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#include <iostream>
#include <cmath>
#include <iomanip>
#define pi 3.1415927
using namespace std;
int main()
{
double r,v;
while(cin >> r)
{
v = 4 * pi * pow(r,3)/3;
cout << fixed << setprecision(3) << v << endl;
}
return 0;
}
注意：三次方就是pow(r,3); 公式中要把/3放在最后，或者把分子用括号括起来，不能直接用
4/3*pi*pow(r,3);
Ubuntu 20.04下Cube-Studio云原生平台单机部署全攻略（附避坑指南）
本文详细介绍了在Ubuntu 20.04系统上单机部署Cube-Studio云原生平台的完整流程，包括环境准备、Docker配置、Rancher与Kubernetes集群部署以及Cube-Studio核心组件安装。特别提供了官方文档未涵盖的实战技巧和常见问题解决方案，帮助开发者高效完成部署并避免常见陷阱。
杭电 2001 计算两点间的距离
Problem Description 输入两点坐标（X1,Y1）,（X2,Y2）,计算并输出两点间的距离。 Input 输入数据有多组，每组占一行，由4个实数组成，分别表示x1,y1,x2,y2,数据之间用空格隔开。 Output 对于每组输入数据，输出一行，结果保留两位小数。
pytorchDataLoader中调整数据长度不一致问题（lstm等）
数据长度不一致 用pytorch做rnn的时候，如果输入数据不一样长，可以用两种方式解决。 一种是自定义collate方法， #自定义collate_fn dataloader.DataLoader(dataset,4,True,collate_fn=my_collate) 然后里面写数据和标签载入方式即可 def my_collate(batch): data = [item[0] for item in batch] target = [item[1] for item in batc
HDU-2002 计算球体积
Java
STM32F4串口接收ATGM336H GPS数据，手把手教你解析NMEA协议并转换经纬度格式
本文详细介绍了如何使用STM32F4开发板通过串口接收ATGM336H GPS模块的NMEA协议数据，并解析GNRMC语句获取经纬度信息。内容涵盖硬件连接、NMEA协议深度解析、数据校验实现以及经纬度格式转换的数学原理与代码实现，为嵌入式开发者提供完整的GPS数据处理解决方案。
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生成式引擎优化（GEO）全景指南摘要 GEO（生成式引擎优化）是针对AI生成内容（如ChatGPT、文心一言等）的优化策略，通过结构化数据、权威建设与语义优化提升企业内容在AI答案中的引用优先级，实现“无点击曝光”。其核心价值在于从流量竞争转向信任构建，突破传统SEO局限。技术架构依托RAG（检索增强生成）、向量数据库及反馈学习机制，动态优化内容生成与引用逻辑。实施策略包括：部署结构化数据（如JSON-LD）、维护动态知识图谱、适配多模态内容（3D/VR）、联合权威机构发布白皮书。行业案例显示，美妆、智能家
杭电oj2002 C++
#include<iostream> #include<math.h> #include<iomanip> #define PI 3.1415927 using namespace std; int main() { double r,v; while(cin>>r) { v=4*PI*pow(r,3)/3; cout<<fixed&l...
Cadence Orcad Capture原理图断线的检查与避免方法
在设计电路图中，尤其是直接借鉴别人的设计进行二次开发的过程中难免会遇到如下尴尬的情况。有没有眼神好的朋友发现图纸的哪里有不对劲的地方呢？
杭电OJ_2002（C++版）
#include <iostream> #include <iomanip> using namespace std; int main(void){ double r,v,pi(3.1415927); while(cin>>r) { v=(4*pi*r*r*r)/3; cout<<fixed<<setprecision(3)...
优秀的 Verilog/FPGA开源项目介绍（二十六）- ISP (图像信号处理)
介绍查看《ISP算法及架构分析介绍》今天项目的顺序就是先介绍几个关于ISP算法架构的项目，包括python、matlab、c等高级语言实现的ISP算法架构，最后介绍一个FPGA实现ISP的项目。openISPhttps://github.com/cruxopen/openISP项目介绍用python实现的ISP架构，该架构涉及到的知识如下：下图是最新实现的模块：主要模块如...
计算机组成原理（第三版）唐朔飞-第一章第二章-课后习题
计算机组成原理
hduoj 2002
计算球体积 Time Limit: 2000/1000 MS (Java/Others)Memory Limit: 65536/32768 K (Java/Others) Total Submission(s): 236451Accepted Submission(s): 92511 Problem Description 根据输入的半径...
