# Lab 09: Mutable Trees lab09.zip

Due at 11:59pm on Friday, 07/20/2018.

Lab Check-in 5 questions here.

## Starter Files

Download lab09.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.

## Submission

By the end of this lab, you should have submitted the lab with `python3 ok --submit`. You may submit more than once before the deadline; only the final submission will be graded. Check that you have successfully submitted your code on okpy.org.

• Questions 1 through 4 must be completed in order to receive credit for this lab. Starter code for coding questions is in lab09.py.
• Questions 5 and 6 are optional. It is recommended that you complete these problems on your own time. Starter code for these questions is in lab09_extra.py.

# Topics

## Trees (Again)

Recall that a tree is a recursive abstract data type that has a `label` (the value stored in the root of the tree) and `branches` (a list of trees directly underneath the root).

We saw one way to implement the tree ADT -- using constructor and selector functions that treat trees as lists. Another, more formal, way to implement the tree ADT is with a class. Here is part of the class definition for `Tree`, which can be found in `lab09.py`:

``````class Tree:
"""
>>> t = Tree(3, [Tree(2, [Tree(5)]), Tree(4)])
>>> t.label
3
>>> t.branches.label
2
>>> t.branches.is_leaf()
True
"""
def __init__(self, label, branches=[]):
for b in branches:
assert isinstance(b, Tree)
self.label = label
self.branches = list(branches)

def is_leaf(self):
return not self.branches``````

Even though this is a new implementation, everything we know about the tree ADT remains true. That means that solving problems involving trees as objects uses the same techniques that we developed when first studying the tree ADT (e.g. we can still use recursion on the branches!). The main difference, aside from syntax, is that tree objects are mutable.

Here is a summary of the differences between the tree ADT implemented using functions and lists vs. implemented using a class:

- Tree constructor and selector functions Tree class
Constructing a tree To construct a tree given a `label` and a list of `branches`, we call `tree(label, branches)` To construct a tree object given a `label` and a list of `branches`, we call `Tree(label, branches)` (which calls the `Tree.__init__` method)
Label and branches To get the label or branches of a tree `t`, we call `label(t)` or `branches(t)` respectively To get the label or branches of a tree `t`, we access the instance attributes `t.label` or `t.branches` respectively
Mutability The tree ADT is immutable because we cannot assign values to call expressions The `label` and `branches` attributes of a `Tree` instance can be reassigned, mutating the tree
Checking if a tree is a leaf To check whether a tree `t` is a leaf, we call the convenience function `is_leaf(t)` To check whether a tree `t` is a leaf, we call the bound method `t.is_leaf()`. This method can only be called on `Tree` objects.

### Displaying `Tree` instances

Implementing trees as a class gives us another advantage: we can specify how we want them to be output by the interpreter by implementing the `__repr__` and `__str__` magic methods.

Here is the `__repr__` method:

``````def __repr__(self):
if self.branches:
branch_str = ', ' + repr(self.branches)
else:
branch_str = ''
return 'Tree({0}{1})'.format(self.label, branch_str)``````

With this implementation of `__repr__`, a `Tree` instance is displayed as the exact constructor call that created it:

``````>>> t = Tree(4, [Tree(3), Tree(5, [Tree(6)]), Tree(7)])
>>> t
Tree(4, [Tree(3), Tree(5, [Tree(6)]), Tree(7)])
>>> t.branches
[Tree(3), Tree(5, [Tree(6)]), Tree(7)]
>>> t.branches
Tree(3)
>>> t.branches
Tree(5, [Tree(6)])``````

Here is the `__str__` method. You do not need to understand how this function is implemented.

``````def __str__(self):
def print_tree(t, indent=0):
tree_str = '  ' * indent + str(t.label) + "\n"
for b in t.branches:
tree_str += print_tree(b, indent + 1)
return tree_str
return print_tree(self).rstrip()``````

With this implementation of `__str__`, we can pretty-print a `Tree` to see both its contents and structure:

``````>>> t = Tree(4, [Tree(3), Tree(5, [Tree(6)]), Tree(7)])
>>> print(t)
4
3
5
6
7
>>> print(t.branches)
3
>>> print(t.branches)
5
6``````

## Binary Search Trees

A binary search tree (or BST) is a special type of tree that obeys the following constraints:

• Each node has at most two branches, a left and a right branch.
• All labels in the left branch of any node have a value less than or equal to the label at that node.
• All labels in the right branch of any node have a value greater than the label at the node.

Thus, these are BSTs: These are not BSTs: The following class implements BSTs. Because we constrain BSTs to have at most two branches, we will store each branch as `left` and `right` instance attributes rather than in a list. `BST` objects are recursive: each branch is also a `BST`. Note that there exists an empty BST, which wasn't true with regular trees; this allows us to construct trees with less than two branches.

