x0=zeros(l,1) ;
opts=optimset('MaxIter',10000) ;
ceq=0 ;

X = Train(:,1:20) ;
Y = Train(:,21) ;

l = 48 ;
C = 1 ;
K = X * X' ;

H = diag(Y) * K * diag(Y) ;
A = [] ;
c = [] ;
Aeq = Y' ;
cu = C * ones(l,1) ;
cl = zeros(l,1) ;
f = -ones(l,1) ;
warning off all

answer = quadprog(H,f,A,c,Aeq,ceq,cl,cu,x0,opts);

%----------------------------------------------------

% demostrating margin of error of Training Set

p = answer .* Y ;
total = 0 ;
for i = 1:48
    total = p(i) + total 
end
total;

% counting up total correctly classified in Training Set

count = 0;
for i = 1:48
    if (answer(i) > 0 && answer(i) < C)
        count = count + 1;
    end
end
count

%-----------------------------------------------------

% calculating b 
TestData = 1;
x = Test(TestData,1:20) ;
k = 1;
b1 = Y(k,:);
b2=0;
for i = 1:l
    b2 = ((Y(i,:)*answer(i,:)*X(i,:)) .* X(k,:))  +b2;
end
b = b1 - b2;

%Calculate prediction on Test set with kernal

solution = 0;
d = 5;

    for i = 1:l
        solution = solution + ((answer(i,:)*Y(i,:)) * ((1 + (X(i,:) * x'))).^d) + b;
    end

solution