Start with the recursive algorithm (pseudocode) : traverse(node): if node != None do: traverse(node.left) print node.value traverse(node.right) endif This is a clear case of tail recursion, so you can easily turn it into a while-loop. traverse(node): while node != None do: traverse(node.left) print node.value node = node.right endwhile You’re left with a recursive call. What … Read more
There’s no prescribed format, as you can usually redefine your data through various accessor functions (such as hierarchy.children) and array.map. But the format you quoted is probably the most convenient representation for trees because it works with the default accessors. The first question is whether you intend to display a graph or a tree. For … Read more
It just an assuption you make for the recursive description of the height of a binary tree. You can consider a tree composed by just a node either with 0 height or with 1 height. If you really want to think about it somehow you can think that it’s 0 if you consider the height … Read more
Here’s a quick closure table example for SQLite. I’ve not included the statements for inserting items into an existing tree. Instead, I’ve just created the statements manually. You can find the insert and delete statements in the Models for hierarchical data slides. For the sake of my sanity when inserting the IDs for the directories, … Read more
An “ordering” is basically a set of rules that determine what items come before, or after, what other items. IE: the relative order items would appear in if they were sorted. For collections that enforce an ordering, that ordering is generally specified in terms of comparison operators (particularly <), interfaces (a la Java’s Comparable<T>), or … Read more
if (node == null) { return 0; } The children of leaf nodes are null. Therefore this is saying that once we’ve gone past the leaves, there are no further nodes. If we are not past the leaf nodes, we have to calculate the height and this code does so recursively. return 1 + The … Read more
Assuming your Random Forest model is already fitted, first you should first import the export_graphviz function: from sklearn.tree import export_graphviz In your for cycle you could do the following to generate the dot file export_graphviz(tree_in_forest, feature_names=X.columns, filled=True, rounded=True) The next line generates a png file os.system(‘dot -Tpng tree.dot -o tree.png’)
It’s just a missing case that will presumably eventually be filled in. There is no reason not to do it, and in certain cases it would be considerably faster than the immutable tree (since modifications require log(n) object creations with an immutable tree and only 1 with a mutable tree). Edit: and in fact it … Read more
Think about how to prove the result for a full binary tree, and you’ll see how to do it in general. For the full binary tree, say of height h, the number of nodes N is N = 2^{h+1} – 1 Why? Because the first level has 2^0 nodes, the second level has 2^1 nodes, … Read more