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Java Solution for LeetCode algorithm problems, continually updating
146\. LRU Cache
Medium
Design a data structure that follows the constraints of a **[Least Recently Used (LRU) cache](https://en.wikipedia.org/wiki/Cache_replacement_policies#LRU)**.
Implement the `LRUCache` class:
* `LRUCache(int capacity)` Initialize the LRU cache with **positive** size `capacity`.
* `int get(int key)` Return the value of the `key` if the key exists, otherwise return `-1`.
* `void put(int key, int value)` Update the value of the `key` if the `key` exists. Otherwise, add the `key-value` pair to the cache. If the number of keys exceeds the `capacity` from this operation, **evict** the least recently used key.
The functions `get` and `put` must each run in `O(1)` average time complexity.
**Example 1:**
**Input** ["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"] [[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]]
**Output:** [null, null, null, 1, null, -1, null, -1, 3, 4]
**Explanation:**
LRUCache lRUCache = new LRUCache(2);
lRUCache.put(1, 1); // cache is {1=1}
lRUCache.put(2, 2); // cache is {1=1, 2=2}
lRUCache.get(1); // return 1
lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
lRUCache.get(2); // returns -1 (not found)
lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
lRUCache.get(1); // return -1 (not found)
lRUCache.get(3); // return 3
lRUCache.get(4); // return 4
**Constraints:**
* `1 <= capacity <= 3000`
* 0 <= key <= 104
* 0 <= value <= 105
* At most 2 * 105 calls will be made to `get` and `put`.