Hi, I am trying to solve the following question in Matlab.

Assume a new iris sample S has a feature vector of [5.0000, 3.5000, 1.4600, 0.2540]. Calculate the distances between the new sample and the 150 samples in the iris dataset using the distance functions. Calculate Mahalanobis distances and plot the obtained distances

I did the following code, but for some reason its not working, can you please help?

load iris.mat feat_S=[5,3.50,1.460,0.2540]; Nsp = length(label); P =cov(feat); Dlist=[];

for i = 1:Nsp feat_i = feat(i, :); m_dist = mahal_distance(feat_S, feat_i,P); mahal_distance = (feat_S-feati) * P * (feat_S-feat_i)'; Dlist(i) = m_dist; end

figure; plot(Dlist); xlabel('Samples in each class') ylabel('Euclidean Distance'); title('Distance between Sample S and the Samples in Iris Dataset');

figure; c = unique(label);

for ic = c' idx= find(label==ic); Dvec = Dlist(idx); if ic==1; plot(Dvec, 'r'); end if ic==2; plot(Dvec, 'k'); end if ic==3; plot(Dvec, 'g'); end hold on; end

xlabel('Samples in each class'); ylabel('Distance of Sample S to Samples of each class'); legend('Class 1', 'Class 2', 'Class 3');