JNI基础知识(四)C++构造函数及浅拷贝、深拷贝问题

大数据开发者必学:C++组合类构造函数原理与实战应用 在C++编程中,组合类构造函数是面向对象编程与系统资源管理的关键桥梁。它通过初始化列表机制,确保成员对象按声明顺序正确构造,直接决定了对象的初始状态与内存布局。理解其执行顺序与初始化原理,不仅能提升代码性能,还能避免内存泄漏与未定义行为,是构建健壮、高效软件的基础。对于大数据开发领域,这种底层知识尤为重要。许多高性能组件如RocksDB、Apache Arrow的核心实现都依赖C++,其内部大量使用组合类来管理数据块、缓冲区等资源。掌握组合类构造,有助于开发者深入理解Spark Tungsten、Flink 阅读详情

C++的语法之特别,连其构造方法都有三个:构造函数、析构函数、拷贝构造函数,与Java相比确实有其独到之处。

构造函数:声明的时候就会调用

class Teacher{
private:
	char *name;
	int age;
public:
	//无参构造函数(写了,就会覆盖默认的无参构造函数)
	Teacher(){
		cout << "无参构造函数" << endl;
	}
	//有参构造函数会覆盖默认的构造函数
	Teacher(char *name, int age){
		this->name = name;
		this->age = age;
		cout << "有参构造函数" << endl;
	}	
};

析构函数:对象要被释放时会被调用

//析构函数
class Teacher{
private:
	char *name;
	int age;
public:
	//无参构造函数赋默认值
	Teacher(){
		this->name = (char*)malloc(100);
		strcpy(name,"jack walson");
		age = 20;
		cout << "无参构造函数" << endl;
	}
	//析构函数
	//当对象要被系统释放时,析构函数被调用
	//作用:善后处理
	~Teacher(){
		cout << "析构" << endl;
		//释放内存
		free(this->name);
	}
};

 拷贝构造函数:

调用时期:

1、声明时赋值

2、作为参数传入,实参给形参赋值

3、作为函数返回值返回,给变量初始化赋值

class Teacher{
private:
	char *na
Allan方差读图分析IMU误差指标 Allan方差标定IMU参数实测分析 阅读详情

相关推荐

如何修复Windows卡在“正在准备Windows”的问题?这里有详细步骤

如何修复Windows卡在“正在准备Windows”的问题?这里有详细步骤。

wyxtx的博客 5661

HRnet的pytorch实现

HRnet的pytorch实现

奋土的博客 1953

基于单片机控制的开关电源设计

采用开关电源技术和MCU模块,凭借可靠性高、易于调适的特点,可以更高效地发挥出二者的优势,从而实现更好的组合。stm32的起动模式是通过Boot0和Boot1两个管脚的高低电平来决定的,当stm32被供电之后,这两个管脚的电平。在 VCC上,键盘的一头(列线)被连接到 VCC上,而在“0”的编程输出中,则用来进。,在开关管打开的情况下,由输入电力供给电流,感应装置存储   Buck变换器是一种非隔离式的电压转换器[10]整流电路对输入电压进行整流,再经过滤波电路,即Dc电压是直流/直流变换器的直流输入。

QQ1694456187的博客 2308

树莓派4B玩转ROS2 Humble:Ubuntu Desktop一键安装与配置教程

本文详细介绍了在树莓派4B上安装和配置ROS2 Humble的完整教程,包括硬件准备、Ubuntu Desktop系统安装、一键部署ROS2、网络配置优化以及常见问题解决方案。通过简化安装流程和性能调优技巧,帮助开发者快速搭建机器人开发环境,充分发挥树莓派4B在ROS2应用中的潜力。

w8x9y0z1的博客 963

PowerDesigner16.5下载和安装教程

一、软件下载 1.下载地址(不用复制,直接点击下载链接即可):链接:https://pan.baidu.com/s/1MSnWHWt3SO2Uz5dt9AANAg提取码:z9kk 二、软件安装 1.下载安装包(包含安装文件、汉化包、注册文件) 2.下载后文件内容如下 3....

cicong2374的博客 10万+

AI 模型训练核心方法论:零基础也能落地的 8 个关键阶段

本文是为 AI 零基础学习者打造的模型训练入门指南,以生活化语言拆解核心概念,规避专业术语壁垒。内容先通俗阐释模型训练、监督学习、数据集划分等基础概念,再按问题定义、数据准备、模型选择、环境搭建、训练、评估、调参、部署 8 个阶段,拆解完整训练流程,搭配 PyTorch 代码示例与实战案例,同时提供资源预算、常见问题解决方案及学习路径。指南强调数据的核心重要性,推荐新手优先使用迁移学习和免费 GPU 资源,兼顾理论与实操,帮助学习者快速上手,避开训练误区,独立完成从模型搭建到部署的全流程。

weixin_52741870的博客 468

YOLO26科研优化:多损失函数(EIoU、SIoU等)融合,让边界框回归精度再上台阶

将原有损失函数替换为我们编写的MultiLoss。例如,在bbox_loss。

LXY_LOVE36的博客 171

Steam崩溃:ntdll.dll报错0xc0000374...如何解决?

🏆本文收录于 《全栈 Bug 调优(实战版)》 专栏。专栏聚焦真实项目中的各类疑难 Bug,从成因剖析 → 排查路径 → 解决方案 → 预防优化全链路拆解,形成一套可复用、可沉淀的实战知识体系。无论你是初入职场的开发者,还是负责复杂项目的资深工程师,都可以在这里构建一套属于自己的「问题诊断与性能调优」方法论,助你稳步进阶、放大技术价值。

**My Coding Family** 1156

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")

efm32 入门.pdf

本PDF共计22页,介绍的是操作驱动API,下载与否自己斟酌。

锂电池充电器电路设计 里面有电路

锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

上一篇: JNI基础知识(三)C++的特别之处
下一篇: FFMPEG实战——配置踩过的坑
钱多多~吖
博客等级 码龄13年 4粉丝 21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