Thursday, July 7, 2016

LeetCode OJ - LRU Cache

Problem:

Please find the problem here.

Analysis:

Ideally, we can access the item in $ O(1) $ time, the key challenge is to find the least recently used item. Keeping just one least recently used item is easy, but what happen after we evicted the least recently used item already?

Solution:

There are two key ideas - we need to have a queue of keys ordered by recency. With the queue, we can easily evict the least recently used item and automatically know which item is next least recently used. The another key idea is that we can update that queue in $ O(1) $ time by making a queue a doubly linked list and leave the pointer of the node in the unordered_map, that will make the whole operation $ O(1) $ time. Apparently, that is optimal.

Code:


#include "stdafx.h"

// https://leetcode.com/problems/lru-cache/

#include "LEET_LRU_CACHE.h"
#include <map>
#include <iostream>
#include <sstream>
#include <vector>
#include <string>
#include <unordered_map>

using namespace std;

namespace _LEET_LRU_CACHE
{
    class LRUCache
    {
    private:
        struct Node
        {
            Node* prev;
            Node* next;
            int key;
            int value;
        };
        Node* m_front;
        Node* m_back;
        int m_capacity;
        unordered_map<int, Node*> m_cache;

        void ExtractNodeFromList(Node* n)
        {
            n->next->prev = n->prev;
            n->prev->next = n->next;
        }

        void EnqueueNode(Node* n)
        {
            n->prev = this->m_front;
            n->next = this->m_front->next;
            n->prev->next = n;
            n->next->prev = n;
        }
    public:
        LRUCache(int capacity) : m_capacity(capacity)
        {
            this->m_front = new Node();
            this->m_back = new Node();
            this->m_front->prev = this->m_back->next = nullptr;
            this->m_front->next = this->m_back;
            this->m_back->prev = this->m_front;
        }

        int get(int key)
        {
            auto probe = this->m_cache.find(key);
            if (probe == this->m_cache.end())
            {
                return -1;
            }
            else
            {
                Node* item_node = probe->second;
                this->ExtractNodeFromList(item_node);
                this->EnqueueNode(item_node);
                return item_node->value;
            }
        }

        void set(int key, int value)
        {
            if (this->m_capacity > 0)
            {
                auto probe = this->m_cache.find(key);
                if (probe == this->m_cache.end())
                {
                    Node* new_item_node;
                    if (this->m_cache.size() == this->m_capacity)
                    {
                        // Full cache, evict the least recently used item
                        Node* least_recently_used_node = this->m_back->prev;
                        int least_recently_used_key = least_recently_used_node->key;

                        this->m_cache.erase(least_recently_used_key);
                        this->ExtractNodeFromList(least_recently_used_node);

                        // Reuse the node to avoid allocation
                        new_item_node = least_recently_used_node;
                    }
                    else
                    {
                        new_item_node = new Node();
                    }

                    new_item_node->key = key;
                    new_item_node->value = value;
                    this->m_cache.insert(pair<int, Node*>(key, new_item_node));
                    EnqueueNode(new_item_node);
                }
                else
                {
                    Node* old_item_node = probe->second;
                    old_item_node->value = value;
                    this->ExtractNodeFromList(old_item_node);
                    this->EnqueueNode(old_item_node);
                }
            }
        }
    };
};

using namespace _LEET_LRU_CACHE;

int LEET_LRU_CACHE()
{

    return 0;
}

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