``````class BST:
"""
>>> bst1 = BST(4, BST(2, BST(1)))
>>> bst1.max()
4
>>> bst1.min()
1
>>> bst2 = BST(6, BST(2, BST(1), BST(4)), BST(7, BST.empty, BST(9)))
>>> bst2.max()
9
>>> bst2.min()
1
>>> 9 in bst2
True
>>> 10 in bst2
False
>>> bst3 = BST(6, BST(2, BST(1), BST(4)), BST(8, BST(7), BST(9)))
>>> 7 in bst3
True
>>> 10 in bst3
False
"""
empty = ()

def __init__(self, label, left=empty, right=empty):
assert left is BST.empty or isinstance(left, BST)
assert right is BST.empty or isinstance(right, BST)

self.label = label
self.left, self.right = left, right

if left is not BST.empty:
assert left.max() <= label
if right is not BST.empty:
assert label < right.min()

def is_leaf(self):
return self.left is BST.empty and self.right is BST.empty

def __repr__(self):
if self.is_leaf():
return 'BST({0})'.format(self.label)
elif self.right is BST.empty:
left = repr(self.left)
return 'BST({0}, {1})'.format(self.label, left)
else:
left, right = repr(self.left), repr(self.right)
if self.left is BST.empty:
left = 'BST.empty'
template = 'BST({0}, {1}, {2})'
return template.format(self.label, left, right)

def max(self):
"""Returns max element in BST."""
if self.right is BST.empty:
return self.label
return self.right.max()

def min(self):
"""Returns min element in BST."""
if self.left is BST.empty:
return self.label
return self.left.min()

def __contains__(self, e):
if self.label == e:
return True
elif e > self.label and self.right is not BST.empty:
return e in self.right
elif self.left is not BST.empty:
return e in self.left
return False``````

# Required Questions

## What Would Python Display?

### Q1: WWPD: Trees

Use Ok to test your knowledge with the following "What Would Python Display?" questions:

``python3 ok -q trees -u``

Enter `Function` if you believe the answer is `<function ...>`, `Error` if it errors, and `Nothing` if nothing is displayed. Recall that `Tree` instances will be displayed the same way they are constructed.

``````>>> from lab07 import *
>>> t = Tree(1, Tree(2))
______Error
>>> t = Tree(1, [Tree(2)])
>>> t.label
______1
>>> t.branches
______Tree(2)
>>> t.branches.label
______2
>>> t.label = t.branches.label
>>> t
______Tree(2, [Tree(2)])
>>> t.branches.append(Tree(4, [Tree(8)]))
>>> len(t.branches)
______2
>>> t.branches
______Tree(2)
>>> t.branches
______Tree(4, [Tree(8)])``````

## Coding Practice

### Q2: Cumulative Sum

Write a function `cumulative_sum` that mutates the Tree `t` so that each node's label becomes the sum of all labels in the subtree rooted at the node.

``````def cumulative_sum(t):
"""Mutates t so that each node's label becomes the sum of all labels in
the corresponding subtree rooted at t.

>>> t = Tree(1, [Tree(3, [Tree(5)]), Tree(7)])
>>> cumulative_sum(t)
>>> t
Tree(16, [Tree(8, [Tree(5)]), Tree(7)])
"""
for b in t.branches:
cumulative_sum(b)
t.label = sum([b.label for b in t.branches]) + t.label``````

Use Ok to test your code:

``python3 ok -q cumulative_sum``

### Q3: Leaves

Write a function `leaves` that returns a list of all the label values of the leaf nodes of a `Tree`.

``````def leaves(t):
"""Returns a list of all the labels of the leaf nodes of the Tree t.

>>> leaves(Tree(1))

>>> leaves(Tree(1, [Tree(2, [Tree(3)]), Tree(4)]))
[3, 4]
"""
if t.is_leaf():
return [t.label]
all_leaves = []
for b in t.branches:
all_leaves += leaves(b)
return all_leaves``````

Use Ok to test your code:

``python3 ok -q leaves``

### Q4: Insert

Implement the function `insert`, which takes in a `BST` and an value `v` and inserts `v` into the correct position in the `BST` by mutating it.

Hint: Try drawing out a few examples of inserting into a BST. What do you notice about every node that is inserted? Will an inserted value ever become the label of an inner (i.e. non-leaf) node?

Note: To check if a BST is empty, compare its identity to `BST.empty`:

