function [k] = findBestK (trainMat)
% trainMat = n*m+1: n objects of dimension m; last column is class
% returns the value of k that returns the best value of kNN

numObjects = size(trainMat,1);
numFeatures = size(trainMat,2)-1;
% trying k= 1 to numObjects-1
numCorrect = zeros(1, numObjects-1);

for i=1:numObjects-1
    for j=1:numObjects
        % k = i, try on each object j
        remTrainMat = train
        result = simplekNN(trainMat(j,1:numFeatures), trainMat, 1);