Design HashSet

[hiteshmadapathi]

No description provided.

[super30admin]

Implement Hash Set (Design HashSet.py)

Your solution has the advantage of being simple and achieving O(1) time complexity for all operations. However, there are a few issues to address:

1. Collision Handling: Your current implementation does not handle collisions correctly. For instance, if two keys map to the same index (e.g., key=0 and key=1000000 both map to index 0), storing one will overwrite the other. This breaks the HashSet property of storing unique keys. To fix this, you need to use a data structure that can handle collisions, such as a list of buckets where each bucket is a list (or another set) of keys that hash to the same index.

2. Space Efficiency: Your array has a fixed size of 1,000,000, which is the maximum key value. This is inefficient if only a small number of keys are stored. For example, if only 100 keys are stored, you are still using 1,000,000 spaces. Consider using a smaller array and handling collisions with chaining (e.g., each index points to a linked list). Alternatively, you can use a dynamic array that grows as needed, but that might complicate the hash function.

3. Key Storage: You are storing the key itself in the array. However, for a HashSet, you only need to know if the key is present (a boolean). So you could use an array of booleans. But note that even with booleans, the size is fixed at 1e6, which is 1e6 booleans (about 1e6 bytes = 1 MB), which is acceptable in Python? Actually, in Python, a boolean might use more memory, but it's still manageable. However, the collision issue remains.

4. Improvements: Consider implementing a more space-efficient solution like the reference solution, which uses double hashing. It uses a 2D array of booleans with 1000 primary buckets and 1000 secondary buckets (with an extra bucket for the 0th primary index to handle key=1000000). This reduces the space to about 1e6 booleans, but allocated only when needed. For example, if you have 10,000 keys, it will allocate only the necessary secondary arrays, which might be around 10,000 booleans plus the primary array (1000 pointers). This is much more efficient.

Alternatively, you can use chaining with a list of lists. Here's a sketch:

• Initialize an array of size 1000 (or any reasonable number) of lists.
• For a key, compute hash = key % 1000.
• Then, in the list at index hash, check if the key exists (for contains), add if not present (for add), or remove if present (for remove).
• This would use space proportional to the number of keys.

Overall, your solution is correct for keys that do not collide (which is almost all except key=0 and key=1000000), but it fails for those two. Given the constraints, it might pass all test cases if the test cases don't include key=1000000? But the problem says keys can be up to 10^6, so key=1000000 is allowed.

VERDICT: NEEDS_IMPROVEMENT

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Implement Min Stack

It appears that you have submitted a solution for the "Design HashSet" problem instead of the "Implement Min Stack" problem. Please note that the problem requires designing a stack that supports push, pop, top, and retrieving the minimum element in constant time.

For the Min Stack problem, you need to implement a class with the following methods:

• MinStack(): initializes the stack object.
• void push(int val): pushes the element val onto the stack.
• void pop(): removes the element on the top of the stack.
• int top(): gets the top element of the stack.
• int getMin(): retrieves the minimum element in the stack.

Your current solution implements a hash set with add, remove, and contains methods, which does not meet the requirements.

I recommend revisiting the problem statement and implementing a stack that can also track the minimum element efficiently. One common approach is to use two stacks: one to store all the values and another to store the current minimum values. Alternatively, you can store pairs (value, current_min) in a single stack.

Please ensure you are solving the correct problem. If you have any questions about the problem requirements or need guidance on how to approach the Min Stack problem, feel free to ask.

VERDICT: NEEDS_IMPROVEMENT