``````>>> b = BST(3, BST(2))
>>> b is BST.empty
False
>>> b.right is BST.empty
True``````
``````def insert(bst, v):
"""
>>> bst = BST(5, BST(3, BST(1), BST(4)), BST(10, BST.empty, BST(11)))
>>> insert(bst, 2)
>>> 2 in bst
True
>>> insert(bst, 7)
>>> insert(bst, 6)
>>> bst
BST(5, BST(3, BST(1, BST.empty, BST(2)), BST(4)), BST(10, BST(7, BST(6)), BST(11)))
"""
if v <= bst.label:
if bst.left is BST.empty:
bst.left = BST(v)
else:
insert(bst.left, v)
else:
if bst.right is BST.empty:
bst.right = BST(v)
else:
insert(bst.right, v)``````

Use Ok to test your code:

``python3 ok -q insert``

# Optional Questions

## More trees practice

### Q5: Same Shape

Write a function `same_shape` that returns `True` if two `Tree`s have the same shape. Two trees have the same shape if and only if they have the same number of branches and each pair of corresponding children have the same shape.

``````def same_shape(t1, t2):
"""Returns whether two Trees t1, t2 have the same shape. Two trees have the
same shape iff they have the same number of branches and each pair
of corresponding branches have the same shape.

>>> t, s = Tree(1), Tree(3)
>>> same_shape(t, t)
True
>>> same_shape(t, s)
True
>>> t = Tree(1, [Tree(2), Tree(3)])
>>> same_shape(t, s)
False
>>> s = Tree(4, [Tree(3, [Tree(2, [Tree(1)])])])
>>> same_shape(t, s)
False
"""
return len(t1.branches) == len(t2.branches) and \
all([same_shape(b1, b2) for b1, b2 in zip(t1.branches, t2.branches)])``````

Use Ok to test your code:

``python3 ok -q same_shape``

### Q6: Reverse Other

Write a function `reverse_other` that mutates the tree such that labels on every other (odd-depth) level are reversed. For example, `Tree(1,[Tree(2, [Tree(4)]), Tree(3)])` becomes `Tree(1,[Tree(3, [Tree(4)]), Tree(2)])`. Notice that the nodes themselves are not reversed; only the labels are.

``````def reverse_other(t):
"""Mutates the tree such that nodes on every other (odd-depth) level
have the labels of their branches all reversed.

>>> t = Tree(1, [Tree(2), Tree(3), Tree(4)])
>>> reverse_other(t)
>>> t
Tree(1, [Tree(4), Tree(3), Tree(2)])
>>> t = Tree(1, [Tree(2, [Tree(3, [Tree(4), Tree(5)]), Tree(6, [Tree(7)])]), Tree(8)])
>>> reverse_other(t)
>>> t
Tree(1, [Tree(8, [Tree(3, [Tree(5), Tree(4)]), Tree(6, [Tree(7)])]), Tree(2)])
"""
def reverse_helper(t, need_reverse):
if t.is_leaf():
return
new_labs = [child.label for child in t.branches][::-1]
for i in range(len(t.branches)):
child = t.branches[i]
reverse_helper(child, not need_reverse)
if need_reverse:
child.label = new_labs[i]
reverse_helper(t, True)``````

Use Ok to test your code:

``python3 ok -q reverse_other``

## More BSTs practice

### Q7: Next Smallest Element

Write a function `next_element` that takes in a BST and a number N. It returns the smallest element that is larger than N. For example, if you have this tree:

``````  --8---
/      \
3-    10-
/  \      \
1  6     14
/ \   /
4 7  13  ``````

Then `next_element(3)` is 4, `next_element(6)` is 7, and `next_element(7)` is 8.

``````def next_element(bst, n):
"""
This function takes in a BST and a number N and it returns the smallest
element that is greater than N, or None if it has no such element.

>>> t = BST(8, BST(3, BST(1), BST(6, BST(4), BST(7))), BST(10, BST.empty, BST(14, BST(13))))
>>> next_element(t, 1)
3
>>> next_element(t, 3)
4
>>> next_element(t, 5)
6
>>> next_element(t, 7)
8
>>> next_element(t, 10)
13
>>> next_element(t, 14)
>>> result = 
>>> a = next_element(t, 1)
>>> while a:
...   result += [a]
...   a = next_element(t, a)
>>> result
[1, 3, 4, 6, 7, 8, 10, 13, 14]
"""
def next_or_default(t, n, default):
"""The smallest element in t greater than n, or default
if there is no such element."""
if t is BST.empty:
return default
elif t.label > n:
return next_or_default(t.left, n, t.label)
else:
return next_or_default(t.right, n, default)
return next_or_default(bst, n, None)``````

Use Ok to test your code:

``python3 ok -q next_element``