原型模式

克隆以后我就成了你——探究原型模式 孙悟空拔一根毫毛就可以变出好多一模一样的孙悟空!!!中国的克隆技术其实是世界领先的! 即便Jungle读书少,Jungle也清晰地记得中学生物课本上提到过的克隆羊“多利”。虽然多利寿命不长,但它的出现对“克隆(Clone)”技术意义重大。克隆,直观说就是从原有生物体上取体细胞,然后无性繁殖出有完全相同基因的个体或种群。这么说来中国的克隆技术其实是世界领先的,因为孙悟空拔一根毫毛变出许多一模一... 阅读详情

1、示例图

在这里插入图片描述

2、代码

实现原型模式的核心类

/**
 * 1、实现Cloneable接口
 * 2、实现clone方法
 */
public class Sheep implements Cloneable{
    private String name;
    private int age;

    public Sheep(String name, int age) {
        this.name = name;
        this.age = age;
    }

    //clone该实例使用默认的方法clone
    @Override
    protected Object clone() {
        Sheep sheep = null;
        try{
            sheep = (Sheep) super.clone();
        }catch(Exception e){
            System.out.println(e.toString());
        }
        return sheep;
    }
}

继承了Sheep

public class SheepImpl extends Sheep {
    public SheepImpl(String name, int age) {
        super(name, age);
    }
}

运行测试原型模式

public class ProtoTypeTest {
    public static void main(String[] args) {
        SheepImpl sheep = new SheepImpl("洋洋",1);
        SheepImpl cpSheep = (SheepImpl) sheep.clone();
        System.out.println(sheep == cpSheep); //为false 不是同一个对象
    }
}

3、优点

1)在解决克隆羊的问题上比用传统方式效率高,减少了在类初始化对于资源的消耗

4、浅拷贝

如果在Sheep中包含一个其他的对象的属性,在clone之前对该对象的该属性赋值,即该属性是引用对象,当该属性被修改时会影响到其他clone的对象。(注:这里的clone方法都是默认的clone方式)

Sheep类新增Sheep属性

public class Sheep implements Cloneable{
    private String name;
    private int age;
    private Sheep sheep;
}

运行代码

public class ProtoTypeTest {
    public static void main(String[] args) {
        SheepImpl sheep = new SheepImpl("洋洋",1);
        SheepImpl friend = new SheepImpl("哈哈",2);
        sheep.setSheep(friend);

        //默认使用默认的clone
        SheepImpl cpSheep = (SheepImpl) sheep.clone();

        //新增的属性Sheep的hashCode相同,即浅拷贝
        System.out.println(sheep.getName() + "--" + sheep.getAge() + "--" + sheep.getSheep().hashCode());
        System.out.println(cpSheep.getName() + "--" + cpSheep.getAge() + "--" + cpSheep.getSheep().hashCode());
        System.out.println(sheep == cpSheep); //false 不是同一个对象
    }
}

运行结果

洋洋--1--460141958
洋洋--1--460141958
false

4、深拷贝的两种方法

主要的深拷贝方法都在这个类中

public class DeepClone implements Cloneable, Serializable {
    private static final long serialVersionUID = -1464966484283440495L;
    private String name;
    private int age;
    private DeepCloneTarget deepCloneTarget;
    public DeepClone(String name, int age) {
        this.name = name;
        this.age = age;
    }
    public String getName() {
        return name;
    }
    public void setName(String name) {
        this.name = name;
    }
    public int getAge() {
        return age;
    }
    public void setAge(int age) {
        this.age = age;
    }
    public DeepCloneTarget getDeepCloneTarget() {
        return deepCloneTarget;
    }
    public void setDeepCloneTarget(DeepCloneTarget deepCloneTarget) {
        this.deepCloneTarget = deepCloneTarget;
    }

