analisys.m

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

sprintf('%0.5g out of %0.5g mistaken identifing 5',size(samples52,2)-size(find(L==5),2),size(samples52,2))
size(find(L==5),2)/size(samples52,2)
correct5=correct5+size(find(L==5),2)/size(samples52,2);
[L,sc] = osuSVMclass(samples62,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 6',size(find(L==6),2),size(samples62,2))
sprintf('%0.5g out of %0.5g mistaken identifing 6',size(samples62,2)-size(find(L==6),2),size(samples62,2))
size(find(L==6),2)/size(samples62,2)
correct6=correct6+size(find(L==6),2)/size(samples62,2);
[L,sc] = osuSVMclass(samples72,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 7',size(find(L==7),2),size(samples72,2))
sprintf('%0.5g out of %0.5g mistaken identifing 7',size(samples72,2)-size(find(L==7),2),size(samples72,2))
size(find(L==7),2)/size(samples72,2)
correct7=correct7+size(find(L==7),2)/size(samples72,2);

%%%%%%%%%% checking samples?3 %%%%%%%%%%%%%
l_data=[l_data1 l_data2 l_data4 l_data5];
s_data=[s_data1 s_data2 s_data4 s_data5];
[TS.alpha, TS.sv, TS.b, TS.params, TS.n]=LinearSVC(s_data,l_data);
[L,sc] = osuSVMclass(samples13,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 1',size(find(L==1),2),size(samples13,2))
sprintf('%0.5g out of %0.5g mistaken identifing 1',size(samples13,2)-size(find(L==1),2),size(samples13,2))
size(find(L==1),2)/size(samples13,2)
correct1=correct1+size(find(L==1),2)/size(samples13,2);
[L,sc] = osuSVMclass(samples23,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 2',size(find(L==2),2),size(samples23,2))
sprintf('%0.5g out of %0.5g mistaken identifing 2',size(samples23,2)-size(find(L==2),2),size(samples23,2))
size(find(L==2),2)/size(samples23,2)
correct2=correct2+size(find(L==2),2)/size(samples23,2);
[L,sc] = osuSVMclass(samples33,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 3',size(find(L==3),2),size(samples33,2))
sprintf('%0.5g out of %0.5g mistaken identifing 3',size(samples33,2)-size(find(L==3),2),size(samples33,2))
size(find(L==3),2)/size(samples33,2)
correct3=correct3+size(find(L==3),2)/size(samples33,2);
[L,sc] = osuSVMclass(samples43,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 4',size(find(L==4),2),size(samples43,2))
sprintf('%0.5g out of %0.5g mistaken identifing 4',size(samples43,2)-size(find(L==4),2),size(samples43,2))
size(find(L==4),2)/size(samples43,2)
correct4=correct4+size(find(L==4),2)/size(samples43,2);
[L,sc] = osuSVMclass(samples53,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 5',size(find(L==5),2),size(samples53,2))
sprintf('%0.5g out of %0.5g mistaken identifing 5',size(samples53,2)-size(find(L==5),2),size(samples53,2))
size(find(L==5),2)/size(samples53,2)
correct5=correct5+size(find(L==5),2)/size(samples53,2);
[L,sc] = osuSVMclass(samples63,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 6',size(find(L==6),2),size(samples63,2))
sprintf('%0.5g out of %0.5g mistaken identifing 6',size(samples63,2)-size(find(L==6),2),size(samples63,2))
size(find(L==6),2)/size(samples63,2)
correct6=correct6+size(find(L==6),2)/size(samples63,2);
[L,sc] = osuSVMclass(samples73,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 7',size(find(L==7),2),size(samples73,2))
sprintf('%0.5g out of %0.5g mistaken identifing 7',size(samples73,2)-size(find(L==7),2),size(samples73,2))
size(find(L==7),2)/size(samples73,2)
correct7=correct7+size(find(L==7),2)/size(samples73,2);

