Completed Design-2

[ManasviReddy25]

No description provided.

[super30admin]

Create Queue using Stacks (Problem1.py)

Strengths:

• Correct implementation of the two-stack queue pattern
• Good explanatory comments at the top
• Proper handling of edge cases (checking empty in pop)
• Clear and logical code structure

Areas for Improvement:

• Use Pythonic truthiness: if not self.outstack: instead of if len(self.outstack)==0:
• The pop() method could be slightly cleaner by combining the peek call and pop operation
• Consider adding docstrings for better documentation

Overall, this is a solid implementation that correctly solves the problem with appropriate time and space complexity.

VERDICT: PASS

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Implement Hash Map (Problem2.py)

EVALUATION

1. Correctness

The solution correctly implements all required operations:

• put(key, value): Stores value at the computed position
• get(key): Returns -1 if bucket doesn't exist or value is -1 (which could be ambiguous)
• remove(key): Sets the position back to -1

Issue: The solution uses -1 as a sentinel value to indicate "not found", but the problem states that values can be in the range 0 <= value <= 10^6. This means a legitimate value of -1 cannot be stored, and the get method cannot distinguish between "key not found" and "key exists with value -1". However, since the problem constraints allow values from 0 to 10^6, this is not a practical issue for this problem.

2. Time Complexity

• Reference Solution: Amortized O(1) - uses linked lists with potential O(n) worst case
• Student Solution: O(1) for all operations - direct indexing into 2D array

The student solution achieves true O(1) time complexity for all operations, which is actually better than the reference solution's amortized O(1).

3. Space Complexity

• Reference Solution: O(n) where n is the number of entries
• Student Solution: O(n) where n is the number of entries

Both solutions use similar space. The student solution pre-allocates a fixed outer array of 1000 slots but only creates inner arrays when needed, which is memory-efficient.

4. Code Quality

The code is well-structured with:

• Clear method names (primary, secondary instead of getHash)
• Good inline comments explaining each step
• Clean separation of concerns

Minor improvements:

• Variable name hash1 and hash2 could be more descriptive (e.g., bucket_count, inner_size)
• The comment about "+1 handles key 1000000" is slightly misleading - it's actually because key // 1000 can produce 1000 for key 1,000,000

5. Efficiency

The solution is highly efficient:

• Direct array access provides O(1) lookup
• No collision handling needed
• Memory is allocated lazily (only when needed)

FEEDBACK

Strengths:

1. Excellent use of a 2D array structure for O(1) access without collision handling
2. Lazy initialization of inner arrays saves memory
3. Clean, readable code with good comments
4. Achieves true O(1) time complexity (better than reference's amortized O(1))

Areas for Improvement:

1. The sentinel value approach (-1) works for this problem but is a potential design flaw in general
2. Variable naming could be more descriptive (e.g., outer_buckets instead of hash1)
3. Consider adding input validation or handling edge cases more explicitly

Minor Note:
The comment "keeps growing whenever values are entered" is incorrect - the outer array is fixed at 1000 slots, and inner arrays only grow when a new bucket is accessed for the first time.

The solution is correct, efficient, and well-implemented. It achieves O(1) time complexity for all operations through direct indexing, which is a clever and effective approach for this problem. The code is readable and follows good practices. While there are minor suggestions for improvement, the solution successfully solves the problem within the given constraints.

VERDICT: PASS