Unit 8 Session 1 Advanced (Click for link to problem statements)
Understand what the interviewer is asking for by using test cases and questions about the problem.
- Established a set (2-3) of test cases to verify their own solution later.
- Established a set (1-2) of edge cases to verify their solution handles complexities.
- Have fully understood the problem and have no clarifying questions.
- Have you verified any Time/Space Constraints for this problem?
HAPPY CASE
Input: Binary tree with nodes ["Plantae", "Non-flowering", "Flowering", "Mosses", "Ferns", "Gymnosperms", "Angiosperms", "Monocots", "Dicots"]
Output: ['Mosses', 'Ferns', 'Gymnosperms', 'Monocots', 'Dicots']
Explanation: The leaf nodes, which are the most specific categories, are ["Mosses", "Ferns", "Gymnosperms", "Monocots", "Dicots"].
EDGE CASE
Input: Binary tree with only one node ["Plantae"]
Output: ['Plantae']
Explanation: The tree has only the root, so the only classification is ["Plantae"].
Match what this problem looks like to known categories of problems, e.g. Linked List or Dynamic Programming, and strategies or patterns in those categories.
For Tree Leaf Node problems, we want to consider the following approaches:
Plan the solution with appropriate visualizations and pseudocode.
General Idea: Traverse the tree recursively, collecting the values of nodes that have no children (leaf nodes).
1) If the current node is None, return an empty list.
2) If the current node has no left and right children, it's a leaf node, so return a list containing its value.
3) Recursively collect leaf nodes from the left subtree.
4) Recursively collect leaf nodes from the right subtree.
5) Combine the lists of leaf nodes from the left and right subtrees and return the result.
⚠️ Common Mistakes
Implement the code to solve the algorithm.
class TreeNode:
def __init__(self, value, left=None, right=None):
self.val = value
self.left = left
self.right = right
def get_most_specific(taxonomy):
if taxonomy is None:
return []
# If the current node is a leaf node, return its value
if taxonomy.left is None and taxonomy.right is None:
return [taxonomy.val]
# Recursively collect leaf nodes from left and right subtrees
left_leaves = get_most_specific(taxonomy.left)
right_leaves = get_most_specific(taxonomy.right)
# Combine the leaf nodes and return
return left_leaves + right_leaves
Review the code by running specific example(s) and recording values (watchlist) of your code's variables along the way.
Evaluate the performance of your algorithm and state any strong/weak or future potential work.
Assume N
represents the number of nodes in the binary tree.
O(N)
because the algorithm needs to visit every node in the tree.O(H)
where H
is the height of the tree, due to the recursive call stack.