leetcode || 146、LRU Cache
problem:
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.
thinking:
(1)这道题我自己借助unordered_map和list实现了一个版本,小数据测试通过,大数据测试时(连续set、get),由于频繁的操作map、list,超时了。
最高效的做法是将list嵌入到节点的结构体中。
我的思路:构建一个包含key值和访问次数visited的结构体,每次访问结构体对象时,访问次数+1,在节点数未达到上限时,将新节点插入到list的头部。在达到上限时,
将访问次数最小的节点移到list的末尾,移除尾部节点,在list头部插入新的节点。使用unordered_map提高超找效率。
code:
class node{ public: int _key; int _visited; node(int key, int visited):_key(key),_visited(visited){} }; class LRUCache{ private: unordered_map<int,list<node *>::iterator> map_record; list<node *> list_record; int size; public: LRUCache(int capacity) { size=capacity; } int get(int key) { if(map_record.count(key)!=0) { unordered_map<int,list<node *>::iterator>::iterator it_a=map_record.find(key); list<node *>::iterator it_b=it_a->second; node *node_tmp=*it_b; node_tmp->_visited+=1; adjust(); return key; } else return -1; } void set(int key, int value) { if(map_record.count(key)!=0) { unoredered_map<int,list<node *>::iterator>::iterator it_a=map_record.find(key); list<node *>::iterator it_b=it_a->second; node *node_tmp=*it_b; node_tmp->_key=value; node_tmp->_visited+=1; } else { if(list_record.size()<size) { node *tmp_node=new node(value,1); list_record.push_front(tmp_node); list<node *>::iterator it=find(list_record.begin(),list_record.end(),tmp_node); map_record.insert(make_pair(value,it)); } else { adjust(); node *tmp_node=new node(value,1); list_record.push_front(tmp_node); map_record.clear(); for(list<node *>::iterator it=list_record.begin();it!=list_record.end();++it) { node *tmp=*it; map_record.insert(make_pair(tmp->_key,it)); } } } } protected: void adjust() { if(list_record.empty()) return; node *index=list_record.back(); list<node *>::iterator min_it; int min_visited=index->_visited; for(list<node *>::iterator it=list_record.begin();it!=list_record.end();++it) { node *tmp_node = *it; if(tmp_node->_visited<min_visited) min_it=it; } list<node *>::iterator last=list_record.end(); swap(min_it,--last); list_record.pop_back(); } };
一个高效且测试通过的版本:
struct CacheNode { int key; int value; CacheNode* next; CacheNode* prev; CacheNode(int _key, int _value) { key = _key; value = _value; next = nullptr; prev = nullptr; } }; class LRUCache{ public: LRUCache(int capacity) { _capacity = capacity; head = new CacheNode(INT_MIN,-1); tail = head->next; len = 0; } int get(int key) { auto found = cache.find(key); if (found != cache.end()) { if (found -> second == tail) { tail = tail->prev; } insertToHead(found->second); if (tail == head) tail = head -> next; return found -> second -> value; } return -1; } void set(int key, int value) { auto found = cache.find(key); if (found != cache.end()) { found->second->value = value; if (found -> second == tail) { tail = tail->prev; } insertToHead(found->second); if (tail == head) tail = head->next; return ; } if (len == _capacity) { CacheNode* tmp = tail -> prev; cache.erase(tail->key); deleteNodeLink(tail); delete tail; tail = tmp; insertToHead(new CacheNode(key, value)); return ; } insertToHead(new CacheNode(key, value)); if (tail == nullptr) tail = head->next; len++; } private: CacheNode* head; CacheNode* tail; int _capacity; int len; unordered_map<int, CacheNode*> cache; void deleteNodeLink(CacheNode* node) { CacheNode* prev = nullptr; CacheNode* next = nullptr; if (node -> prev) prev = node->prev; if (prev) { prev -> next = node -> next; } if(node->next) next = node -> next; if(next) { next -> prev = prev; } } void insertToHead(CacheNode* node) { deleteNodeLink(node); node->prev = head; node->next = head->next; if (head -> next) head->next->prev = node; head->next = node; cache[node->key] = node; } };
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