leetcode Roman& Integer

【HALCON工业视觉应用探索】02. C# + Halcon铸件毛刺机器人打磨引导:点云处理从实验室到工业现场的全流程落地 摘要:铸件毛刺的自动化打磨是机械制造领域的重要难题,其随机性(高度0.1-3mm)和复杂工况导致人工打磨效率低(3分钟/件)、工伤率高。本文基于Halcon 24.11与.NET 6,构建了一套点云处理方案:通过线激光扫描仪获取三维点云,经预处理、毛刺检测、路径规划三大模块,引导机器人完成打磨。虚拟实验测试显示,对高度>1mm的毛刺检出率达100%,定位误差±0.08mm,但工业现场受油污、粉尘影响,性能降至检出率68-75%、处理时间>45秒。 阅读详情

参考:http://blog.youkuaiyun.com/ljiabin/article/details/39968583

1、罗马字母与数值的对应关系,罗马数字的范围0~3999

int toNumber(char ch) {
        switch (ch) {
            case 'I': return 1;
            case 'V': return 5;
            case 'X': return 10;
            case 'L': return 50;
            case 'C': return 100;
            case 'D': return 500;
            case 'M': return 1000;
        }
        return 0;
    }

2、CDL ==>550

转化方式:从右往左,如果左值比右值小,则减;如果左值比右值大,则加

public int romanToInt(String s) {
        char[] array=s.toCharArray();
        int result=toNumber(array[array.length-1]);
        for(int i=array.length-2;i>=0;i--){
            int t2=toNumber(array[i+1]);
            int t1=toNumber(array[i]);
            if(t1>=t2){
                result+=t1;
            }
            else{
                result-=t1;
            }
        }
        return result;
    }
3、转化方式:数值每个位置的数都表示一个罗马数字,如69(LXIX),其中LX和IX分别表示60和9.

roman的数组,表示{1,2,3,。。。10,20,30.。。。100,200,300.。。}

    public String intToRoman(int num) {
        String result="";
        String[][] roman={
            {"","I","II","III","IV","V","VI","VII","VIII","IX"},
            {"","X","XX","XXX","XL","L","LX","LXX","LXXX","XC"},
            {"","C","CC","CCC","CD","D","DC","DCC","DCCC","CM"},
            {"","M","MM","MMM"}
        };
        int col=0;
        while(num!=0){
            int remain=num%10;
            result=roman[col][remain]+result;
            col++;
            num=num/10;
        }
        return result;
    }

还有一种暴力方式:

class Solution {  
public:  
    string intToRoman(int num) {  
        string str;    
        string symbol[]={"M","CM","D","CD","C","XC","L","XL","X","IX","V","IV","I"};    
        int value[]=    {1000,900,500,400, 100, 90,  50, 40,  10, 9,   5,  4,   1};   
        for(int i=0;num!=0;++i)  
        {  
            while(num>=value[i])  
            {  
                num-=value[i];  
                str+=symbol[i];  
            }  
        }  
        return str;  
    }  
};  



【异常】Cursor IDE 扩展主机超时错误处理文档The agent execution provider did not respond within 4 seconds. 该错误通常是 Cursor IDE 的临时性问题,不影响项目代码。大多数情况下,重新加载窗口或重启 IDE 即可解决。如果问题持续,可按上述进阶方案排查。重新加载窗口(最快)重启 IDE检查系统资源清理缓存检查扩展更多关于 AI+教育领域的干货,可以关注我的微信公众号第一时间获取哦~ 阅读详情

相关推荐

矢量量化的基本原理:高效数据压缩与信息检索的关键矢量量化(Vector Quantization,简称VQ)是一种数据压缩和信

此外,在矢量量化过程中,可能会引入一定的信息损失,对一些精细特征的表示可能存在一定的误差。它通过将高维数据映射到低维码本空间中,并将相似的数据映射到相同的码本向量中,实现对数据的高效表示和存储。最重要的是,矢量量化可以用于数据的检索和分析,提供了高效的数据查询和处理手段。通过使用码本索引,我们可以高效地存储和检索大量的数据。在图像处理中,矢量量化可以用于图像压缩,将复杂的图像数据转化为较少的码字,实现图像的存储和传输。矢量量化通过将连续的高维数据分割并映射到有限的码本集合中,实现数据的离散表示和压缩。

m0_70886513的博客 1373

[LeetCode]Roman to Integer

[LeetCode]Roman to Integer

个人技术博客 930

Chipyard------介绍与环境搭建

Chipyard是一个开源的、可扩展的、集成化的芯片设计平台,旨在帮助芯片设计人员快速、高效地开发自己的定制芯片。由伯克利大学架构研究小组开发,它基于Chisel语言和Scala编程语言,提供了一整套硬件设计工具和流程,包括RTL设计、验证、物理布局、仿真等,以及支持多种芯片体系结构的库和模块。

qq_38798111的博客 9479

LeetCode [Roman to Integer]

Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M. Symbol Value I 1 V 5 X 10 L 50 C 100 D ...

