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.get(2); // returns -1 (not found)
cache.put(4, 4); // evicts key 1
cache.get(1); // returns -1 (not found)
cache.get(3); // returns 3
cache.get(4); // returns 4
使用鏈表 + 哈希表
使用鏈表模擬LRU的寄存器,每次從鏈表中取出某個元素之後,然後將該元素移動到鏈表的表頭,表頭代表最近訪問的元素。當put元素的時候,如果沒有達到容量,則直接put,如果達到容量,則去除鏈表的尾元素。
使用哈希表存儲key,存儲容量和裏面的key。
import java.util.HashMap;
import java.util.LinkedList;
class LRUCache {
int capacity;
LinkedList ageList;
HashMap<Integer, Integer> cache;
public LRUCache(int capacity) {
this.capacity = capacity;
ageList = new LinkedList<Integer>();
cache = new HashMap<>();
}
public int get(int key) {
Integer result = cache.get(key);
if (result == null)
return -1;
ageList.remove(new Integer(key));
ageList.addFirst(key);
return result;
}
public void put(int key, int value) {
cache.put(key, value);
if (cache.size() > capacity){
cache.remove(ageList.removeLast());
}
ageList.remove(new Integer(key));
ageList.addFirst(key);
}
}
/**
* Your LRUCache object will be instantiated and called as such:
* LRUCache obj = new LRUCache(capacity);
* int param_1 = obj.get(key);
* obj.put(key,value);
*/