Java实现的朴素贝叶斯算法示例

本⽂实例讲述了Java实现的朴素贝叶斯算法。分享给⼤家供⼤家参考，具体如下：

对于朴素贝叶斯算法相信做数据挖掘和推荐系统的⼩伙们都⽿熟能详了，算法原理我就不啰嗦了。我主要想通过java代码实现朴素贝叶斯算法，思想：

1. ⽤javabean +Arraylist 对于训练数据存储2. 对于样本数据训练具体的代码如下：

package NB;/**

* 训练样本的属性 javaBean * */

public class JavaBean { int age;

String income; String student;

String credit_rating; String buys_computer; public JavaBean(){ }

public JavaBean(int age,String income,String student,String credit_rating,String buys_computer){ this.age=age;

this.income=income; this.student=student;

this.credit_rating=credit_rating;

this.buys_computer=buys_computer;}

public int getAge() { return age;}

public void setAge(int age) { this.age = age;}

public String getIncome() { return income;}

public void setIncome(String income) { this.income = income;}

public String getStudent() { return student;}

public void setStudent(String student) { this.student = student;}

public String getCredit_rating() { return credit_rating;}

public void setCredit_rating(String credit_rating) { this.credit_rating = credit_rating;}

public String getBuys_computer() { return buys_computer;}

public void setBuys_computer(String buys_computer) { this.buys_computer = buys_computer;}

@Override

public String toString() {

return \"JavaBean [age=\" + age + \ + student + \ + buys_computer + \"]\";}}

算法实现的部分：

package NB;

import java.io.BufferedReader;import java.io.File;

import java.io.FileReader;import java.util.ArrayList;public class TestNB { /**data_length * 算法的思想 */

public static ArrayList list = new ArrayList();; static int data_length=0;

public static void main(String[] args) { // 1.读取数据，放⼊list容器中 File file = new File(\"E://test.txt\"); txt2String(file); //数据测试样本

testData(25,\"Medium\ }

// 读取样本数据

public static void txt2String(File file) { try {

BufferedReader br = new BufferedReader(new FileReader(file));// 构造⼀个BufferedReader类来读取⽂件 String s = null;

while ((s = br.readLine()) != null) {// 使⽤readLine⽅法，⼀次读⼀⾏ data_length++; splitt(s); }

br.close();

} catch (Exception e) { e.printStackTrace(); } }

// 存⼊ArrayList中

public static void splitt(String str){ String strr = str.trim();

String[] abc = strr.split(\"[\\\\p{Space}]+\"); int age=Integer.parseInt(abc[0]);

JavaBean bean=new JavaBean(age, abc[1], abc[2], abc[3], abc[4]); list.add(bean); }

// 训练样本，测试

public static void testData(int age,String a,String b,String c){ //训练样本

int number_yes=0; int bumber_no=0; // age情况 个数

int num_age_yes=0; int num_age_no=0; // income

int num_income_yes=0; int num_income_no=0; // student

int num_student_yes=0; int num_stdent_no=0; //credit

int num_credit_yes=0; int num_credit_no=0; //遍历List 获得数据

for(int i=0;iif(bb.getBuys_computer().equals(\"Yes\")){ //Yes number_yes++;

if(bb.getIncome().equals(a)){//income num_income_yes++; }

if(bb.getStudent().equals(b)){//student num_student_yes++; }

if(bb.getCredit_rating().equals(c)){//credit num_credit_yes++; }

if(bb.getAge()==age){//age num_age_yes++; }

}else {//No

bumber_no++;

if(bb.getIncome().equals(a)){//income num_income_no++; }

if(bb.getStudent().equals(b)){//student num_stdent_no++; }

if(bb.getCredit_rating().equals(c)){//credit num_credit_no++; }

if(bb.getAge()==age){//age num_age_no++; } } }

System.out.println(\"购买的历史个数:\"+number_yes); System.out.println(\"不买的历史个数:\"+bumber_no); System.out.println(\"购买+age:\"+num_age_yes); System.out.println(\"不买+age:\"+num_age_no);

System.out.println(\"购买+income:\"+num_income_yes); System.out.println(\"不买+income:\"+num_income_no); System.out.println(\"购买+stundent:\"+num_student_yes); System.out.println(\"不买+student:\"+num_stdent_no); System.out.println(\"购买+credit:\"+num_credit_yes);

System.out.println(\"不买+credit:\"+num_credit_no); //// 概率判断

double buy_yes=number_yes*1.0/data_length; // 买的概率 double buy_no=bumber_no*1.0/data_length; // 不买的概率 System.out.println(\"训练数据中买的概率:\"+buy_yes); System.out.println(\"训练数据中不买的概率:\"+buy_no); /// 未知⽤户的判断

double nb_buy_yes=(1.0*num_age_yes/number_yes)*(1.0*num_income_yes/number_yes)*(1.0*num_student_yes/number_yes)*(1.0*num_credit_yes/number_yes)*buy_yes; double nb_buy_no=(1.0*num_age_no/bumber_no)*(1.0*num_income_no/bumber_no)*(1.0*num_stdent_no/bumber_no)*(1.0*num_credit_no/bumber_no)*buy_no; System.out.println(\"新⽤户买的概率:\"+nb_buy_yes); System.out.println(\"新⽤户不买的概率:\"+nb_buy_no); if(nb_buy_yes>nb_buy_no){

System.out.println(\"新⽤户买的概率⼤\"); }else {

System.out.println(\"新⽤户不买的概率⼤\"); } }}

对于样本数据：

25 High No Fair No25 High No Excellent No33 High No Fair Yes41 Medium No Fair Yes41 Low Yes Fair Yes41 Low Yes Excellent No33 Low Yes Excellent Yes25 Medium No Fair No25 Low Yes Fair Yes41 Medium Yes Fair Yes25 Medium Yes Excellent Yes33 Medium No Excellent Yes33 High Yes Fair Yes41 Medium No Excellent No对于未知⽤户的数据得出的结果：

购买的历史个数:9不买的历史个数:5购买+age:2不买+age:3购买+income:4不买+income:2购买+stundent:6不买+student:1购买+credit:6不买+credit:2

训练数据中买的概率:0.6428571428571429训练数据中不买的概率:0.35714285714285715新⽤户买的概率:0.028218694885361547新⽤户不买的概率:0.006857142857142858新⽤户买的概率⼤

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