LRU算法

LRU 算法就是⼀种缓存淘汰策略，全称是 Least Recently Used，也就是说我们认为最近使⽤过的 数据应该是是「有⽤的」，很久都没⽤过的数据应该是⽆⽤的，内存满了就优先删那些很久没⽤过的数据。

 

 

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type LRUCache struct {     size int     capacity int     cache map[int]*DLinkedNode     head, tail *DLinkedNode }   type DLinkedNode struct {     key, value int     prev, next *DLinkedNode }   func initDLinkedNode(key, value int) *DLinkedNode {     return &DLinkedNode{         key: key,         value: value,     } }   func Constructor(capacity int) LRUCache {     l := LRUCache{         cache: map[int]*DLinkedNode{},         head: initDLinkedNode(0, 0),         tail: initDLinkedNode(0, 0),         capacity: capacity,     }     l.head.next = l.tail     l.tail.prev = l.head     return l }   func (this *LRUCache) Get(key int) int {     if _, ok := this.cache[key]; !ok {         return -1     }     node := this.cache[key]     this.moveToHead(node)     return node.value }     func (this *LRUCache) Put(key int, value int)  {     if _, ok := this.cache[key]; !ok {         node := initDLinkedNode(key, value)         this.cache[key] = node         this.addToHead(node)         this.size++         if this.size > this.capacity {             removed := this.removeTail()             delete(this.cache, removed.key)             this.size--         }     } else {         node := this.cache[key]         node.value = value         this.moveToHead(node)     } }   func (this *LRUCache) addToHead(node *DLinkedNode) {     node.prev = this.head     node.next = this.head.next     this.head.next.prev = node     this.head.next = node }   func (this *LRUCache) removeNode(node *DLinkedNode) {     node.prev.next = node.next     node.next.prev = node.prev }   func (this *LRUCache) moveToHead(node *DLinkedNode) {     this.removeNode(node)     this.addToHead(node) }   func (this *LRUCache) removeTail() *DLinkedNode {     node := this.tail.prev     this.removeNode(node)     return node }