how to make a knn classifer using minkowski distance function

Need to make a knn classifer without using fitcknn for K = 3, 5, 7, that uses minkowski distance for the order of 1, 2 and 5

[Walter Roberson]

Do you mean that you have been given an assignment to write knn classification code yourself?

If so then it would defeat the purpose if we were to give you knn classification code.

[Apurva Jariwala]

I am trying to make a knn classifier and train and test it using the Iris dataset. The objective is to find accuracy and the confusion matrix. Please read the code below and let me know what changes can I make

IrisD = readtable('irisdata.csv');
classes = categorical(IrisD{:,5});
Icats = categories(classes);
setosa = IrisD(strcmp(IrisD{:,5},Icats(1)),:);
Ttest1 = setosa(1:40,:);
Ttrain1 = setosa(41:50,:);
versicolor = IrisD(strcmp(IrisD{:,5},Icats(2)),:);
Ttest2 = versicolor(1:40,:);
Ttrain2 = versicolor(41:50,:);
virginica = IrisD(strcmp(IrisD{:,5},Icats(3)),:);
Ttest3 = virginica(1:40,:);
Ttrain3 = virginica(41:50,:);
Ttest = [Ttest1; Ttest2; Ttest3];
Ttrain = [Ttrain1; Ttrain2; Ttrain3];
testlabel = Ttest(:,5);
trainlabel = Ttrain(:,5);
C = unique(trainlabel);
testf = Ttest(:,1:4);
trainf = Ttrain(:,1:4);
K = 3;
r = 2;
Lpred = [];
for i = 1:size(testf,1)
    Ftest = testf(i,:);
    Ns = size(trainf, 1);
    dmat = abs(trainf-repmat(Ftest, Ns, 1));
    dlist = nthroot(sum(dmat.^r, 2), r);
    [dsort, isort] = sort(dlist, 'ascend');
    Lknn = trainlabel(isort(1:K));
    Ncl = []; 
    for iC = 1:length(C) 
        cl = C(iC);
        ncl = length(find(Lknn==cl)); 
        Ncl = [Ncl; ncl, cl];
    end
    [vmax, imax] = max(Ncl(:,1));
    Cpred = Ncl(imax, 2);
    Lpred = [Lpred; Cpred];
end