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.
public class LRUCache {
class Node{
int key;
int val;
Node pre;
Node next;
Node(int key,int val){
this.key = key;
this.val = val;
}
}
Node head = new Node(-1,-1);
Node tail = new Node(-1,-1);
private void moveToTail(Node no){
this.removeNode(no);
no.pre = tail.pre;
no.next = tail;
tail.pre.next = no;
tail.pre = no;
}
private void addNode(Node no){
no.pre = tail.pre;
no.next = tail;
tail.pre.next = no;
tail.pre = no;
}
private void removeNode(Node no){
no.pre.next = no.next;
no.next.pre = no.pre;
}
private Node getHead(){
return head.next;
}
Map<Integer,Node> map = new HashMap<>();
int cap;
int count = 0;
public LRUCache(int capacity) {
cap = capacity;
head.next = tail;
tail.pre = head;
}
public int get(int key) {
Node res = map.get(key);
if(res == null) {
return -1;
}else{
int val = res.val;
this.moveToTail(res);
return val;
}
}
public void set(int key, int value) {
if(map.containsKey(key)){
Node res = map.get(key);
res.val = value;
this.moveToTail(res);
}else{
count++;
if(count>cap){
Node rem = this.getHead();
map.remove(rem.key);
rem.key = key;
rem.val = value;
this.moveToTail(rem);
map.put(key, rem);
}else{
Node nod = new Node(key,value);
this.addNode(nod);
map.put(key, nod);
}
}
}
}
原文:http://blog.csdn.net/guorudi/article/details/43857323