本文實例講述了Java實現的樸素貝葉斯算法。分享給大家供大家參考，具體如下：
對于樸素貝葉斯算法相信做數據挖掘和推薦系統的小伙們都耳熟能詳了，算法原理我就不啰嗦了。我主要想通過java代碼實現樸素貝葉斯算法，思想：
1. 用javabean +Arraylist 對于訓練數據存儲
2. 對于樣本數據訓練
具體的代碼如下：
package NB;/** * 訓練樣本的屬性 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;}@Overridepublic String toString() { return "JavaBean [age=" + age + ", income=" + income + ", student=" + student + ", credit_rating=" + credit_rating + ", buys_computer=" + buys_computer + "]";}}算法實現的部分：
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<JavaBean> list = new ArrayList<JavaBean>();; static int data_length=0; public static void main(String[] args) { // 1.讀取數據，放入list容器中 File file = new File("E://test.txt"); txt2String(file); //數據測試樣本 testData(25,"Medium","Yes","Fair"); } // 讀取樣本數據 public static void txt2String(File file) { try { BufferedReader br = new BufferedReader(new FileReader(file));// 構造一個BufferedReader類來讀取文件 String s = null; while ((s = br.readLine()) != null) {// 使用readLine方法，一次讀一行 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); } // 訓練樣本，測試 public static void testData(int age,String a,String b,String c){ //訓練樣本 int number_yes=0; int bumber_no=0; // age情況 個數 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; //遍歷List 獲得數據 for(int i=0;i<list.size();i++){ JavaBean bb=list.get(i); if(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("購買的歷史個數:"+number_yes); System.out.println("不買的歷史個數:"+bumber_no); System.out.println("購買+age:"+num_age_yes); System.out.println("不買+age:"+num_age_no); System.out.println("購買+income:"+num_income_yes); System.out.println("不買+income:"+num_income_no); System.out.println("購買+stundent:"+num_student_yes); System.out.println("不買+student:"+num_stdent_no); System.out.println("購買+credit:"+num_credit_yes); System.out.println("不買+credit:"+num_credit_no); //// 概率判斷 double buy_yes=number_yes*1.0/data_length; // 買的概率 double buy_no=bumber_no*1.0/data_length; // 不買的概率 System.out.println("訓練數據中買的概率:"+buy_yes); System.out.println("訓練數據中不買的概率:"+buy_no); /// 未知用戶的判斷 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("新用戶買的概率:"+nb_buy_yes); System.out.println("新用戶不買的概率:"+nb_buy_no); if(nb_buy_yes>nb_buy_no){ System.out.println("新用戶買的概率大"); }else { System.out.println("新用戶不買的概率大"); } }}對于樣本數據：
25 High No Fair No
25 High No Excellent No
33 High No Fair Yes
41 Medium No Fair Yes
41 Low Yes Fair Yes
41 Low Yes Excellent No
33 Low Yes Excellent Yes
25 Medium No Fair No
25 Low Yes Fair Yes
41 Medium Yes Fair Yes
25 Medium Yes Excellent Yes
33 Medium No Excellent Yes
33 High Yes Fair Yes
41 Medium No Excellent No
對于未知用戶的數據得出的結果：
購買的歷史個數:9
不買的歷史個數:5
購買+age:2
不買+age:3
購買+income:4
不買+income:2
購買+stundent:6
不買+student:1
購買+credit:6
不買+credit:2
訓練數據中買的概率:0.6428571428571429
訓練數據中不買的概率:0.35714285714285715
新用戶買的概率:0.028218694885361547
新用戶不買的概率:0.006857142857142858
新用戶買的概率大
希望本文所述對大家java程序設計有所幫助。
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