tubesduaai.TesFFNN.java Source code

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Here is the source code for tubesduaai.TesFFNN.java

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package tubesduaai;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.Serializable;
import java.util.Random;
import java.util.Scanner;
import weka.classifiers.Evaluation;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

/**
 *
 * @author Nugroho Satriyanto <massatriya@gmail.com>
 */
public class TesFFNN {

    public static Instances data;
    public static Evaluation eval;
    public static Scanner sc = new Scanner(System.in);

    public static Evaluation cross_validation(FFNN x) throws Exception {
        eval = new Evaluation(data);
        eval.crossValidateModel(x, data, 10, new Random(1));
        return eval;
    }

    public static void tes() throws Exception {
        BufferedReader reader = new BufferedReader(new FileReader("C:\\Program Files\\Weka-3-8\\data\\iris.arff"));
        data = new Instances(reader);
        reader.close();
        // setting class attribute
        data.setClassIndex(data.numAttributes() - 1);
        Instances dummy = null;
        FFNN nn = new FFNN("C:\\Program Files\\Weka-3-8\\data\\iris.arff", 0);
        boolean[] nom = nn.cek_nominal();
        System.out.println("ingin load?: ");
        String load = sc.nextLine();
        if (load.equalsIgnoreCase("y")) {
            nn.load_model();
            eval = cross_validation(nn);
            nn.print_perceptron();
        } else {
            nn.buildClassifier(data);
            nn.print_perceptron();
            eval = cross_validation(nn);
            nn.print_perceptron();
        }
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        double[] attValues1 = { 5.1, 3.5, 1.4, 0.2 };
        Instance i1 = new DenseInstance(1.0, attValues1);
        double[] attValues2 = { 7.0, 3.2, 4.7, 1.4 };
        Instance i2 = new DenseInstance(1.0, attValues2);
        double[] attValues3 = { 6.3, 3.3, 6.0, 2.5 };
        Instance i3 = new DenseInstance(1.0, attValues3);
        i1.setDataset(data);
        i2.setDataset(data);
        i3.setDataset(data);
        //hasil harusnya 0 1 2
        System.out.println(nn.classifyInstance(i1));
        System.out.println(nn.classifyInstance(i2));
        System.out.println(nn.classifyInstance(i3));
        System.out.println("ingin save?: ");
        String save = sc.nextLine();
        if (save.equalsIgnoreCase("y")) {
            nn.save_model();
        }
    }

    public static void main(String[] args) {
        try {
            tes();
        } catch (Exception e) {
            System.out.println(e.toString());
        }
    }
}