ex1.m

svm classification and regression code

N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m
fschange('C:\MATLAB6p1\work\Untitled.m');
clear untitled
Untitled

N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m

diary out.m

N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m
fschange('C:\MATLAB6p1\work\Untitled.m');
clear untitled
Untitled

N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m

diary out.m

Support Vector Classification
_____________________________
Constructing ...
Optimising ...
Execution time:  0.1 seconds
Status : OPTIMAL_SOLUTION
|w0|^2    : 27965586.131396
Margin    : 0.000378
Sum alpha : 27965586.182051
Support Vectors : 18 (90.0%)

h =

  123.0004  143.0004


N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m
fschange('C:\MATLAB6p1\work\Untitled.m');
clear untitled
Untitled

N=20;
%X=[1 1; 1 0; -1 0;1 -1;0 0;
 %  3 3; -3 3;-3 -3;3 -3;4 1];
X=[1 1;1.5 0.5;2 1;2.5 1.5;3 2;3.5 2.5;4 2;4.5 1.5;5 1;6 0.5;
    2.5 1;3 1.5;3.5 2;4 1.5;4.5 1;5 0.5;6 0;5 1.5;4.5 1.9;4 1.5];
Y=zeros(N./2,1)+1;
Y=[Y;zeros(N./2,1)-1];
global p1
global Yt
p1=5.0;
ker='rbf';
[nsv, alpha, bias,Xs,Ys,alphas] = svc(X,Y,ker);
[h] = svcplot(X,Y,ker,alpha,bias)
trnX=X;
trnY=Y;
tstX=X;
predictedY =svcoutput(trnX,trnY,tstX,ker,alpha,bias);
Untitled


diary ex1.m

clear
diary ex1.m

diary out.m