tucil.dua.ai.TucilDuaAi.java Source code

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Here is the source code for tucil.dua.ai.TucilDuaAi.java

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package tucil.dua.ai;

import java.util.Scanner;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.J48;
import weka.core.Debug.Random;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.converters.ConverterUtils.DataSource;
import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.Remove;

/**
 *
 * @author Toshiba
 */
public class TucilDuaAi {

    static Instances datas;

    public static void LoadData() throws Exception {
        datas = DataSource.read("C:\\Program Files\\Weka-3-8\\data\\iris.arff");
        datas.setClassIndex(datas.numAttributes() - 1); //Set label atribute
    }

    public static Instances Discretize() throws Exception {
        Discretize discretize = new Discretize();
        String[] options = new String[2];
        options[0] = "-R";
        options[1] = "1";
        discretize.setOptions(options);
        discretize.setInputFormat(datas);
        return Filter.useFilter(datas, discretize);
    }

    public static void fullTrainingSet() throws Exception {
        Classifier j48 = new J48();
        j48.buildClassifier(datas);

        Evaluation eval = new Evaluation(datas);
        eval.evaluateModel(j48, datas);
        System.out.println("=====Run Information======");
        System.out.println("======Classifier Model======");
        System.out.println(j48.toString());
        System.out.println(eval.toSummaryString("====Stats======\n", false));
        System.out.println(eval.toClassDetailsString("====Detailed Result=====\n"));
        System.out.println(eval.toMatrixString("======Confusion Matrix======\n"));
    }

    public static void addInstance() {

    }

    public static void crossValidation() throws Exception {
        Evaluation evaluation = new Evaluation(datas);
        Classifier attr_tree = new J48();
        attr_tree.buildClassifier(datas);
        evaluation.crossValidateModel(attr_tree, datas, 10, new Random(1));
        System.out.println("=====Run Information======");
        System.out.println("======Classifier Model======");
        System.out.println(attr_tree.toString());
        System.out.println(evaluation.toSummaryString("====Stats======\n", false));
        System.out.println(evaluation.toClassDetailsString("====Detailed Result=====\n"));
        System.out.println(evaluation.toMatrixString("======Confusion Matrix======\n"));
    }

    public static void saveModel(String path, Classifier cls) throws Exception {
        weka.core.SerializationHelper.write(path, cls);
    }

    public static Classifier loadModel(String path) throws Exception {
        return (Classifier) weka.core.SerializationHelper.read(path);
    }

    public static void addInstance(Instances d) {
        Scanner input = new Scanner(System.in);
        Instance temp = new DenseInstance(d.numAttributes());
        for (int i = 0; i < (d.numAttributes() - 1); i++) {
            System.out.print(d.attribute(i) + " = ");
            Double x = input.nextDouble();
            temp.setValue(d.attribute(i), x);
        }
        System.out.print(d.attribute(d.numAttributes() - 1) + " = ");
        String x = input.next();
        temp.setValue(d.attribute(d.numAttributes() - 1), x);
        d.add(temp);
    }

    public static void main(String[] args) {
        // TODO code application logic here
        TucilDuaAi test = new TucilDuaAi();
        try {
            LoadData();
            System.out.println(datas);
            System.out.println("=====");
            addInstance(datas);
            System.out.println(datas.numInstances());
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

}