bag-of-words model的java实现，bag-of-wordsmodel

bag-of-words model的java实现，bag-of-wordsmodel

bag-of-words model的java实现
为了验证paragraphVector的优势，需要拿bag-of-words model来对比。 实验数据：京东的评论，经人工挑选，分为“正面评论”和“负面评论”，中性的去掉。 分别拿这两个模型，来对每段“评论”做特征抽取，然后拿SVM来分类。 实验结果：400条训练，254条测试。bag-of-words模型的准确率是0.66，paraVector模型的准确率是0.84.

下面给出bag-of-words model的实现。其实很简单，原理之前在《数学之美》看过。具体可以参考http://www.cnblogs.com/platero/archive/2012/12/03/2800251.html。

训练数据： 1 文件good：正面评论 2 文件bad：负面评论 3 文件dict：其实就是good+bad，把正面评论和负面评论放在一起，主要遍历这个文件，找出所有词汇，生成词典。

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.io.UnsupportedEncodingException;
import java.util.StringTokenizer;

public class BowModel 
{

	
	Dict dict;
	DocFeatureFactory dff;
	
	public BowModel(String path) throws Throwable
	{
		dict = new Dict();
		dict.loadFromLocalFile(path);		
		dff = new DocFeatureFactory(dict.getWord2Index());
	}
	
	

	
	
	double[][] featureTable;
	private void generateFeature(String docsFile,int docNum) throws IOException
	{
		featureTable = new double[docNum][];
		int docIndex=0;
		File file = new File(docsFile);
		BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(file),"utf-8"));
		while(true)
		{
			String line=br.readLine();
			if(line == null)
				break;
			featureTable[docIndex++] = dff.getFeature(line);
		}
		br.close();		
	}
	
	private void nomalizeFeature()
	{
		double sum=0;
		double var =0;
		for(int col=0;col<featureTable[0].length;col++)//一列代表一个维度
		{
			sum =0;
			for(int row=0;row<featureTable.length;row++)
			{
				sum+= featureTable[row][col];
			}
			sum/=featureTable.length;//均值
			var =0;
			for(int row=0;row<featureTable.length;row++)
			{
				var+= (featureTable[row][col]-sum)*(featureTable[row][col]-sum);
			}
			var = Math.sqrt(var/featureTable.length);//标准差
			if(var == 0) continue;
			for(int row=0;row<featureTable.length;row++)
			{
				featureTable[row][col] = (featureTable[row][col] -sum)/var;
			}
		}
	}
	
	private void saveFeature(String path,String label) throws IOException
	{
		File file=new File(path);
		BufferedWriter br= new BufferedWriter(new OutputStreamWriter(new FileOutputStream(file)));
		for(int i=0;i<featureTable.length;i++)
		{
			br.append(label+" ");
			for(int j=0;j<featureTable[0].length;j++)
			{
				br.append(Integer.toString(j+1)+":"+featureTable[i][j]+" ");
			}
			br.append("\n");
		}
		br.close();
	}
	
	public void train() throws IOException
	{
		generateFeature("/media/linger/G/sources/comment/test/good",340);
		nomalizeFeature();
		saveFeature("svm_good","1");
		
		generateFeature("/media/linger/G/sources/comment/test/bad",314);
		nomalizeFeature();
		saveFeature("svm_bad","-1");
	}
	
	
	public static void main(String[] args) throws Throwable 
	{
		// TODO Auto-generated method stub
		BowModel bm = new BowModel("/media/linger/G/sources/comment/test/dict");
		bm.train();
	}

}

import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.UnsupportedEncodingException;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Hashtable;
import java.util.StringTokenizer;

public class Dict 
{
	HashMap<String,Integer> word2Index =null;
	Hashtable<String,Integer> word2Count = null;
	void loadFromLocalFile(String path) throws IOException
	{
		word2Index = new HashMap<String,Integer>();
		word2Count = new Hashtable<String,Integer>();
		int index = 0;
		File file = new File(path);
		BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(file),"utf-8"));
		while(true)
		{
			String line=br.readLine();
			if(line == null)
				break;
			StringTokenizer tokenizer=new StringTokenizer(line," ");
			while(tokenizer.hasMoreElements())
			{
				String term=tokenizer.nextToken();
				if(word2Count.containsKey(term))
				{
					
					int freq=word2Count.get(term)+1;
					word2Count.put(term, freq);
					
				}
				else
				{
					word2Count.put(term, 1);
					word2Index.put(term, index++);
				}
			}
		}
		br.close();
	}
	
	public HashMap<String,Integer> getWord2Index() throws Throwable
	{
		if(word2Index==null)
			throw new Exception("has not loaded file!");
		return word2Index;
	}
	
	public static void main(String[] args) 
	{
		// TODO Auto-generated method stub

	}

}

import java.util.HashMap;
import java.util.StringTokenizer;

public class DocFeatureFactory 
{
	HashMap<String,Integer> word2Index;
	double[] feature;
	int dim;
	public DocFeatureFactory(HashMap<String,Integer> w2i)
	{
		word2Index = w2i;
		dim = w2i.size();
	}
	
	double[] getFeature(String doc)
	{
		feature = new double[dim];
		StringTokenizer tokenizer=new StringTokenizer(doc," ");
		while(tokenizer.hasMoreElements())
		{
			String term =tokenizer.nextToken();
			feature[word2Index.get(term)]++;
		}	
		return feature;
	}
	
	public static void main(String[] args) 
	{
		// TODO Auto-generated method stub

	}

}

本文作者：linger 本文链接：http://blog.csdn.net/lingerlanlan/article/details/38333687

不能，只能用set方法和 有参构造 来添加到twomodel中，因为没有两个对象的参数不一样，
不过不想玛法的话看下边

在twomodel 定义一个方法
public void fuzhi(onemodel o){
this.id=o.id;
this.name=o.name;
this.age=o.age;
}

当你想添加onemodel 到twomodel 中，只要调用这个方法，再把onemodel 对象传到里边就行了
如
onemodel a=new onemodel ();
twomodel t=new twomodel ();
//开始复制
t.fuzhi(a);
//调用方法就ok
 

Model1是用jsp+javabean开发；Model2是MVC模式开发。我想这问题在google上一搜一大堆更详细的答案的，为什么不去自己搜一下呢？！