Leetcode-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.
Analysis:
Use double linked list.
Solution:
1 class Node{ 2 int key; 3 int val; 4 Node pre; 5 Node next; 6 public Node(int k, int v){ 7 key= k; 8 val = v; 9 pre = null; 10 next = null; 11 } 12 } 13 14 public class LRUCache { 15 16 17 Map<Integer,Node> record; 18 Node preHead,end; 19 int maxCapa; 20 int curCapa; 21 22 public LRUCache(int capacity) { 23 record = new HashMap<Integer,Node>(); 24 preHead = new Node(-1,-1); 25 end = null; 26 maxCapa = capacity; 27 curCapa = 0; 28 } 29 30 public int get(int key) { 31 if (!record.containsKey(key)) return -1; 32 Node cur = record.get(key); 33 if (cur==end) return cur.val; 34 35 cur.pre.next = cur.next; 36 cur.next.pre = cur.pre; 37 end.next = cur; 38 cur.next = null; 39 cur.pre = end; 40 end = cur; 41 return cur.val; 42 } 43 44 public void set(int key, int value) { 45 if (maxCapa==0) return; 46 47 if (record.containsKey(key)){ 48 Node cur = record.get(key); 49 cur.val = value; 50 this.get(key); 51 } else { 52 if (curCapa==maxCapa){ 53 Node cur = preHead.next; 54 preHead.next = cur.next; 55 record.remove(cur.key); 56 curCapa--; 57 } 58 59 curCapa++; 60 Node cur = new Node(key,value); 61 record.put(key,cur); 62 if (curCapa==1){ 63 preHead.next = cur; 64 cur.pre = preHead; 65 end = cur; 66 } else { 67 end.next = cur; 68 cur.pre = end; 69 end = cur; 70 } 71 } 72 } 73 }
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