see-70. Climbing Stairs

VSCode+PlatformIO新建工程卡成狗?3步提速大法实测有效(附镜像源配置) 针对VSCode+PlatformIO新建工程时因依赖下载缓慢导致的加载慢问题,本文提供了三步核心提速方案。通过配置Python环境变量、修改PlatformIO插件设置以使用国内镜像源,并优化pip配置文件,实测可大幅缩短工程创建时间,有效解决创建工程卡顿的痛点。 阅读详情

实质上是求解斐波那契数列问题:F(n)=F(n-1)+F(n-2),其中F(0)=F(1)=1

class Solution {
public:
    int climbStairs(int n) {
       // if(n==1||n==0)
         //   return 1;
       // return climbStairs(n-1)+climbStairs(n-2);
        vector<int> table;
        table.push_back(1);
        table.push_back(1);
        for(int i=2;i<=n;i++)
        {
            table.push_back(table[i-1]+table[i-2]);
        }
        return table[n];
    }
};

 

Android 13.0 双屏时触摸失效问题排查与修复 摘要:本文记录了RK3588 Android12开发板上主屏触摸失效问题的排查与解决过程。通过分析发现,USB触摸设备被错误关联到辅屏(displayId=3)而非主屏(displayId=0)。解决方案是修改TouchInputMapper.cpp,强制触摸设备使用INTERNAL viewport(主屏),并保持设备在viewport不活跃时仍启用。该修改涉及约40行代码,有效解决了多显示器环境下触摸设备错误映射的问题,同时提供了详细的调试方法和注意事项。 阅读详情

相关推荐

深入解析ACS SPiiPlus运动控制器的托管API架构与实战应用

本文深入解析了ACS SPiiPlus运动控制器的托管API架构,详细介绍了其核心模块、设计哲学与实战应用。通过C#/.NET封装的托管接口,开发者能够高效实现多轴协同运动控制、EtherCAT总线集成以及事件驱动的异步编程,显著提升高精度工业自动化设备的开发效率与系统稳定性。

jwt8token的博客 862

【Leetcode】70. Climbing Stairs 【动态规划】

70. Climbing Stairs Total Accepted: 119494Total Submissions: 319815Difficulty: Easy You are climbing a stair case. It takes n steps to reach to the top. Each time you can either c

renxingzhadan的专栏 417

ExpressLRS 915MHz vs 2.4GHz:实测对比与TX12遥控器的最佳搭配方案

本文深入对比了ExpressLRS 915MHz与2.4GHz两种频段在穿透能力、延迟、抗干扰等方面的实测表现,并结合RadioMaster TX12遥控器的硬件特性,提供了从微型花飞到极限远航等不同场景下的最佳搭配方案与EdgeTX系统配置指南,帮助FPV玩家根据核心飞行需求做出精准选择。

milk8的博客 447

70 - Climbing Stairs

You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Subscribe to see which c

hellochenlu的博客 315

用STM32手搓一个体温计

市面上大多额温计、测温计精度。

m0_61687959的博客 2376

MEMS惯性传感器关键参数解析与误差优化策略

本文深入解析了MEMS惯性传感器的关键性能参数,如零偏稳定性、噪声与随机游走、振动整流误差等,并阐述了这些误差如何导致设备导航漂移、姿态不稳等问题。文章重点探讨了从硬件选型、系统设计到校准补偿、算法融合(如卡尔曼滤波)以及温度补偿等实战优化策略,旨在帮助工程师有效提升自动驾驶、无人机等设备的测量精度与稳定性。

weixin_28363123的博客 415

LeetCode 70. Climbing Stairs

题目要求: You are climbing a stair case. It takes n steps to reach to the top.Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top?Subscribe to see which compani

ncst 459

让 ABAP 开发者也能做出高质量 SAP Fiori:到底需要掌握多少前端知识?

文章摘要: 随着SAP技术演进,ABAP开发者的角色边界正在重构。传统SAP GUI时代的全栈开发模式逐渐转向RAP+Fiori Elements的新范式,其中关键技术变化包括: 开发模式转变:从前后端耦合转向RAP模型驱动开发,通过CDS注解实现UI意图表达 分工优化:Fiori Elements模板处理80%标准UI需求,Flexible Programming Model解决20%差异化需求 效率提升:注解驱动开发可在RAP建模阶段同步定义UI结构,大幅缩短交付周期 工具整合:SAP提供从建模到部署的

