Java读取Properties文件的六种方法

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Java读取properties文件 【转】

使用J2SE API读取Properties文件的六种方法

1。使用java.util.Properties类的load()方法
示例: InputStream in = lnew BufferedInputStream(new FileInputStream(name));
Properties p = new Properties();
p.load(in);

2。使用java.util.ResourceBundle类的getBundle()方法
示例: ResourceBundle rb = ResourceBundle.getBundle(name, Locale.getDefault());

3。使用java.util.PropertyResourceBundle类的构造函数
示例: InputStream in = new BufferedInputStream(new FileInputStream(name));
ResourceBundle rb = new PropertyResourceBundle(in);

4。使用class变量的getResourceAsStream()方法
示例: InputStream in = JProperties.class.getResourceAsStream(name);
Properties p = new Properties();
p.load(in);

5。使用class.getClassLoader()所得到的java.lang.ClassLoader的getResourceAsStream()方法
示例: InputStream in = JProperties.class.getClassLoader().getResourceAsStream(name);
Properties p = new Properties();
p.load(in);

6。使用java.lang.ClassLoader类的getSystemResourceAsStream()静态方法
示例: InputStream in = ClassLoader.getSystemResourceAsStream(name);
Properties p = new Properties();
p.load(in);

补充

Servlet中可以使用javax.servlet.ServletContext的getResourceAsStream()方法
示例:InputStream in = context.getResourceAsStream(path);
Properties p = new Properties();
p.load(in);

完整的示例,可以参考附件文件
如何上传文件,谁知道请告诉以下。 只好把source都贴上来了
JProperties.java文件



package com.kindani;

//import javax.servlet.ServletContext;
import java.util.*;
import java.io.InputStream;
import java.io.IOException;
import java.io.BufferedInputStream;
import java.io.FileInputStream;


public class JProperties {

public final static int BY_PROPERTIES = 1;
public final static int BY_RESOURCEBUNDLE = 2;
public final static int BY_PROPERTYRESOURCEBUNDLE = 3;
public final static int BY_CLASS = 4;
public final static int BY_CLASSLOADER = 5;
public final static int BY_SYSTEM_CLASSLOADER = 6;

public final static Properties loadProperties(final String name, final int type) throws IOException {
Properties p = new Properties();
InputStream in = null;
if (type == BY_PROPERTIES) {
in = new BufferedInputStream(new FileInputStream(name));
assert (in != null);
p.load(in);
} else if (type == BY_RESOURCEBUNDLE) {
ResourceBundle rb = ResourceBundle.getBundle(name, Locale.getDefault());
assert (rb != null);
p = new ResourceBundleAdapter(rb);
} else if (type == BY_PROPERTYRESOURCEBUNDLE) {
in = new BufferedInputStream(new FileInputStream(name));
assert (in != null);
ResourceBundle rb = new PropertyResourceBundle(in);
p = new ResourceBundleAdapter(rb);
} else if (type == BY_CLASS) {
assert (JProperties.class.equals(new JProperties().getClass()));
in = JProperties.class.getResourceAsStream(name);
assert (in != null);
p.load(in);
// return new JProperties().getClass().getResourceAsStream(name);
} else if (type == BY_CLASSLOADER) {
assert (JProperties.class.getClassLoader().equals(new JProperties().getClass().getClassLoader()));
in = JProperties.class.getClassLoader().getResourceAsStream(name);
assert (in != null);
p.load(in);
// return new JProperties().getClass().getClassLoader().getResourceAsStream(name);
} else if (type == BY_SYSTEM_CLASSLOADER) {
in = ClassLoader.getSystemResourceAsStream(name);
assert (in != null);
p.load(in);
}

if (in != null) {
in.close();
}
return p;

}

// ---------------------------------------------- servlet used


// ---------------------------------------------- support class


public static class ResourceBundleAdapter extends Properties {
public ResourceBundleAdapter(ResourceBundle rb) {
assert (rb instanceof java.util.PropertyResourceBundle);
this.rb = rb;
java.util.Enumeration e = rb.getKeys();
while (e.hasMoreElements()) {
Object o = e.nextElement();
this.put(o, rb.getObject((String) o));
}
}

private ResourceBundle rb = null;

public ResourceBundle getBundle(String baseName) {
return ResourceBundle.getBundle(baseName);
}

public ResourceBundle getBundle(String baseName, Locale locale) {
return ResourceBundle.getBundle(baseName, locale);
}

public ResourceBundle getBundle(String baseName, Locale locale, ClassLoader loader) {
return ResourceBundle.getBundle(baseName, locale, loader);
}

public Enumeration getKeys() {
return rb.getKeys();
}

public Locale getLocale() {
return rb.getLocale();
}

public Object getObject(String key) {
return rb.getObject(key);
}

public String getString(String key) {
return rb.getString(key);
}

public String[] getStringArray(String key) {
return rb.getStringArray(key);
}

protected Object handleGetObject(String key) {
return ((PropertyResourceBundle) rb).handleGetObject(key);
}

