Use a SFA classification model (stored in opts$*Filename), predict & evaluate on new data (xtst,realc_tst). Author of orig. matlab version: Wolfgang Konen, Jan 2011-Mar 2011. See also [Berkes05] Pietro Berkes: Pattern recognition with Slow Feature Analysis. Cognitive Sciences EPrint Archive (CogPrint) 4104, http://cogprints.org/4104/ (2005)
sfaClassPredict(xtst, realcTst, opts)NTST x IDIM, test input data
1 x NTST, test class labels
list with several parameter settings:
[* = s,g,x] from where to load the models (see sfaClassify)
list res containing
1 x 2 matrix: error rate with / w/o SFA on test set
output from SFA when applied to test data
predictions with SFA + GaussClassifier on test set
predictions w/o SFA (only GaussClassifier) on test set (only if opts.xFilename exists)