题目内容如下(链接:https://leetcode.com/problems/lru-cache/description/)
Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.
get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.put(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.
Follow up:
Could you do both operations in O(1) time complexity?
Example:
LRUCache cache = new LRUCache( 2 /* capacity */ ); cache.put(1, 1); cache.put(2, 2); cache.get(1); // returns 1 cache.put(3, 3); // evicts key 2 cache.get(2); // returns -1 (not found) cache.put(4, 4); // evicts key 1 cache.get(1); // returns -1 (not found) cache.get(3); // returns 3 cache.get(4); // returns 4
经典面试算法题,用list+unordered_map即可轻松实现,代码如下:
class LRUCache {
private:
typedef list<int>::iterator IT;
int m_iCapacity;
list<int> m_list;
unordered_map<int, pair<int, IT> > m_umap;
public:
LRUCache(int capacity) {
m_iCapacity = capacity;
m_list = list<int>();
m_umap = unordered_map<int, pair<int, IT> >();
}
int get(int key) {
if (m_umap.find(key) == m_umap.end())
{
return -1;
}
m_list.erase(m_umap[key].second);
m_list.push_front(key);
m_umap[key].second = m_list.begin();
return m_umap[key].first;
}
void put(int key, int value) {
// the key already exist
if(m_umap.find(key) != m_umap.end())
{
m_list.erase(m_umap[key].second);
m_umap.erase(key);
}
else if(m_iCapacity == m_list.size())
{
m_umap.erase(m_list.back());
m_list.pop_back();
}
m_list.push_front(key);
m_umap[key] = pair<int, IT>(value, m_list.begin());
}
};
本文介绍了一种使用list和unordered_map实现的LRU(最近最少使用)缓存算法,该算法能够在O(1)时间复杂度下完成get和put操作。通过具体的代码示例,展示了如何设计和实现这一数据结构。
1116

被折叠的 条评论
为什么被折叠?



