K-NN algorithm using Matlab

knn.m

function rate = knn(trainSet, testSet, k)

m = size(trainSet, 1);
n = size(testSet, 1);
predict = [];

for test=1:n
    for train=1:m
        Eulidist(train) = norm(testSet(test, 1:end-1) - trainSet(train, 1:end-1));
    end
        
    [sortedDist, indices] = sort(Eulidist);
    kDist = sortedDist(1:k);
    kInd = indices(1:k);
    kPredict = trainSet(kInd, end);
    
    votes = zeros(max(kPredict), 1);
    
    for i=1:size(votes, 1)
        votes(kPredict(i)) = votes(kPredict(i)) + 1;
    end
    
    predict(test) = max(votes);
end

actual = testSet(:, end);
rate = 100 * sum(actual == predict') / n;