java发送outlook邮件

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记录下发送outlook邮件

Exchange Web服务(EWS)Java API提供了一个托管接口,

中文文档地址为

https://docs.microsoft.com/zh-cn/exchange/client-developer/exchange-web-services/get-started-with-ews-managed-api-client-applications

首先项目中下载在pom.xml添加jar

<dependency>
    <groupId>com.microsoft.ews-java-api</groupId>
    <artifactId>ews-java-api</artifactId>
    <version>2.0</version>
</dependency>
 

1 然后编写发送邮件代码 

import microsoft.exchange.webservices.data.core.ExchangeService;
import microsoft.exchange.webservices.data.core.enumeration.misc.ExchangeVersion;
import microsoft.exchange.webservices.data.core.service.item.EmailMessage;
import microsoft.exchange.webservices.data.credential.ExchangeCredentials;
import microsoft.exchange.webservices.data.credential.WebCredentials;
import microsoft.exchange.webservices.data.property.complex.MessageBody;

import java.net.URI;
public class SendMailTest {
    public static void main(String[] args) throws Exception {

        ExchangeService service = new ExchangerServiceSSLVerify(ExchangeVersion.Exchange2010_SP2);
        String userName = "";  //用户名
        String password = ""; //密码
        String domain = ""; //域
        String url = ""; //邮件服务器地址
        ExchangeCredentials credentials = new WebCredentials(userName, password, domain);
        service.setCredentials(credentials);
        service.setUrl(new URI("https://" + "邮箱服务器地址" + "/EWS/Exchange.asmx"));
        
        EmailMessage msg = new EmailMessage(service);
        msg.setSubject("标题");
        msg.setBody(MessageBody.getMessageBodyFromText("测试内容"));
        msg.getToRecipients().add("xxx@xx.com.cn"); //接受人
        msg.getCcRecipients().add("xxxx@xx.com.cn"); //抄送
        msg.send();  //发送现有电子邮件
    }
}

2编写接受邮件功能 

import microsoft.exchange.webservices.data.core.ExchangeService;
import microsoft.exchange.webservices.data.core.PropertySet;
import microsoft.exchange.webservices.data.core.enumeration.misc.ExchangeVersion;
import microsoft.exchange.webservices.data.core.enumeration.property.WellKnownFolderName;
import microsoft.exchange.webservices.data.core.service.folder.Folder;
import microsoft.exchange.webservices.data.core.service.item.EmailMessage;
import microsoft.exchange.webservices.data.core.service.item.Item;
import microsoft.exchange.webservices.data.credential.ExchangeCredentials;
import microsoft.exchange.webservices.data.credential.WebCredentials;
import microsoft.exchange.webservices.data.search.FindItemsResults;
import microsoft.exchange.webservices.data.search.ItemView;

import java.net.URI;

/**
 * @author 疯信子
 * @version 1.0
 * @date 2018/11/28.
 */
public class ReadMailTest {
     public static void main(String[] args) throws Exception {
         ExchangeService service = new ExchangerServiceSSLVerify(ExchangeVersion.Exchange2010_SP2);
         String userName = "";  //用户名
         String password = ""; //密码
         String domain = ""; //域
         String url = ""; //邮件服务器地址
         ExchangeCredentials credentials = new WebCredentials(userName, password, domain);
         service.setCredentials(credentials);
         service.setUrl(new URI("https://" + "邮箱服务器地址" + "/EWS/Exchange.asmx"));

         Folder inbox=Folder.bind(service, WellKnownFolderName.Inbox);

         ItemView view=new ItemView(1);

         FindItemsResults<Item> findResults = service.findItems(inbox.getId(), view);
         service.loadPropertiesForItems(findResults, PropertySet.FirstClassProperties);
         for (Item item : findResults.getItems()) {
             EmailMessage message = EmailMessage.bind(service, item.getId());
             System.out.println("发送人-->"+message.getSender());
             System.out.println("Sub -->" + item.getSubject());
             System.out.println("内容-->"+item.getBody().toString());
         }

       }
}

3 忽略证书验证,使用HttpClient访问HTTPS接口

import microsoft.exchange.webservices.data.EWSConstants;
import microsoft.exchange.webservices.data.core.ExchangeService;
import microsoft.exchange.webservices.data.core.enumeration.misc.ExchangeVersion;
import org.apache.http.config.Registry;
import org.apache.http.config.RegistryBuilder;
import org.apache.http.conn.socket.ConnectionSocketFactory;
import org.apache.http.conn.socket.PlainConnectionSocketFactory;
import org.apache.http.conn.ssl.SSLConnectionSocketFactory;

import javax.net.ssl.SSLContext;
import javax.net.ssl.TrustManager;
import javax.net.ssl.X509TrustManager;
import java.security.GeneralSecurityException;
import java.security.KeyManagementException;
import java.security.NoSuchAlgorithmException;
import java.security.cert.CertificateException;
import java.security.cert.X509Certificate;

public class ExchangerServiceSSLVerify extends ExchangeService {

    public ExchangerServiceSSLVerify(ExchangeVersion requestedServerVersion) {
        super(requestedServerVersion);
    }

    @Override
    protected Registry<ConnectionSocketFactory> createConnectionSocketFactoryRegistry() {
        try {
            SSLContext sslContext = createSSL();
            return RegistryBuilder.<ConnectionSocketFactory>create()
                    .register(EWSConstants.HTTP_SCHEME, PlainConnectionSocketFactory.INSTANCE)
                    .register(EWSConstants.HTTPS_SCHEME, new SSLConnectionSocketFactory(sslContext))
                    .build();
        } catch (GeneralSecurityException e) {
            throw new RuntimeException(
                    "Could not initialize ConnectionSocketFactory instances for HttpClientConnectionManager", e
            );
        }
    }

    private SSLContext createSSL() throws NoSuchAlgorithmException, KeyManagementException {

        //Secure Protocol implementation.
        SSLContext ctx = SSLContext.getInstance("SSL");
        //Implementation of a trust manager for X509 certificates
        X509TrustManager tm = new X509TrustManager() {
            @Override
            public void checkClientTrusted(X509Certificate[] xcs,
                                           String string) throws CertificateException {
            }

            @Override
            public void checkServerTrusted(X509Certificate[] xcs,
                                           String string) throws CertificateException {
            }

            @Override
            public X509Certificate[] getAcceptedIssuers() {
                return null;
            }
        };
        ctx.init(null, new TrustManager[]{tm}, null);
        return ctx;
    }
}

以上就可以完成邮件发送和接受。

注意邮件服务器使用的https地址,需要忽略https证书.

java 通过 microsoft graph 调用outlook(一) 废话不多说。 阅读详情

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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; % 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]); 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