算法描述

（1）扫描训练样本数据集，分别统计训练集中类别 Ci 的个数 Di 和属于类别Ci 的样本中属性Ak取值Xk为 Dik 的实例样本个数，构成统计表；
（2）计算先验概率和条件概率，构成概率表；
（3）构建分类模型；
（4）扫描待分类的样本数据集，调用已得到的统计表、概率表以及构建好的分类准则，得出分类结果；

代码

public class Bayes {//将训练集按巡逻集合的最后一个值进行分类Map<String, ArrayList<ArrayList<String>>> datasOfClass(ArrayList<ArrayList<String>> datas){Map<String, ArrayList<ArrayList<String>>> map = new HashMap<String, ArrayList<ArrayList<String>>>();ArrayList<String> t = null;String c = "";for (int i = 0; i < datas.size(); i++) {t = datas.get(i);c = t.get(t.size() - 1);if(c.length()==0) continue;if (map.containsKey(c)) {map.get(c).add(t);} else {ArrayList<ArrayList<String>> nt = new ArrayList<ArrayList<String>>();nt.add(t);map.put(c, nt);}}return map;}//在训练数据的基础上预测测试元组的类别 ，testT的各个属性在结果集里面出现的概率相乘最高的，即是结果public String predictClass(ArrayList<ArrayList<String>> datas, ArrayList<String> testT) {Map<String, ArrayList<ArrayList<String>>> doc = this.datasOfClass(datas);//将训练集元素划分保存在数据里Object classes[] = doc.keySet().toArray();double maxP = 0.00;int maxPIndex = -1;//testT的各个属性在结果集里面出现的概率相乘最高的，即使结果集for (int i = 0; i < doc.size(); i++) {String c = classes[i].toString();ArrayList<ArrayList<String>> d = doc.get(c);BigDecimal b1 = new BigDecimal(Double.toString(d.size()));BigDecimal b2 = new BigDecimal(Double.toString(datas.size()));//b1除以b2得到一个精度为3的双浮点数double pOfC = b1.divide(b2,10,BigDecimal.ROUND_HALF_UP).doubleValue();for (int j = 0; j < testT.size(); j++) {double pv = this.pOfV(d, testT.get(j), j);if(pv==0) pv=1/(double)d.size();BigDecimal b3 = new BigDecimal(Double.toString(pOfC));BigDecimal b4 = new BigDecimal(Double.toString(pv));//b3乘以b4得到一个浮点数pOfC=b3.multiply(b4).doubleValue();}if(pOfC > maxP){maxP = pOfC;maxPIndex = i;}}return classes[maxPIndex].toString();}// 计算指定属性到训练集出现的频率private double pOfV(ArrayList<ArrayList<String>> d, String value, int index) {double p = 0.00;int count = 0;int total = d.size();for (int i = 0; i < total; i++) {if(Double.parseDouble(d.get(i).get(index))==Double.parseDouble(value)){count++;}}BigDecimal b1 = new BigDecimal(Double.toString(count));BigDecimal b2 = new BigDecimal(Double.toString(total));//b1除以b2得到一个精度为3的双浮点数p = b1.divide(b2,10,BigDecimal.ROUND_HALF_UP).doubleValue();return p;}
}

public class TestBayes {static String res;//读取测试元组public ArrayList<String> readTestData(String string) throws IOException{ArrayList<String> candAttr = new ArrayList<String>();String str = "";str = string;//string分析器String[] tokenizer = str.split(",");for(int i=0;i<tokenizer.length-1;i++){candAttr.add(tokenizer[i]);}res=tokenizer[tokenizer.length-1];return candAttr;}//读取训练集public ArrayList<ArrayList<String>> readData() throws IOException {ArrayList<ArrayList<String>> list=new ArrayList<>();try { // 防止文件建立或读取失败，用catch捕捉错误并打印，也可以throw/* 读入TXT文件 */File filename = new File("src/bp/trainBayes.txt"); // 要读取以上路径的input。txt文件InputStreamReader reader = new InputStreamReader(new FileInputStream(filename)); // 建立一个输入流对象readerBufferedReader br = new BufferedReader(reader); // 建立一个对象，它把文件内容转成计算机能读懂的语言String line = "";line = br.readLine();while (line != null) {String[] temp=line.split(",");ArrayList<String> arrayList = new ArrayList<>(Arrays.asList(temp));list.add(arrayList);line = br.readLine();}} catch (Exception e) {e.printStackTrace();}return list;}public static  List<String> readTxt(String fileName){List<String> list=new ArrayList<>();try { // 防止文件建立或读取失败，用catch捕捉错误并打印，也可以throw/* 读入TXT文件 */File filename = new File(fileName); // 要读取以上路径的input。txt文件InputStreamReader reader = new InputStreamReader(new FileInputStream(filename)); // 建立一个输入流对象readerBufferedReader br = new BufferedReader(reader); // 建立一个对象，它把文件内容转成计算机能读懂的语言String line = "";line = br.readLine();while (line != null) {if(line.length()>0){list.add(line);}line = br.readLine();}} catch (Exception e) {e.printStackTrace();}return list;}public static void main(String[] args) {TestBayes tb = new TestBayes();int righr=0,total;ArrayList<ArrayList<String>> datas = null;ArrayList<String> testT = null;Bayes bayes = new Bayes();try {datas = tb.readData();List <String> l=  readTxt("src/bp/testBayes.txt");for(String c:l) {testT = tb.readTestData(c);String k = bayes.predictClass(datas, testT);if(k.equals(res))righr++;}double re=(double) righr/(double)l.size();System.out.println("测试集的数量："+ (new Double(l.size())).intValue());System.out.println("分类正确的数量："+(new Double(righr)).intValue());System.out.println("算法的分类正确率为："+ re);} catch (IOException e) {e.printStackTrace();}}
}