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

Functions

addNoisyCopies Man page
customRep Man page
customRepmat Man page
customSize Man page
etaval Man page
gaussClassifier Man page
gaussCreate Man page
gaussLoad Man page
gaussSave Man page
lcovCreate Man page
lcovFix Man page
lcovPca Man page
lcovPca2 Man page
lcovTransform Man page
lcovUpdate Man page
nlDim Man page
nlExpand Man page
rSFA Man page
rSFA-package Man page
sfa1 Man page
sfa1Create Man page
sfa1Step Man page
sfa2 Man page
sfa2Create Man page
sfa2Step Man page
sfaBSh Man page
sfaCheckCondition Man page
sfaClassify Man page
sfaClassPredict Man page
sfaExecute Man page
sfaExpand Man page
sfaGetHf Man page
sfaGetIntRange Man page
sfaLoad Man page
sfaNlRegress Man page
sfaPBootstrap Man page
sfaPreproc Man page
sfaSave Man page
sfaStep Man page
sfaTimediff Man page
xpDim Man page

Files

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

Please suggest features or report bugs with the GitHub issue tracker.

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