LRU Cache
Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and set.
get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
set(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.
Tips:
考察对基本数据结构哈希表、链表的掌握,
为了使查找、插入和删除都有较高的性能,首先定义一个Node节点存储key, value, prev, next。使用一个哈希表。每次进行操作以后把最近操作的放到队尾。
复杂度:
get()采用哈希表查找,时间复杂度O(1),set()采用双向链表,删除插入时间复杂度O(1)
Code:
public class LRUCache {
private class Node{
Node prev;
Node next;
int key;
int value;
public Node(int key, int value) {
this.key = key;
this.value = value;
this.prev = null;
this.next = null;
}
}
private int capacity;
private HashMap<Integer, Node> hs = new HashMap<Integer, Node>();
private Node head = new Node(-1, -1);
private Node tail = new Node(-1, -1);
public LRUCache(int capacity) {
this.capacity = capacity;
tail.prev = head;
head.next = tail;
}
public int get(int key) {
if( !hs.containsKey(key)) {
return -1;
}
// remove current
Node current = hs.get(key);
current.prev.next = current.next;
current.next.prev = current.prev;
// move current to tail
move_to_tail(current);
return hs.get(key).value;
}
public void set(int key, int value) {
if( get(key) != -1) {
hs.get(key).value = value;
return;
}
if (hs.size() == capacity) {
hs.remove(head.next.key);
head.next = head.next.next;
head.next.prev = head;
}
Node insert = new Node(key, value);
hs.put(key, insert);
move_to_tail(insert);
}
private void move_to_tail(Node current) {
current.prev = tail.prev;
tail.prev = current;
current.prev.next = current;
current.next = tail;
}
}