%%%%%%%%%% checking samples?4   %%%%%%%%%%%%%
l_data=[l_data1 l_data2 l_data3 l_data5];
s_data=[s_data1 s_data2 s_data3 s_data5];
[TS.alpha, TS.sv, TS.b, TS.params, TS.n]=LinearSVC(s_data,l_data);
[L,sc] = osuSVMclass(samples14,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 1',size(find(L==1),2),size(samples14,2))
sprintf('%0.5g out of %0.5g mistaken identifing 1',size(samples14,2)-size(find(L==1),2),size(samples14,2))
size(find(L==1),2)/size(samples14,2)
correct1=correct1+size(find(L==1),2)/size(samples14,2);
[L,sc] = osuSVMclass(samples24,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 2',size(find(L==2),2),size(samples24,2))
sprintf('%0.5g out of %0.5g mistaken identifing 2',size(samples24,2)-size(find(L==2),2),size(samples24,2))
size(find(L==2),2)/size(samples24,2)
correct2=correct2+size(find(L==2),2)/size(samples24,2);
[L,sc] = osuSVMclass(samples34,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 3',size(find(L==3),2),size(samples34,2))
sprintf('%0.5g out of %0.5g mistaken identifing 3',size(samples34,2)-size(find(L==3),2),size(samples34,2))
size(find(L==3),2)/size(samples34,2)
correct3=correct3+size(find(L==3),2)/size(samples34,2);
[L,sc] = osuSVMclass(samples44,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 4',size(find(L==4),2),size(samples44,2))
sprintf('%0.5g out of %0.5g mistaken identifing 4',size(samples44,2)-size(find(L==4),2),size(samples44,2))
size(find(L==4),2)/size(samples44,2)
correct4=correct4+size(find(L==4),2)/size(samples44,2);
[L,sc] = osuSVMclass(samples54,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 5',size(find(L==5),2),size(samples54,2))
sprintf('%0.5g out of %0.5g mistaken identifing 5',size(samples54,2)-size(find(L==5),2),size(samples54,2))
size(find(L==5),2)/size(samples54,2)
correct5=correct5+size(find(L==5),2)/size(samples54,2);
[L,sc] = osuSVMclass(samples64,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 6',size(find(L==6),2),size(samples64,2))
sprintf('%0.5g out of %0.5g mistaken identifing 6',size(samples64,2)-size(find(L==6),2),size(samples64,2))
size(find(L==6),2)/size(samples64,2)
correct6=correct6+size(find(L==6),2)/size(samples64,2);
[L,sc] = osuSVMclass(samples74,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 7',size(find(L==7),2),size(samples74,2))
sprintf('%0.5g out of %0.5g mistaken identifing 7',size(samples74,2)-size(find(L==7),2),size(samples74,2))
size(find(L==7),2)/size(samples74,2)
correct7=correct7+size(find(L==7),2)/size(samples74,2);
%%%%%%%%%% checking samples?5   %%%%%%%%%%%%%
l_data=[l_data1 l_data2 l_data3 l_data4];
s_data=[s_data1 s_data2 s_data3 s_data4];
[TS.alpha, TS.sv, TS.b, TS.params, TS.n]=LinearSVC(s_data,l_data);
[L,sc] = osuSVMclass(samples15,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 1',size(find(L==1),2),size(samples15,2))
sprintf('%0.5g out of %0.5g mistaken identifing 1',size(samples15,2)-size(find(L==1),2),size(samples15,2))
size(find(L==1),2)/size(samples14,2)
correct1=correct1+size(find(L==1),2)/size(samples14,2);
[L,sc] = osuSVMclass(samples25,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 2',size(find(L==2),2),size(samples25,2))
sprintf('%0.5g out of %0.5g mistaken identifing 2',size(samples25,2)-size(find(L==2),2),size(samples25,2))
size(find(L==2),2)/size(samples25,2)
correct2=correct2+size(find(L==2),2)/size(samples25,2);
[L,sc] = osuSVMclass(samples35,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 3',size(find(L==3),2),size(samples35,2))
sprintf('%0.5g out of %0.5g mistaken identifing 3',size(samples35,2)-size(find(L==3),2),size(samples35,2))
size(find(L==3),2)/size(samples35,2)
correct3=correct3+size(find(L==3),2)/size(samples35,2);
[L,sc] = osuSVMclass(samples45,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 4',size(find(L==4),2),size(samples45,2))
sprintf('%0.5g out of %0.5g mistaken identifing 4',size(samples45,2)-size(find(L==4),2),size(samples45,2))
size(find(L==4),2)/size(samples45,2)
correct4=correct4+size(find(L==4),2)/size(samples45,2);
[L,sc] = osuSVMclass(samples55,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 5',size(find(L==5),2),size(samples55,2))
sprintf('%0.5g out of %0.5g mistaken identifing 5',size(samples55,2)-size(find(L==5),2),size(samples55,2))
size(find(L==5),2)/size(samples55,2)
correct5=correct5+size(find(L==5),2)/size(samples55,2);
[L,sc] = osuSVMclass(samples65,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
sprintf('%0.5g out of %0.5g correct identifing 6',size(find(L==6),2),size(samples65,2))
sprintf('%0.5g out of %0.5g mistaken identifing 6',size(samples65,2)-size(find(L==6),2),size(samples65,2))
[L,sc] = osuSVMclass(samples75,TS.n, TS.alpha, TS.sv,TS.b, TS.params);
size(find(L==6),2)/size(samples65,2)
correct6=correct7+size(find(L==6),2)/size(samples65,2);
sprintf('%0.5g out of %0.5g correct identifing 7',size(find(L==7),2),size(samples75,2))
sprintf('%0.5g out of %0.5g mistaken identifing 7',size(samples75,2)-size(find(L==7),2),size(samples75,2))
size(find(L==7),2)/size(samples75,2)
correct7=correct7+size(find(L==7),2)/size(samples75,2);
sprintf('THIS ARE THE RESULTS:')
correct1/5
correct2/5
correct3/5
correct4/5
correct5/5
correct6/5
correct7/5