Godfryfry的博客 395

3个国产AI大模型实战:如何用DeepSeek+豆包+千问批量生成高质量测试用例(附完整代码)

本文详细介绍了如何利用DeepSeek、豆包和千问三大国产AI大模型批量生成高质量测试用例的实战方法。通过Python环境配置、多模型SDK安装和统一接口封装,实现高效测试用例生成,并结合结构化提示词设计和多模型结果对比分析,显著提升测试效率和覆盖率。

weixin_29197699的博客 712

【SPI-NAND】Linux驱动移植实战:从规格书解析到内核适配

本文详细解析了SPI-NAND Flash的Linux驱动移植实战,从规格书关键参数提取到内核MTD子系统适配。内容涵盖存储结构解析、命令集映射、设备树配置及厂商驱动开发,特别强调ECC校验、坏块管理等NAND特性处理,并提供了性能优化与测试验证的实用技巧。

weixin_27076351的博客 351

LeetCode13——Roman to Integer

Given a roman numeral, convert it to an integer. Input is guaranteed to be within the range from 1 to 3999. 题目大意 给你个罗马数字,把它转换成一个int数。输入限定在[1, 3999]。 难度系数:容易 实现 一次性通过,:) int getVal(c

代码菌的blog 3162

【目录】专栏文章导航

从最强工具链Davinci Configurator模块学习出发掌握AUTOSAR

weixin_47571898的博客 3301

leetcode-13. Roman to Integer

leetcode12题的变式,罗马数字转换为阿拉伯数字 题目类型: HashMap,字符串 题意: Given a roman numeral, convert it to an integer. Input is guaranteed to be within the range from 1 to 3999. 我的思路: 66% 步骤: 将字符串的各位转化为...

NNnora的博客 491

基于Simulink的柔性关节人形机器人振动抑制控制仿真

本文完成了基于Simulink的柔性关节人形机器人振动抑制控制仿真,实现了:✅ 建立二质量柔性关节动力学模型✅ 设计加速度反馈主动阻尼控制器✅ 对比陷波滤波与状态反馈效果✅ 验证大幅运动下的快速振动衰减能力✅ 为高动态人形机器人操作(如击掌、搬运)提供平滑执行基础。

xiaoheshang_123的博客 131

【实战篇】【硅基流动API+Cherry Studio搭建个人知识库全流程解析:从零到精通的超详细指南】

在人工智能技术高速发展的今天,基于大语言模型的知识库已成为开发者提效的必备工具。然而云端服务存在响应延迟、数据隐私、硬件依赖等痛点。通过硅基流动API与的深度整合,我们既能享受大模型的强大能力,又能实现本地化部署与高效管理。本文将从环境配置到高阶调优,手把手带你构建企业级知识库系统。

商务合作|问题讨论|交流学习 请联系作者微信,加微信请务必注明来意,博客主页有联系方式 1082

【Leetcode 13】Roman to Integer

Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M. Symbol Value I 1 V 5 X 10 L 50 C 100 D ...

Zlase的博客 488

【WRF数据准备】生成 LCZ 对应的FRC_URB2D

WRF 静态地理数据准备

WW、forever的博客 412

Integer to Roman (罗马数字转换) 【leetcode】

题目:Given an integer, convert it to a roman numeral. Input is guaranteed to be within the range from 1 to 3999. 科普了一下表达方式,理解了就不复杂了。 I = 1; V = 5; X = 10; L = 50; C = 100; D = 500; M = 1000;

Havenoidea 的专栏 1万+

IDEA的使用指南

IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.

