119. Pascal's Triangle II

磁盘I/O %util特别高 阅读详情

Given an index k, return the kth row of the Pascal's triangle.

For example, given k = 3,
Return [1,3,3,1].

Note:
Could you optimize your algorithm to use only O(k) extra space?

Subscribe to see which companies asked this question.

思路:

       思路与Pascal's Triangle 一样 利用上一层的值改变当前层的值 只是遍历改变值由后往前遍历了 只是空间变为O(k)

C++ 代码:

class Solution {
public:
    vector<int> getRow(int rowIndex) {
        vector<int> res(rowIndex+1,1);
        for(int i=1;i<rowIndex;i++){
            for(int j=i;j>0;j--){
                res[j] += res[j-1];
            }
        }
        return res;
    }
};


Opencv2.4.9源码分析——HoughCircles 图形可以用一些参数进行表示,标准霍夫变换的原理就是把图像空间转换成参数空间(即霍夫空间),例如霍夫变换的直线检测就是在距离-角度空间内进行检测。圆可以表示成: (x-a)2+(y-b)2=r2                   (1) 其中a和b表示圆心坐标,r表示圆半径,因此霍夫变换的圆检测就是在这三个参数组成的三维空间内进行检测。 原则上,霍夫变换可以检测任何形状。但复杂的形状需要的 阅读详情

相关推荐

LabVIEW电压信号采集系统:从零搭建到科研实战

本文详细介绍了如何从零开始搭建一个基于LabVIEW的电压信号采集系统。内容涵盖硬件选型(如NI USB-6002数据采集卡)、软件环境配置、核心程序开发(包括多通道同步采集、数据自动保存)以及高级功能优化。系统支持自定义采样率与时长,并提供了完整的LabVIEW程序框图和配套的Python数据分析脚本,旨在帮助科研人员和工程师快速构建稳定、高效的专属数据采集方案,提升实验自动化水平。

gold8的专栏 461

Leetcode 119 Pascal's Triangle II(水)

题目连接:Leetcode 119 Pascal's Triangle II解题思路:空间复杂度可以优化到o(n),每次在数组末尾追加一个1,然后从右往左遍历,每个数加上它前一个数。这样的操作执行n次。class Solution { public: vector&lt;int&gt; getRow(int rowIndex) { vector&lt;int&gt; ans; a...

不慌不忙、不急不躁 241

AI开发提效10倍:从需求到部署的全流程实战指南(附Prompt模板)

用AI开发不是“偷懒”,而是把时间花在更有价值的地方——原来花80%时间写重复代码,现在用AI搞定后,能把精力放在“优化用户体验”“提升系统性能”这些核心工作上,这才是AI给开发者的真正红利。如果你还没开始用AI做开发,建议从“写一个简单接口”“补一段注释”开始尝试,先用起来再优化方法。记住:AI是工具,不是对手,学会驾驭它,你会发现开发效率提升的同时,还能告别无效加班。

这里只有干货:从 Modbus 通信到 MES 对接,从 YOLO 训练到工控机部署,带你搞定工业软件开发全流程。 1531

LeetCode 119. Pascal's Triangle II(杨辉三角II) -- c语言

119. Pascal's Triangle II Given a non-negativeindexkwherek≤33, return thekthindex row of the Pascal's triangle. Note that the row index starts from0. In Pascal's triangle, each number is ...

d_benhua的博客 441

【FPGA】定点数运算中的位宽优化与截取策略

本文深入探讨了FPGA定点数运算中的核心挑战——数据位宽优化与截取策略。针对定点数在加法、乘法等运算中位宽膨胀的问题,文章详细分析了位宽变化规律,并对比了直接截断、四舍五入和收敛舍入三大截取策略的优缺点及硬件实现方法。通过实战指南与避坑经验,旨在帮助开发者在保证计算精度的同时,高效利用FPGA资源,实现性能与面积的平衡。

weixin_29181861的博客 116

ESP-01S获取网络时间踩坑全记录:AT指令调试、JSON解析乱码、时区处理,一个都不能少

本文详细记录了使用ESP-01S WIFI模块获取网络时间的完整过程,包括AT指令调试、JSON解析乱码处理以及时区转换等关键步骤。通过实战案例和代码示例,帮助开发者避开常见陷阱,实现稳定的网络时间同步功能。

weixin_30460489的博客 295

LeetCode119—Pascal's Triangle II

LeetCode119—Pascal’s Triangle II

上善若水 626

Vivado静态时序分析学习笔记(1)

