Hard Margin one-norm SVM

function [ weights bias ] = TannSchmidVectorMachineHardMarginUno( K, H, labels)
%Toggle details which kernel we use

[xm xn] = size(K);
[ym yn] = size(labels);

%check to make sure training & labels have same dimension and toggle is
%valid

if xm ~= ym
    display('Sorry, this is an idiot proof function. Try feeding in valid parameters next time, doof!');
    return;
end

%allocate space for different parts
f = -ones(xm, 1);
A = zeros(xm +2, xm);
bias = zeros(xm +2, 1);

%build constraints matrix
A(1,:) = labels';
A(2,:) = -labels';
for i = 1:xm
    A(i+2, i) = -1;
end
          
[weights v] = quadprog(H, f, A, bias);

%find the bias
bias = getHardMarginBias(weights, K, labels);

save('recordedResults0', 'weights', 'bias', 'K');