testklr.m

The goal of SPID is to provide the user with tools capable to simulate, preprocess, process and clas

% De verschillende outcomes/klassen k moeten genummerd zijn van 1 tot k,
% dus bij binaire classificatie niet -1/1 of 0/1!

% De opties instellen
pars.ker='RBF_kernel';
pars.sig2=10;
pars.lambda=0.001;
pars.maxitt002=1000;
pars.treshold=0.98;
pars.deltak=1;
pars.epsil=1e-6;
pars.prior=0;
pars.info=0;
pars.optim='full';%'full','coordinate'
pars.maxbacktrack=10;
pars.redds=0;
pars.bisectie=0;

% Cross validation om gamma en sigma te bepalen
for1=exp(3:-1:-5);%gamma
for2=exp(-1:1:6);%sigma
fold=5;
[lambdaLH,sig2LH]=xval(traindata,traintarget,fold,pars,for1,for2)

% De bekomen gamma en sigma in de opties steken
pars.sig2=sig2LH;
pars.lambda=lambdaLH;

% KLR model fitten
alpha=fsirlssvm(traindata,traintarget,pars,testdata,testtarget);
% Probabiliteiten berekenen
[pt,lhte]=predictprobs(traindata,traintarget,alpha,testdata,testtarget,pars);

% Plotje maken (indien twee inputs)
plotprobs(traindata,traintarget,alpha,pars);