382. Linked List Random Node

Given a singly linked list, return a random node's value from the linked list. Each node must have the same probability of being chosen.

Follow up:
What if the linked list is extremely large and its length is unknown to you? Could you solve this efficiently without using extra space?

Example:

// Init a singly linked list [1,2,3].
ListNode head = new ListNode(1);
head.next = new ListNode(2);
head.next.next = new ListNode(3);
Solution solution = new Solution(head);

// getRandom() should return either 1, 2, or 3 randomly. Each element should have equal probability of returning.
solution.getRandom();
如果n-1个元素复合要求,即n-1每个元素取到的概率是1/(n - 1),那么如果保证第n个元素取到的概率是1/n,那么对于前n-1个元素,每个元素取到的概率是1/(n - 1) * (n - 1)/n。一个比较好的讲解:

When I first got this question, I went through some articles, but it is painful for me to understand abstract notations like i, k, m, n, n-1, k+1...

After I read this one: http://blog.jobbole.com/42550/, it comes with a simple example and I understood suddenly, and write the code by myself. I translate it to English, so more people can benefit from it.

Start...
When we read the first node head, if the stream ListNode stops here, we can just return the head.val. The possibility is 1/1.

When we read the second node, we can decide if we replace the result r or not. The possibility is 1/2. So we just generate a random number between 0 and 1, and check if it is equal to 1. If it is 1, replace r as the value of the current node, otherwise we don't touch r, so its value is still the value of head.

When we read the third node, now the result r is one of value in the head or second node. We just decide if we replace the value of r as the value of current node(third node). The possibility of replacing it is 1/3, namely the possibility of we don't touch r is 2/3. So we just generate a random number between 0 ~ 2, and if the result is 2 we replace r.

We can continue to do like this until the end of stream ListNode.

代码如下:
/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode(int x) { val = x; }
 * }
 */
public class Solution {
    ListNode head;
    Random random;

    /** @param head The linked list's head.
        Note that the head is guaranteed to be not null, so it contains at least one node. */
    public Solution(ListNode head) {
        this.head = head;
        random = new Random();
    }
    
    /** Returns a random node's value. */
    public int getRandom() {
        ListNode curr = head;
        int res = curr.val;
        for (int i = 1; curr.next != null; i ++) {
            curr = curr.next;
            if (random.nextInt(i + 1) == i)
                res = curr.val;
        }
        return res;
    }
}

/**
 * Your Solution object will be instantiated and called as such:
 * Solution obj = new Solution(head);
 * int param_1 = obj.getRandom();
 */

内容概要:本文详细阐述了DeepSeek大模型在服装行业的应用方案,旨在通过人工智能技术提升服装企业的运营效率和市场竞争力。文章首先介绍了服装行业的现状与挑战,指出传统模式难以应对复杂的市场变化。DeepSeek大模型凭借其强大的数据分析和模式识别能力,能够精准预测市场趋势、优化供应链管理、提升产品设计效率,并实现个性化推荐。具体应用场景包括设计灵感生成、自动化设计、虚拟试衣、需求预测、生产流程优化、精准营销、智能客服、用户体验提升等。此外,文章还探讨了数据安全与隐私保护的重要性,以及技术实施与集成的具体步骤。最后,文章展望了未来市场扩展和技术升级的方向,强调了持续优化和合作的重要性。 适用人群:服装行业的企业管理层、技术负责人、市场和销售团队、供应链管理人员。 使用场景及目标:①通过市场趋势预测和用户偏好分析,提升设计效率和产品创新;②优化供应链管理,减少库存积压和生产浪费;③实现精准营销,提高客户满意度和转化率;④通过智能客服和虚拟试衣技术,提升用户体验;⑤确保数据安全和隐私保护,建立用户信任。 阅读建议:此资源不仅涵盖技术实现的细节,还涉及业务流程的优化和管理策略的调整,建议读者结合实际业务需求,重点关注与自身工作相关的部分,并逐步推进技术的应用和创新。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值