tucil2ai.Tucil2AI.java Source code

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Here is the source code for tucil2ai.Tucil2AI.java

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package tucil2ai;

import java.io.File;
import java.util.Random;
import java.util.Scanner;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.J48;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import weka.core.SerializationHelper;

/**
 *
 * @author t420s
 */

public class Tucil2AI {

    /**
     *
     * @param cls
     * @throws Exception
     */
    protected static void saveModel(Classifier cls) throws Exception {
        weka.core.SerializationHelper.write("res/tucilAI2j48.model", cls);
    }

    /**
     *
     * @return
     * @throws Exception
     */
    protected static Classifier loadModel() throws Exception {
        return (Classifier) weka.core.SerializationHelper.read("res/tucilAI2j48.model");
    }

    /**
    * @param filename
    * @param f
    * @return 
    * @throws java.lang.Exception
    */
    protected static Instances loadfile(String filename, Discretize f) throws Exception {
        DataSource source = new DataSource(filename);
        Instances data;
        data = source.getDataSet();
        data.setClassIndex(data.numAttributes() - 1);

        Instances data_filtered = Filter.useFilter(data, f);
        return data_filtered;
    }

    /**
     *
     * @param cls
     * @param data
     * @param cross
     * @return
     * @throws Exception
     */
    protected static Evaluation evalJ48(Classifier cls, Instances data, boolean cross) throws Exception {
        Evaluation E;
        E = new Evaluation(data);
        if (cross == false) {
            E.evaluateModel(cls, data);
        } else {
            E.crossValidateModel(cls, data, 10, new Random(0x100)); /*crossValidateModel*/
        }
        return E;
    }

    /**
     *
     * @param E
     * @throws Exception
     */
    public static void printEval(Evaluation E) throws Exception {
        System.out.println(E.toSummaryString("\nResults\n======\n", false));
        System.out.println(E.toClassDetailsString());
        System.out.println(E.toMatrixString());
    }

    /**
     *
     * @param cls
     * @param filename
     * @param f
     * @throws Exception
     */
    public static void ClassifyJ48(Classifier cls, String filename, Discretize f) throws Exception {
        Instances unlabeled = loadfile(filename, f);
        Instances labeled = new Instances(unlabeled);
        for (int i = 0; i < unlabeled.numInstances(); ++i) {
            double clsLabel = cls.classifyInstance(unlabeled.instance(i));
            labeled.instance(i).setClassValue(clsLabel);
        }

        System.out.println(labeled.toString());
    }

    public static void menu() {
        System.out.println("Apa yang mau anda lakukan:");
        System.out.println("1. Load model");
        System.out.println("2. Pelajari dataset");
        System.out.println("3. Exit");
    }

    /**
     *
     * @param args
     * @throws Exception
     */
    public static void main(String[] args) throws Exception {
        // TODO code application logic here
        DataSource source;
        Instances data;
        Instances data_filtered;
        Discretize filter;
        Classifier clsJ48 = null;

        source = new DataSource("res/iris.arff");
        data = source.getDataSet();
        data.setClassIndex(data.numAttributes() - 1);
        System.out.println("Data berhasil dibaca");
        // filter
        filter = new Discretize();
        filter.setInputFormat(data);
        data_filtered = Filter.useFilter(data, filter);
        System.out.println("Data berhasil difilter");

        int pil = 0;
        while (pil != 3) {
            menu();
            Scanner sc = new Scanner(System.in);
            pil = sc.nextInt();
            switch (pil) {
            case 1:
                clsJ48 = loadModel();
                System.out.println("Model berhasil dimuat");
                break;
            case 2:
                clsJ48 = new J48();
                clsJ48.buildClassifier(data_filtered);
                int pil3;
                System.out.println("Apakah Anda mau menyimpan model ini?");
                System.out.println("1. Ya");
                System.out.println("2. Tidak");
                pil3 = sc.nextInt();
                if (pil3 == 1) {
                    saveModel(clsJ48);
                }
                break;
            default:
                break;
            }
            if (pil == 3)
                continue;
            System.out.println("Pilih metode evaluasi:");
            System.out.println("1. 10-fold cross validation");
            System.out.println("2. Full-test");
            System.out.println("3. Klasifikasikan datatest");

            int pilmethod;
            pilmethod = sc.nextInt();
            while (pilmethod < 1 || pilmethod > 3) {
                System.out.println("Masukan salah!");
                pilmethod = sc.nextInt();
            }
            if (pilmethod != 3) {
                Evaluation eval = evalJ48(clsJ48, data_filtered, pilmethod == 1);
                printEval(eval);
            } else {
                ClassifyJ48(clsJ48, "res/datatest.arff", filter);
                System.out.println("Terbentuk 3 instance baru untuk diklasifikasi");
            }
        }
    }
}