静态时序分析分为片内时序分析(寄存器与寄存器之间的时序关系)和片外时序分析(IO时序分析)。(1)建立时间 setup time在时钟事件(上升沿/下降沿)发生之前数据信号应保持稳定的时间。把实际电路抽象成前级D触发器+组合逻辑+后级D触发器。

Fantaasky的博客 4376

LeetCode 119:Pascal's Triangle II

Given an index k, return the kth row of the Pascal's triangle. For example, given k = 3, Return [1,3,3,1]. 题目要求计算杨辉三角某一行的元素,这个也是二项式系数的计算问题。 class Solution { public: vector getRow(int row

哈哈的个人专栏 2717

302.QoS实现工具之MQC

QoS 技术可以对网络中报文进行分类处理,根据优先级提供不同的差分服务,如何实现这种差分服务呢?我们有一种强大的配置方法 - 模块化 QoS 命令行 MQC(Modular QoS Command-Line)。下面就来介绍一下 MQC。

迷逝 1393

WIFI和BT测试(射频工程师)

买不了吃亏买不了上当,我卖的是技能,你收获的是经验。

m0_58834499的博客 599

leetcode:119. Pascal's Triangle II(Java)解答

转载请注明出处:z_zhaojun的博客 原文地址:http://blog.csdn.net/u012975705/ 题目地址:https://leetcode.com/problems/pascals-triangle-ii/ Pascal’s Triangle II Given an index k, return the kth row of the Pascal's tri

z_zhaojun的博客 2593

大模型的应用与微调:如何调用 LLM?从 OpenAI API 到本地部署

本篇文章介绍了如何调用 OpenAI、DeepSeek、Manus、通义千问等大模型 API,并探讨了 LLM 本地部署的方法。在实际应用中,可以根据需求选择云端 API 或本地化部署方案,以满足不同的计算需求和隐私要求。下一篇:大模型微调与 RAG 技术(LoRA、QLoRA、向量数据库)

晴天彩虹雨的博客 1157

【leetcode】119. Pascal's Triangle II

O(k)的空间复杂度,构建Pascal Triangle

小do在努力 514

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)

ssm框架搭建

这个是一个在idea上成功跑起来的ssm框架项目,保存在这里方便以后使用直接下载

【LeetCode】119 Pascal's Triangle II

Pascal’s TriangleGiven an index k, return the kth row of the Pascal’s triangle.For example, given k = 3, Return [1,3,3,1].Note: Could you optimize your algorithm to use only O(k) extra space?Method 1

正努力学English的程序猿 406

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")

Java [Leetcode 119]Pascal's Triangle II

题目描述: Given an indexk, return thekthrow of the Pascal's triangle. For example, givenk= 3,Return[1,3,3,1]. 解题思路: 每次在上一个list前面插入1,然后后面的每两个间相加赋值给前一个数。 代码描述: public class Solution { ...

dkk37351的博客 177

IMSL CNL 7.0.0 (x86-64)Crack

IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件,这是C++版本的破解文件

leetcode 119 Pascal's Triangle II C++

求杨辉三角的某一行,这次不用记录全部的三角,只要纪录上一行就可以了。 vector getRow(int rowIndex) { vector pre; for(int i=0;i<rowIndex+1;i++){ vector tmp(i+1); tmp[0]=tmp[i]=1;

wcxdell的专栏 605

【LeetCode】解题119:Pascal's Triangle II

LeetCode解题 118:Pascal's Triangle II Problem 118: Pascal's Triangle II [Easy]解题思路Solution (Java) Problem 118: Pascal’s Triangle II [Easy] Given a non-negative index k where k ≤ 33, return the kth index...

Fayedily的博客 446

LeetCode - 119. Pascal's Triangle II

这道题目与118. Pascal's Triangle很像,所不同的是这道题要求的是打印出制定某行,而上一题则是将所有的全都打印出来。一个非常简单的想法自然是先计算出整个三角形,然后再打印出某个指定的行,但是这样肯定会消耗很多的空间,所以这种做法并不是很推荐。 在这道题目上,一开始的时候我的思维走向了歪路,帕斯卡三角形有一个性质就是它的每一行都是二项展开式的系数,所以可以根据这个来直接计算出某一

找不到工作了... 463
上一篇: 118. Pascal's Triangle
下一篇: 219. Contains Duplicate II
Mike_CSU
博客等级 码龄10年 6粉丝 39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