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 Roman to Integer

// //  main.cpp //  Integer to Roman // //  Created by zhukunjie on 14-7-8. //  Copyright (c) 2014年 zhukunjie. All rights reserved. // #include using namespace std;

moyedamo的专栏 430

TMS_WebGMaps_1.9.8.0_Full_Source

TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder

每日leetcode--(12) Integer to Roman

我的想法是,把900,400,90,40,9,4都放到数字列表中,从大到小访问该数字列表,不断取模即可,但是速度上比较慢。 #include &lt;iostream&gt; #include &lt;string&gt; #include &lt;stdio.h&gt; using namespace std; class Solution { public: int N...

ssdut_209的博客 534

编程题 字符串A在字符串B中出现的次数

腾讯笔试题之一,过了70%,超时。 输入:整数k, 字符串a,字符串b 输出:字符串a中k个字符在b出现的次数。 int main(){ int k; string a; string b; set&lt;string&gt; tempset; while(cin &gt;&gt;k &gt;&gt; a &gt;&gt; b){ i...

github_38818603的博客 6372

bfs和dfs的区别?

参考: https://blog.youkuaiyun.com/yishuige/article/details/51769997     实现方法 基本思想 解决问题 N规模 DFS 栈/递归 回溯法,一次访问一条路,更接近人的思维方式, 所有解问题,或连通性问题 不能太大,&lt;=200 BFS 队列 分治限界法,一次访问多条路,...

github_38818603的博客 5233

leetcode 二叉树 两个结点的最短距离

参考链接 https://www.cnblogs.com/wxdjss/p/5698071.html

github_38818603的博客 4944

红黑树与平衡二叉树

红黑树和平衡二叉树区别如下: 1、红黑树放弃了追求完全平衡,追求大致平衡,在与平衡二叉树的时间复杂度相差不大的情况下,保证每次插入最多只需要三次旋转就能达到平衡,实现起来也更为简单。 2、平衡二叉树追求绝对平衡,条件比较苛刻,实现起来比较麻烦,每次插入新节点之后需要旋转的次数不能预知。 平衡二叉树介绍:https://blog.youkuaiyun.com/lpp0900320123/article/det...

github_38818603的博客 3202

网格索引 和四叉树索引

参考链接:https://blog.youkuaiyun.com/zhshulin/article/details/19031177 问题:在一个平面层上面,给出一个矩形,找出这个矩形里的所有点。 暴力法:遍历所有点,判断是否在矩形里面 网格索引:划分网格,对于每个网格元素,作为集合对象,记录包含在这个网格里面的所有图元的信息(地址或引用)。 缺点:如果索引一条线的时候,一条线的引用在多个网格中都被记录...

github_38818603的博客 2717

PCA与LDA的异同

PCA和LDA虽然都用到数据降维的思想。 监督方式不一样,PCA是无监督的,LDA是有监督的。 目的也不一样。PCA是为了去除原始数据集中冗余的维度,让投影子空间的各个维度的方差尽可能大,也就是熵尽可能大。LDA(线性判别式分析)关注的是标注,希望投影之后不同类别的数据点距离尽量大,相同类别的数据点距离尽量紧凑。 ...

github_38818603的博客 2596

二叉树的构造

struct Node{ char data; Node* right, *left; }; class Bitree{ public: Bitree(){ root = new Node(); root-&gt;left = nullptr; root-&gt;right = nullptr; } void...

github_38818603的博客 1614

c++ 数组指针的使用

定义一个字符串数组,按首字母从小到大进行排序。 void sort(char **p, int n){ char *temp; for(int i = 0; i &lt; n-1; i++){ for(int j = 0; j &lt; n - i -1; j++){ cout&lt;&lt; *(p+j)&lt;&lt;","&lt;&...

github_38818603的博客 1558

关于走格子的问题

dfs: 所有可能的路径,是否存在某一条路径 bfs: 遍历图,最短路径 深度优先搜索利用了栈进行计算,而宽度优先搜索则利用了队列进行计算 1.简单版:从(0,0)走到(n,m),只能往下或往右走,最短的路径。(这里最短路径的意思就是只会走m+n个空格)注意这里的矩阵大小是(m+1)*(n+1) 多少种走法:C(m+n,n),或者用动态规划的方式求解。 记录路径:用dfs typed...

github_38818603的博客 1502

手写 queue 和dequeue

参考链接  https://blog.youkuaiyun.com/foreyes_1001/article/details/52215243

github_38818603的博客 1270

leetcode z形遍历二叉树

key:  use two stack s1,s2  /** * Definition for binary tree * struct TreeNode { * int val; * TreeNode *left; * TreeNode *right; * TreeNode(int x) : val(x), left(NULL), right(NU...

github_38818603的博客 890
上一篇: Java中的String 和char[] ,int和Integer
下一篇: 函数异步执行,并传递传参
kotomi_du
博客等级 码龄10年 5粉丝 91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