GfG: Missing in a Sorted Array of Natural Numbers

[paragc05]

No description provided.

[super30admin]

Interview Problem: Find Missing Number in a sorted array (Problem12.py)

Strengths:

1. Your solution correctly implements binary search to find the missing number in O(log n) time.
2. The code is clean, well-structured, and easy to understand.
3. Good use of comments to explain the approach.
4. The edge case handling (when n == 1) is a nice touch.
5. The variable naming is clear and descriptive.

Areas for improvement:

1. The example comments in the header are incorrect - they show wrong output values. Make sure to update them to match the actual expected outputs.
2. Consider adding more edge case handling documentation in comments.
3. The condition if n == 1: return 1 handles the case when the array is empty (meaning 1 is missing), but you might want to add a comment explaining this edge case.
4. You could potentially add input validation (e.g., checking if the array is sorted).

VERDICT: PASS

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Interview Problem: Design Min Heap

It appears that you have submitted a solution for a different problem (finding a missing integer in a sorted array) instead of the requested Min Heap implementation problem.

To correctly solve this problem, you need to:

1. Create a class (e.g., MyMinHeap) with an underlying array to store heap elements
2. Implement helper methods for parent, left child, and right child indices
3. Implement insert(val) - adds element at end and bubbles up to maintain heap property
4. Implement getMin() - returns the root element (heap[0])
5. Implement extractMin() - removes root, replaces with last element, and heapifies down
6. Implement a heapify() helper method to restore heap property after removal

Please resubmit with a solution that actually implements the Min Heap data structure with the required operations.

VERDICT: NEEDS_IMPROVEMENT