}

}

JPropertiesTest.java文件


package com.kindani.test;

import junit.framework.*;
import com.kindani.JProperties;

//import javax.servlet.ServletContext;
import java.util.Properties;

public class JPropertiesTest extends TestCase {
JProperties jProperties;
String key = "helloworld.title";
String value = "Hello World!";

public void testLoadProperties() throws Exception {
String name = null;
Properties p = new Properties();

name = "C:\\IDEAP\\Properties4Methods\\src\\com\\kindani\\test\\LocalStrings.properties";
p = JProperties.loadProperties(name, JProperties.BY_PROPERTIES);
assertEquals(value, p.getProperty(key));

name = "com.kindani.test.LocalStrings";
p = JProperties.loadProperties(name,JProperties.BY_RESOURCEBUNDLE);
assertEquals(value, p.getProperty(key));
assertEquals(value,((JProperties.ResourceBundleAdapter)p).getString(key));

name = "C:\\IDEAP\\Properties4Methods\\src\\com\\kindani\\test\\LocalStrings.properties";
p = JProperties.loadProperties(name, JProperties.BY_PROPERTYRESOURCEBUNDLE);
assertEquals(value, p.getProperty(key));
assertEquals(value,((JProperties.ResourceBundleAdapter)p).getString(key));

name = "\\com\\kindani\\test\\LocalStrings.properties";
p = JProperties.loadProperties(name, JProperties.BY_SYSTEM_CLASSLOADER);
assertEquals(value, p.getProperty(key));

name = "\\com\\kindani\\test\\LocalStrings.properties";
p = JProperties.loadProperties(name, JProperties.BY_CLASSLOADER);
assertEquals(value, p.getProperty(key));

name = "test\\LocalStrings.properties";
p = JProperties.loadProperties(name, JProperties.BY_CLASS);
assertEquals(value, p.getProperty(key));
}


}

properties文件与JPropertiesTest.java文件相同的目录下
LocalStrings.properties文件
# $Id: LocalStrings.properties,v 1.1 2000/08/17 00:57:52 horwat Exp $

# Default localized resources for example servlets
# This locale is en_US

helloworld.title=Hello World!

requestinfo.title=Request Information Example
requestinfo.label.method=Method:
requestinfo.label.requesturi=Request URI:
requestinfo.label.protocol=Protocol:
requestinfo.label.pathinfo=Path Info:
requestinfo.label.remoteaddr=Remote Address:

requestheader.title=Request Header Example

requestparams.title=Request Parameters Example
requestparams.params-in-req=Parameters in this request:
requestparams.no-params=No Parameters, Please enter some
requestparams.firstname=First Name:
requestparams.lastname=Last Name:

cookies.title=Cookies Example
cookies.cookies=Your browser is sending the following cookies:
cookies.no-cookies=Your browser isn't sending any cookies
cookies.make-cookie=Create a cookie to send to your browser
cookies.name=Name:
cookies.value=Value:
cookies.set=You just sent the following cookie to your browser:

sessions.title=Sessions Example
sessions.id=Session ID:
sessions.created=Created:
sessions.lastaccessed=Last Accessed:
sessions.data=The following data is in your session:
sessions.adddata=Add data to your session
sessions.dataname=Name of Session Attribute:
sessions.datavalue=Value of Session Attribute:

------------------------------------------------------------------------------------------------------------------------------------------------------------------

