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Design and implement a data structure for . 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.

The cache is initialized with a positive capacity.

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 1cache.put(3, 3);    // evicts key 2cache.get(2);       // returns -1 (not found)cache.put(4, 4);    // evicts key 1cache.get(1);       // returns -1 (not found)cache.get(3);       // returns 3cache.get(4);       // returns 4

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完成一个添加、查找的过程。难点在于有限定长度，如果超了需要删除最久没用过的键值对。（有点类似于内存占用算法）

用collection.OrderedDict()真是很方便。

即有顺序，又能根据key进行pop(key)，又能根据位置popitem(i)。

注意get的时候相当于调用了，所以pop出来重新加到字典尾部。也可以用self.dic.move_to_end(key)来执行，很方便。

class LRUCache:    def __init__(self, capacity: int):        self.maxl=capacity        self.dic = collections.OrderedDict()    def get(self, key: int) -> int:                if key in self.dic.keys():            temp=self.dic.pop(key)            self.dic[key]=temp            return temp        return -1          def put(self, key: int, value: int) -> None:        if key in self.dic.keys():            self.dic.pop(key)        self.dic[key]=value        if len(self.dic)>self.maxl:            self.dic.popitem(0)

 

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