    /**
     * 深拷贝 方式1
     */
    @Override
    protected Object clone() throws CloneNotSupportedException {
        DeepClone deep = null;
        //1、对基本数据类型进行clone(使用默认的clone)
        deep = (DeepClone)super.clone();
        //2、将引用对象进行,单独处理
        deep.setDeepCloneTarget((DeepCloneTarget)deepCloneTarget.clone());
        return deep;
    }

    /**
     * 深拷贝 方式2
     * 通过序列化clone
     */
    public Object deepClone(){
        ByteArrayOutputStream bos = null;
        ObjectOutputStream oos = null;
        ByteArrayInputStream bis = null;
        ObjectInputStream ois = null;
        try{
            //序列化
            bos = new ByteArrayOutputStream();
            oos = new ObjectOutputStream(bos);
            oos.writeObject(this);

            //反序列化
            bis = new ByteArrayInputStream(bos.toByteArray());
            ois = new ObjectInputStream(bis);
            DeepClone cpDeepClone = (DeepClone) ois.readObject();
            return cpDeepClone;
        }catch (Exception e){
            e.printStackTrace();
            return null;
        }finally {
            //关闭流
            try {
                bos.close();
                bis.close();
                oos.close();
                ois.close();
            }catch (Exception e2){
                e2.printStackTrace();
            }
        }
    }
}

DeepClone的属性

public class DeepCloneTarget implements Cloneable, Serializable {
    private static final long serialVersionUID = -4163686254455988446L;
    private String Name;
    private int no;

    public DeepCloneTarget(String name, int no) {
        Name = name;
        this.no = no;
    }
    public String getName() {
        return Name;
    }
    public void setName(String name) {
        Name = name;
    }
    public int getNo() {
        return no;
    }
    public void setNo(int no) {
        this.no = no;
    }

    //在这里全部属性都是基本数据类型,则使用默认的clone即可
    @Override
    protected Object clone() throws CloneNotSupportedException {
        return super.clone();
    }
}

运行代码

public class DeepCloneTest {
    public static void main(String[] args) throws CloneNotSupportedException {
        DeepClone deepClone = new DeepClone("aaa",11);
        deepClone.setDeepCloneTarget(new DeepCloneTarget("vvv",22));

        //方式1 深拷贝
        DeepClone cpDeepClone = (DeepClone) deepClone.clone();

        System.out.println("深拷贝 方式1");
        System.out.println(deepClone == cpDeepClone);
        System.out.println("deepClone.DeepCloneTarget = "+deepClone.getDeepCloneTarget().hashCode());
        System.out.println("cpdeepClone.DeepCloneTarget = "+cpDeepClone.getDeepCloneTarget().hashCode());

        //方式2 深拷贝 序列化
        DeepClone cpDeepCloneSerial = (DeepClone) deepClone.deepClone();

        System.out.println("深拷贝 方式2");
        System.out.println(deepClone == cpDeepCloneSerial);
        System.out.println("deepClone.DeepCloneTarget = "+deepClone.getDeepCloneTarget().hashCode());
        System.out.println("cpDeepCloneSerial.DeepCloneTarget = "+cpDeepCloneSerial.getDeepCloneTarget().hashCode());
    }
}

运行结果

深拷贝 方式1
false
deepClone.DeepCloneTarget = 460141958
cpdeepClone.DeepCloneTarget = 1163157884
深拷贝 方式2
false
deepClone.DeepCloneTarget = 460141958
cpDeepCloneSerial.DeepCloneTarget = 2065951873
Java 原型模式 文章目录原型模式原型模式Demo使用原型模式的好处浅克隆和深克隆浅克隆深克隆 原型模式 原型模式属于创建型模式,用原型实例指定待创建对象的类型,并且通过复制这个原型来创建新的对象!(但是学姐是不可复制的哦,只有我这里有,还不赶快关注我的 Java精选频道 ) 原型模式Demo 下了几次棋,学弟把学姐征服了多少次?这里是以次为单位,我们需要把这个枢纽创建出来,为下棋计单位! /** * 枢纽:... 阅读详情

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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")

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