<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:context="http://www.springframework.org/schema/context"
xmlns:tx="http://www.springframework.org/schema/cache"
xsi:schemaLocation="http://www.springframework.org/schema/beans
https://www.springframework.org/schema/beans/spring-beans.xsd
http://www.springframework.org/schema/context
https://www.springframework.org/schema/context/spring-context.xsd
http://www.springframework.org/schema/mvc http://www.springframework.org/schema/cache http://www.springframework.org/schema/cache/spring-cache.xsd">
<!-- 创建数据源 -->
<bean id="dataSource" class="org.apache.commons.dbcp.BasicDataSource">
<property name="driverClassName" value="com.mysql.jdbc.Driver"></property>
<property name="url" value="jdbc:mysql://localhost:3306/mydb"/>
<property name="username" value="root"/>
<property name="password" value="123456"/>
</bean>
<!-- mybatis配置,sqlsession -->
<bean id="dataSource" class="org.mybatis.spring.SqlSessionFactoryBean">
<property name="dataSource" ref="dataSource"></property>
<!-- 配置mybatis的数据类型 -->
<property name="typeAliases" value="com.zll.entity"></property>
<!-- 配置加载mybatis的xml文件 -->
<property name="mapperLocations" value="classpath:com/zll/dao/*.xml"></property>
</bean>
<!-- 配置mybatis数据输入映射资源 -->
<bean class="org.mybatis.spring.mapper.MapperScannerConfigurer">
<property name="basePackage" value="com.zll.dao"></property>
</bean>
<bean id="txManager" class="org.springframework.jdbc.datasource.DataSourceTransactionManager">
<property name="dataSource" ref="dataSource"></property>
</bean>
<!-- 开启事务的注解 -->
<tx:annotation-driven/>
</beans>
相关推荐
PCIe 物理层(Physical Layer)详解
PCIe物理层关键技术解析 PCIe物理层作为协议栈底层核心,承担着比特流传输、链路训练与电气特性管理等关键功能。该层采用分层架构,包含逻辑物理层(负责编解码/加扰)和电气物理层(处理信号收发)。关键技术亮点包括: 智能链路管理:通过LTSSM状态机实现11种状态转换,涵盖链路检测(Detect)、协商(Polling/Configuration)、功耗管理(L0s/L1)等全生命周期控制 高效编码方案:Gen1/Gen2采用8b/10b编码(20%开销),Gen3+升级为128b/130b(仅1.5%开销
Spring-Xml配置
Spring-Xml配置
【WebApi + Sqlserver】 使用C#编写WebApi并打开sqlserver数据库进行访问,代码适用于简单练习,真实开发需自行完善
【WebApi + Sqlserver】 使用C#编写WebApi并打开sqlserver数据库进行访问,代码适用于简单练习,真实开发需自行完善
Spring XML配置文件详解
spring配置文件是用于指导Spring工厂进行Bean生产、依赖关系注入(装配)及Bean实例分发的"图纸"。Spring框架的配置文件是基于xml的,Spring强大的功能依赖于类型繁多的配置项,这些配置项纷繁复杂难以记忆,下面将常用的配置项示例记录下来,以备后续查看使用。 一、Spring配置文件示例 <?xml version="1.0" encoding="UTF-8"?&...
Yolo8行人车辆目标检测实战分享 完整项目代码可直接运行
Yolo8行人车辆目标检测实战分享 完整项目代码可直接运行
华为OD机试2025C卷 - 字符串解密 (C++ & Python & JAVA & JS & GO)
华为OD机试 2025C卷 字符串解密,提供Java、C++、Python、Go、JavaScript源码实现,并提供完整思路讲解
Spring框架源码(一) 如何加载并解析spring.xml配置文件?
目录 XmlBeanFactory XmlBeanDefinitionReader DefaultBeanDefinitionDoucmentReader doRegisterBeanDefinitions BeanDefinitionParserDelegate BeanDefinitionHolder DefaultListableBeanFactory 早期我们使用Spring框架做开发时,经常会用到xml去配置bean,这些bean首先在xml文件里配置好,然后由...
