# CSPT23 Lecture 12
## [Max Depth](https://leetcode.com/problems/maximum-depth-of-binary-tree/)
```
from collections import deque
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
"""
Plan
Use recursion to do a depth-first traversal and return the max depth you found
"""
def maxDepth(self, root: Optional[TreeNode]) -> int:
if root == None:
return 0
self.maxDepthFoundSoFar = 0
self.maxDepthHelper(root, 1)
return self.maxDepthFoundSoFar
def maxDepthHelper(self, node, depth):
if node.left == None and node.right == None and depth > self.maxDepthFoundSoFar:
self.maxDepthFoundSoFar = depth
return
if node.left:
self.maxDepthHelper(node.left, depth + 1)
if node.right:
self.maxDepthHelper(node.right, depth + 1)
def maxDepthLevelOrder(self, root: Optional[TreeNode]) -> int:
if root == None:
return 0
queue = deque()
queue.append((root, 1))
maxDepthFoundSoFar = 0
while len(queue) > 0:
curr = queue.popleft()
currNode, currDepth = curr[0], curr[1]
if currDepth > maxDepthFoundSoFar:
maxDepthFoundSoFar = currDepth
if currNode.left:
queue.append((currNode.left, currDepth + 1))
if currNode.right:
queue.append((currNode.right, currDepth + 1))
return maxDepthFoundSoFar
def maxDepthIterativeDepthFirst(self, root: Optional[TreeNode]) -> int:
if root == None:
return 0
stack = deque()
stack.append((root, 1))
maxDepthFoundSoFar = 0
while len(stack) > 0:
curr = stack.pop()
currNode, currDepth = curr[0], curr[1]
if currDepth > maxDepthFoundSoFar:
maxDepthFoundSoFar = currDepth
if currNode.right:
stack.append((currNode.right, currDepth + 1))
if currNode.left:
stack.append((currNode.left, currDepth + 1))
return maxDepthFoundSoFar
```
## [Kth Smallest](https://leetcode.com/problems/kth-smallest-element-in-a-bst/)
```
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
from collections import deque
class Solution:
"""
brute-force
traverse the entire tree and push the values onto a list
sort that list, return the k - 1 index
runtime: O(nlogn)
space: O(n)
better approach
do an inorder traversal and append values to the list, index
correctly into the list afterwards
runtime: O(n)
space: O(n)
best approach
do an inorder traversal and keep track of how many nodes you've processed
once you're processing the kth node, then you know that's the kth smallest
value
runtime: O(n)
space: O(n)
"""
def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:
self.curr = 0
return self.kthSmallestHelper(root, k)
def kthSmallestHelper(self, node, k): # returns None or the kth node's value
if node.left:
leftSubtree = self.kthSmallestHelper(node.left, k)
if leftSubtree != None:
return leftSubtree
self.curr += 1
if self.curr == k:
return node.val
if node.right:
rightSubtree = self.kthSmallestHelper(node.right, k)
if rightSubtree != None:
return rightSubtree
def kthSmallestRecursiveNonOptimal(self, root: Optional[TreeNode], k: int) -> int:
values = []
self.kthSmallestHelperRecursiveNonOptimal(root, values)
return values[k - 1]
def kthSmallestHelperRecursiveNonOptimal(self, node, values):
if node.left:
self.kthSmallestHelper(node.left, values)
values.append(node.val)
if node.right:
self.kthSmallestHelper(node.right, values)
def kthSmallestBruteForce(self, root: Optional[TreeNode], k: int) -> int:
values = []
stack = deque()
stack.append(root)
while len(stack) > 0:
curr = stack.pop()
values.append(curr.val)
if curr.left:
stack.append(curr.left)
if curr.right:
stack.append(curr.right)
values.sort()
return values[k - 1]
```