Description

Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.

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 <= 10^4
0 <= value <= 10^5
At most 2 * 105 calls will be made to get and put.

Solution

dict + heap

Use a dict to store the key-value pair, and also store the timestamp of the calling of functions. If the capacity is exceeded, then pop from the heap, compare the timestamp in heap with timestamp in dict, if they are the same, pop from the dict.

The time complexity of put function is not strictly $o(1)$ though.

dict + double-sided linked list

Use head and tail for the linked list!!

Code

dict + heap

class LRUCache:
    import heapq

    def __init__(self, capacity: int):
        self.key_value = {}
        self.time_heap = []
        self.timestamp = 0
        self.capacity = capacity

    def get(self, key: int) -> int:
        if key not in self.key_value:
            return -1
        self.timestamp += 1
        self.key_value[key][1] = self.timestamp
        heapq.heappush(self.time_heap, (self.timestamp, key))
        return self.key_value[key][0]

    def put(self, key: int, value: int) -> None:
        self.timestamp += 1
        self.key_value[key] = [value, self.timestamp]
        heapq.heappush(self.time_heap, (self.timestamp, key))
        while len(self.key_value) > self.capacity:
            timestamp, key = heapq.heappop(self.time_heap)
            if self.key_value[key][1] == timestamp:
                self.key_value.pop(key)       


# Your LRUCache object will be instantiated and called as such:
# obj = LRUCache(capacity)
# param_1 = obj.get(key)
# obj.put(key,value)

Linked List

class LinkedList:
    def __init__(self, key: int, val: int):
        self.key = key
        self.val = val
        self.prev = None
        self.next = None

class LRUCache:

    def __init__(self, capacity: int):
        self.head = LinkedList(-1, -1)
        self.tail = LinkedList(-1, -1)
        self.head.next = self.tail
        self.tail.prev = self.head
        self.capacity = capacity
        self.key_value = {}

    def _remove(self, node) -> None:
        prev = node.prev
        pn = node.next
        prev.next = pn
        pn.prev = prev

    def _add(self, node) -> None:
        self.tail.prev.next = node
        node.prev = self.tail.prev
        self.tail.prev = node
        node.next = self.tail

    def get(self, key: int) -> int:
        if key not in self.key_value:
            return -1
        node = self.key_value[key]
        self._remove(node)
        self._add(node)
        return node.val
        
    def put(self, key: int, value: int) -> None:
        if key in self.key_value:
            self._remove(self.key_value[key])
        node = LinkedList(key, value)
        self._add(node)
        self.key_value[key] = node
        while len(self.key_value) > self.capacity:
            head_next = self.head.next
            self._remove(head_next)
            self.key_value.pop(head_next.key)


# Your LRUCache object will be instantiated and called as such:
# obj = LRUCache(capacity)
# param_1 = obj.get(key)
# obj.put(key,value)