ml.ann.MainPTR.java Source code

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Here is the source code for ml.ann.MainPTR.java

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package ml.ann;

import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
import java.io.Serializable;
import java.util.Scanner;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.Normalize;

/**
 *
 * @author Ivana Clairine
 */
public class MainPTR implements Serializable {

    public static NominalToBinary m_nominalToBinaryFilter;
    public static Normalize m_normalize;

    public static void main(String[] args) throws FileNotFoundException, IOException, Exception {
        boolean randomWeight;
        double weightawal = 0.0;
        double learningRate = 0.0001;
        double threshold = 0.00;
        double momentum = 0.00;
        int maxEpoch = 100000;
        int nCrossValidate = 2;

        m_nominalToBinaryFilter = new NominalToBinary();
        m_normalize = new Normalize();

        Scanner in = new Scanner(System.in);
        System.out.println("Lokasi file: ");

        String filepath = in.nextLine();
        filepath = "test-arffs/iris.arff";
        System.out.println("--- Algoritma ---");
        System.out.println("1. Perceptron Training Rule");
        System.out.println("2. Delta Rule Incremental");
        System.out.println("3. Delta Rule Batch");
        System.out.println("Pilihan Algoritma (1/2/3) : ");
        int choice = in.nextInt();
        String temp = in.nextLine();

        System.out.println("Apakah Anda ingin memasukkan nilai weight awal? (YES/NO)");
        String isRandom = in.nextLine();
        System.out.println("Apakah Anda ingin memasukkan konfigurasi? (YES/NO)");
        String config = in.nextLine();

        if (config.equalsIgnoreCase("yes")) {
            System.out.print("Masukkan nilai learning rate: ");
            learningRate = in.nextDouble();
            System.out.print("Masukkan nilai threshold: ");
            threshold = in.nextDouble();
            System.out.print("Masukkan nilai momentum: ");
            momentum = in.nextDouble();
            System.out.print("Masukkan jumlah epoch: ");
            threshold = in.nextInt();
            System.out.print("Masukkan jumlah folds untuk crossvalidate: ");
            nCrossValidate = in.nextInt();
        }

        randomWeight = isRandom.equalsIgnoreCase("yes");

        if (randomWeight) {
            System.out.print("Masukkan nilai weight awal: ");
            weightawal = Double.valueOf(in.nextLine());
        }

        //print config
        if (isRandom.equalsIgnoreCase("yes")) {
            System.out.print("isRandom | ");
        } else {
            System.out.print("Weight " + weightawal + " | ");
        }

        System.out.print("L.rate " + learningRate + " | ");
        System.out.print("Max Epoch " + maxEpoch + " | ");
        System.out.print("Threshold " + threshold + " | ");
        System.out.print("Momentum " + momentum + " | ");
        System.out.print("Folds " + nCrossValidate + " | ");
        System.out.println();

        FileReader trainreader = new FileReader(filepath);
        Instances train = new Instances(trainreader);
        train.setClassIndex(train.numAttributes() - 1);

        m_nominalToBinaryFilter.setInputFormat(train);
        train = new Instances(Filter.useFilter(train, m_nominalToBinaryFilter));

        m_normalize.setInputFormat(train);
        train = new Instances(Filter.useFilter(train, m_normalize));

        MultiClassPTR tempMulti = new MultiClassPTR(choice, randomWeight, learningRate, maxEpoch, threshold);
        tempMulti.buildClassifier(train);

        Evaluation eval = new Evaluation(new Instances(train));
        eval.evaluateModel(tempMulti, train);
        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());
    }

}