27. Remove Element

CATIA二次开发实战:我用VBA写了个BOM表自动导出工具,效率提升80% 本文详细介绍了如何利用VBA进行CATIA二次开发,构建BOM表自动导出工具的全过程。通过智能解析算法、异常处理机制和性能优化技巧,该工具将BOM表生成时间从2.5小时缩短至8分钟,效率提升80%。文章还探讨了与PDM系统集成和定制化报告生成等扩展应用,为机械设计工程师提供了实用的自动化解决方案。 阅读详情

Given an array and a value, remove all instances of that value in place and return the new length.

Do not allocate extra space for another array, you must do this in place with constant memory.

The order of elements can be changed. It doesn't matter what you leave beyond the new length.

Example:
Given input array nums = [3,2,2,3], val = 3

Your function should return length = 2, with the first two elements of nums being 2.



class Solution {
public:
    int removeElement(vector<int>& nums, int val) {
        int cnt = 0;
        for(int i = 0 ; i < nums.size() ; ++i) {
            if(nums[i] == val)
                cnt++;//统计重复的val
            else
                nums[i-cnt] = nums[i];//修改nums
        }
    return nums.size()-cnt;
    }
};

嵌入式系统开发笔记96:将STM32CubeIDE设置为中文 本文介绍将STM32CubeIDE设置为中文的方法。 阅读详情

相关推荐

GlobalPlatform Technology Secure Channel Protocol ‘03‘ 标准 - 安全通道协议简介

按照GlobalPlatform Technology Secure Channel Protocol '03' (SCP03)标准,描述了如何利用S-MAC,S-RMAC,S-ENC进行安全通信的过程。

mmmx777的专栏 6092

leetcode记录27.Remove Element

27.Remove Element √ Accepted √ 113/113 cases passed (8 ms) √ Your runtime beats 37.73 % of cpp submissions √ Your memory usage beats 100 % of cpp submissions (8.5 MB) class Solution { public: ...

我的博客 229

数字孪生变电站如何制作?虚拟现实项目强荐广州华锐互动

变电站数字孪生系统可以为提高变电站设备与环境的实时感知能力、在线诊断设备健康状态、促进设备隐藏故障定位和维修效率的提高、实现设备全生命周期提供有力支撑。通过对设备状态的准确评估,可以延长设备的生命周期,实现资产增值。1、3D可视化:建立可视化平台,集中展示所有3D模型信息、数据信息、环境信息和人员信息,包括设备数字模型、建筑外形及内部、周边地形、人员动态、天气、时间等。4、智能监控:采用先进的AI摄像头,通过设备的图像识别,自动智能分析图像,准确分析设备状态、运行动态、外围控制情况。...

gzvrnew的博客 776

Leetcode27.Remove Element

27.Remove Element 题目 原题链接:https://leetcode.com/problems/remove-element/ 题目内容: Given an array nums and a value val, remove all instances of that value in-place and return the new length. ...

chenguiyuan1234的博客 212

手把手教你使用ESM-2进行蛋白质结构预测:从安装到实战

本文详细介绍了如何使用ESM-2蛋白质语言模型进行蛋白质结构预测,从环境准备、模型安装到实战应用。通过逐步指导,读者可以掌握ESM-2的配置、单序列预测、结果可视化及高级优化技巧,适用于生物信息学研究和药物开发。

weixin_29306875的博客 534

哈工大2024春大数据挖掘期末考试回忆版试题

(3)现有一个用户数量远多于物品(还是物品远多于用户,忘了是哪个了)的情景,应该使用基于user-user的推荐系统还是item-itmen的推荐系统,说明原因。(1)L是n*n拉普拉斯矩阵,x是一个n*1列向量,证明对任意的x,(x)^TLx>=0(6分)(3)证明:p到o的K-可达距离,至少是o的K-distance(2分)(2)(1)的结果说明竞争率的上界是多少?(2)考Gain的意义,如何利用Gain选择划分点(2分)(2)说明如何利用划分后的数据进行验证(2分)(2)k可达距离是对称的吗?

ymd2002的博客 1891

[Easy] 27. Remove Element

27. Remove Element Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modify...

Bungehurst 386

奈曼-皮尔逊准则

奈曼-皮尔逊准则 如果各个假设的先验概率P(Hi)(i=0,1)P(H_i)(i=0,1)P(Hi​)(i=0,1)已知,并且每种判决的代价函数已知,这时,可以用贝叶斯准则进行判决。 如果进一步假设正确判决不付出的代价,而错误判决代价相同,即c00=c11=0,c01=c10=1c_{00}=c_{11}=0,c_{01}=c_{10}=1c00​=c11​=0,c01​=c10​=1,这时可以用最小平均错误概率准则进行判决。 在不知道先验概率P(Hi)(i=0,1)P(H_i)(i=0,1)P(Hi​)(

little_soldier的专栏 1万+

Leetcode 27. Remove Element

题目描述:移除数组中数位val的元素,并返回一个长度,前长度个元素不包括该元素val. 题目链接:Leetcode 27. Remove Element 思路和前面一题的思路一样,那就是判断是否相等,然后从0下标开始置位移动。 代码如下 class Solution { public int removeElement(int[] nums, int val) { i...

