交叉验证的Java weka实现，并保存和重载模型

//直接调用Evaluation即可完成
Evaluation eval = null;
for (int i = 0; i < 10; i++) {
   eval = new Evaluation(Train);
   eval.crossValidateModel(m_classifier, Train, 10, new Random(i),
args);// 实现交叉验证模型
}
System.out.println(eval.toSummaryString());// 输出总结信息
System.out.println(eval.toClassDetailsString());// 输出分类详细信息
System.out.println(eval.toMatrixString());// 输出分类的混淆矩阵

SerializationHelper.write("LibSVM.model", classifier4);//参数一为模型保存文件，classifier4为要保存的模型

Classifier classifier8 = (Classifier) weka.core.SerializationHelper.read("LibSVM.model");

package weka_test;
import java.io.File;
import java.io.IOException;
import weka.classifiers.Classifier;
import weka.classifiers.trees.J48;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffLoader;
import weka.experiment.InstanceQuery;
import weka.classifiers.Evaluation;
import java.util.Random;

public class test {
   /**
    * oracleInput
    * @return data
    * @throws Exception
*/
   public static Instances oracleInput() throws Exception{
      InstanceQuery query = new InstanceQuery();
      String sql = "SELECT to_char(z.cydate,'yyyy/mm') AS d,sum(z.bcmoney) as c FROM zybc z"
+ " WHERE to_char(z.cydate,'yyyy/mm') IS NOT NULL"
+ " GROUP BY to_char(z.cydate,'yyyy/mm') ORDER BY to_date(to_char(z.cydate,'yyyy/mm'),'yyyy/mm') ASC";
      //System.out.println(sql);
      query.setCustomPropsFile(new File("weka/weka_oracle.props"));
      query.setDatabaseURL("jdbc:oracle:thin:@192.168.2.133:1521/XE");
      query.setUsername("***");
      query.setPassword("***");
      query.setQuery(sql);
      Instances data = query.retrieveInstances();          
return data;
   }
   /**
    * mysqlInput
    * @return data
    * @throws Exception
*/
   public static Instances mysqlInput() throws Exception{
      InstanceQuery query = new InstanceQuery();
      String sql = "SELECT * FROM iris";
      //System.out.println(sql);
      query.setCustomPropsFile(new File("weka/weka_mysql.props"));
      query.setDatabaseURL("jdbc:mysql://localhost:3306/test");
      query.setUsername("***");
      query.setPassword("***");
      query.setQuery(sql);
      Instances data = query.retrieveInstances();    
return data;
   }

   /**
    * @param args
    * @throws Exception 
*/
   public static void main(String[] args) throws Exception {
      // TODO Auto-generated method stub
      Classifier m_classifier = new J48();

        /*File inputFile = new File("D://Program Files//Weka-3-7//data//iris.arff");//训练语料文件
        ArffLoader atf = new ArffLoader(); 
        atf.setFile(inputFile);
        Instances instancesTrain = atf.getDataSet(); // 读入训练文件    */ 

      Instances Train = mysqlInput();
      Instances Test = mysqlInput();
        Test.setClassIndex(4); //设置分类属性所在行号（第一行为0号），instancesTest.numAttributes()可以取得属性总数
        double sum = Test.numInstances(),//测试语料实例数
        right = 0.0f;
        Train.setClassIndex(4);
        m_classifier.buildClassifier(Train); //训练          
        //System.out.println(m_classifier.toString());

      //2、利用模型进行预测 
int a=0,b=0,c=0,d=0;//记录每个类别的个数，方便计算评价指标
for (int i = 0; i < Test.numInstances(); i++) {
         double classification = m_classifier.classifyInstance(Train.instance(i));
         double classValue = Train.instance(i).classValue();
if (classification == 0.0 && classValue == 0.0) {
            a++;
         } else if (classification == 0.0 && classValue == 1.0) {
            b++;
         } else if (classification == 1.0 && classValue == 0.0) {
            c++;
         } else if (classification == 1.0 && classValue == 1.0) {
            d++;
         }
      }
      // 3、得出预测效果评测指标
      double precision = (double) a / (a + b);
      double recall = (double) a / (a + c);
      double fMeasure = 2 * precision * recall / (precision + recall);
      System.out.println("precision\trecall\tF-Measure");
      System.out.println((precision) + "\t\t"
+ (recall) + "\t"
+ (fMeasure));

for(int  i = 0;i