import java.io.*;

import weka.classifiers.*;

import weka.classifiers.meta.Vote;

import weka.core.Instance;

import weka.core.Instances;

import weka.core.SelectedTag;

import weka.core.converters.ArffLoader;

public class test {

/**

* @param args

*/

public static void main(String[] args) {

// TODO Auto-generated method stub

Instances tranIns=null; //训练数据

Instances testIns=null; //测试数据

Classifier cfs1=null; //分类器1

Classifier cfs2=null; //分类器2

Classifier cfs3=null; //分类器3

Classifier []cfsArray=new Classifier[3]; //分类器数组

try

{

File file=new File("C://Program Files//Weka-3-6//data//segment-challenge.arff"); //训练数据

ArffLoader loader=new ArffLoader();

loader.setFile(file);

tranIns=loader.getDataSet(); //读入数据

file=new File("C://Program Files//Weka-3-6//data//segment-test.arff"); //测试数据

loader.setFile(file);

testIns=loader.getDataSet();

testIns.setClassIndex(testIns.numAttributes()-1); //设置类别的位置

tranIns.setClassIndex(tranIns.numAttributes()-1);

cfs1=(Classifier)Class.forName("weka.classifiers.bayes.NaiveBayes").newInstance(); //算法

cfs2=(Classifier)Class.forName("weka.classifiers.trees.J48").newInstance();

cfs3=(Classifier)Class.forName("weka.classifiers.rules.ZeroR").newInstance();

cfsArray[0]=cfs1;

cfsArray[1]=cfs2;

cfsArray[2]=cfs3;

//分类器的决策方式

Vote ensemble=new Vote();

SelectedTag tag1=new SelectedTag(Vote.MAJORITY_VOTING_RULE,Vote.TAGS_RULES);//（投票）

ensemble.setCombinationRule(tag1);

ensemble.setClassifiers(cfsArray);

ensemble.setSeed(2); //设置随机种子

ensemble.buildClassifier(tranIns); //训练分类器

Instance testInst;

Evaluation testingEvaluation1=new Evaluation(testIns); //检测分类模型的类

Evaluation testingEvaluation2=new Evaluation(testIns);

Evaluation testingEvaluation3=new Evaluation(testIns);

Evaluation testingEvaluation4=new Evaluation(testIns);

int length=testIns.numInstances();

//单独学习

for(int i=0;i<length;i++)

{

testInst=testIns.instance(i);

testingEvaluation1.evaluateModelOnceAndRecordPrediction(cfs1, testInst);

}

System.out.println("分类正确率："+(1- testingEvaluation1.errorRate()));

for(int i=0;i<length;i++)

{

testInst=testIns.instance(i);

testingEvaluation2.evaluateModelOnceAndRecordPrediction(cfs2, testInst);

}

System.out.println("分类正确率："+(1- testingEvaluation2.errorRate()));

for(int i=0;i<length;i++)

{

testInst=testIns.instance(i);

testingEvaluation3.evaluateModelOnceAndRecordPrediction(cfs3, testInst);

}

System.out.println("分类正确率："+(1- testingEvaluation3.errorRate()));

//集成学习

for(int i=0;i<length;i++)

{

testInst=testIns.instance(i);

testingEvaluation4.evaluateModelOnceAndRecordPrediction(ensemble, testInst);

}

System.out.println("分类正确率："+(1- testingEvaluation4.errorRate()));

}

catch(Exception e)

{

e.printStackTrace();

}

}

}