Java对properties配置文件的操作


package com.yorsun;

import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Properties;

import javax.servlet.ServletContext;
import javax.servlet.http.HttpServlet;


public class PropertiesUnit {
private String filename;

private Properties p;

private FileInputStream in;

private FileOutputStream out;

public PropertiesUnit(String filename) {
this.filename = filename;
File file = new File(filename);
try {
in = new FileInputStream(file);
p = new Properties();
p.load(in);
in.close();
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
System.err.println("配置文件config.properties找不到!");
e.printStackTrace();
} catch (IOException e) {
// TODO Auto-generated catch block
System.err.println("读取配置文件config.properties错误!");
e.printStackTrace();
}
}

public static String getConfigFile(HttpServlet hs) {
return getConfigFile(hs, "config.properties");
}


private static String getConfigFile(HttpServlet hs, String configFileName) {
String configFile = "";
ServletContext sc = hs.getServletContext();
configFile = sc.getRealPath("/" + configFileName);
if (configFile == null || configFile.equals("")) {
configFile = "/" + configFileName;
}
// TODO Auto-generated method stub
return configFile;
}

public void list() {
p.list(System.out);
}

public String getValue(String itemName) {
return p.getProperty(itemName);
}

public String getValue(String itemName, String defaultValue) {
return p.getProperty(itemName, defaultValue);
}

public void setValue(String itemName, String value) {
p.setProperty(itemName, value);
}

public void saveFile(String filename, String description) throws Exception {
try {
File f = new File(filename);
out = new FileOutputStream(f);
p.store(out, description);
out.close();
} catch (IOException ex) {
throw new Exception("无法保存指定的配置文件:" + filename);
}
}

public void saveFile(String filename) throws Exception{
saveFile(filename,"");
}

public void saveFile() throws Exception{
if(filename.length()==0)
throw new Exception("需指定保存的配置文件名");
saveFile(filename);
}

public void deleteValue(String value){
p.remove(value);
}

public static void main(String args[]){
String file="/eclipse/workspace/NewsTest/WEB-INF/config.properties";
// String file="D:\\eclipse\\workspace\\NewsTest\\WEB-INF\\config.properties";
PropertiesUnit pu=new PropertiesUnit(file);
pu.list();
}

}

--------------------------------------------------------------------------------------------------------------------------------------------------------------------

package com.test.TestClass;

import java.io.BufferedInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import java.util.Properties;

public class ReadPropertiesFile ...{

public void readPropertiesFile(String fileName) throws FileNotFoundException ...{
String str = "";
Properties prop = new Properties();

InputStream stream = null;

//读取这个类在同一包中的properties文件
//stream = getClass().getClassLoader().getResourceAsStream(fileName);
System.out.println("path:" + getClass().getResource(fileName));


//读取SRC下的的properties文件
String path = getClass().getResource("/").getPath();
stream = new BufferedInputStream(new FileInputStream(new File(path+fileName)));

try ...{
prop.load(stream);
str = prop.getProperty("localname");
System.out.println("localname:" + str);
System.out.println("properties:" + prop);
stream.close();

} catch (IOException e) ...{
// TODO Auto-generated catch block
e.printStackTrace();
}
}


public static void main(String[] args) throws FileNotFoundException ...{
// TODO Auto-generated method stub
new ReadPropertiesFile().readPropertiesFile("config.properties");

}

}

--------------------------------------------------------------------------------------------------------------------------------------

//=================sprin配置文件================================
<bean
id="userService"
class="com.thtf.ezone.ezesb.jmx.admin.service.impl.UserServiceImpl">
<property
name="filePath"
value="config/userInfo.properties" />
</bean>

//=================java文件================================

package com.thtf.ezone.ezesb.jmx.admin.service.impl;

import java.io.FileInputStream;
import java.io.IOException;
import java.util.Properties;

public class UserServiceImpl implements UserService {

String filePath = null;

public void setFilePath(String filePath) {
this.filePath = filePath;
}

public static void main(String dd[])throws Exception{

Properties p = new Properties();
FileInputStream ferr=new FileInputStream((getClass().getClassLoader()
.getResource("") + filePath).toString().substring(6));// 用subString(6)去掉:file:/try{
p.load(ferr);
ferr.close();
Set s = p.keySet();
Iterator it = s.iterator();
while(it.hasNext()){
String id = (String)it.next();
String value = p.getProperty(id);
System.out.println(id+":="+value);
}
}catch(Exception e){
e.printStackTrace();
}
}
}

//==============databaseconfing.properties 文件=====================

#----------------------------------
# sql server 2000数据厍连接信息
#----------------------------------
dp.sd.DataBaseTyp=mssql
DataBaseHost=127.0.0.1:1433
DataBase=formpro
UserName=sa
PassWord=01
#----------------------------------
# mysql 数据厍连接信息
#----------------------------------
#DataBaseHost=127.0.0.1:3306
#DataBaseTyp=mysql
#DataBase=snow
#UserName=root
#PassWord=01
//==========================运行结果=======================

PassWord:=01
DataBaseHost:=127.0.0.1:1433
DataBase:=formpro
dp.sd.DataBaseTyp:=mssql
UserName:=sa

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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]); 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