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.
思路：get函数在Cache查找key的值，如果存在于Cache中，则将该键值移到Cache首位置，并返回值value反之，则返回-1；set(key,value)函数，如果key存在，则更新相应的value把该元素放到最前面。如果不存在，则创建，并放到最前面，如果容器满了，就把最后那个元素去除。从这可以看出，元素访问的先后是有一定的顺序的，我们可以采用map来对元素进行快速查找，然后定位到查找的结点，使用双向链表来进行移动或删除都很方便。这里使用STL中list容器对于移动或删除都比较容易，代码也比较简洁。
struct CacheNode { int key; int value; CacheNode(int k,int v):key(k),value(v){} }; class LRUCache{ private: int size; list<CacheNode> cacheList; unordered_map<int,list<CacheNode>::iterator > cacheMap; public: LRUCache(int capacity) { this->size=capacity; } int get(int key) { if(cacheMap.find(key)!=cacheMap.end()) { list<CacheNode>::iterator iter=cacheMap[key]; cacheList.splice(cacheList.begin(),cacheList,iter); cacheMap[key]=cacheList.begin(); return cacheList.begin()->value; } else return -1; } void set(int key, int value) { if(cacheMap.find(key)==cacheMap.end()) { if(cacheList.size()==size) { cacheMap.erase(cacheList.back().key); cacheList.pop_back(); } cacheList.push_front(CacheNode(key,value)); cacheMap[key]=cacheList.begin(); } else { list<CacheNode>::iterator iter=cacheMap[key]; cacheList.splice(cacheList.begin(),cacheList,iter); cacheMap[key]=cacheList.begin(); cacheList.begin()->value=value; } } };
原文地址：http://www.cnblogs.com/awy-blog/p/3833427.html
