Java利用樸素貝葉斯分類算法實現信息分類

 
 
import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.InputStreamReader;
import java.util.*;
import java.util.stream.Collectors;
 
/**
 * @author liuya
 */
public class NaiveBayesModel {
 
    //樣本數據
    private static List<List<String>> data = new ArrayList<>();
    //樣本數據
    private static Set<List<String>> dataSet = new HashSet<>();
    //分類模型
    public static Map<String,String> modelMap = new HashMap<>();
    //樣本數據集
    private static String path = "./src/data.txt";
 
    public static void main(String[] args) {
        //訓練模型
        trainingModel();
        //識別
        classification("帥","好","高","上進","有錢");
        classification("不帥","不好","不高","不上進","不有錢");
    }
 
    /**
     * 導入數據
     * @param path
     * @return
     */
    public static void readData(String path){
        List<String> row = null;
        try {
            InputStreamReader isr = new InputStreamReader(new FileInputStream(new File(path)));
            BufferedReader br = new BufferedReader(isr);
            String str = null;
            while((str = br.readLine()) != null){
                row = new ArrayList<>();
                String[] str1 = str.split(",");
                for(int i = 0; i < str1.length ; i++) {
                    row.add(str1[i]);
                }
                dataSet.add(row);
                data.add(row);
            }
            br.close();
            isr.close();
        } catch (Exception e) {
            e.printStackTrace();
            System.out.println("讀取文件內容出錯！");
        }
    }
 
    public static void trainingModel() {
        readData(path);
        String category1="中意";
        String category2="不中意";
        dataSet.forEach(e->{
          double categoryP1=  calculateBayesian(e.get(0),e.get(1),e.get(2),e.get(3),e.get(4),category1);
          double categoryP2=  calculateBayesian(e.get(0),e.get(1),e.get(2),e.get(3),e.get(4),category2);
            String result=categoryP1>categoryP2?category1:category2;
            modelMap.put(e.get(0)+"-"+e.get(1)+"-"+e.get(2)+"-"+e.get(3)+"-"+e.get(4),result);
        });
    }
 
    /**
     * 分類的識別
     * */
    public static void  classification(String look, String character, String height, String progresses, String wealthy){
        String key=look+"-"+character+"-"+height+"-"+progresses+"-"+wealthy;
        String result=modelMap.get(key);
        System.out.println("特征為"+look+","+character+","+height+","+progresses+","+wealthy+"的對象，女生"+result);
    }
 
 
    /**
     * 分類的核心是比較樸素貝葉斯的結果值，結果值大的認為就屬於該分類（會有誤差，數據集量越大，結果判定的準確率就會越高）由於分母相同可以直接比較分子來確定分類
     * */
    public static double calculateBayesian(String look, String character, String height, String progresses, String wealthy,String category) {
        //獲取P(x|y)的分母
      //  double denominator = getDenominator(look,character,height,progresses,wealthy);
        //獲取P(x|y)的分子
        double molecule = getMolecule(look,character,height,progresses,wealthy,category);
        return molecule/1;
    }
 
    /**
     * 獲取p(x|y)分子
     * @return
     */
    public static double getMolecule(String look, String character, String height, String progresses, String wealthy,String category) {
        double resultCP = getProbability(5, category);
        double lookCP = getProbability(0, look, category);
        double characterCP = getProbability(1, character, category);
        double heightCP = getProbability(2, height, category);
        double progressesCP = getProbability(3, progresses, category);
        double wealthyCP = getProbability(4, wealthy, category);
        return lookCP * characterCP * heightCP * progressesCP * wealthyCP * resultCP;
 
    }
 
    /**
     * 獲取p(x|y)分母
     * @return
     */
    public static double getDenominator(String look, String character, String height, String progresses, String wealthy) {
        double lookP = getProbability(0, look);
        double characterP = getProbability(1, character);
        double heightP = getProbability(2, height);
        double progressesP = getProbability(3, progresses);
        double wealthyP = getProbability(4, wealthy);
        return lookP * characterP * heightP * progressesP * wealthyP;
    }
 
 
    /**
     * 獲取某特征的概率
     * @return
     */
    private static double getProbability(int index, String feature) {
        int size = data.size();
        int num = 0;
        for (int i = 0; i < size; i++) {
            if (data.get(i).get(index).equals(feature)) {
                num++;
            }
        }
        return (double) num / size;
    }
 
    /**
     * 獲取某類別下某特征的概率
     * @return
     */
    private static double getProbability(int index, String feature, String category) {
        List<List<String>> filterData=data.stream().filter(e -> e.get(e.size() - 1).equals(category)).collect(Collectors.toList());
        int size =filterData.size();
        int num = 0;
        for (int i = 0; i < size; i++) {
            if (data.get(i).get(index).equals(feature)) {
                num++;
            }
        }
        return (double) num / size;
    }
}