[LeetCode]Longest Common Prefix

Anomalib实战:从零开始构建自定义数据集的异常检测模型 本文详细介绍了如何使用Anomalib 2.1.0从零开始构建自定义数据集的异常检测模型。通过实战演示,指导读者准备符合规范的数据集结构,快速上手训练Patchcore等先进算法,并完成模型推理与调优,为工业质检、医疗影像等场景提供高效的视觉异常检测解决方案。 阅读详情
class Solution {
//1. find out the minimum size string as the pivot string
//2. check every other string and update the minSize
public:
    string longestCommonPrefix(vector<string> &strs) {
        // Start typing your C/C++ solution below
        // DO NOT write int main() function
        if(strs.size() == 0) return string("");
		int minSize = strs[0].size();
		int pivotIdx = 0;
		for(int i = 0; i < strs.size(); ++i)
			if(strs[i].size() < minSize)
			{
				minSize = strs[i].size();
				pivotIdx = i;
			}

		for(int i = 0; i < strs.size(); ++i)
		{
			if(i == pivotIdx) continue;
			for(int j = 0; j < minSize; ++j)
			{
				if(strs[i][j] != strs[pivotIdx][j])
				{
					minSize = j;
					break;
				}
			}
		}
		return strs[pivotIdx].substr(0, minSize);
    }
};

second time

class Solution {
public:
    string longestCommonPrefix(vector<string> &strs) {
        // Start typing your C/C++ solution below
        // DO NOT write int main() function
        if(strs.size() == 0) return string("");
        
        int strIdx = 0;
        //choose the smallest string first
        for(int i = 1; i < strs.size(); ++i)
            if(strs[i].size() < strs[strIdx].size()) strIdx = i;

        int commonLen = strs[strIdx].size();
        for(int i = 0; i < strs.size(); ++i)
        {
            if(i == strIdx) continue;
            int curLen = 0;
            for(int j = 0; j < strs[i].size() && j < commonLen; ++j)
            {
                if(strs[strIdx][j] == strs[i][j]) curLen++;
                else break;
            }
            commonLen = curLen;
        }
        
        return strs[strIdx].substr(0, commonLen);
    }
};


LightRAG实战:5分钟快速搭建一个轻量级法律问答系统(附完整代码) 本文通过实战指南,详细介绍了如何使用LightRAG技术快速搭建一个轻量级法律问答系统。该方案通过构建扁平化实体关系网络和混合检索策略,在5分钟内即可完成从环境准备到智能问答的完整流程,并附有完整代码,为中小团队和个人开发者提供了高效、低成本的AI应用解决方案。 阅读详情

相关推荐

【gateway nc adapter虚拟网卡 win11 联想】问题的解决

由于需要登录公司内网,于是启用奇安信VPN。但启动后报出网关未连接的问题,于是我检查了我的网络适配器,并尝试解决,以下给出几种我实践过程中的行动,并附带最后成功的解决方法。

m0_73602417的博客 6553

LeetCode 14. Longest Common Prefix

题目来源:https://leetcode.com/problems/longest-common-prefix/ 问题描述 14.Longest Common Prefix Easy Write a function to find the longest common prefix string amongst an array of strings. If there is no...

da_kao_la的博客 208

LayaBox CommandBuffer描边使用详细

项目需要使用LayaBox做一个给3D物体动态描边的一个功能,前前后后踩了一些坑算是把这个东西给做出来了把我改好的代码贴上,分享给大家。 (1)想要使用描边你的引擎版本必须 >=2.9.0 (2)你必须下载 Laya官方的BlurShader库才能使用这个功能,下载地址:https://github.com/layabox/layaair-demo/tree/master/h5/3d/ts/LayaAir3D_PostProcess/BlurShader 下载完之后把BlurShader放到自己项目的

小智队长 1735

【LeetCode】14. Longest Common Prefix

Problem Algorithmic thinking 方法一:水平扫描法 Java解法: public String longestCommonPrefix(String[] strs) { if (strs.length == 0) return ""; String prefix = strs[0]; for (int i = 1; i &lt; strs....

