Nothing
#This demo is based on class_demo1.m as provided with sfa-tk in matlab
# % demonstrates how classification can be done with SFA
# %
# % see [Berkes05] Pietro Berkes: Pattern recognition with Slow Feature
# % Analysis. Cognitive Sciences EPrint Archive (CogPrint) 4104, http://cogprints.org/4104/ (2005)
## get path of test project
path<-find.package("rSFA")
#path<-file.path(testPath,"demo01")
## show path
path<-paste(path,"demoFiles",sep="/")
## source the demo function (you can look up the function in "path/demoFiles/")
source(paste(path,"/class.R",sep=""))
opts=list()
opts$idim=5; #% number of input dimensions
opts$nclass=4; #% number of classes
opts$ALIGNED=1; #% =1: align the Gaussian classifiers with coordinate axes
#% (recommended, this is more robust if the data distribution deviates
#% largely from multivariate Gaussian).
#% =0: don't align, can be more precise if data are Gaussian.
opts$dographics=2; #% =0: no plots, =1: some plots, =2: more plots
opts$epsC=0;#1e-7#0;
pars=list()
pars$npatt=120; #% number of patterns per class
pars$sigma=0.10; #% sigma of Gaussian noise added to prototypes
pars$generateP=1; #% =1: generate synthetic class patterns x (& save on path/classPat.dat)
#% =0: load synthetic class patterns x from path/classPat.dat
#% =5: generate totally random synthetic data
pars$MIXING=2; #% =1: nonlinear mixing of input variables, same for all classes
#% =2: nonlinear mixing in x1 and x2, different for all classes
#% other: no mixing
pars$tstfrac=0.4; #% which fraction of the patterns becomes test patterns
pars$tstvarseed=1; #% =1: vary seed, =0: always seed 0, for random number generator
#% in case of random divide
result<-class1(opts,pars,path)
#To document: for generateP=5 80% error on test set is expected (5 classes -> 20% right by random guessing)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.