声明变量baijq的HTML5同时

别再乱调NvM_WriteBlock了!手把手教你搞懂Autosar NVM状态机与读写时序 本文深入解析Autosar NVM状态机与读写时序,帮助开发者避免常见错误。通过详细的状态机解读、最佳实践代码示例和异常处理策略,提升嵌入式系统中数据存储的可靠性。特别针对NvM_WriteBlock的调用逻辑和状态机机制进行实战讲解,解决工程师在实际开发中遇到的异步写入问题。 阅读详情

聋子,安排在左右当侍者,可避免泄露重要军事机密;哑巴,派他传递密信,一旦被敌人抓住,除了搜去密信之外,再也问不出更多的HTML5东西;瘸子,命令他去守护炮台,坚守阵地,他很难弃阵而逃;瞎子,听觉特别好,命他战前伏在阵前qie听敌军的动静,担负侦察任务。

例如,intbaijq;baijq=250;//声明变量baijq的HTML5同时,系统给baijq分配了空间。引用类型就不是了,只给变量分配了引用空间,数据空间没有分配,因为谁都不知道数据是什么啊,整数,字符?我们看一个错误的例子:mydatetoday;today day=4;//发生错误,因为today对象的数据空间未分配。

作者: 时间: 出处: 缩略图: 标题:vector用法 (二) 迭代器内容: 迭代器就是一个来回遍历的HTML5指针,这么想就方便理解了。#include&lt;iostream&gt;#include&lt;vector&gt;using namespace std;int main(){vector&lt;int&gt; ivec(5,1);/* iterator 感觉就相当于一个指针* 指针类型根据每一个容器有所不同 * iter接受所有指针操作的方法 * 采用begin、end的赋值方法,可以避免容器为空产生的问题 */vector&lt;int&gt;::iterator iter1 = ivec begin();int i = 1;for(vector&lt;int&gt;::iterator iter = ivec begin(); iter != ivec end(); iter++){*iter = i++;}// 常量迭代器,不能更改指向的值for(vector&lt;int&gt;::const_iterator iter = ivec begin(); iter != ivec end(); iter++){cout&lt;&lt;*iter&lt;&lt;endl;}cout&lt;&lt;ivec[2]&lt;&lt;endl;cout&lt;&lt;*(++iter1)&lt;&lt;endl;return 0;}输出结果:1234532 本篇博客出自 阿修罗道,转载请注明出处:blog youkuaiyun.com/fansongy/article/details/7284462作者: 时间: 出处: 缩略图: 标题:getting runtime information in weblogic server by wlst 内容: getting runtime informationthis chapter describes how to use weblogic scripting tool (wlst) to retrieve information that weblogic server instances produce to describe their run-time state this chapter includes the following sections:accessing runtime information: main stepsconfiguring loggingworking with the weblogic diagnostics frameworkaccessing runtime information: main stepsthe administration server hosts the domain run-time hierarchy which provides access to any mbean on any server in the weblogic domain  if the administration server is not running for a weblogic domain, wlst can connect to individual managed servers to retrieve run-time data accessing the run-time information for a weblogic domain includes the following main steps:invoke wlst and connect to a running administration server instance  see invoking wlst navigate to the domain run-time mbean hierarchy by entering the domainruntime command wls:/mydomain/serverconfig&gt;domainruntime() the domainruntime command places wlst at the root of the domain-wide run-time management objects,domainruntimembean navigate to serverruntimes and then to the server instance which you are interested in monitoring wls:/mydomain/domainruntime&gt;cd(`serverruntimes/myserver`) at the server instance, navigate to and interrogate run-time mbeans wls:/mydomain/domainruntime/serverruntimes/myserver&gt;cd(`jvmruntime/myserver`)&gt; wls:/mydomain/domainruntime/serverruntimes/myserver/jvmruntime/myserver&gt;ls() -rheapfreecurrent191881368-rheapfreepercent87-rheapsizecurrent259588096-rheapsizemax518979584-rjavavmvendorsun microsystems inc -rjavavendorsun microsystems inc -rjavaversion1 6 0_21-rname adminserver-rosnamewindows xp-rosversion 5 1-rtype jvmruntime-ruptime409141-r-xprederegistervoid :the following sections provide example scripts for retrieving run-time information about weblogic server server instances and weblogic domain resources script for monitoring server statethe wlst online script in example 8-1 navigates the domain run-time hierarchy and checks the status of a managed server every 5 seconds  it restarts the server if the server state changes fromrunning to any other status  it assumes that wlst is connected to the weblogic domain`s administration server example 8-1 monitoring server state# node manager needs to be running to run this script import threadimport timedef checkhealth(servername):while 1:slbean = getslcrt(servername)status = slbean getstate()print `status of managed server is `&#43;statusif status != &quot;running&quot;: print `starting server `&#43;servername start(servername, block=&quot;true&quot;)time sleep(5) def getslcrt(svrname):domainruntime()slrbean = cmo lookupserverlifecycleruntime(svrname)return slrbeancheckhealth(&quot;myserver&quot;)script for monitoring the jvmthe wlst online script in example 8-2 monitors the hjvmheapsize for all running servers in a weblogic domain; it checks the heap size every 3 minutes and prints a warning if the heap size is greater than a specified threshold  it assumes that the url for the weblogic domain`s administration server is t3://localhost:7001 for information on how to run this script, see invoking wlst example 8-2 monitoring the jvm heap sizewaittime=180000threshold=300000000uname = &quot;weblogic&quot;pwd = &quot;welcome1&quot;url = &quot;t3://localhost:7001&quot;def monitorjvmheapsize():connect(uname, pwd, url)while 1:servernames = getrunningservernames()domainruntime()for name in servernames:print `now checking `&#43;name getname()try:cd(&quot;/serverruntimes/&quot;&#43;name getname()&#43;&quot;/jvmruntime/&quot;&#43;name getname())heapsize = cmo getheapsizecurrent()if heapsize &gt; threshold:# do whatever is neccessary, send alerts, send email etc print `warning: the heapsize is greater than the threshold`else: print heapsizeexcept wlstexception,e:# this typically means the server is not active, just ignore# pass print &quot;ignoring exception &quot; &#43; e getmessage()java lang thread sleep(waittime) def getrunningservernames():# only returns the currently running servers in the domainreturn domainruntimeservice getserverruntimes() if __name__== &quot;main&quot;:monitorjvmheapsize()configuring loggingusing wlst, you can configure a server instance`s logging and message output to determine which log attributes can be configured, see &quot;logmbean&quot; and &quot;logfilembean&quot; in the oracle weblogic server mbean reference  the reference also indicates valid values for each attribute the wlst online script in example 8-3 sets attributes of logmbean (which extends logfilembean)  for information on how to run this script, seeinvoking wlst example 8-3 configuring logging# connect to the serverconnect(&quot;weblogic&quot;,&quot;welcome1&quot;,&quot;t3://localhost:7001&quot;)edit()startedit()# set cmo to the server log configcd(&quot;servers/myserver/log/myserver&quot;)ls ()# change logmbean attributesset(&quot;filecount&quot;, 5)set(&quot;fileminsize&quot;, 400)# list the current directory to confirm the new attribute valuesls ()# save and activate the changessave()activate()# all doneexit()working with the weblogic diagnostics frameworkthe weblogic diagnostic framework (wldf) is a monitoring and diagnostic framework that can collect diagnostic data that servers and applications generate  you configure wldf to collect the data and store it in various sources, including log records, data events, and harvested metrics  for more information, see configuring and using the diagnostics framework for oracle weblogic server for example scripts that demonstrate using wlst to configure the weblogic diagnostic framework, see&quot;weblogic scripting tool examples&quot; in configuring and using the diagnostics framework for oracle weblogic server to view the collected diagnostics information using wlst, use one of the following commands to export the data from the wldf repositories:from wlst offline, use the exportdiagnosticdata command (see &quot;exportdiagnosticdata&quot; in weblogic scripting tool command reference) from wlst online, use the exportdiagnosticdatafromserver command (see&quot;exportdiagnosticdatafromserver&quot; in weblogic scripting tool command reference)) weblogic management runtimeinterface domainruntimembeanpublic interface domainruntimembeanthis class is used for monitoring a weblogic domain  a domain may contain zero or more clusters  a cluster may be looked up by a logical name deprecation of mbeanhome and type-safe interfacesthis is a type-safe interface for a weblogic server mbean, which you can import into your client classes and access throughweblogic management mbeanhome  as of 9 0, the mbeanhome interface and all type-safe interfaces for weblogic server mbeans are deprecated  instead, client classes that interact with weblogic server mbeans should use standard jmx design patterns in which clients use the javax management mbeanserverconnection interface to discover mbeans, attributes, and attribute types at runtime method summarydategetactivationtime()the time when the domain became active appruntimestateruntimembeangetappruntimestateruntime()returns a service from which it is possible to determine the state applications throughout the domain coherenceserverlifecycleruntimembean[]getcoherenceserverlifecycleruntimes()the coherenceserverlifecycleruntimembean for all configured coherence servers in the domain consoleruntimembeangetconsoleruntime()return the mbean which provides access to console runtime services deployerruntimembeangetdeployerruntime()deprecated 9 0 0 0deploymentmanagermbeangetdeploymentmanager()provides access to the service interface to the interface that is used to deploy new customer applications or modules into this domain logruntimembeangetlogruntime()return the mbean which provides access to the control interface for wls server logging messagedrivencontrolejbruntimembeangetmessagedrivencontrolejbruntime()the messagedrivencontrolejbruntimembean for this server migratableservicecoordinatorruntimembeangetmigratableservicecoordinatorruntime()returns the service used for coordinating the migraiton of migratable services migrationdataruntimembean[]getmigrationdataruntimes()returns a history of server migrations wseepolicysubjectmanagerruntimembeangetpolicysubjectmanagerruntime()serverlifecycleruntimembean[]getserverlifecycleruntimes()the serverlifecycleruntimembean for all configured servers in the domain servicemigrationdataruntimembean[]getservicemigrationdataruntimes()returns all the service migrations done in the domainsnmpagentruntimembeangetsnmpagentruntime()return the mbean which provides access to the monitoring statistics for wls snmp agent coherenceserverlifecycleruntimembeanlookupcoherenceserverlifecycleruntime(stringname)returns the coherence server life cycle run-time mbean for the specified server serverlifecycleruntimembeanlookupserverlifecycleruntime(stringname)returns the server life cycle run-time mbean for the specified server voidrestartsystemresource(systemresourcembeanresource)restarts a system resource on all nodes to which it is deployed voidsetpolicysubjectmanagerruntime(wseepolicysubjectmanagerruntimembeanbean)作者: 时间: 出处: 缩略图: 标题:<font color="red">[置顶]</font>千万别惹程序员内容: 好久没有发娱乐性质的技术文章了,搞得气氛有点严肃了,考虑到程序员们都是比较严肃和容易较真的类书呆子的群体,所以,需要更新一个有娱乐性质的文章了。

一键熄火:在 SAP ABAP 平台停用 OAuth 2.0 Client 的正确姿势 本文是一篇面向SAP ABAP平台运维与开发团队的实战指南,重点讲解了OAuth 2.0客户端的停用操作。文章首先阐述了停用的必要性——当客户端存在泄漏风险或配置错误时,停用能在阻断访问的同时保留配置。随后详细介绍了通过SOAUTH2事务码执行停用的具体步骤,并区分了"停用"、"删除"和"撤销令牌"三个关键概念及其适用场景。 文章提供了两个真实案例:一个是怀疑客户端泄漏后的紧急处置流程,另一个是合规审计期间的冻结与恢复操作。最后总结了一份验证清单, 阅读详情

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深度学习工具包 DeepLearningToolbox

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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; 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