E.W的博客 1232

LTspice基础教程-009.LTspice波形测量

<<==上一篇:LTspice基础教程-008.LTspice PWL设置 >>==下一篇:LTspice基础教程-010. 首篇博客文末提供所有相关资源百度网盘分享链接

gaoyong_wang的博客 4万+

STM32F103电压采集proteus仿真

基于HAL库,仿真单片机型号为stm32f103R6,代码keil5。通过单片机的ADC采集功能,测量四路电压,数据经由DMA传输,并将结果通过串口输出,结果比较确,误差在0.01V左右。目前仿真采集四路比较稳定,多了会乱码。proteus版本8.11安装包链接:https://pan.baidu.com/s/1yhNKLl1lGSU9KU0tTuAxcg?pwd=dxe8提取码:dxe8项目下载地址:.

weixin_42490599的博客 4768

【leetcode】14. 最长公共前缀(Longest Common Prefix)

题目描述 【leetcode】14. 最长公共前缀(Longest Common Prefix) 编写一个函数来查找字符串数组中的最长公共前缀。 如果不存在公共前缀,返回空字符串 “”。 第一次解答 看到题目直觉就是挨个遍历完所有字符串的第1元素,第2元素,第3元素…直到有不同,或者某个字符串遍历完毕。 判断是否共同前缀,想法是:如果大家是共同前缀,那么都应该一样,所以随便取某个字符串的元素作...

Liber-coder的博客 312

PRIDE PPP-AR软件实战:从ZTD解算到PWV反演的全流程避坑指南

本文详细解析了使用PRIDE PPP-AR软件进行GNSS数据处理的全流程,重点涵盖从ZTD解算到PWV反演的关键步骤与精度评估。文章深入剖析了软件部署、数据准备、参数配置中的常见“坑点”,并提供了与IGS ZTD及ERA5产品进行交叉验证的实用方法,旨在帮助用户高效、准确地获取高精度大气可降水量产品。

p8q9r0的博客 606

Leetcode14. Longest Common Prefix 最长公共前缀

14. Longest Common Prefix 最长公共前缀 标签: Leetcode 题目地址:https://leetcode-cn.com/problems/longest-common-prefix/ 题目描述 编写一个函数来查找字符串数组中的最长公共前缀。 如果不存在公共前缀,返回空字符串&amp;nbsp;&quot;&quot;。 示例&amp;nbsp;1: 输入: [&quot;flower&quot;,&quo

CoderWangSon 276

用业务语义织就企业数据之网:SAP Datasphere 的定位、架构与落地场景全解析

SAP Datasphere作为SAP业务数据云的核心组件,通过语义层整合企业内外部数据,支持实时分析与AI应用。其核心功能包括数据集成、语义建模、虚拟化与仓库能力,通过Spaces实现资源隔离,Data Builder和Business Builder分别面向技术和业务建模。该平台与SAP HANA Cloud、SAC等深度集成,支持财务统一分析、供应链可视化等典型场景,并可通过BW bridge实现传统BW资产云化迁移。Datasphere强调开放互联,能与Databricks等生态伙伴协同,为企业提供

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 1365

某AI推荐系统项目管理复盘:架构师总结的6大方法论与调优策略

我是张三,某大厂资深AI架构师,主导过5个千万级用户的推荐系统项目(覆盖电商、内容、视频、音乐、外卖等场景)。我的公众号“AI架构师笔记”会分享更多AI项目管理和技术实战经验,欢迎关注。最后的话:推荐系统不是“一锤子买卖”,而是“持续优化的过程”。希望这篇文章能帮你少踩坑,多做对——毕竟,把复杂的事做成,才是架构师的核心能力。

2501_92132293的博客 362

[Leetcode] Longest Common Prefix

Longest Common Prefix 题解 题目来源:https://leetcode.com/problems/longest-common-prefix/description/ Description Write a function to find the longest common prefix string amongst an array of strings. Solut...

weixin_30847271的博客 144

【100%通过率】华为OD机试真题 Python 实现【网上商城优惠活动 / 模拟商场优惠打折II】

某网上商场举办优惠活动,发布了满减、打折、无门槛3种 优惠券,分别为: 1:每满100元优惠10元,无使用数限制,如100~199元可以使用1张减10元,200~299可使用2张减20元,以此类推; 2:92折券,1次限使用1张,如100元,则优惠后为92元, 3:无门槛5元优惠券,无使用数限制,直接减5元。

MISAYAONE的博客 9372

Leetcode刷题笔记之 Longest Common Prefix(python实现)

Leetcode刷题笔记之 Longest Common Prefix(python实现)问题描述程序实现总结 问题描述 Write a function to find the longest common prefix string amongst an array of strings. If there is no common prefix, return an empty strin...

weixin_44239314的博客 422

迈瑞BS系列全自动生化分析仪LIS协议接口手册(内含HL7和ASTM)

