460. LFU Cache
LeetCode 460. LFU Cache
Description
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.
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=[3,4], cnt(4)=2, cnt(3)=3Constraints:
0 <= capacity, key, value <= 10^4At most
10^5calls will be made togetandput.
Follow up: Could you do both operations in O(1) time complexity?
Tags
Design
Solution
See Reference 1.
Complexity
Time complexity:
Space complexity:
Code
type LFUCache struct {
cap, minFreq int
nodes map[int]*list.Element // key2node
lists map[int]*list.List // freq2list
}
type node struct {
key, value, freq int
}
func Constructor(capacity int) LFUCache {
return LFUCache{
cap: capacity,
minFreq: 0,
nodes: make(map[int]*list.Element),
lists: make(map[int]*list.List),
}
}
func (this *LFUCache) Get(key int) int {
v, ok := this.nodes[key]
if !ok {
return -1
}
curNode := v.Value.(*node)
this.lists[curNode.freq].Remove(v)
curNode.freq++
if _, ok := this.lists[curNode.freq]; !ok {
this.lists[curNode.freq] = list.New()
}
listNode := this.lists[curNode.freq].PushFront(curNode)
this.nodes[key] = listNode
if curNode.freq-1 == this.minFreq && this.lists[curNode.freq-1].Len() == 0 {
this.minFreq++
}
return curNode.value
}
func (this *LFUCache) Put(key int, value int) {
if this.cap == 0 {
return
}
if _, ok := this.nodes[key]; ok {
this.nodes[key].Value.(*node).value = value
this.Get(key) // update freq
return
}
// delete if full
if this.cap == len(this.nodes) {
obsoleteNode := this.lists[this.minFreq].Back()
delete(this.nodes, obsoleteNode.Value.(*node).key)
this.lists[this.minFreq].Remove(obsoleteNode)
}
this.minFreq = 1 // set minFreq = 1 due to new node insertion
curNode := &node{
key: key,
value: value,
freq: 1,
}
if _, ok := this.lists[1]; !ok {
this.lists[1] = list.New()
}
listNode := this.lists[1].PushFront(curNode)
this.nodes[key] = listNode
}Reference
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