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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.
为了查找高效O(1)，考虑使用哈希表hash， 为了使插入/删除高效O(1)，考虑使用双链表list, 因此采用双链表和哈希表结合。
GET(key):
hash命中节点node，把node从list中移动到表头，否则返回-1
SET(key, value):
hash命中node，只需更新hash中的node，并把node从list中移动到表头。
hash不命中，则需要判断capacity. 如果cache满，需要替换cache中的node，并删除list的表尾元素，cache未满，将node插入表头，更新cache。
1 struct CacheNode 2 { 3 int key; 4 int value; 5 CacheNode(int k, int v):key(k), value(v){} 6 }; 7 8 class LRUCache{ 9 public: 10 LRUCache(int capacity) 11 { 12 cacheSize = capacity; 13 } 14 15 int get(int key) 16 { 17 if (cacheMap.find(key) == cacheMap.end()) // hit fail 18 return -1; 19 cacheList.splice(cacheList.begin(), cacheList, cacheMap[key]); 20 cacheMap[key] = cacheList.begin(); 21 return cacheMap[key]->value; 22 } 23 24 void set(int key, int value) 25 { 26 if (cacheMap.find(key) == cacheMap.end()) // hit fail 27 { 28 CacheNode cacheItem(key, value); 29 if (cacheMap.size() == cacheSize) // cache is full 30 { 31 cacheMap.erase(cacheList.back().key); 32 cacheList.pop_back(); 33 } 34 cacheList.push_front(cacheItem); 35 cacheMap[key] = cacheList.begin(); 36 } 37 else // hit success 38 { 39 cacheMap[key]->value = value; 40 cacheList.splice(cacheList.begin(), cacheList, cacheMap[key]); 41 cacheMap[key] = cacheList.begin(); 42 } 43 } 44 45 private: 46 int cacheSize; 47 unordered_map<int, list<CacheNode>::iterator> cacheMap; 48 list<CacheNode> cacheList; 49 };
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原文地址：http://www.cnblogs.com/ym65536/p/4230589.html
