Java实现朴素贝叶斯分类器

package com.company; /******************************************************************************** * * 李勇志 2014301500370 * 数据挖掘实验，分类实验 * 朴素贝叶斯分类 * *训练样本示例格式：（R,1,5,5,4） */ import java.io.File; import java.util.Scanner; import java.util.Vector; public class Bayes { static Vector<String> indata = new Vector<>();//读入数据 static Vector<int[]> catagory_R = new Vector<>();//存储类别R的所有数据 static Vector<int[]> catagory_L = new Vector<>();//存储类别L的所有数据 static Vector<int[]> catagory_B = new Vector<>();//存储类别B的所有数据 public static boolean loadData(String url) {//加载测试的数据文件 try { Scanner in = new Scanner(new File(url));//读入文件 while (in.hasNextLine()) { String str = in.nextLine();//将文件的每一行存到str的临时变量中 indata.add(str);//将每一个样本点的数据追加到Vector 中 } return true; } catch (Exception e) { //如果出错返回false return false; } } public static void pretreatment(Vector<String> indata) { //数据预处理，将原始数据中的每一个属性值提取出来存放到Vector<double[]> data中 int i = 0; String t; while (i < indata.size()) {//取出indata中的每一行值 int[] tem = new int[4]; t = indata.get(i); String[] sourceStrArray = t.split(",", 5);//使用字符串分割函数提取出各属性值 switch (sourceStrArray[0]) { case "R": { for (int j = 1; j < 5; j++) { tem[j - 1] = Integer.parseInt(sourceStrArray[j]); } catagory_R.add(tem); break; } case "L": { for (int j = 1; j < 5; j++) { tem[j - 1] = Integer.parseInt(sourceStrArray[j]); } catagory_L.add(tem); break; } case "B": { for (int j = 1; j < 5; j++) { tem[j - 1] = Integer.parseInt(sourceStrArray[j]); } catagory_B.add(tem); break; } } i++; } } public static double bayes(int[] x, Vector<int[]> catagory) { double[] ai_y = new double[4]; int[] sum_ai = new int[4]; for (int i = 0; i < 4; i++) { for (int j = 0; j < catagory.size(); j++) { if (x[i] == catagory.get(j)[i]) sum_ai[i]++; } } for (int i = 0; i < 4; i++) { ai_y[i] = (double) sum_ai[i] / (double) catagory.size(); } return ai_y[0] * ai_y[1] * ai_y[2] * ai_y[3]; } public static void main(String[] args) { loadData("balance-scale.data"); pretreatment(indata); double p_yR = (double) catagory_R.size() / (double) (indata.size());//表示概率p（R） double p_yB = (double) catagory_B.size() / (double) (indata.size());//表示概率p（B） double p_yL = (double) catagory_L.size() / (double) (indata.size());//表示概率p（L） int[] x = new int[4]; double x_in_R, x_in_L, x_in_B; int sumR=0, sumL=0, sumB=0; double correct=0; System.out.println("请输入样本x格式如下：\n 1 1 1 1\n"); int r = 0; while (r < indata.size()) { for (int i = 0; i < 4; i++) //读取数字放入数组的第i个元素 x[i] = Integer.parseInt(indata.get(r).split(",", 5)[i + 1]); x_in_B = bayes(x, catagory_B) * p_yB; x_in_L = bayes(x, catagory_L) * p_yL; x_in_R = bayes(x, catagory_R) * p_yR; if (x_in_B == Math.max(Math.max(x_in_B, x_in_L), x_in_R)) { System.out.println("输入的第"+r+"样本属于类别：B"); sumB++; if(indata.get(r).split(",",5)[0].equals("B")) correct++; } else if (x_in_L == Math.max(Math.max(x_in_B, x_in_L), x_in_R)) { System.out.println("输入的第"+r+"样本属于类别：L"); sumL++; if(indata.get(r).split(",",5)[0].equals("L")) correct++; } else if (x_in_R == Math.max(Math.max(x_in_B, x_in_L), x_in_R)) { System.out.println("输入的第"+r+"样本属于类别：R"); sumR++; if(indata.get(r).split(",",5)[0].equals("R")) correct++; } r++; } System.out.println("使用训练样本进行分类器检验得到结果统计如下："); System.out.println("R类有："+sumR+" 实际有R类样本"+catagory_R.size()+"个"); System.out.println("L类有："+sumL+" 实际有L类样本"+catagory_L.size()+"个"); System.out.println("B类有："+sumB+" 实际有B类样本"+catagory_B.size()+"个"); System.out.println("分类的正确率为"+correct*1.0/indata.size()*100+"%"); } }