leetcode majority element

高动态范围结构光三维测量技术:从多曝光融合到深度学习的演进与应用 本文深入探讨了高动态范围结构光三维测量技术的演进与应用,从传统的多曝光融合技术到深度学习驱动的创新方案。通过工业案例展示了该技术在提升测量精度和效率方面的显著优势,特别是在处理高反光、复杂表面时的突破性表现。文章还提供了针对不同工业场景的技术选型指南,帮助工程师优化三维测量解决方案。 阅读详情

题目:

Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times.

You may assume that the array is non-empty and the majority element always exist in the array.

找到一个数组中出现次数超过数组元素数量一半的元素的值。

很快想到了第一种非常简单的方法,就是把数组排序,然后输出下标是中间的元素:

public int majorityElement(int[] num) {
		Arrays.sort(num,0,num.length);
		return num[num.length/2];
}
只有两行代码,可谓是一个非常漂亮的解。

不过可以看一下java的源码,Arrays的sort方法用的是快排:

public static void sort(int[] a, int fromIndex, int toIndex) {
        rangeCheck(a.length, fromIndex, toIndex);
        DualPivotQuicksort.sort(a, fromIndex, toIndex - 1);
}
所以平均时间复杂度会有n*O(logn),能AC。

但是总觉得有点不够,于是看了一下别人的solution。

方法二:

时间复杂度O(n)

用一个计数器counter记数,用一个变量candidate存储候选值,遍历数组,如果当前值和前一个值相同,counter+1,如果不同,counter-1,如果counter==0了,candidate移动到当前元素。设想极度乱序的情况,那个元素被尽可能多的隔开了,但是至少有一组一定是两个以上连续的那个元素,所以最后的candidate就是我们需要的值。

<span style="white-space:pre">	</span>int candidate = 0;
        int counter = 0;
        for(int i=0;i<num.length;i++)
        {
        	if(counter==0)
        	{
        		candidate = num[i];
        		counter++;
        	}
        	else{
        		if(num[i]==candidate)counter++;
        		else counter--;
        	}
        }
<span style="white-space:pre">	</span>return candidate;






KEPServerEX 6 之 EX 6.11.718.0 发布说明 KEPServerEX 6.11.718.0 发布说明 阅读详情

相关推荐

基于ESP32-S3的电子吧唧DIY:蓝牙传图、触摸交互与动态显示全解析

本文详细解析了如何利用ESP32-S3芯片DIY一个功能丰富的电子吧唧(智能徽章)。项目核心实现了通过蓝牙低功耗(BLE)接收手机小程序发送的图片,并存储至文件系统,最终在带触摸功能的TFT屏幕上进行动态显示与交互。教程涵盖了从硬件选型、软件开发环境搭建,到蓝牙通信、图片处理与触摸功能实现的完整步骤,并分享了实用的调试技巧,适合创客和嵌入式爱好者动手实践。

weixin_42510243的博客 674

【leetcode 哈希表】Majority Element

leetcode新題,Majority Element ,难度easy。题意:给定一个长度为n的数组,找出majority element,所谓majority element就是出现次数大于n/2的那个数。 很简单的题目,解法很多: Runtime: O(n2) — Brute force solution: Check each element if it is the majority element. Runtime: O(n), Space: O(n) — Hash table: Maintain

wepon的专栏 4847

modbus-esp8266库深度解析:工业级Modbus协议栈实现

Modbus是一种广泛应用于工业自动化领域的通用串行通信协议,其核心在于标准化的功能码、寄存器模型与帧结构设计。基于RTU、ASCII和TCP三种传输变体,Modbus协议通过主从架构实现设备间可靠数据交互,具备低开销、高兼容性与强实时性等技术价值。在边缘计算与工业物联网场景中,它被广泛用于PLC通信、传感器数据采集、HMI集成及远程固件升级(FOTA)等关键任务。针对ESP8266/ESP32等Wi-Fi微控制器平台,modbus-esp8266库提供了完整协议栈支持,涵盖多实例并发、回调驱动架构、零拷贝

weixin_42315569的博客 349

Leetcode 229 Majority Element II

Leetcode 229 Majority Element II题目思路 题目 Given an integer array of size n, find all elements that appear more than ⌊ n/3 ⌋ times. Note: The algorithm should run in linear time and in O(1) space. Example 1: Input: [3,2,3] Output: [3] Example 2: Input: [1,

weixin_43796689的博客 206

DQN网络代码调用env.reset()后存储观测信息报错问题ValueError: setting an array element with a sequence.解决处理

**reset()函数返回的是一个array类型以及其中数据的type!模拟游戏进行学习任务。因此需要将observation指定为真正需要的array信息即可,得到的的变量observation类型与Agent存储时的。因此怀疑返回的observation有问题,于是通过。报错信息提示为数据维度不对应,也即最初通过。在强化学习DQN网络代码实现过程中,针对。

Vulcan_Q的博客 4092

保姆级教程:用CH34xSerCfg工具修改USB转串口芯片的VID/PID,避免驱动冲突

本文详细介绍了如何使用CH34xSerCfg工具修改USB转串口芯片的VID/PID,解决驱动冲突问题。通过实战案例和配置技巧,帮助开发者高效管理嵌入式设备,避免常见的USB设备识别问题,提升开发效率。

weixin_30924239的博客 224

LeetCode Majority Element

给定一个非空的数组,其中某一数值的数量超过数组长度的一半,请找出这个值。

DRFish 2456

全功能爬虫框架:Botasaurus 的详细使用(现代化、反检测、高并发的智能爬虫框架)

