fivefolddetect.m

eye detection sample in matlab. Eyetrackers can analyze a driver’s level of attentiveness while driv

function [all] = fivefold(samples,labels)

%[TYPES.alpha, TYPES.sv, TYPES.b, TYPES.params, TYPES.n]=LinearSVC(samples(:,51:350),labels(:,51:350));
%load sldtTestDB
[samples,labels]=balagan(samples,labels);
start= [ 1  524  1047 1593 2116];
stop = [523 1046 1592 2115 2616];
all = [];
samples1 = samples(:,start(1):stop(1));
samples2 = samples(:,start(2):stop(2));
samples3 = samples(:,start(3):stop(3));
samples4 = samples(:,start(4):stop(4));
samples5 = samples(:,start(5):stop(5));
labels1 = labels(:,start(1):stop(1));
labels2 = labels(:,start(2):stop(2));
labels3 = labels(:,start(3):stop(3));
labels4 = labels(:,start(4):stop(4));
labels5 = labels(:,start(5):stop(5));
load FC

%1. test first fold
sam=[samples2 samples3 samples4 samples5];
lab=[labels2 labels3 labels4 labels5];
[FC.alpha, FC.sv, FC.b, FC.params, FC.n]=osuSVMTrain(sam,lab, FC.params,2);
[L,sc] = osuSVMclass(samples1,FC.n, FC.alpha, FC.sv,FC.b, FC.params);
tmp=[labels1' L'];
all = [all;tmp];
%2. test fold 2
sam=[samples1 samples3 samples4 samples5];
lab=[labels1 labels3 labels4 labels5];
[FC.alpha, FC.sv, FC.b, FC.params, FC.n]=osuSVMTrain(sam,lab, FC.params,2);
[L,sc] = osuSVMclass(samples2,FC.n, FC.alpha, FC.sv,FC.b, FC.params);
tmp=[labels2' L'];
all = [all;tmp];
%3. test fold 3
sam=[samples1 samples2 samples4 samples5];
lab=[labels1 labels2 labels4 labels5];
[FC.alpha, FC.sv, FC.b, FC.params, FC.n]=osuSVMTrain(sam,lab, FC.params,2);
[L,sc] = osuSVMclass(samples3,FC.n, FC.alpha, FC.sv,FC.b, FC.params);
tmp=[labels3' L'];
all = [all;tmp];
%4. test fold 4
sam=[samples1 samples2 samples4 samples5];
lab=[labels1 labels2 labels4 labels5];
[FC.alpha, FC.sv, FC.b, FC.params, FC.n]=osuSVMTrain(sam,lab, FC.params,2);
[L,sc] = osuSVMclass(samples4,FC.n, FC.alpha, FC.sv,FC.b, FC.params);
tmp=[labels4' L'];
all = [all;tmp];
%5. test fold 5
sam=[samples1 samples2 samples3 samples4];
lab=[labels1 labels2 labels3 labels4];
[FC.alpha, FC.sv, FC.b, FC.params, FC.n]=osuSVMTrain(sam,lab, FC.params,2);
[L,sc] = osuSVMclass(samples5,FC.n, FC.alpha, FC.sv,FC.b, FC.params);
tmp=[labels5' L'];
all = [all;tmp];


%all;