Design and implement a data structure for Least Frequently Used (LFU) 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 reaches its capacity, it should invalidate the least frequently used item before inserting a new item. For the purpose of this problem, when there is a tie (i.e., two or more keys that have the same frequency), the least recently used key would be evicted.
Note that the number of times an item is used is the number of calls to the get and put functions for that item since it was inserted. This number is set to zero when the item is removed.
Follow up:
Could you do both operations in O(1) time complexity?
Use double linked list for O(1) updating frequency and evict least frequently used item.
Caution: In STL's list, after adjacent items of an iterator has been removed or changed, the behavior of this iterator would change(Not as expected). So do not use ++/-- after the next/previous item is removed/changed(because of inserion).
using Freq = list<pair<int, list<int>>>;
struct Node {
int key, value;
Freq::iterator freq_it;
list<int>::iterator pos_it;
Node(int k = 0, int v = 0) : key(k), value(v) {}
};
class LFUCache {
public:
Freq freq;
unordered_map<int, Node> cache;
const int capacity;
LFUCache(int capacity) : capacity(capacity) {
}
int get(int key) {
if (!cache.count(key))
return -1;
int res = cache[key].value;
update(key, res);
return res;
}
void put(int key, int value) {
if (!cache.count(key)) {
if (freq.size() && cache.size() == capacity) {
auto & items = freq.back().second;
cache.erase(*items.begin());
items.pop_front();
if (items.empty())
freq.pop_back();
}
// insert a new element but with no space
if (cache.size() >= capacity)
return;
}
update(key, value);
}
void update(int key, int value) {
Freq::iterator fit;
int newfreq;
if (cache.count(key)) {
cache[key].value = value;
fit = cache[key].freq_it;
newfreq = fit->first + 1;
fit->second.erase(cache[key].pos_it);
if (fit->second.empty())
// return the next iterator of the removed element
fit = freq.erase(fit);
}
else {
cache[key] = Node(key, value);
fit = freq.end();
newfreq = 1;
}
if (fit == freq.begin() || prev(fit)->first != newfreq)
// return the iterator of the inserted element
fit = freq.insert(fit, {newfreq, {}});
else
--fit;
cache[key].freq_it = fit;
cache[key].pos_it = fit->second.insert(fit->second.end(), key);
}
};
/**
* Your LFUCache object will be instantiated and called as such:
* LFUCache* obj = new LFUCache(capacity);
* int param_1 = obj->get(key);
* obj->put(key,value);
*/
or
OrderedDict in python
Use OrderedDict to represent double linked list to maintain the visiting order of items.
from collections import OrderedDict, defaultdict
class LFUCache(dict):
def __init__(self, capacity: int):
self.cap = capacity
self.freq = defaultdict(Node)
self.start = Node(1000000000)
self.end = Node(0, self.start)
self.start.next = self.end
def get(self, key: int) -> int:
if key not in self:
return -1
self.update(key, self.freq[self[key]][key])
return self.freq[self[key]][key]
def put(self, key: int, value: int) -> None:
if key not in self:
if len(self) == self.cap and len(self) > 0:
last = self.end.prev
kv = last.popitem(last=False)
del self[kv[0]]
if not last:
del self.freq[last.freq]
last.delete()
if len(self) >= self.cap:
return
self.update(key, value)
def update(self, key: int, value: int = None) -> None:
if key in self:
cur = self.freq[self[key]]
nextcount = self[key] + 1
del cur[key]
if not cur:
del self.freq[cur.freq]
cur = cur.delete()
else:
cur = self.end
nextcount = 1
if cur.prev == self.start or cur.prev.freq != nextcount:
cur = cur.insert(nextcount)
self.freq[nextcount] = cur
else:
cur = cur.prev
cur[key] = value
self[key] = nextcount
class Node(OrderedDict):
def __init__(self, freq, prev=None, next=None):
self.freq = freq
self.prev = prev
self.next = next
def delete(self):
self.prev.next = self.next
self.next.prev = self.prev
return self.next
def insert(self, freq: int):
self.prev.next = Node(freq, self.prev, self)
self.prev = self.prev.next
return self.prev
# Your LFUCache object will be instantiated and called as such:
# obj = LFUCache(capacity)
# param_1 = obj.get(key)
# obj.put(key,value)