迈瑞BS系列全自动生化分析仪LIS协议接口手册,内含HL7和ASTM)

老版QQ原生表情gif动态

这是老版的QQ表情,不过全是动态的的,Windows照片查看器是不会动的,可以右击用浏览器打开查看,再说一遍是老版的,只是分享给宁愿用老版也要动态的人。

【力扣Leetcode题解系列之0014—Longest Common Prefix 最长公共前缀】

本文介绍了求解字符串数组最长公共前缀的多种解法及其多语言实现。主要方法包括:1)遍历前缀子串法,逐个检查公共前缀;2)利用集合特性判断公共前缀;3) 基于最短字符串遍历比较;4)排序后比较首尾字符串。文章提供了Python、Java和C语言的实现代码,展示了不同语言下的解决方案。这些方法各有优缺点,适用于不同场景,其中遍历最短字符串和排序法相对高效。

youngerwang的博客 439

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

Leetcode 3485. Longest Common Prefix of K Strings After Removal

Leetcode 3485. Longest Common Prefix of K Strings After Removal

codename_cys的博客 376

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] Longest Common Prefix

[Problem] Write a function to find the longest common prefix string amongst an array of strings. [Analysis] Trie树的应用,不了解的可以先学一学Trie树,然后就可以秒杀这一题。 [Solution]class Solution {public: // definition of

1.02^365的成长裂变 3618

[勇者闯LeetCode] 14. Longest Common Prefix

[勇者闯LeetCode] 14. Longest Common PrefixDescription Write a function to find the longest common prefix string amongst an array of strings. Information Tags: String Difficulty: Easy Solution列向扫描字符是否相同,

fanguzhai的博客 293

LeetCode Longest Common Prefix

问题网址:https://leetcode.com/problems/longest-common-prefix/description/问题描述: 编写一个函数来查找字符串数组中最长的公共前缀字符串。LeetCode这道问题也是没有给出非常明确的输入输出,也是显得意义不明下面给出解法水平扫描法首先,我们将描述找到由一组字符串LCP(S_1 \ Lots S_n)LCP(S 1 … S n)共享的

weixin_37806277的博客 249

Leetcode学习笔记:#14. Longest Common Prefix

Leetcode学习笔记:#14. Longest Common Prefix Write a function to find the longest common prefix string amongst an array of strings. If there is no common prefix, return an empty string “”. 实现: public Strin...

ccystewart的博客 160

LeetCode之LCP(Longest Common Prefix)问题

LeetCode之LCP(Longest Common Prefix)问题 这个也是简单题目。可是关键在于题意的理解。 题目原文就一句话:Write a function to find the longest common prefix string amongst an array of strings. 题意是给一...

weixin_30614109的博客 93

LeetCode力扣 14. 最长公共前缀 Longest Common Prefix 题解代码 JavaScript

问题https://leetcode-cn.com/problems/longest-common-prefix/ 练习使用JavaScript解答 其中字符串我手动转换为了String对象。按道理应该是解释器自动转换的,而且在LeetCode上执行测试用例的时候也可以正常自动转换,但是提交后就报错,于是手动转换了。 /** * @param {string[]} strs * @return {string} */ var longestCommonPrefix = function(st.

Coding home - 漂流瓶jz 494

leetcode014 Longest Common Prefix

14. Longest Common Prefix Write a function to find the longest common prefix string amongst an array of strings.

jnuyanfa的博客 403

leetcode Longest Common Prefix

Longest Common Prefix Write a function to find the longest common prefix string amongst an array of strings. package String; import java.util.HashMap; import java.util.Iterator; impor

820

【Leetcode】Longest Common Prefix

题目链接:https://leetcode.com/problems/longest-common-prefix/ 题目: Write a function to find the longest common prefix string amongst an array of strings. 算法: [java] view plain c

业精于勤,荒于嬉 1654

LeetCode——Longest Common Prefix

Write a function to find the longest common prefix string amongst an array of strings. » Solve this problem class Solution { public: string longestCommonPrefix(vector &strs) { // S

788
上一篇: [LeetCode]Letter Combinations of a Phone Number
下一篇: [LeetCode]Longest Consecutive Sequence
AI记忆
AI记忆 优质创作者: 人工智能技术领域 优质创作者: 人工智能技术领域 领域专家: 算法与数据结构技术领域 领域专家: 算法与数据结构技术领域
博客等级 码龄19年 6685粉丝 429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

打赏作者

AI记忆

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

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