面向对象程序设计C++ 期末考试程序题复习

C++期末考试复习一 一、选择题(每小题2分,共40分) 1、C++是( )。 A. 面向对象的程序设计语言 B. 面向过程的程序设计语言 C. 既支持面向对象的程序设计又支持面向过程的程序设计的混合型语言 D. 非结构化的程序设计语言 2、面向对象程序设计思想的主要特征中不包括( )。 A. 封装性 B. 多态性 C. 继承性 D. 功能分解,逐步求精 3、若定义:string str; 当语句cin>>str; 执行时,从键盘输入: Microsoft Visual Studio 阅读详情

7.5 定义一个基类Shape,在此基础上派生出Rectangle和Circle,二者都有getArea()函数计算对象的面积。使用Rectangle类创建一个派生类Square。

#include<iostream>
using namespace std;
//定义Shape基类 
class Shape
{
   
   
	public:
		virtual double getArea()=0;
 };
 
//由Shape类派生Rectangle类
class Rectangle:public Shape      
{
   
   
	public:
		Rectangle(double a,double b) //构造函数 
		{
   
   
			length=a;
			width=b;
		}
		~Rectangle(){
   
   }     //析构
		double getArea()    //GetArea()函数计算Rectangle面积 
		{
   
   
			return length*width;
		}
	private:
		double length;
		double width;
 };
 //由Shape类派生Circle类
class Circle:public Shape
{
   
   
	public:
		Circle(double a)
		{
   
   
			r=a;
		}
		~Circle(){
   
   }
		double getArea()
		{
   
   
			return 3.14*r*r;
		}
	private:
		double r;	
 };
 //由
C++面向对象程序设计总复习 改文总结了c++面向对象程序设计知识点,网课、博客以及阅读《c++面向对象程序设计-谭浩强第3版》的笔记,文末附有一些筛选了的选择判断的期末考试题。具体请看目录,希望获得能够对你有帮助... 阅读详情

相关推荐

C++期末复习与提升资料包

经典题目往往是历年来的考试中的高频考点,通过练习这些题目,我们可以加深对核心知识点的理解和记忆。更重要的是,通过经典题目的练习,我们能够拓展解题思路,提高解决实际问题的能力。练习时可以按照以下步骤:知识回顾:在开始之前,先快速回顾相关的知识点,为解题做好准备。独立解题:尝试独立完成题目,不要急于查看答案或求助于他人。代码实现:在理解了题目要求后,将解题思路转化为代码。代码优化:完成初稿后,回顾代码并思考是否有更优解法。阅读答案。

weixin_31569663的博客 1037

c++面向对象程序设计语言重点难点复习题及答案.doc

c++面向对象程序设计语言重点难点复习题及答案。

面向对象程序设计C++期末考试题及答案

很好的一套面向对象程序设计C++期末考试,适合于大学程序设计的各种考试复习

《面向对象程序设计C++》期末试卷(B)标准答案

《面向对象程序设计C++》期末试卷(B)标准答案,我么考试就是考这个的,我们用的教材是邵兰洁主编的,北京邮电大学出版社的《C++程序设计》

《面向对象程序设计C++》期末试卷及标准答案(好的).pdf

《面向对象程序设计C++》期末试卷及标准答案(好的).pdf

《面向对象程序设计C++》期末试卷及标准答案(A).pdf

《面向对象程序设计C++》期末试卷及标准答案(A).pdf

5G-A无源物联网:深度解析“不插电“智能的底层技术原理

北京移动推出5G-A无源物联网技术,实现电动车标签"零功耗"运行。该技术通过射频能量收集(利用5G-A基站高功率信号转换为电能)和反向散射通信(调制反射信号传输数据)两大核心技术,使设备无需电池即可工作。5G-A相比4G在信号能量密度、通信速率和定位精度等方面显著提升,结合通感一体等创新技术,为无源物联网提供商用基础。该方案已在北京小区试点应用,实现电动车精准定位管理,标签寿命可达5年以上。专家预测,这项技术将大幅降低物联网部署成本,推动"万物智联"时代的到来。

