关于Java实现朴素贝叶斯算法

package naiveBayesian;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.IOException;import java.io.InputStreamReader;import java.util.ArrayList;public class NativeBeyes {private ArrayList<ArrayList<String>> datas=new ArrayList<ArrayList<String>>();//训练数据集private ArrayList<String> test=new ArrayList<String>();//测试数据集private static ArrayList<ArrayList<String>> subdatas=new ArrayList<ArrayList<String>>();//同类别数据集public ArrayList<Double> p=new ArrayList<Double>();//不同类别对应概率public ArrayList<String> kinds=new ArrayList<String>();//记录种类值private ArrayList<Double> p1=new ArrayList<Double>();//不同类别对给定多维向量特征的概率public NativeBeyes(ArrayList<ArrayList<String>> datas,ArrayList<String> test) {this.datas=datas;this.test=test;}/* * 计算类别种数计算各类别的概率 */public int sorts(ArrayList<ArrayList<String>> datas,ArrayList<String> test) {int a=0,b=0,c=datas.size();//类别种类计数器及首个类别计数while(datas.isEmpty()!=true) {for(int i=0;i<datas.size();i++) {if(datas.get(0).get(datas.get(0).size()-1).equals(datas.get(i).get(datas.get(0).size()-1))==true) {this.subdatas.add(datas.get(i));b++;if(b>=2) {datas.remove(i);i--;}}}datas.remove(0);p.add((double)b);b=0;a++;}for(int i=0;i<a;i++) {p.add(p.get(i)/c);}for(int i=0;i<a;i++) {p.remove(0);}numerator(test);return a;}//***********************************///* * 计算分母（denominator）的值，即相应属性值在对应属性集中的概率值  */public double denominator(ArrayList<ArrayList<String>> datas,ArrayList<String> test) {double pp=1.0;int a=0;for(int i=0;i<test.size();i++) {for(int j=0;j<datas.size();j++) {if(datas.get(j).get(i).equals(test.get(i))==true) {a++;}}pp*=(double)a/datas.size();a=0;}return pp;}//************************************///* * 计算相应类别下不同特征属性值的条件概率(部分分子numerator值) */public void numerator(ArrayList<String> test) {double p=1.0;int b=0,c=0;while(this.subdatas.isEmpty()!=true) {for(int i=0;i<this.subdatas.size();i++) {if(this.subdatas.get(0).get(this.subdatas.get(0).size()-1).equals(this.subdatas.get(i).get(this.subdatas.get(0).size()-1))==true) {b++;}else {break;}}kinds.add(this.subdatas.get(0).get(this.subdatas.get(0).size()-1));for(int i=0;i<test.size();i++) {for(int j=0;j<b;j++) {if(test.get(i).equals(this.subdatas.get(j).get(i))) {c++;}}p*=(double)c/b;c=0;}p1.add(p);p=1.0;String str=this.subdatas.get(0).get(this.subdatas.get(0).size()-1);for(int i=0;i<b;i++) {this.subdatas.remove(0);}b=0;}}//******************************************///* * 比较得出概率最大的可能分类 */public int compare(ArrayList<ArrayList<String>> datas,ArrayList<String> test,int c,double q,ArrayList<Double> p,ArrayList<Double> p1) {double[] num=new double[c];//1int t=0;double a=0.0;for(int i=0;i<num.length;i++) {num[i]=(double)p.get(i)*p1.get(i)/q;}a=num[0];for(int i=0;i<num.length;i++) {if(a<num[i]) {a=num[i];t=i;}}System.out.printf("该分类可信度为：%.2f     ",num[t]);return t;}//********************************************//public String predect(ArrayList<ArrayList<String>> datas,ArrayList<String> test) throws IOException {        NativeBeyes beyes=new NativeBeyes(datas,test);    beyes.numerator(test);//p(类别下的条件概率)    double bb=beyes.denominator(datas,test);    String str2=beyes.kinds.get(beyes.compare(datas,test,beyes.sorts(datas,test),bb,beyes.p,beyes.p1));    System.out.println("分类为："+str2);            return str2;}}

package naiveBayesian;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.IOException;import java.io.InputStreamReader;import java.util.ArrayList;public class Mian {public static void main(String[] args) throws IOException {ArrayList<ArrayList<String>> datas=new ArrayList<ArrayList<String>>();//训练数据集ArrayList<String> answer=new ArrayList<String>();String str=null;int count=0,c=0;FileInputStream fis = new FileInputStream("C:\\Users\\zfw\\Desktop\\java项目\\datas.txt");         InputStreamReader isr = new InputStreamReader(fis, "UTF-8");         BufferedReader br = new BufferedReader(isr);         while((str=br.readLine())!=null) {        String[] strs=str.split(",");        ArrayList<String> array=new ArrayList<String>();        for(int i=0;i<strs.length;i++)        array.add(strs[i]);        datas.add(array);        }        br.close();        FileInputStream fis2 = new FileInputStream("C:\\Users\\zfw\\Desktop\\java项目\\test.txt");         InputStreamReader isr2 = new InputStreamReader(fis2, "UTF-8");         BufferedReader br2 = new BufferedReader(isr2);         while((str=br2.readLine())!=null) {        c++;        String[] strs=str.split(",");        ArrayList<String> test=new ArrayList<String>();        for(int i=0;i<strs.length-1;i++)        test.add(strs[i]);        NativeBeyes beyes=new NativeBeyes(datas,test);        if(beyes.predect(datas,test).equals(strs[strs.length-1])) {        count++;        }    FileInputStream fis1 = new FileInputStream("C:\\Users\\zfw\\Desktop\\java项目\\datas.txt");             InputStreamReader isr1 = new InputStreamReader(fis1, "UTF-8");             BufferedReader br1 = new BufferedReader(isr1);             while((str=br1.readLine())!=null) {            String[] strs1=str.split(",");            ArrayList<String> array=new ArrayList<String>();            for(int i=0;i<strs1.length;i++)            array.add(strs1[i]);            datas.add(array);            }            br1.close();        }        br2.close();             System.out.printf("正确率为：%.2f",(double)count/datas.size()*100);        System.out.println("%");}}

关于对朴素贝叶斯算法的理解可参考：

https://mp.weixin.qq.com/s?__biz=MzI4MDYzNzg4Mw==&mid=2247