## Description

Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.

get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1. put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.

The cache is initialized with a positive capacity.

Follow up: Could you do both operations in O(1) time complexity?

Example:

LRUCache cache = new LRUCache( 2 /* capacity */ );

cache.put(1, 1);
cache.put(2, 2);
cache.get(1);       // returns 1
cache.put(3, 3);    // evicts key 2
cache.put(4, 4);    // evicts key 1
cache.get(3);       // returns 3
cache.get(4);       // returns 4


## Solutions

### 1. Hash Table + Double Linked List

# Time: O(1)
# Space: O(n)

class ListNode():
def __init__(self, key, value):
self.key = key
self.val = value
self.pre = None
self.next = None

def __init__(self):
self.tail = None

self.tail = node
else:

def remove(self, node):
pre, next = node.pre, node.next
if pre:
pre.next = next
else:

if next:
next.pre = pre
else:
self.tail = pre

node.pre = None
node.next = None
# del node

def remove_last(self):
if not self.tail:
return None
tail = self.tail
self.remove(tail)
return tail

class LRUCache:

def __init__(self, capacity: int):
self.capacity = capacity
self.cache = dict()
self.size = 0

def get(self, key: int) -> int:
if key not in self.cache:
return -1
node = self.cache[key]
return node.val

def put(self, key: int, value: int) -> None:
if key not in self.cache:
self.size += 1
node = ListNode(key, value)
self.cache[key] = node
else:
node = self.cache[key]
node.val = value

if self.size > self.capacity:
self.size -= 1