基于STC8G1K08A单片机的PCA捕获外部信号（100~1000HZ）程序实现
基于STC8G1K08A单片机的PCA捕获外部信号（100~1000HZ）程序实现
杭电OJ 2002（C++）
计算球体积 #include<iostream> #include<cmath> #include<iomanip> using namespace std; #define PI 3.1415927; int main() { cout<<setiosflags(ios::fixed); cout.precision(3); double r,sum = 0; while(cin>>r) { sum = r * r * r
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');```[](https://bitdeli.com/free "Bitdeli Badge")
老版QQ原生表情gif动态
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杭电OJ2002
#include <iostream> #include <iomanip> using namespace std; #define PI 3.1415927 int main() { double r, v; while (cin >> r) { v = PI*r*r*r*(4.0 / 3); cout << setprecisi...
lwip-2.0.3
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杭电ACM2002C++
#include<iostream> #include<iomanip> #define PI 3.1415927 using namespace std; int main() { double r,v; while(cin>>r) { v=PI*4/3*r*r*r; cout<<setiosflag...
win10 跳过U8环境检测的IE WebControls.rar
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杭电oj-2002(计算球体积)
用C++格式控制小数点的位数 #include<iostream> #include<iomanip> using namespace std; #define PI 3.1415927 int main(){ double r; while(cin>>r){ cout<<fixed << setprecision(3)...
杭电oj刷题第一阶段答案
文章目录输入输出练习1000 A+B1089 A+B for Input-Output Practice (I)1090 A+B for Input-Output Practice (II)1091 A+B for Input-Output Practice (III)1092 A+B for Input-Output Practice (IV)1093 A+B for Input-Output ...
【杭电oj】练习自用
很搞笑，我居然这个代码还能写错，真的是太久没写了，手生的要死。 Problem 2001 Problem 2002 Problem 2004 好简单的题，大学读了四年还是在做这种题，好累呀，生活好可怕。 你好！ 这是你第一次使用 Markdown编辑器 所展示的欢迎页。如果你想学习如何使用Markdown编辑器, 可以仔细阅读这篇文章，了解一下Markdown的基本语法知识。我们对Markdown编辑器进行了一些功能拓展与语法支持，除了标准的Markdown编辑器功能，我们增加了如下几点新功能，帮助
杭电OJ平台刷题全记录
#include int main() { printf("这是一篇文章，用于记录我在杭电OJ平台刷题时的题解与困惑，希望能够参加ACM。"); }
杭电oj2000-2009C++实现（南昌理工学院ACM集训队）
2000
杭电OJ2002 球的体积
杭电OJ2002 球的体积 #include <iostream> #include <iomanip> #define PI 3.1415927 using namespace std; int main() { double v,r; while(cin>>r) { v=4*PI*r*r*r/3; co...
杭电oj部分题 · 2000~2011、2039
sort// 自定义结构体int age;// 自定义比较函数：按年龄降序排序// 用自定义规则排序// 输出：Charlie(25) → Alice(20) → Bob(18)return 0;是 C++ 算法库，封装了排序、查找、最值等通用算法，避免重复造轮子；核心函数：新手优先掌握sort()swap()，覆盖 80% 的基础场景；关键规则：算法的范围是 “左闭右开”，自定义排序需写比较函数；通用性：支持数组、vectorstring。
杭电ACM 2000->2099 100道题 详细解题报告出炉
我去年暑假花了5天，把杭电ACM网站上2000到2099这100道题全AC了，又花了10来天精心写解题报告。里面包括题目、解题思路、编程技巧以及参考源码。所有代码都是使用C/C++写的。最近整理资料时无意间发现，打包成chm文件和大家分享。我已经上传到优快云上了。下载地址：http://download.youkuaiyun.com/source/492194也可到我的Google Sites上下载
杭电oj编码2002
问题描述： 根据输入的半径值，计算球的体积。 输入： 输入数据有多组，每组占一行，每行包括一个实数，表示球的半径。 输出： 输出对应的球的体积，对于每组输入数据，输出一行，计算结果保留三位小数。 样例输入： 1 1.5 样例输出： 4.189 14.137 提示： #define PI 3.1415927 分析： 注意输出格式的要求，使用库函数
杭电oj2002
#include #include #include using namespace std; int main() { double r,V,pi=3.1415927; while(cin>>r) { V=4.0/3*pi*r*r*r; cout } return 0; }
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