LFU Cache
Problem Statement - link #
Design and implement a data structure for a Least Frequently Used (LFU) cache.
Implement the LFUCache class:
LFUCache(int capacity)Initializes the object with thecapacityof the data structure.int get(int key)Gets the value of thekeyif thekeyexists in the cache. Otherwise, returns-1.void put(int key, int value)Update the value of thekeyif present, or inserts thekeyif not already present. When the cache reaches itscapacity, it should invalidate and remove the least frequently used key before inserting a new item. For this problem, when there is a tie (i.e., two or more keys with the same frequency), the least recently usedkeywould be invalidated.
To determine the least frequently used key, a use counter is maintained for each key in the cache. The key with the smallest use counter is the least frequently used key.
When a key is first inserted into the cache, its use counter is set to 1 (due to the put operation). The use counter for a key in the cache is incremented either a get or put operation is called on it.
The functions get and put must each run in O(1) average time complexity.
Examples #
Example 1:
Input
["LFUCache", "put", "put", "get", "put", "get", "get", "put", "get", "get", "get"]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [3], [4, 4], [1], [3], [4]]
Output
[null, null, null, 1, null, -1, 3, null, -1, 3, 4]
Explanation
// cnt(x) = the use counter for key x
// cache=[] will show the last used order for tiebreakers (leftmost element is most recent)
LFUCache lfu = new LFUCache(2);
lfu.put(1, 1); // cache=[1,_], cnt(1)=1
lfu.put(2, 2); // cache=[2,1], cnt(2)=1, cnt(1)=1
lfu.get(1); // return 1
// cache=[1,2], cnt(2)=1, cnt(1)=2
lfu.put(3, 3); // 2 is the LFU key because cnt(2)=1 is the smallest, invalidate 2.
// cache=[3,1], cnt(3)=1, cnt(1)=2
lfu.get(2); // return -1 (not found)
lfu.get(3); // return 3
// cache=[3,1], cnt(3)=2, cnt(1)=2
lfu.put(4, 4); // Both 1 and 3 have the same cnt, but 1 is LRU, invalidate 1.
// cache=[4,3], cnt(4)=1, cnt(3)=2
lfu.get(1); // return -1 (not found)
lfu.get(3); // return 3
// cache=[3,4], cnt(4)=1, cnt(3)=3
lfu.get(4); // return 4
// cache=[4,3], cnt(4)=2, cnt(3)=3
Constraints #
0 <= capacity <= 10^40 <= key <= 10^50 <= value <= 10^9- At most
2 * 10^5calls will be made togetandput.
Solutions #
class LFUCache {
public:
unordered_map <int,list<pair<int,int>>> lst_map; //will store frequency as key and list of all <key,value> with that frequency
unordered_map <int,list<pair<int,int>>::iterator> key_map; //will store key and it's respective address
unordered_map <int,int> frequency_map; //will store key and it's frequency
int cap=0,size=0,min_frequency=0;
LFUCache(int capacity)
{
cap=capacity;
}
int get(int key)
{
auto found= key_map.find(key);
if(found==key_map.end())
return -1;
else
{ auto temp = found->second->second;
put(key,temp);
return temp;
}
};
void put(int key, int value)
{
auto found = key_map.find(key);
if(found== key_map.end())
{ if(cap!=0)
{
if(size>=cap)
{ //removing page
auto temp = lst_map[min_frequency].back(); //making copy of popped element
lst_map[min_frequency].pop_back(); //popping it out
key_map.erase(temp.first); //popping from key_map
frequency_map.erase(temp.first); //popping from frequency_map;
// done removing
}
else
size++;
lst_map[1].emplace_front(make_pair(key,value)); // adding node in list
min_frequency=1;
key_map[key]= lst_map[1].begin(); // adding new key iterator pair in key map
frequency_map[key]=1;
}
}
else
{
auto frequency = frequency_map[key];
lst_map[frequency].erase(found->second); //removed from old list
if(lst_map[frequency].size()==0) //in case list becomes empty it might change min_frequency
{
if(min_frequency ==frequency)
min_frequency++;
}
lst_map[++frequency].emplace_front(key,value); //added in new list
key_map[key]=lst_map[frequency].begin(); //updated in keymap
frequency_map[key]=frequency;
}
}
};