LeetCode刷题笔录Remove Duplicates from Sorted List II

SystemVerilog学习总结 因为选择的offer是IC验证,以前学的是设计,所以只能再学systemverilog(SV)和UVM了。在学习SV和UVM之前,我认为需要学习以下几个部分:数字电路、c++和verilog。因为IC验证确实有不少别的专业之前没学过数字电路和Verilog,所以还是最好先学习一下。 知识点的总结主要是通过systemverilog验证-测试平台编写指南这本书进行的,所以请看书。通过对这本书的目录进行重点的划分,然后再进行相关知识点的总结。最后,给出自己收集的别的博主IC EDA环境的虚拟机,以及使用虚拟机需 阅读详情

Given a sorted linked list, delete all nodes that have duplicate numbers, leaving only distinct numbers from the original list.

For example,
Given 1->2->3->3->4->4->5, return 1->2->5.

Given 1->1->1->2->3, return 2->3.

基本的双指针题,注意实现时候的一些细节就好了。重点是两个连续元素不相等和相等的处理方式是不太一样的。

/**
 * Definition for singly-linked list.
 * public class ListNode {
 *     int val;
 *     ListNode next;
 *     ListNode(int x) {
 *         val = x;
 *         next = null;
 *     }
 * }
 */
public class Solution {
    public ListNode deleteDuplicates(ListNode head) {
        ListNode dummyHead = new ListNode(-1);
        dummyHead.next = head;
        ListNode p = dummyHead;
        ListNode q = dummyHead;
        while(p.next != null){
            q = p.next;
            if(q.next == null){
                if(p.next == q){
                    break;
                }
                else{
                    p.next = null;
                    break;
                }
            }
            if(q.next.val != q.val){
                p = q;
                continue;
            }
            else{
                while(q.next != null && q.next.val == q.val)
                    q = q.next;
                p.next = q.next;
            }
        }
        
        return dummyHead.next;
    }
}


基于激光雷达的目标跟随功能移植到自己的ROS机器人小车上 提示:文章写完后,目录可以自动生成,如何生成可参考右边的帮助文档 文章目录前言一、下载目标跟随的源码二、修改话题名称1.修改/cmd_vel话题名称2.修改/scan话题名称3.修改双电机正反转4.目标跟随功能测试 前言 本人尝试把github上的一个目标跟随项目移植到自己的机器人小车上,并且成功了,最终小车可以跟着人移动,在这里分享一下心得。基于激光雷达的目标跟随,通过获取激光雷达的信息,跟随最近的物体。 提示:以下是本篇文章正文内容,下面案例可供参考 一、下载目标跟随的源码 源码地址:https: 阅读详情

相关推荐

最优化方法 25:PDHG

前面的章节要么从原始问题出发,要么从对偶问题出发,通过求解近似点或者一个子优化问题进行迭代,而且推导过程中我们发现根据问题的参数特征,比如矩阵 AAA 是瘦高型的还是矮胖型的,采用对偶和原始问题的复杂度会不一样,可以选择一个更简单的。而这一节,我们将要从原始对偶问题出发来优化,什么是原始对偶问题呢?就是原始优化变量和对偶优化变量(原始函数和共轭函数)混合在一块,看下面的原理就知道了。 1. 原始对偶问题 现在考虑原始优化问题,其中 f,gf,gf,g 为闭凸函数 min⁡f(x)+g(Ax) \min \q

Glooow的博客 4599

LeetCode # 有序数组中的单一元素

给定一个只包含整数的有序数组,每个元素都会出现两次,唯有一个数只会出现一次,找出这个数。 示例 1: 输入: [1,1,2,3,3,4,4,8,8] 输出: 2 示例 2: 输入: [3,3,7,7,10,11,11] 输出: 10 解题思路:如果数组中每个元素都出现两次,那么数组总元素个数是偶数,且偶数个元素一定与其前一个元素相等,与其后一个元素不等。因此,每次二分折半的时候控制前半个元素个数为奇数个,若mid元素与后一个元素相等,说明单个元素在后半段,设start=mid+2;若不相

婷子的博客 356

BrainVision Recorder 和 BrainVision Analyzer 2.2 安装

本篇为《EEG处理》系列博客的第二篇,该系列博客主要记录EEG信号处理的学习过程中的软件安装和学习过程,方便以后查阅。本篇为BrainVision Recorder 和 BrainVision Analyzer 2.2 的安装过程。 BrainVision Recorder 和 BrainVision Analyzer 2.2 安装安装前奏Brain Vision Analyzer分析软件 安装Brain Vision Recorder记录软件 安装推荐几款个人使用的科研软件 安装前奏 Brain V

梁小憨憨的博客 5059

[算法题] Remove Duplicates from Sorted Array ii

题目内容 本题来源LeetCode Follow up for "Remove Duplicates": What if duplicates are allowed at most twice? For example, Given sorted array nums = [1,1,1,2,2,3], Your function should return length = 5, wi...

