% implements KNN, returns the test error for the k-nearest neighbors
% algorithms when using a specified number of neighbors (k) for
% classification using a majority rules with tie-breaking.
function [test_err] = KNN(k, training_data, test_data, training_labels, test_labels)
  
  n = length(test_data(:,1)); % get number of rows in test data
  preds = zeros(length(test_labels),1); % predict labels for each test point

  % TODO: compute pairwise euclidean distance between the test data and the
  % training data

  % for each data point (row) in the test data
  for t = 1:n
    
    % TODO: compute k-nearest neighbors for data point


    % TODO: classify test point using majority rule. Include tie-breaking
    % using whichever class is closer by distance. Fill in preds with the
    % predicted label.

  end

  test_err = sum(preds ~= test_labels)/n; % error rate

end % Function end