贝叶斯算法(文本分类算法)java源码

package com.vista;import java.io.IOException;import jeasy.analysis.MMAnalyzer;/*** 中文分词器*/public class ChineseSpliter{/*** 对给定的文本进行中文分词* @param text 给定的文本* @param splitToken 用于分割的标记,如"|"* @return 分词完毕的文本*/public static String split(String text,String splitToken){String result = null;MMAnalyzer analyzer = new MMAnalyzer();try{result = analyzer.segment(text, splitToken);}catch (IOException e){e.printStackTrace();}return result;}}停用词处理去掉文档中无意思的词语也是必须的一项工作,这里简单的定义了一些常见的停用词，并根据这些常用停用词在分词时进行判断。

package com.vista;/*** 停用词处理器* @author phinecos**/public class StopWordsHandler{private static String stopWordsList[] ={"的", "我们","要","自己","之","将","“","”","，","（","）","后","应","到","某","后","个","是","位","新","一","两","在","中","或","有","更","好",""};//常用停用词public static boolean IsStopWord(String word){for(int i=0;i<stopWordsList.length;++i){if(word.equalsIgnoreCase(stopWordsList[i]))return true;}return false;}}训练集管理器我们的系统首先需要从训练样本集中得到假设的先验概率和给定假设下观察到不同数据的概率。

package com.vista;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.util.Properties;import java.util.logging.Level;import java.util.logging.Logger;/*** 训练集管理器*/public class TrainingDataManager{private String[] traningFileClassifications;//训练语料分类集合private File traningTextDir;//训练语料存放目录private static String defaultPath = "D:\\TrainningSet";public TrainingDataManager(){traningTextDir = new File(defaultPath);if (!traningTextDir.isDirectory()){throw new IllegalArgumentException("训练语料库搜索失败！ [" +defaultPath + "]");}this.traningFileClassifications = traningTextDir.list(); }/*** 返回训练文本类别，这个类别就是目录名* @return训练文本类别*/public String[] getTraningClassifications(){return this.traningFileClassifications;}/*** 根据训练文本类别返回这个类别下的所有训练文本路径（full path）* @param classification 给定的分类* @return给定分类下所有文件的路径（full path）*/public String[] getFilesPath(String classification){File classDir = new File(traningTextDir.getPath() +File.s eparator +classification);String[] ret = classDir.list();for (int i = 0; i < ret.length; i++){ret[i] = traningTextDir.getPath() +File.separator +cl assification +File.separator +ret[i];}return ret;}/*** 返回给定路径的文本文件内容* @param filePath 给定的文本文件路径* @return文本内容* @throws java.io.FileNotFoundException* @throws java.io.IOException*/public static String getText(String filePath) throws FileNotF oundException,IOException{InputStreamReader isReader =new InputStreamReader(new Fil eInputStream(filePath),"GBK");BufferedReader reader = new BufferedReader(isReader);String aline;StringBuilder sb = new StringBuilder();while ((aline = reader.readLine()) != null){sb.append(aline + " ");}isReader.close();reader.close();return sb.toString();}/*** 返回训练文本集中所有的文本数目* @return训练文本集中所有的文本数目*/public int getTrainingFileCount(){int ret = 0;for (int i = 0; i < traningFileClassifications.length; i+ +){ret +=getTrainingFileCountOfClassification(traningFil eClassifications[i]);}return ret;}/*** 返回训练文本集中在给定分类下的训练文本数目* @param classification 给定的分类* @return训练文本集中在给定分类下的训练文本数目*/public int getTrainingFileCountOfClassification(String classi fication){File classDir = new File(traningTextDir.getPath() +File.s eparator +classification);return classDir.list().length;}/*** 返回给定分类中包含关键字／词的训练文本的数目* @param classification 给定的分类* @param key 给定的关键字／词* @return给定分类中包含关键字／词的训练文本的数目*/public int getCountContainKeyOfClassification(String classifi cation,String key){int ret = 0;try{String[] filePath = getFilesPath(classification);for (int j = 0; j < filePath.length; j++){String text = getText(filePath[j]);if (text.contains(key)){ret++;}}}catch (FileNotFoundException ex){Logger.getLogger(TrainingDataManager.class.getName()).log (Level.SEVERE, null,ex);}catch (IOException ex){Logger.getLogger(TrainingDataManager.class.getName()) .log(Level.SEVERE, null,ex);}return ret;}}先验概率先验概率是我们需要计算的两大概率值之一package com.vista;/*** 先验概率计算* <h3>先验概率计算</h3>* P(c_j)=N(C=c_j)<b>/</b>N <br>* 其中，N(C=c_j)表示类别c_j中的训练文本数量；* N表示训练文本集总数量。