weixin_30834019的博客 139

手把手教你用OpenRouter免费模型:从DeepSeek V3.1到Llama 3.3的实战指南

本文详细介绍了如何利用OpenRouter平台免费接入DeepSeek V3.1、Llama 3.3等53个AI模型,提供从账号注册到实战应用的完整指南。通过文本摘要、客服对话生成和代码生成等具体案例,帮助开发者高效利用免费模型资源,提升AI应用开发效率。

weixin_29258297的博客 3547

【毕设】基于LoRa的物联网控制器:从原理图到PCB的完整硬件设计指南

本文提供了一份完整的基于LoRa的物联网控制器硬件设计指南,涵盖从系统框图、原理图设计到PCB布局布线的全流程。详细讲解了电源电路、单片机最小系统、信号接口以及LoRa无线模块等核心模块的设计要点与抗干扰技巧,并分享了实用的调试心得,旨在帮助电子、物联网专业学生顺利完成毕业设计,掌握低功耗远距离物联网节点的硬件开发实践。

weixin_42126677的博客 306

【LeetCode】26. Remove Duplicates from Sorted Array 解题小结

题目: Given a sorted array, remove the duplicates in place such that each element appear onlyonceand return the new length. Do not allocate extra space for another array, you must do this in place w...

weixin_30279315的博客 120

HFSS实战(二)——精准仿真高速PCB差分线的信号完整性

本文深入探讨了使用HFSS进行高速PCB差分线信号完整性仿真的关键技术与实战经验。从设计文件导入、模型裁剪到差分端口设置,详细解析了精准仿真的全流程,特别强调了材料特性和3D电磁场仿真在解决微带线损耗等问题中的重要性。文章还提供了常见问题排查指南和效率优化技巧,帮助工程师提升高速PCB设计的仿真精度与效率。

weixin_38170829的博客 572

LeetCode 刷题笔录 Add Two Numbers

You are given two linked lists representing two non-negative numbers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a link

likecool21的专栏 1806

第4章 Stata参数检验

目录 4.1单一样本T检验 案例延伸 4.2独立样本T检验 案例延伸 1.改变置信水平 2.在异方差假定条件下进行假设检验 4.3配对样本T检验 案例延伸 1.改变置信水平 4.4单一样本方差的假设检验 案例延伸 4.5双样本方差的假设检验 参数检验(Parameter Test)是指对参数的平均值、方差、比率等特征进行的统计检验。参数检验一般假设统计总体的具体分布为已知,但是其中一些参数或者取值范围不确定,分析的主要目的是估计这些未知参数的取值,或者对这些参数进...

qq_45112156的博客 1万+

uniapp(全端兼容) - 实现点击输入框时完美顶起键盘,获取键盘高度动态计算顶起距离,解决软键盘弹出时输入框被覆盖问题,uniapp微信小程序/移动端h5网页/安卓苹果app(示例代码,一键复制

uniapp,支付宝微信小程序,移动端H5网页网站,软键盘弹出位置不对,软键盘挡住了输入框怎么办,获取键盘高度计算弹出距离,uniapp如何解决输入框input激活后键盘弹出遮挡问题,uniapp输入框偶尔被键盘遮挡,解决uniapp在App环境下输入框被软键盘遮挡的问题,uniapp 完美解决苹果和安卓input框遮挡兼容问题,uniapp输入框拉起软键盘时,输入框显示不全,被软键盘挡住一半,input被键盘遮挡的解决方案,软键盘遮挡输入框,uniapp软键盘顶不起来输入框,输入框没有被软键盘弹出来怎么办

✅ 精选推荐文章 1万+

LeetCode刷题笔录 Merge Two Sorted Lists

Merge two sorted linked lists and return it as a new list. The new list should be made by splicing together the nodes of the first two lists. 这题不能像merge

likecool21的专栏 840

【时空图神经网络 & 交通】相关模型3:STFGNN | 空间-时间依赖性 | 基于数据驱动的方法来生成“时间图” | 时空融合图模块,门控卷积模块 | 动态时间规整 | 顶会文章解析

STFGNN(时空融合图神经网络)在交通流量预测中的优势主要体现在以下几个方面:首先,它提出了一种数据驱动的方法生成“时间图”,弥补了传统空间图可能无法反映的某些相关性。其次,STFGNN通过并行处理不同时间段的空间图和时间图,有效地学习隐藏的时空依赖关系。此外,它将融合图模块与门控卷积模块整合到一个统一层中,通过层层堆叠处理长序列,进一步增强了模型的表达能力。实验结果表明,STFGNN在多个交通数据集上表现出色,优于其他基准方法,展示了其在实际应用中的强大潜力。

