Unit 9 Session 1 (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?
inventory
tree is None
?
False
since there are no baked goods to fulfill the order.True
if the node's value matches the order_size
, otherwise return False
.order_size
be zero or negative?
order_size
is a positive integer.HAPPY CASE
Input: quantities = [5,4,8,11,None,13,4,7,2,None,None,None,1], order_size = 22
Output: True
Explanation: The path 5 -> 4 -> 11 -> 2 sums to 22, so the order can be fulfilled.
Input: quantities = [5,4,8,11,None,13,4,7,2,None,None,None,1], order_size = 2
Output: False
Explanation: No root-to-leaf path sums to 2, so the order cannot be fulfilled.
EDGE CASE
Input: quantities = [], order_size = 10
Output: False
Explanation: The tree is empty, so return False.
Input: quantities = [5], order_size = 5
Output: True
Explanation: The tree has only one node with value 5, which matches the order size.
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 problems involving finding a root-to-leaf path that sums to a given value in a binary tree, we can consider the following approaches:
order_size
.order_size
and check if the remaining value can be found along any path in the left or right subtree.Plan the solution with appropriate visualizations and pseudocode.
1) Base Case:
inventory
tree is None
, return False
.order_size
, return True
.
2) Recursive Check:order_size
and recursively check the left and right subtrees.order_size
, return True
.
3) Return False
if no valid path is found.Pseudocode:
1) Define the base case:
* If `inventory` is `None`, return `False`.
* If the current node is a leaf and its value equals `order_size`, return `True`.
2) Subtract the current node's value from `order_size` (`remaining_order = order_size * inventory.val`).
3) Recursively check the left and right subtrees:
* Return `True` if either subtree can fulfill the remaining order size.
4) Return `False` if no valid path is found.
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 can_fulfill_order(inventory, order_size):
if not inventory:
return False
# Check if it's a leaf node and the order size matches the node's value
if not inventory.left and not inventory.right:
return inventory.val == order_size
# Recur on the left and right subtrees
remaining_order = order_size * inventory.val
return (can_fulfill_order(inventory.left, remaining_order) or
can_fulfill_order(inventory.right, remaining_order))
Review the code by running specific example(s) and recording values (watchlist) of your code's variables along the way.
quantities = [5,4,8,11,None,13,4,7,2,None,None,None,1]
and order_size = 22
:
Evaluate the performance of your algorithm and state any strong/weak or future potential work.
Assume N
represents the number of nodes in the tree.
O(N)
because each node in the tree must be visited once.O(N)
due to the recursive call stack in the worst case, assuming a balanced tree.