Botasaurus是一个基于 Python 的全栈式智能数据采集框架。它深度融合了以下关键技术: 无头浏览器自动化(Headless Browser Automation):基于 Playwright 或 Puppeteer,支持 Chromium、Firefox、WebKit; 高级反检测引擎(Anti-Detection Engine):自动绕过 WebDriver、Canvas、AudioContext 等 50+ 种指纹检测; 声明式任务模型(Declarative Task Model):用装

数据知道的博客 4370

leetcode 169. Majority Element

leetcode 169. Majority Element题目 Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times. You may assume that the array is n

Decorator2015的博客 700

反激电源及其变压器计算与绕制

本内容以一个宽电压输入 24V3A 输出的 72W 反激电源为例来计算电路的各个参数和器件选型,包含反激变压器的计算与绕制方法。

让你爱上电路设计 1935

YOLOv8结合GhostNet实现轻量化目标检测:模型设计与性能评测

GhostNet系列的核心是**“幽灵特征图(Ghost Feature Map)”** 概念:通过简单的线性变换(如深度卷积)生成“幽灵特征”,这些特征与原始特征融合后,能以极低的计算成本带来丰富的语义信息。为了突出GhostNet系列的必要性,设计消融实验:分别测试“仅主卷积”“仅幽灵卷积”“GhostNet(主卷积+幽灵卷积)”三种方案的性能,用数据证明“幽灵特征”的不可替代性。简单来说,引入GhostNet系列的YOLOv8,既能体现你的技术深度,又能让毕设的“落地性”大幅提升。

m0_71589036的博客 114

LeetCode 169. Majority Element

LeetCode 169. Majority Element 问题来源LeetCode 169. Majority Element 问题描述 Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times.

yanqinghe123的专栏 320

基于YOLOv11的手术器械计数系统:从数据集构建到Web应用部署

m2cai16-tool数据集:包含15种腹腔镜手术器械的标注图像Cholec80数据集:包含80个胆囊切除手术视频,标注了7种器械SurgicalTools数据集:包含超过10,000张手术器械图像,涵盖21类常见器械手术剪(Scissors)止血钳(Hemostat)持针器(Needle Holder)手术刀(Scalpel)镊子(Forceps)拉钩(Retractor)吸引器(Suction)

YOLO 395

leetcode 169 Majority Element 多数元素

leetcode 169 Majority Element leetcode 2020年3月 每日一题打卡 思路: python 细节: 查找list某元素的位置:list.index(i) 向下取整:int() 代码: class Solution(object): def majorityElement(self, nums): """ :type ...

weixin_43973433的博客 173

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")

Devexpress16.2.6破解工具

Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具

[LeetCode]169. Majority Element

[LeetCode]169. Majority Element题目描述思路遍历计数,返回大于n/2的值 update1 既然数的数目超过n/2,那数必然是中位数,排序之后去num[n/2] update2 分治算法,类似快排的思路,见代码代码class Solution { public: int majorityElement(vector<int>& nums) {

Lcharon的博客 3191

廖雪峰Python教程[完整版]

廖雪峰的python教程整理的,已经排好版了,而且还是完整的,适合小白

leetcode-169. Majority Element

leetcode-169. Majority Element Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times. You may assume that...

anchouman0644的博客 263

LeetCode:Majority Element

LeetCode:Majority Element  Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times. You may assume that the array is non

feiqiangs的专栏 459

leetcode题解:第169题Majority Element

https://leetcode-cn.com/problems/majority-element/ 解法一 最容易想到的解法,用哈希表存下每个元素出现的次数,当次数超过n/2时,找到答案,算法效率也高,复杂度均为O(n)O(n)O(n)。 class Solution { public: int majorityElement(vector<int>& nums) { unordered_map<int, int> count; fo

chenf1999的博客 287

LeetCode: 169. Majority Element

LeetCode: 169. Majority Element Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times. You may assume that the array is

qha0731的专栏 284

LeetCode 169. Majority Element and LeetCode 229. Majority Element II解题报告

LeetCode 169.Majority Element LeetCode 229. Majority Element II

My Blog 1638

Leetcode169. Majority Element

给一个数组,存在一个数字超过了半数,找出这个数。

magic_jiayu的博客 285

LeetCode169. Majority Element

依旧尝试用分治法解决问题。这次选中的是LeetCode169. Majority Element。难度为easy。题目要求如下: Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times. Yo

reborncgy的博客 269

leetcode169. Majority Element

题目leetcode169. Majority Element 难度等级:easy思路由于majority member 出现次数大于总数一半,实际上就是求数组的中位数。最简单的思路,就是先排序,然后二分查找到中位数。但其实,可以直接利用快排算法的partition函数,就可以把中位数找出来。具体的做法就是,分块,但分块的中间元素index 等于 中位数的index时,结束。这样,时间复杂度虽然和

alxandral_brother的博客 310

Leetcode169: Majority Element

Leetcode169:Majority Element Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊n/2⌋ times. You may assume that the array is non-e

~\(≧▽≦)/~ 272

【leetcode】【medium】229. Majority Element II​​​​​​​

229. Majority Element II Given an integer array of sizen, find all elements that appear more than⌊ n/3 ⌋times. Note:The algorithm should run in linear time and in O(1) space. Example 1: Input...

lemonade13的博客 107
上一篇: java中String的相等判断(==和equals())详解
下一篇: leetcode: Triangle
MR_LUXY
博客等级 码龄16年 20粉丝 43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