clear

load trainresult;
load testing;

[N,M] = size(testset);   % N is the size of test set
Ns = length(weight);
X = supportvectors;

for i=1:N
    for j=1:Ns
        switch kernel
            case 'radial'
                K(i,j) = exp( - norm( testset(i,:) - X(j,:) )^2 / 2*eta2 );
            case 'poly'
                K(i,j) = ( 1 + dot( testset(i,:),X(j,:) ) )^2;
            otherwise
                disp('Unknown kernel.');
        end
    end
end
F = K*weight + bias;

switch obj
    case  'regression'
        source = testdata(M+1:N+M-1);
        len = length(source);
        T = 0:len-1;
        figure(1)
        plot(T,source,'b');
        hold on
        plot(T,F(1:len),'r');
        
        for i=1:len-setn
            err1(i) = ( F(i) - source(i) )^2;
        end
        rmse1 = sqrt(sum(err1)/len)
        for i=len-(setn-1):len
            err2(i-len+setn) = ( F(i) - source(i) )^2;
        end
        rmse2 = sqrt(sum(err2)/len)
        
    otherwise
        disp('The end.');
end