追光者♂:记录、分享、总结、提升,现象级专栏《Python从入门到人工智能》作者,无惧黑暗,坚信曙光 383

LeetCode刷题笔录Swap Nodes in Pairs

Given a linked list, swap every two adjacent nodes and return its head. For example, Given 1->2->3->4, you should return the list as 2->1->4->3. Your algorithm should use only constant space. Y

likecool21的专栏 765

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

android 蓝牙通讯demo

android 下蓝牙通讯demo,实现了发现蓝牙,使能蓝牙,通过蓝牙发送消息。

LeetCode刷题笔录 Reorder List

Given a singly linked list L: L0→L1→…→Ln-1→Ln, reorder it to: L0→Ln→L1→Ln-1→L2→Ln-2→… You must do this in-place without altering the nodes' values. For example, Given {1,2,3,4}, reorder it to 

likecool21的专栏 755

visio安装包

Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表,创建专业级图表以简化复杂的信息。

LeetCode刷题笔录Remove Duplicates From Sorted List

Given a sorted linked list, delete all duplicates such that each element appear only once. For example, Given 1->1->2, return 1->2. Given 1->1->2->3->3, return 1->2->3. 先写了一个用extra space的方法,

likecool21的专栏 683

LeetCode刷题笔录Sort List

Sort a linked list in O(n log n) time using constant space complexity. 挺有意思的题,这里用了merge sort,可以利用到merge sorted list 那题的代码 /** * Definition for singly-linked list. * class ListNode { *

likecool21的专栏 679

LeetCode刷题笔录Linked List Cycle II

Given a linked list, return the node where the cycle begins. If there is no cycle, return null. Follow up: Can you solve it without using extra space?

likecool21的专栏 658

LeetCode刷题笔录 Rotate List

Given a list, rotate the list to the right by k places, where k is non-negative. For example: Given 1->2->3->4->5->NULL and k = 2, return 4->5->1->2->3->NULL. 这题xiang

likecool21的专栏 641

LeetCode刷题笔录Remove Nth Node from End of List

Given a linked list, remove the nth node from the end of list and return its head. For example, Given linked list: 1->2->3->4->5, and n = 2. After removing the second node from the end, the

likecool21的专栏 633

LeetCode刷题笔录Partition List

Given a linked list and a value x, partition it such that all nodes less than x come before nodes greater than or equal to x. You should preserve the original relative order of the nodes in each of

likecool21的专栏 606

LeetCode刷题笔录Reverse Linked List II

Reverse a linked list from position m to n. Do it in-place and in one-pass. For example: Given 1->2->3->4->5->NULL, m = 2 and n = 4, return 1->4->3->2->5->NULL. Note: Given m, n satisfy t

likecool21的专栏 583

求集合的交集/差集

先利用外部排序使两个磁盘文件有序,然后顺序读取两个文件,使用双指针求交集。在进阶 1 中,我们是通过双指针去做的,最坏情况下我们需要完整遍历。时,O(n log m) 可能明显优于 O(n + m)。的,这就使得我们可以通过从 1~1000 遍历来确定交集。,它们已经按非降序排序,请你返回两个数组的。和上一题的区别在于,这里需要找到重复的元素。返回一个数组,数组中的每个元素在。的每个字符串中都出现的共用字符(**注意:**列表中的整数可以按。较小,可以完整加载到内存,而。正整数组成的一个非空数组,按。

jyyyyx的算法之旅 112

LeetCode 面试经典 150_多维动态规划_交错字符串(97_C++_中等)

LeetCode 面试经典 150_多维动态规划_交错字符串(97_C++_中等) 题目描述: 给定三个字符串 s1、s2、s3,请你帮忙验证 s3 是否是由 s1 和 s2 交错 组成的。 两个字符串 s 和 t 交错 的定义与过程如下,其中每个字符串都会被分割成若干 非空 子字符串: - s = s1 + s2 + ... + sn - t = t1 + t2 + ... + tm - |n - m| <= 1 - 交错 是 s1 + t

huayimenghan的博客 204

LeetCode 每日一题 2026/11/2-2026/11/8

记录了初步解题思路 以及本地实现代码;并不一定为最优 也希望大家能一起探讨 一起进步

AlphaTao的博客 175

LeetCode Hot 100 二叉树专题(二)3 题(Java)

本篇文章共 3 题:543. 二叉树的直径、102. 二叉树的层序遍历、108. 将有序数组转换为二叉搜索树。 每道题都统一按「① 题目分析 & 解题思路 → ② 常见解法(附代码)及讲解 → ③ 知识点总结」的顺序组织;代码使用 Java 编写,节点定义沿用 LeetCode 默认的 TreeNode。

2401_88612756的博客 362

leetcode 3736. 最小操作次数使数组元素相等 III 简单

中的所有元素都等于 3。中的所有元素都等于 5。在一步操作中,你可以将任意单个元素。返回使数组中的所有元素都。

2401_88085478的博客 202
上一篇: LeetCode刷题笔录Reverse Linked List II
下一篇: LeetCode刷题笔录Permutations II
likecool21
博客等级 码龄14年 14粉丝 123原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
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
实付元
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