tree_tester

   % fill in the rest of your code here.
    trainPart = make_xval_partition(600, 4);
    xTest = X(find(trainPart == 1), :);
    yTest = Y(find(trainPart == 1));
    xTrain = X(find(trainPart ~= 1), :);
    yTrain = Y(find(trainPart ~= 1));

    K_vals = [1,2,3,4,5,6,7,8,9];
    errors = zeros(9, 2);

%
%     for j = 1:9
%         K = K_vals(j);
%         part = make_xval_partition(450, 10);
%         tree = dt_train(xTrain, yTrain, K);
%
%         %now find errors
%         train_errors = zeros(size(xTrain, 1), 1);
%         for v = 1:size(xTrain, 1)
%             train_errors(v) = abs(yTrain(v, 1) - dt_value(tree, xTrain(v, :)));
%         end
%         errors(j, 1) = mean(train_errors);
%
%         test_errors = zeros(size(xTest, 1), 1);
%         for v = 1:size(xTest, 1)
%             test_errors(v) = abs(yTest(v, 1) - dt_value(tree, xTest(v, :)));
%         end
%         errors(j, 2) = mean(test_errors);
%     end
%
%     errors

    %optimal is level 7

    opt_tree = dt_train(xTrain, yTrain, 7);
    train_errors = zeros(size(xTrain, 1), 1);
    for i = 1 : size(xTrain, 1)
        train_errors(i) = abs(yTrain(i, 1) - dt_value(opt_tree, xTrain(i, :)));
    end

    test_errors = zeros(size(xTest, 1), 1);
    for i = 1 : size(xTest, 1)
        test_errors(i) = abs(yTest(i, 1) - dt_value(opt_tree, xTest(i, :)));
    end

    a = mean(train_errors)
    b = mean(test_errors)