LeetCode-146. LRU Cache

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问题描述

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.

Example 1:

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Input
["LRUCache", "put", "put", "get", "put", "get", "put", "get", "get", "get"]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [4, 4], [1], [3], [4]]
Output
[null, null, null, 1, null, -1, null, -1, 3, 4]

Explanation
LRUCache lRUCache = new LRUCache(2);
lRUCache.put(1, 1); // cache is {1=1}
lRUCache.put(2, 2); // cache is {1=1, 2=2}
lRUCache.get(1);    // return 1
lRUCache.put(3, 3); // LRU key was 2, evicts key 2, cache is {1=1, 3=3}
lRUCache.get(2);    // returns -1 (not found)
lRUCache.put(4, 4); // LRU key was 1, evicts key 1, cache is {4=4, 3=3}
lRUCache.get(1);    // return -1 (not found)
lRUCache.get(3);    // return 3
lRUCache.get(4);    // return 4

Constraints:

  • 1 <= capacity <= 3000
  • 0 <= key <= 104
  • 0 <= value <= 105
  • At most 2 * 105 calls will be made to get and put.

解答

哈希表快速查找的特性和双向链表的有序性相结合

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/*
 * @Description: 146. LRU Cache
 * @Author: libk
 * @Github: https://github.com/libk
 */
/**
 * Your LRUCache object will be instantiated and called as such:
 * var obj = new LRUCache(capacity)
 * var param_1 = obj.get(key)
 * obj.put(key,value)
 */
/**
 * @param {number} key
 * @param {number} value
 */
const DListNode = function (key, value) {
  this.key = key
  this.value = value
  this.next = null
  this.pre = null
}
/**
 * @param {number} capacity
 */
const LRUCache = function (capacity) {
  this.hash = {}
  this.capacity = capacity
  this.count = 0
  this.dummyHead = new DListNode()
  this.dummyTail = new DListNode()
  this.dummyHead.next = this.dummyTail
  this.dummyTail.pre = this.dummyHead
}

/**
 * @param {number} key
 * @return {number}
 */
LRUCache.prototype.get = function (key) {
  let curNode = this.hash[key]
  if (curNode) {
    this.moveToHead(curNode)
    return curNode.value
  } else {
    return -1
  }
}

/**
 * @param {number} key
 * @param {number} value
 * @return {void}
 */
LRUCache.prototype.put = function (key, value) {
  let curNode = this.hash[key]
  if (curNode) {
    curNode.value = value
    this.moveToHead(curNode)
  } else {
    curNode = new DListNode(key, value)
    this.hash[key] = curNode
    this.addToHead(curNode)
    this.count++
    if (this.count > this.capacity) {
      let tailNode = this.dummyTail.pre
      this.removeFromList(tailNode)
      delete this.hash[tailNode.key]
      this.count--
    }
  }
}

/**
 * @param {DListNode} curNode
 * @return {void}
 */
LRUCache.prototype.removeFromList = function (curNode) {
  curNode.pre.next = curNode.next
  curNode.next.pre = curNode.pre
}

/**
 * @param {DListNode} curNode
 * @return {void}
 */
LRUCache.prototype.addToHead = function (curNode) {
  curNode.pre = this.dummyHead
  curNode.next = this.dummyHead.next
  this.dummyHead.next.pre = curNode
  this.dummyHead.next = curNode
}

/**
 * @param {DListNode} curNode
 * @return {void}
 */
LRUCache.prototype.moveToHead = function (curNode) {
  this.removeFromList(curNode)
  this.addToHead(curNode)
}