LeetCode-146. LRU Cache [C++][Java]

LeetCode-146. LRU CacheLevel up your coding skills and quickly land a job. This is the best place to expand your knowledge and get prepared for your next interview.https://leetcode.com/problems/lru-cache/

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:

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

【C++】

class LRUCache {
    unordered_map<int, listint, int>>::iterator> hash;
    listint, int>> cache;
    int size;
public:
    LRUCache(int capacity):size(capacity) {}
    
    int get(int key) {
        auto it = hash.find(key);
        if (it == hash.end()) {
            return -1;
        }
        cache.splice(cache.begin(), cache, it->second);
        return it->second->second;
    }
    
    void put(int key, int value) {
        auto it = hash.find(key);
        if (it != hash.end()) {
            it->second->second = value;
            return cache.splice(cache.begin(), cache, it->second);
        }
        cache.insert(cache.begin(), make_pair(key, value));
        hash[key] = cache.begin();
        if (cache.size() > size) {
            hash.erase(cache.back().first);
            cache.pop_back();
        }
    }
};
 
/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache* obj = new LRUCache(capacity);
 * int param_1 = obj->get(key);
 * obj->put(key,value);
 */

或者懒一点

class LRUCache {
private:
	int capacity;
	listint, int>> recent; //pair:key,value
	unordered_map<int, listint, int>>::iterator> pos; //key, iterator
public:
	LRUCache(int capacity): capacity(capacity) {}
	int get(int key) {
		if(pos.find(key) != pos.end()) {
			int value = pos[key]->second;
			put(key, value);
			return value;
		}
		return -1;
	}
	void put(int key, int value) {
		if(pos.find(key) != pos.end()) recent.erase(pos[key]);
		else if (recent.size() >= capacity) {
			int old = recent.back().first;
			recent.pop_back();
			pos.erase(old);
		}
		recent.push_front(make_pair(key, value));
		pos[key] = recent.begin();
	}
};
 
/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache* obj = new LRUCache(capacity);
 * int param_1 = obj->get(key);
 * obj->put(key,value);
 */

【Java】

class LRUCache {
int cap;
    LinkedHashMap cache = new LinkedHashMap<>();
    public LRUCache(int capacity) {
        this.cap = capacity;
    }
    
    public int get(int key) {
        if(!cache.containsKey(key)){
            return -1;
        }
        makeRecently(key);
        return cache.get(key);
    }
    
    public void put(int key, int value) {
        if(cache.containsKey(key)){
            cache.put(key, value);
            makeRecently(key);
            return;
        }
        if(cache.size() >= this.cap){
            int oldestKey = cache.keySet().iterator().next();
            cache.remove(oldestKey);
        }
        cache.put(key, value);
    }
 
    private void makeRecently(int key){
        int val = cache.get(key);
        cache.remove(key);
        cache.put(key, val);
    }
}
 
/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache obj = new LRUCache(capacity);
 * int param_1 = obj.get(key);
 * obj.put(key,value);
 */

参考文献

【1】常见缓存算法和LRU的c++实现_banyan3646的博客-CSDN博客