import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;

import weka.classifiers.Evaluation;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.core.Instances;
import weka.core.converters.ArffLoader;

public class SMO {

    Instances trainData;
    Instances testData;

    Instances loadArffData(String path){
        // load data
        ArffLoader loader = new ArffLoader();
        try {
            loader.setFile(new File(path));
            Instances structure = loader.getStructure();
            return structure;
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        return null;

    }

    public void doSVMTest(){
        //trainData= loadArffData("I:\\Programs\\Weka\\Weka-3-6\\data\\faultvector.arff");
        //testData= loadArffData("I:\\Programs\\Weka\\Weka-3-6\\data\\test-fault.arff");
         // create new instance of scheme
         weka.classifiers.functions.SMO scheme = new weka.classifiers.functions.SMO();
         // set options
         try {
            //scheme.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
            //scheme.buildClassifier(trainData);
             trainData = new Instances(
                     new BufferedReader(new FileReader("I:\\Programs\\Weka\\Weka-3-6\\data\\faultvector.arff")));
             testData = new Instances(
                     new BufferedReader(new FileReader("I:\\Programs\\Weka\\Weka-3-6\\data\\test-fault.arff")));
             trainData.setClassIndex(trainData.numAttributes() - 1);
             testData.setClassIndex(testData.numAttributes() - 1);
            PolyKernel kernel = new PolyKernel();
            String[] options = weka.core.Utils.splitOptions("-E "+ 4.5);
            kernel.setOptions(options);
            //SMO svm = new SMO();
            options = weka.core.Utils.splitOptions("-N 0 -V 5 -C 4");
            scheme.setOptions(options); //svm.setOptions(options);
            scheme.setKernel(kernel);
            scheme.buildClassifier(trainData);

            Evaluation eval = new Evaluation(trainData);
            eval.evaluateModel(scheme, testData);
             System.out.println(eval.toSummaryString("\nResults\n======\n", false));
         } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }

     public static void main(String[] args) throws Exception {
         SMO testsvm = new SMO();
         testsvm.doSVMTest();
     }

}