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OpenAI GPT-5 mini VS Google Gemini 2.5 Flash-Lite

Implement a Least Recently Used (LRU) Cache

Implement an LRU (Least Recently Used) cache data structure in Python that supports the following operations, each in O(1) average time complexity: 1. `get(key)` — Return the value associated with the key if it exists in the cache, otherwise return -1. Accessing a key marks it as recently used. 2. `put(key, value)` — Insert or update the key-value pair. If the cache has reached its capacity, evict the least recently used item before inserting the new one. Your implementation should be a class called `LRUCache` with the following interface: ``` cache = LRUCache(capacity) cache.put(key, value) result = cache.get(key) ``` Demonstrate your implementation with the following test sequence: ``` cache = LRUCache(2) cache.put(1, 10) cache.put(2, 20) print(cache.get(1)) # Expected: 10 cache.put(3, 30) # Evicts key 2 print(cache.get(2)) # Expected: -1 cache.put(4, 40) # Evicts key 1 print(cache.get(1)) # Expected: -1 print(cache.get(3)) # Expected: 30 print(cache.get(4)) # Expected: 40 ``` Requirements: - Do NOT use `functools.lru_cache` or `collections.OrderedDict`. Implement the underlying data structure yourself. - Use a combination of a hash map and a doubly linked list. - Include clear comments explaining your approach. - Handle edge cases such as capacity of 0 or 1. - Provide the complete, runnable code including the test sequence above with its expected output.

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Mar 12, 2026 19:00

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