This article describes the naive Bayes algorithm implemented in Java. Share it for your reference, as follows:
I believe that the young men who do data mining and recommendation systems are familiar with the Naive Bayes algorithm, so I won’t talk about the algorithm principles. I mainly want to implement the naive Bayes algorithm through java code, and the idea is:
1. Use javabean +Arraylist for training data storage
2. For sample data training
The specific code is as follows:
package NB;/** * Properties of training samples 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 + "]";}}The algorithm implementation part:
package NB;import java.io.BufferedReader;import java.io.File;import java.io.FileReader;import java.util.ArrayList;public class TestNB { /**data_length * Thoughts of algorithm*/ public static ArrayList<JavaBean> list = new ArrayList<JavaBean>();; static int data_length=0; public static void main(String[] args) { // 1. Read data and put it in the list container File file = new File("E://test.txt"); txt2String(file); //Data test sample testData(25,"Medium","Yes","Fair"); } // Read sample data public static void txt2String(File file) { try { BufferedReader br = new BufferedReader(new FileReader(file));// Construct a BufferedReader class to read the file String s = null; while ((s = br.readLine()) != null) {// Use the readLine method to read one line at a time data_length++; splitt(s); } br.close(); } catch (Exception e) { e.printStackTrace(); } } // Save in ArrayList 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); } // Training sample, test public static void testData(int age,String a,String b,String c){ //Training sample int number_yes=0; int bucket_no=0; //The number of age cases 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; //Travel List to get data 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("The number of purchases:"+number_yes); System.out.println("The number of history not purchased:"+bumber_no); System.out.println("Buy+age:"+num_age_yes); System.out.println("Buy+income:"+num_age_no); System.out.println("Buy+income:"+num_income_yes); System.out.println("Buy+income:"+num_income_yes); System.out.println("Buy+stundent:"+num_student_yes); System.out.println("Buy+student:"+num_stdent_no); System.out.println("Buy+stundent:"+num_stdent_no); System.out.println("Buy+credit:"+num_credit_yes); System.out.println("Buy+credit:"+num_credit_no); //// Probability judgment double buy_yes=number_yes*1.0/data_length; // Probability purchase double buy_no=bumber_no*1.0/data_length; // Probability not to buy System.out.println("Buy probability in training data:"+buy_yes); System.out.println("Buy probability in training data:"+buy_no); /// Unknown user's 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)*(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("Probability of new users to buy:"+nb_buy_yes); System.out.println("Probability of new users not to buy:"+nb_buy_no); if(nb_buy_yes>nb_buy_no){ System.out.println("Probability of new users not to buy"); }else { System.out.println("Probability of new users not to buy"); } }}For sample data:
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
Results for unknown user data:
Number of purchases: 9
No history of not buying: 5
Purchase +age:2
Don't buy +age:3
Purchase +income:4
Don't buy +income:2
Purchase +stundent:6
Don't buy +student:1
Buy + credit:6
Don't buy + credit:2
Probability of buying in training data: 0.6428571428571429
Probability of not buying in training data: 0.35714285714285715
The probability of new users buying: 0.028218694885361547
The probability of new users not buying: 0.006857142857142858
New users have a high probability of buying
For more information about Java algorithms, readers who are interested in this site can view the topics: "Java Data Structure and Algorithm Tutorial", "Summary of Java Operation DOM Node Tips", "Summary of Java File and Directory Operation Tips" and "Summary of Java Cache Operation Tips"
I hope this article will be helpful to everyone's Java programming.