Za七杂⑧ 228

华为OD机试真题 新系统 - 勇攀数字高峰 (Java/Py/C/C++/Js/Go)

本文整理华为 OD 机试新系统机考真题库,涵盖高频考点精析与完整题目内容,提供 C 语言、Java、C++、Python、Go、JavaScript 多语言源码实现,搭配详细解题思路讲解,并支持在线 OJ 刷题练习,助力高效备考华为 OD 机试新系统。

算法大师! 2096

openlayers全面教程306+【目录】

vue+openlayers系列教程旨在为开发者提供简单快捷的示例。在每一个示例中,解释相应的API知识点。

大剑师兰特的GIS世界 2万+

leetcode 27 Remove Element

27 Remove Element描述代码思考 描述 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this b...

u010659877的专栏 217

【人工智能】架构演进:基于本体论与 LLM Agent 的供应链备件决策闭环实践

本文探讨了如何将本体论(Ontology)与大语言模型(LLM)Agent结合,构建供应链备件管理的智能决策系统。针对LLM在垂直领域存在的幻觉和逻辑缺陷问题,提出通过本体论建立结构化知识框架,形成四层架构:知识建模、动态感知、深度分析和决策闭环。系统利用图数据库实例化领域本体,融合多源数据实现实时感知,结合图谱推理和时序预测进行确定性分析,最终通过工具调用完成决策执行。技术选型推荐Gemini 1.5 Pro、Neo4j和LangChain等工具,实现从自动化到智能化的升级。该架构为工业领域提供了可解释、

奶爸的编程之路,也就一周冷个三天 170

[LeetCode] 27.Remove Element

[LeetCode] 27.Remove Element 题目描述 解题思路 实验代码 题目描述Given an array and a value, remove all instances of that value in-place and return the new length.Do not allocate extra space for another array, you must

user_name_is_so_long的博客 223

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

leetcode解题之 27. Remove Element Java版

27. Remove Element Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modify...

he_zhen_的博客 309

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

LeetCode 27 Remove Element题解

27. Remove Element Total Accepted: 158828Total Submissions: 434799Difficulty: EasyContributors: Admin Given an array and a value, remove all instances of that value in place and retu

赞美月亮的专栏 913

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")

LeetCode 27.Remove Element

LeetCode 27.Remove ElementDescription: Given an array and a value, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must

My Blog 122

【LeetCode】解题27:Remove Element

LeetCode解题 27:Remove Element Problem 27: Remove Element [Easy]解题思路Solution (Java) Problem 27: Remove Element [Easy] Given an array nums and a value val, remove all instances of that value in-place and...

Fayedily的博客 248

LeetCode Notes_#27 Remove Element

LeetCode Notes_#27 Remove Element Contents 题目思路和解答思路解答 题目 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra...

baji9701的博客 159

LeetCode 27. Remove Element

问题链接 LeetCode 27. Remove Element 题目解析 删除数组中等于特定值的元素。 解题思路 特简单题。遍历一次,直接计数即可。大都一样的解法,也可参考https://leetcode.com/problems/remove-element/solution/。 参考代码 class Solution { public: int removeElement(vecto...

weixin_30301449的博客 86

27. Remove Element【leetcode】

27. Remove Element【leetcode】 Given an array and a value, remove all instances of that value in place and return the new length. Do not allocate extra space for another array, you must do this i...

diaocongdie2176的博客 98

LeetCode记录之27——Remove Element

LeetCode记录之27——Remove Element 这道题跟26题很类似,并且有官方的答案。看了官方的答案之后发现写得特别巧,自己做的题太少思路太窄。有意思的是我的算法的时间复杂度是O(N^2),官方的是O(N),我的实际运行时间还少了2ms。 iven an array and a value, remove all instance...

三分自留地 134

LeetCode | 27. Remove Element

Description Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modifying the...

susielau1998的博客 170

leetcode-27. Remove Element

leetcod-27. Remove ElementGiven an array and a value, remove all instances of that value in place and return the new length. Do not allocate extra space for another array, you must do this in place wit

myan991的博客 230

[勇者闯LeetCode] 27. Remove Element

[勇者闯LeetCode] 27. Remove ElementDescription Given an array and a value, remove all instances of that value in place and return the new length. Do not allocate extra space for another array, you mu

fanguzhai的博客 266
上一篇: 392. Is Subsequence
下一篇: 372. Super Pow
Tomjx
博客等级 码龄10年 1粉丝 19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