基于weka3.8.6平台的朴素贝叶斯分类代码，要求：不调用平台自带的NaiveBayes（）函数，自我实现

好的，以下是基于Weka 3.8.6平台的朴素贝叶斯分类代码，不使用平台自带的NaiveBayes()函数，自我实现：

import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

public class NaiveBayesClassifier {
   public static void main(String[] args) throws Exception {
      // 加载数据集
      DataSource source = new DataSource("path/to/your/dataset.arff");
      Instances dataset = source.getDataSet();
      
      // 设置类别属性
      if (dataset.classIndex() == -1)
         dataset.setClassIndex(dataset.numAttributes() - 1);
      
      // 计算类别先验概率和条件概率
      double[] classCounts = new double[dataset.numClasses()];
      double[][][] condProbs = new double[dataset.numAttributes()-1][dataset.numClasses()][];
      for (int i = 0; i < dataset.numInstances(); i++) {
         Instance instance = dataset.instance(i);
         int classValue = (int)instance.classValue();
         classCounts[classValue]++;
         for (int j = 0; j < dataset.numAttributes()-1; j++) {
            int attrValue = (int)instance.value(j);
            if (condProbs[j][classValue] == null) {
               condProbs[j][classValue] = new double[dataset.attribute(j).numValues()];
            }
            condProbs[j][classValue][attrValue]++;
         }
      }
      for (int i = 0; i < dataset.numClasses(); i++) {
         for (int j = 0; j < dataset.numAttributes()-1; j++) {
            if (condProbs[j][i] != null) {
               for (int k = 0; k < condProbs[j][i].length; k++) {
                  condProbs[j][i][k] = (condProbs[j][i][k] + 1) / (classCounts[i] + dataset.attribute(j).numValues());
               }
            }
         }
      }
      
      // 对测试数据进行分类
      for (int i = 0; i < dataset.numInstances(); i++) {
         Instance instance = dataset.instance(i);
         int predictClass = -1;
         double maxProb = Double.NEGATIVE_INFINITY;
         for (int j = 0; j < dataset.numClasses(); j++) {
            double prob = Math.log(classCounts[j]);
            for (int k = 0; k < dataset.numAttributes()-1; k++) {
               int attrValue = (int)instance.value(k);
               if (condProbs[k][j] != null) {
                  prob += Math.log(condProbs[k][j][attrValue]);
               }
            }
            if (prob > maxProb) {
               maxProb = prob;
               predictClass = j;
            }
         }
         System.out.println("Instance " + i + " is classified as class " + predictClass);
      }
   }
}

其中，"path/to/your/dataset.arff"需要替换成你自己的数据集路径。这段代码首先计算了类别先验概率和条件概率，然后对每个测试实例进行分类，并输出分类结果。