Completed BFS-1 homework

[shaurya22c]

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[super30admin]

Binary Tree Level Order Traversal (level_order_traversal.py)

EVERTS.0

EVALUATION:
The student's solution correctly implements the level order traversal of a binary tree using BFS with a queue. Let me analyze each criterion:

1. Correctness: The solution is correct. It handles all edge cases:

   • Empty tree (returns [])
   • Single node tree
   • Multi-level trees
   • It correctly processes nodes level by level, left to right

2. Time Complexity: O(n) - matches the reference solution. Every node is visited exactly once.

3. Space Complexity: O(n) - matches the reference solution. The queue can hold up to one level of nodes (in the worst case, the last level can have up to n/2 nodes), and the result stores all node values.

4. Code Quality:

   • Excellent documentation with clear comments explaining the approach, pattern, time/space complexity, and an example
   • Well-structured code with meaningful variable names
   • Uses collections.deque which is the optimal Python data structure for queue operations (O(1) for popleft)
   • Proper handling of edge cases
   • Clean separation of concerns

5. Efficiency: The solution is efficient. Using collections.deque is actually better than using a regular list as a queue (which would have O(n) for pop(0)). The algorithm is optimal for this problem.

The solution is essentially a Python translation of the reference C++ solution with even better documentation.

FEEDMENT:
Your solution is excellent! Here's what stands out:

Strengths:

• Correct implementation: The BFS approach with level size tracking is implemented correctly
• Excellent documentation: Your comments explaining the approach, pattern, complexity, and providing an example are very helpful for understanding
• Optimal Python implementation: Using collections.deque is the right choice for queue operations in Python (O(1) for popleft vs O(n) for list pop(0))
• Clean code structure: Variable names are descriptive and the logic is easy to follow
• Edge case handling: Properly handles the empty tree case

Minor suggestions (optional):

• The import for collections and List, Optional from typing are typically expected to be at the top of the file, but since this is a LeetCode-style solution, it's understood that these are imported elsewhere
• You could potentially use type hints more explicitly, but this is a stylistic choice

Overall, this is a textbook-quality solution that demonstrates a strong understanding of BFS and level order traversal.

VERDICT: PASS

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Course Schedule (course_schedule.py)

Great work! Your solution demonstrates a strong understanding of graph algorithms and correctly applies Kahn's algorithm for topological sorting. The documentation is exemplary - the docstring clearly explains the approach, pattern, time/space complexity, and even provides a worked example. This level of documentation is excellent practice for technical interviews and collaborative coding.

Strengths:

• Correct algorithm choice and implementation
• Excellent documentation and comments
• Clear, readable code structure
• Good variable naming

Areas for improvement (minor):

1. Consider combining the two loops over prerequisites into one for slightly better efficiency:

   for course, prereq in prerequisites:
       graph[prereq].append(course)
       indegree[course] += 1

2. Instead of the final loop checking indegrees, you could track a counter of processed courses and compare it to numCourses:

   processed = 0
   while queue:
       current_course = queue.popleft()
       processed += 1
       for neighbor in graph[current_course]:
           indegree[neighbor] -= 1
               if indegree[neighbor] == 0:
                   queue.append(neighbor)
   return processed == numCourses

3. Make sure to include import collections at the top of your file if it's not already there.

Overall, this is a high-quality solution that correctly solves the problem with optimal time and space complexity.

VERDICT: PASS