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.
import java.util.Hashtable;
public class LRUCache {
class DLinkedNode {
int key;
int value;
DLinkedNode pre;
DLinkedNode post;
}
private void addNode(DLinkedNode node) {
node.pre = head;
node.post = head.post;
head.post.pre = node;
head.post = node;
}
private void removeNode(DLinkedNode node){
DLinkedNode pre = node.pre;
DLinkedNode post = node.post;
pre.post = post;
post.pre = pre;
}
private void moveToHead(DLinkedNode node){
this.removeNode(node);
this.addNode(node);
}
private DLinkedNode popTail(){
DLinkedNode res = tail.pre;
this.removeNode(res);
return res;
}
private Hashtable<Integer, DLinkedNode>
cache = new Hashtable<Integer, DLinkedNode>();
private int count;
private int capacity;
private DLinkedNode head, tail;
public LRUCache(int capacity) {
this.count = 0;
this.capacity = capacity;
head = new DLinkedNode();
head.pre = null;
tail = new DLinkedNode();
tail.post = null;
head.post = tail;
tail.pre = head;
}
public int get(int key) {
DLinkedNode node = cache.get(key);
if(node == null){
return -1;
}
this.moveToHead(node);
return node.value;
}
public void put(int key, int value) {
DLinkedNode node = cache.get(key);
if(node == null){
DLinkedNode newNode = new DLinkedNode();
newNode.key = key;
newNode.value = value;
this.cache.put(key, newNode);
this.addNode(newNode);
++count;
if(count > capacity){
DLinkedNode tail = this.popTail();
this.cache.remove(tail.key);
--count;
}
}else{
node.value = value;
this.moveToHead(node);
}
}
}