通信行业老兵、物联网从业者,做一个有趣的普通人。 2010

FPGA SPI驱动ADC避坑指南:以ADC128S022为例,详解时序设计与代码调试

本文以ADC128S022为例,详细解析FPGA通过SPI驱动ADC的时序设计与代码调试中的常见问题。从时钟分频计算、SPI模式选择到状态机实现,提供避坑指南和实战技巧,帮助工程师解决数据采集系统中的时序匹配难题。特别针对FPGA与ADC通信中的微妙差异,给出精确的Verilog代码示例和ModelSim仿真验证方法。

weixin_42522374的博客 458

脑电时频分析I:谱分析

1、EEG频带划分 自发的EEG活动在活体大脑中持续存在,通常通过频率进行分类(也叫神经振荡活动) 特定频段的脑活动,即节律性的活动,具有特定的头皮分布和生物学意义 最后还有一个γ波,主要是指30hz~100hz的一个波段,通常这个波段也包含了噪声唁电的成分,比较难提取处理,比较少用到这个波段 这个波段主要是有可能躯体感觉皮层,进行一个多通道的感觉输入的i信号处理,比如我们同时处理视觉和听觉信号的处理,而且γ波与我们记忆的形成有关 除此之外,还有一个特殊的波段,8~12hz的Mu波段主要是特指我们

Kobe123brant的博客 1万+

OSCS开源安全周报第13期:Exchange 高危漏洞公开

OSCS 社区共收录安全漏洞31个,公开漏洞值得关注的是 Apache Commons JXPath 存在代码执行漏洞(CVE-2022-41852),Microsoft Exchange 远程代码执行漏洞(ProxyNotShell(CVE-2022-41040),Apache Tomcat 条件竞争漏洞(CVE-2021-43980),FasterXML jackson-databind 小于2.14.0-rc1拒绝服务漏洞(CVE-2022-42003)。

OSCS开源安全社区 4198

stm32f407VET6 系统学习 day01 GPIO 配置

stm32f407VET6 系统学习 day01 GPIO 配置 LED 蜂鸣器 按键的不讲了,之前讲过了

she666666的博客 8904

AUTOSAR从入门到精通-复杂设备驱动(CDD)

CDD是复杂设备驱动的首字母缩写主要为驱动程序或复杂驱动程序,但不限于驱动程序。复杂驱动程序是一种未被AUTOSAR标准化的软件实体,可以通过AUTOSAR接口和/或基本软件模块api访问。 CDD是位于基础软件复杂驱动层的特定模块,与标准BSW模块或Rte交互。 CDD可能需要与分层软件体系结构的模块连接 分层软件架构的模块可能需要连接到CDD CDD可能需要通过Rte连接swc。

getusushu的博客 4192

Python爬虫教程:如何爬取多平台电商商品信息(价格、评价、库存)

本文介绍了如何编写一个跨平台电商商品爬虫,爬取多个平台的商品信息,包括商品名称、价格、评价等数据。通过Python的爬虫库(如requestsSelenium)和数据处理库(如pandasmatplotlib)的结合,我们可以高效地获取和分析电商平台上的商品信息。

2201_76125261的博客 917

单片机:实现简单的时间片轮询调度(附完整源码)

单片机:实现简单的时间片轮询调度(附完整源码)

希望我的博客,能帮上你解决学习中工作中所遇到的问题 383

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")

IEM模型,地表粗糙度模拟

利用微波反演土壤水分时候,利用IEM模型可以实现对随机地表的模拟

WINCC7.3破解文件

WINCC7.3硬件够破解文件,有需要的下载!WINCC7.3硬件够破解文件,有需要的下载!

Data-Structures-and-Algorithm-Analysis-in-Java.pdf

作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...

上一篇: ZZULIOJ 1063: 最大公约与最小公倍 *
下一篇: 计算机组成原理习题汇总
木右加木
博客等级 码龄7年 300粉丝 121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