function [err,C] = knntest(TrainX, TrainY, TestX, TestY, k, lnorm)
%
% a stub
% your solution should report the total number of errors on the Test
% set using k-nearest neighbors with the supplied k and lnorm
% (lnorm=1 for Manhattan and 2 for Euclidean)
% It should also report C, the confusion matrix.  The i-j element of
% C is the fraction of the total examples who were labeled as class i
% and the true label was class j

[r,c] = size(TestX);
[r1,c1] = size(TrainX);
c1 = c1;
nearList = [];
Dist = 0;
err = 0;
Confusion = zeros(c);

for i = 1:r % track which tuple on testing your working with
    for j = 1:r1 % track which tuple on training your comparing against
        if lnorm == 2
            for l = 1:c  % calculated euclidean distance
                Dist = Dist + (TestX(i,l) - TrainX(j,l))^2;
            end
            Dist = Dist ^ 0.5;
        else
            for l = 1:c   % calculates manhattan distance
                Dist = Dist + abs(TestX(i,l) - TrainX(j,l));
            end
        end
        newPoint = [Dist TrainY(j)]; % creates new point

        [rCheck, cCheck] = size(nearList);
        cCheck = cCheck;
        if rCheck == k
            nearList = sortrows(nearList, 'descend');
            if nearList(1,1) > newPoint(1,1)
                nearList(1,:) = newPoint;
            end
        else
            nearList = [nearList ; newPoint];
        end
        Dist = 0;
    end

    count0 = 0;
    count1 = 0;
    count2 = 0;
    for counting = 1:k    %counting up NN's
            if nearList(counting,2) == 0
                count0 = count0 + 1;
            elseif nearList(counting,2) == 1
                count1 = count1 + 1;
            else
                count2 = count2 + 1;
            end
    end

    score = [0 count0; 1 count1; 2 count2];
    score = sortrows(score,2,'descend');
    if score(1,1) == TestY(i)
        Confusion(score(1,1) + 1,TestY(i) + 1) = Confusion(score(1,1) + 1,TestY(i) + 1) + 1;
    else
        err = err + 1;
        Confusion(score(1,1) + 1,TestY(i) + 1) = Confusion(score(1,1) + 1,TestY(i) + 1) + 1;
    end

end

err = err;
C = Confusion./r;