1. Deep Recursion in FP¶

In the previous section, we limited our treatment of list-processing functions to operating on flat lists of integers, that is, lists that do not themselves contain nested inner lists. In this section, we will consider how to operate on lists that can contain not only integers, but also lists. This will lead to a discussion of deep recursion, which can handle trees represented as lists of lists of … lists of integers nested arbitrarily deep.

A good guideline for deep recursion is the following. When recurring on a list lst of elements that may themselves be lists, recur on both fp.hd(lst) and fp.tl(lst), after making sure that lst is not empty and that fp.hd(lst) is indeed a list.

Consider the following tree_test list as an example:

var tree_test = [ 14,
                  [ 7, [], [12, [], []] ],
                  [ 26,
                    [ 20, [17, [], []], [] ],
                    [ 31, [], [] ]
                  ]
                ]

We want to develop a function that takes in an integer tree represented as a list of lists of … lists of integers and returns the sum of the integers in the tree.

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Now that we have seen a deep recursion example in the preceding slide show, consider the slight modification in the following review problem.

2. Deep Recursion on Binary Search Trees¶

Note that, although our sumTree function would work on an arbitrary nested list, our particular tree_test example is actually a binary search tree under the interpretation that the first nested list following a given number is the left subtree of that number, which contains only values less than or equal to the number, and the second nested list following a number is the right subtree, which contains only numbers greater than the given number. Using this representation of a tree, we could develop the helper functions below that process those nested lists that are binary search trees.

// Return the left subtree
var left = function (bst) {
   return fp.hd(fp.tl(bst));
};

// Return the right subtree
var right = function (bst) {
   return fp.hd(fp.tl(fp.tl(bst)));
};

// Is this tree a leaf node?
var isLeaf = function (tree) {
  return fp.isNull(left(tree)) && fp.isNull(right(tree));
};

Using these helper functions, we can easily write a function path (with one caveat addressed below)

var path = function (n, bst) { … };

where n is a number and bst is a binary search tree that contains the number n. path should return a list of 0’s and 1’s showing how to find the node containing n, where 1 indicates “go right” and 0 indicates “go left”. If n is found at the root, the empty list is returned. Example usage:

> var tree_test = [ 14,
                    [ 7, [], [12, [], []] ],
                    [ 26,
                      [ 20, [17, [], []], [] ],
                      [ 31, [], [] ]
                    ]
                  ]

> path(17, tree_test)
[1, 0, 0]

The caveat in the function we develop is that it is not prepared to return some type of error signal when n is not contained in the tree. We will address this shortcoming in the section on continuation passing style.

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Now that we’ve seen how to use cons in conjunction with lists being returned from deep recursion, consider the following review problem. It also deals with deep recursion and more specifically with the subst function that was described in Recursive List Processing Example: subst( new, old, list ).

3. Practice with Deep Recursion¶

This problem is similar to (and assumes that you have solved) the previous problem.

4. More Practice with Deep Recursion¶

As a final example and to give you a lot more practice with deep recursion, consider the following randomized exercise. You have to solve it correctly three times in a row.