人像美肤 人体美型 CVPR 2022 ABPN
人像美肤 CVPR 2022 ABPN
Spring事务管理(详解+实例)
写这篇博客之前我首先读了《Spring in action》,之后在网上看了很多关于Spring事务管理的文章,具体参考文章如下: 1、将相应的接口依赖图理清出 2、将每个类的具体配置弄明白 3、将具体的配置给出来 4、给出具体的例子(一个声明式的一个编程式的)
面向业务角色的 SAP Fiori Space 与 Page 创建全流程实战指南
业务角色导航设计:从Space与Page规划到实施 摘要:本文探讨了SAP S/4HANA Cloud和BTP ABAP环境中基于业务角色的导航设计方法。传统Fiori Launchpad的Group+Tile模式存在应用堆积问题,而新的Space与Page模型将导航按工作领域(如应付会计、销售代表)组织,每个Space包含多个任务导向的Page。文章详细介绍了从业务角色建模到Space/Page创建、分配的完整流程,包括核心概念解析、规划思路和具体操作步骤。重点阐述了如何根据业务需求选择创建方式(基于预交
PrimeTime low power-多电压设计流程(2)
get_power_switches`、`get_supply_nets` 和 `get_supply_ports` 命令的操作方式类似,并具有类似的命令选项。PrimeTime 根据 UPF 命令构建电源供应网络,包括 create_power_domain、create_supply_net、create_supply_set、create_supply_port、set_domain_supply_net 和 connect_supply_net。电源集句柄是为电源域隐式创建的一个抽象电源集。
spring xml 常用配置解析
文章目录1 Beans概述2 Bean 概述2.1 Bean构造注入方式2.1.1 构造方法注入2.1.2 setter方式的注入2.2 property和constructor-arg常用配置项2.2.1 value2.2.2 ref2.2.3 内部bean2.2.4 list2.2.5 set2.2.6 map2.2.7 props2.2.9 2.3 autowire属性2.4 parent属性2.5 scope属性2.5.1 singleton2.5.2 prototype2.5.3 request
IEC61850规约客户端软件开发实战(第六章)
IEC 61850标准中的GOOSE(Generic Object Oriented Substation Event,面向通用对象的变电站事件)功能是变电站自动化系统的核心通信机制,主要用于实现智能电子设备(IED)间高可靠性、低延时的实时信号传输。本章简述其核心功能与技术特点。
Spring源码-XML配置
Spring容器初始化的所有步骤都在该方法中,如XML配置文件的加载、后置处理器的注册和执行、Bean对象的初始化及注册到容器中等。将beanName作为key, BeanDefinition作为value,保存到 BeanDefinitionRegistry.beanDefinitionMap 中。当Spring配置文件存在自定义属性时能会根据命名空间正确找到对应的 Handler 类, 进而找到 BeanDefinitionParse 将自定义配置解析并注册到容器中。通过alias注册bean。
老版QQ原生表情gif动态
这是老版的QQ表情,不过全是动态的的,Windows照片查看器是不会动的,可以右击用浏览器打开查看,再说一遍是老版的,只是分享给宁愿用老版也要动态的人。
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能:商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传,基本资料下载,开钱箱、小票打印、收款结算,支持多币种付款方式(现金、储值卡、礼券、微信等),支持多种促销。运行环境:.net 4.0登陆信息用户名:1001 密码:123456内置商品信息,商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
spring基于XML的AOP
spring基于XML的AOP
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")
Spring XML的解析
版本说明:Spring 5.2.9(文档引用多是5.3.0) 本文侧重于源码的解读 文章涉及到的关键字: 这里以最简单的ClassPathXmlApplicationContext为例 ApplicationContext context =new ClassPathXmlApplicationContext("applicationContext.xml"); Hello hello=context.getBean("helloImpl",Hello.class); 在XML里我们配置好Bean后,
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
【Spring】XML的解析过程
XML注入Bean的使用 xml配置: <?xml version="1.0" encoding="utf-8" ?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.springframework.org/schema/beans h
基于xml配置spring
XML使用有意义的
spring与xml schema
本文从自定义spring配置文件入手,介绍了spring配置文件头的原理,和spring.schema、spring.handlers两个文件的作用。 下面是一个简单的spring配置文件。我们可以看到,xml文件的前几行这些配置,其实都是xml 结构的定义,通过引入这些定义,使xml文件中的标签可以正常被解析。 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/sch.
Spring Framework:XML Schema 编写
Spring Framework:XML Schema 编写
Spring中XML配置相关
实例: <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="h...
Spring XML 配置
使用。
spring xml解析详解
文件DefaultBeanDefinitionDocumentReader.java 首先解析入口是doRegisterBeanDefinitions这个函数。 1.首先创建一个ParseDelegate的类,第一次创建this.delegate没有被赋值为NULL.相当于 this.delegate = createDelegate(getReaderContext(), root, NUL...
Spring中Xml文件配置以及使用注解消除Xml
快速了解Spring中Xml文件配置以及使用注解消除Xml
spring xml的配置
Spring xml文档头得配置 spring文档头一般是可以复制过来得,刚学习得时候一直看网上有没有配置,然后也没有找到,希望以下过程得学习可以给大家带来帮助!! 1.Spring配置方法:首先写一个source Folder 专门来写xml文档,再建一个spring包 2.右键spring包,--...
Spring XML 配置简介
本文介绍了Spring XML配置中的命名空间与Schema校验机制。主要内容包括:1) xmlns定义默认命名空间避免标签冲突;2) xmlns:xsi引入标准Schema实例命名空间;3) xsi:schemaLocation指定命名空间与XSD文件的映射关系,用于XML校验和IDE智能提示。三者共同作用确保XML配置的正确性,其中xmlns声明"我是谁",xmlns:xsi启用校验机制,xsi:schemaLocation指明校验规则所在位置。
726



被折叠的 条评论
为什么被折叠?



