myclassifier.naiveBayes.java Source code

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Here is the source code for myclassifier.naiveBayes.java

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package myclassifier;

import java.util.Random;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.Utils;
import weka.core.converters.ConverterUtils;

/**
 *
 * @author Fahmi
 */
public class naiveBayes {
    Classifier NBClassifier;
    Instances data;

    public naiveBayes() {
        data = null;
        NBClassifier = new NaiveBayes();
    }

    public naiveBayes(Instances data) {
        this.data = data;
        NBClassifier = new NaiveBayes();
    }

    public void setData(Instances data) {
        this.data = data;
    }

    public void TestData(Instances dataTest) throws Exception {
        if (data != null) {
            Instances train = data;
            // train classifier
            NBClassifier.buildClassifier(train);
            // evaluate classifier and print some statistics
            Evaluation eval = new Evaluation(dataTest);
            System.out.println(eval.toSummaryString("\nResults\n======\n", false));
            System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
            System.out.println(eval.toMatrixString());
        } else {
            System.out.println("Data is null");
        }
    }

    public void CrossValidation() throws Exception {
        if (data != null) {
            Instances train = data;
            // train classifier
            NBClassifier.buildClassifier(train);
            // evaluate classifier and print some statistics
            Evaluation eval = new Evaluation(train);
            eval.crossValidateModel(NBClassifier, train, 10, new Random(1));
            System.out.println(eval.toSummaryString("\nResults\n======\n", false));
            System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
            System.out.println(eval.toMatrixString());
        } else {
            System.out.println("Data is null");
        }
    }

    public void PercentageSplit(double percent) throws Exception {
        // Percent split
        int trainSize = (int) Math.round(data.numInstances() * percent / 100);
        int testSize = data.numInstances() - trainSize;
        Instances train = new Instances(data, 0, trainSize);
        Instances test = new Instances(data, trainSize, testSize);
        // train classifier
        NBClassifier.buildClassifier(train);
        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(NBClassifier, test);
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
        System.out.println(eval.toClassDetailsString("\n=== Detailed Accuracy By Class ===\n"));
        System.out.println(eval.toMatrixString());
    }

    public void SaveModel() throws Exception {
        SerializationHelper.write("NaiveBayes.model", NBClassifier);
        System.out.println("Model has been saved");
    }

    public void LoadModel() throws Exception {
        NBClassifier = (Classifier) SerializationHelper.read("NaiveBayes.model");
        System.out.println("Model has been loaded");
    }

    public void Klasifikasi(String filename) throws Exception {
        // load unlabeled data and set class attribute
        Instances unlabeled = ConverterUtils.DataSource.read("unlabeled_" + filename);
        unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
        // create copy
        Instances labeled = new Instances(unlabeled);
        // label instances
        for (int i = 0; i < unlabeled.numInstances(); i++) {
            double clsLabel = NBClassifier.classifyInstance(labeled.instance(i));
            labeled.instance(i).setClassValue(clsLabel);
        }
        // save newly labeled data
        ConverterUtils.DataSink.write("labeled_" + filename, labeled);

        //print hasil
        System.out.println("Classification Result");
        System.out.println("# - actual - predicted - distribution");
        for (int i = 0; i < labeled.numInstances(); i++) {

            double pred = NBClassifier.classifyInstance(labeled.instance(i));
            double[] dist = NBClassifier.distributionForInstance(labeled.instance(i));
            System.out.print((i + 1) + " - ");
            System.out.print(labeled.instance(i).toString(labeled.classIndex()) + " - ");
            System.out.print(labeled.classAttribute().value((int) pred) + " - ");
            System.out.println(Utils.arrayToString(dist));
        }
    }
}