferer, Patrick Koch; Bug hunting and testing by Ayodele Fasika, Ashwin Kumar, Prawyn Jebakumar
Date of publication2014-12-17 13:07:15
MaintainerMartin Zaefferer <martin.zaefferer@gmx.de>
LicenseGPL (>= 2)
Version1.04
http://gociop.de/research-projects/sfa/

View on CRAN

Man pages

addNoisyCopies: Add noisy copies for parametric bootstrap

customRep: Custom Repeater Function

customRepmat: Custom repmat Function

customSize: Custom Size Function.

etaval: Computes the eta value of a signal (slowness)

gaussClassifier: Classifier for SFA demos

gaussCreate: Create an Gaussian classifier object

gaussLoad: Load a GAUSS object.

gaussSave: Save a GAUSS object.

lcovCreate: Create a new covariance object.

lcovFix: Fix a covariance object

lcovPca: Principal Component Analysis on a covariance object

lcovPca2: Improved Principal Component Analysis on a covariance object

lcovTransform: Transform a covariance object

lcovUpdate: Update a covariance object

nlDim: Custom Nonlinear Dimension Calculation

nlExpand: Expand a signal in the for Nonlinear Expansion demo

rSFA-package: Slow Feature Analysis in R

sfa1: The SFA1 algorithm, linear SFA.

sfa1Create: Create structured list for linear SFA

sfa1Step: A step in the SFA1 algorithm.

sfa2: The SFA2 algorithm, SFA with degree 2 expansion.

sfa2Create: Create structured list for expanded SFA

sfa2Step: A step in the SFA2 algorithm.

sfaBSh: Backslash operator.

sfaCheckCondition: Check Condition of a matrix for SFA

sfaClassify: Predict Class for SFA classification

sfaClassPredict: Predict Class for SFA classification

sfaExecute: Execute learned function for input data

sfaExpand: Degree 2 Expansion

sfaGetHf: Return a SFA function as a quadratic form.

sfaGetIntRange: Helper Function of SFA.

sfaLoad: Load a SFA object.

sfaNlRegress: Perform non-linear regression

sfaPBootstrap: Parametric Bootstrap

sfaPreproc: Preprocessing for SFA classification

sfaSave: Save a SFA object.

sfaStep: Update a step of the SFA algorithm.

sfaTimediff: Calculates the first derivative of signal data

xpDim: Degree 2 Dimension Calculation

Files in this package

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.