Is this a Correct implementation for K-Nearest Neighbors algorithm ?

I implemented K-Nearest Neighbours algorithm, but my experience using matlab is very few. I need you to check the small portion of code and tell me what can be improved or modified ? and hope it is a correct implementation of the algorithm ?

     function test_data = knn(test_data, tr_data,k)

      numoftestdata = size(test_data,1);
      numoftrainingdata = size(tr_data,1);

      for sample=1:numoftestdata

         %Step 1: Computing euclidean distance for each testdata
         R = repmat(test_data(sample,:),numoftrainingdata,1) ;
         euclideandistance  = (R(:,1) - tr_data(:,1)).^2;

         %Step 2: compute k nearest neighbors and store them in an array
          [dist position] = sort(euclideandistance,'ascend');
          knearestneighbors=position(1:k);
          knearestdistances=dist(1:k);

          % Step 3 : Voting 
          for i=1:k
              A(i) = tr_data(knearestneighbors(i),2);  
          end

          M = mode(A);

          if (M~=1)
              test_data(sample,2) = mode(A);
          else 
              test_data(sample,2) = tr_data(knearestneighbors(1),2);
          end
      end

To test it you can use :

 - test_data = [6,0; 2,0; 5,0]
 - tr_data = [1,1;0,2;3,2; 4,4; 5,3]