bi_project2.Classify.java Source code

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Here is the source code for bi_project2.Classify.java

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package bi_project2;

import weka.classifiers.functions.SMOreg;
import weka.core.Instances;
import weka.core.Instance;
import weka.core.converters.ConverterUtils.DataSource;
import java.io.File;
import java.io.IOException;
import weka.core.converters.CSVSaver;

//import java.io.IOException;

public class Classify {
    private static Instances testDataSet; //test dataset
    private static Instances trainDataSet; //train dataset
    private static Instances resultDataSet; // result dataset
    private static Instance instanceVal; //current instance
    private static SMOreg smo; // classifier
    private static CSVSaver saver; //csv saver

    private static String destination_path;

    public Classify() {
        saver = new CSVSaver();
        smo = new SMOreg(); // create a new SMO classifier-create
    }

    // step1: load dataSet
    public void loadDataSet(File Training, File Testing) {
        try {
            // load the training dataset
            DataSource datasource = new DataSource(Training.toString());
            trainDataSet = datasource.getDataSet();
            // set class index
            trainDataSet.setClassIndex(trainDataSet.numAttributes() - 1);
            DataSource newdatasource = new DataSource(Testing.toString());
            testDataSet = newdatasource.getDataSet();
            // set class index
            testDataSet.setClassIndex(testDataSet.numAttributes() - 1);
        } catch (Exception e) {
            System.out.println(e.getMessage());
        }
        destination_path = Testing.getParent();
        System.out.println("destionation path is" + destination_path);
        System.out.println("successfully loaded test and training files");
    }

    //build classifier
    public void buildClassifier() {
        try {
            smo.buildClassifier(trainDataSet); // supply instances to   model                           // Classifier--build model
            System.out.println(smo.toString());
        } catch (Exception e) {
            System.out.println(e.getMessage());
        }
        System.out.print("Successfully build classifier");
    }

    // make predictions on test dataset
    public void makePredictions() {

        resultDataSet = new Instances(testDataSet);
        for (int i = 0; i < testDataSet.numInstances(); i++) {
            instanceVal = testDataSet.instance(i);
            double predict_smo_val = 0;
            try {
                predict_smo_val = smo.classifyInstance(instanceVal);
                resultDataSet.instance(i).setClassValue(predict_smo_val);
            } catch (Exception e) {
                System.out.println("exception thrown !!");
            }
        }

        saver.setInstances(resultDataSet);
        try {
            saver.setFile(new File(destination_path + "/result.csv"));
            saver.writeBatch();
            System.out.println("Successfully wrote results to file: " + destination_path + "/result.csv");
        } catch (IOException e1) {
            System.out.println(e1.getMessage());
        }
    }

    public String getPredictedResults() {
        return destination_path + "/result.csv";
    }
}