146. 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 ('ll 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 is reached its capacity, it should invalidate the least recently used item before inserting a new item.

A difficult design problem is generally double list + Hashmap<key, NODE>[LRU, mid stack] or tree + hashmap <key, node> [Relation tree or Tire

//new node in the tail, old node in the head;classdoublelist{intkey; intVal;    Doublelist prev;    Doublelist Next;  PublicDoublelist (intKeyintval) {         This. Key =key;  This. val =Val; }} Public classLRUCache {intindex = 0; intcapacity;    Doublelist tail;    Doublelist Head; HashMap<integer, doublelist>map;  PublicLRUCache (intcapacity) {        This. Capacity =capacity;  This. Map =NewHashmap<integer, doublelist>();  This. Head =NewDoublelist (0,0);  This. Tail =NewDoublelist (0,0); Head.next=tail; Tail.prev=Head;  This. index = 0; }         Public voidAddFirst (doublelist node) {Node.next=Head.next; Node.next.prev=node; Node.prev=Head; Head.next=node; }         Public voidRemove (doublelist node) {Node.prev.next=Node.next; Node.next.prev=Node.prev; }         Public intGetintkey) {      if(Map.containskey (key)) {doublelist node=Map.get (key);         Remove (node);         AddFirst (node); returnNode.val; }      return-1; }         Public voidSetintKeyintvalue) {        if(Map.containskey (key)) {Map.get (key). Val=value; Doublelist node=Map.get (key);            Remove (node);            AddFirst (node); return; }        Else{doublelist node=Newdoublelist (key, value);            Map.put (key, node); if(Index <capacity) {Index++;            AddFirst (node); }            Else{map.remove (Tail.prev.key);                Remove (Tail.prev);            AddFirst (node); }        }    }       }

146. LRU Cache