decision tree

function [Nodes, node] = decision_tree(X, Y, depth, Nodes, indx)
    if depth == 0
        fprintf('Root\n')
    end
    if length(Y) < 1 % if example is empty
        fprintf('Null\n');
    elseif length(unique(Y)) == 1 % if all example has the same classification
        fprintf('Leaf: Class %d\n', Y(1));
        Nodes(indx) = struct('value', 0, 'infogain', 0, 'left', 0, 'right', 0);
        [node] = 0;
        [Nodes] = Nodes;
    else
        [col, igain] = infogain(X, Y);
        fprintf('split on feature %d (info gain: %f)\n', col, igain);
        features = unique(X(:, col));
        children(1) = 0;
        children(2) = 0;
        for f = features'
            fprintf('Depth: %d - Feature Branch: %d\n', depth, f);
            pos = find(X(:, col) == f);
            [Nodes, children(f)] = decision_tree(X(pos, :), Y(pos, :), depth + 1, Nodes, indx + 1);
        end
        Nodes(indx) = struct('value', col, 'infogain', igain, 'left', children(1), 'right', children(2));
        [node] = col;
        [Nodes] = Nodes;
    end
end