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 350

70. Climbing Stairs

70. Climbing Stairs

fuyetao的博客 205

uniapp - 实现 App 端 “半屏幕“ 连续扫码功能,页面只有半个屏幕是扫码框并且可持续连续扫码获取数据(不使用官方 uni.scanCode API,因为无法连续扫码!)详细示例源码插件教程

uniapp app端实现半屏幕扫码,uniapp 扫一扫半个屏幕,uniapp 扫一扫,uniapp持续扫码实现,uniapp怎么自定义扫码和连续扫码,uni-app App端半屏连续扫码,uniapp 扫一扫半个屏幕,uniapp自定义扫码页面样式,实现连续扫码功能(uniapp),uniapp - 实现 App 端 "半屏幕" 连续扫码功能,页面只有半个屏幕是扫码框并且可持续连续扫码获取数据(不使用官方 uni.scanCode API,因为无法连续扫码!)详细示例源码插件教程。

✅ 精选推荐文章 1万+

Ambari 安装部署教程(手把手教学)【超级详细】

本文为读者提供了一套超详细的 Apache Ambari 安装部署教程,采用手把手教学方式,适合初学者及大数据运维人员。内容涵盖前期准备、环境配置、Ambari Server 与 Agent 安装、集群初始化配置、常见问题排查等全流程操作,确保用户能够顺利搭建稳定的 Hadoop 大数据平台管理系统。

Five的博客 1万+

<LeetCode OJ> 70. Climbing Stairs

Climbing Stairs Total Accepted: 84073 Total Submissions: 236731 Difficulty: Easy You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb

EbowTang的练习场 1670

YOLO+OpenCV强强联手:高精度跌倒检测技术实战解析

(0, 1), (0, 2), (1, 3), (2, 4), # 脖子和头部(3, 5), (5, 6), (5, 7), (7, 9), (5,11),# 左臂(4,6), (6, 12), (6,8) ,(8,10),# 右臂(11, 12), (11, 13), (13, 15), #左腿(12, 14), (14, 16) #右腿keypoints是检测到的人体关键点数据,frame是图像帧,boxes是检测到的边界框信息。定义了人体关键点之间的连接关系,用于绘制人体骨架。

yolo模型训练找我,AI游戏合作找我 2322

【LeetCode】70. Climbing Stairs

Difficulty: Easy You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Su

Juxin_Lin的博客 306

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

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刷题记——70. Climbing Stairs(爬楼梯)

一、题目叙述: You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Note: Giv

爱胡椒的小白兔呢 827

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 70. Climbing Stairs

题目链接:https://leetcode.com/problems/climbing-stairs/ 题目描述: You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinc

yxy 216

数学建模模型总结

这是本人在两年内准备参加数学建模竞赛搜集到的模型资料,已经经过了本人的筛选和整理,希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助!

Leetcode-70. Climbing Stairs

好久没有刷题了,感觉手都生了。今天来道简单点的题目——爬楼梯; 题目如下: You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb

caoyan_12727的博客 564

70. Climbing Stairs Java Solutin

You are climbing a stair case. It takesnsteps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Subscribeto see which comp...

weixin_30615767的博客 84

70. Climbing Stairs Add to List

You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Subscribe to see which comp...

weixin_30821731的博客 89

LeetCode--No.70--Climbing Stairs

You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top? Subscribe to see which c

sophia_tone2w的博客 207

Climbing Stairs

70. Climbing Stairs My Submissions Question Total Accepted: 97743 Total Submissions: 269280 Difficulty: Easy You are climbing a stair case. It takes n steps to reach to the top. Each time you ...

weixin_30398227的博客 68

【刷LeetCode的无聊吐槽】

单纯是一些刷LeetCode的吐槽

weixin_48164517的博客 555

Leetcode题解-算法-动态规划

文章目录1、斐波那契数列1.1爬楼梯 1、斐波那契数列 1.1爬楼梯 70. Climbing Stairs(Easy) You are climbing a stair case. It takes n steps to reach to the top. Each time you can either climb 1 or 2 steps. In how many distinct ways...

桑凯旋的博客 1013

see-88. Merge Sorted Array

利用array1多余的位置,从后向前遍历二个数组: class Solution { public: void merge(vector&lt;int&gt;&amp; nums1, int m, vector&lt;int&gt;&amp; nums2, int n) { int total=m+n-1; m--; n--; ...

leetcodecl 1111

see-1. Two Sum

python版: class Solution: def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ ans = list() ...

leetcodecl 444
上一篇: 387. First Unique Character in a String
下一篇: see-88. Merge Sorted Array
云水谣CS
博客等级 码龄9年 8粉丝 268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