com.Machine_learning.model.MySupportVectorMachine.java Source code

Introduction

Here is the source code for com.Machine_learning.model.MySupportVectorMachine.java

Source

/*
 * To change this license header, choose License Headers in Project Properties.
 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */
package com.Machine_learning.model;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.logging.Level;
import java.util.logging.Logger;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.classifiers.functions.LibSVM;
import static weka.core.Utils.splitOptions;

/**
 *
 * @author filip
 */
public class MySupportVectorMachine extends ClassifierResults {
    LibSVM classifier;
    Instances dataInstances;
    String options = "-C 7 -K 2"; //Default gamma( -G ) = 1/number of attributes

    public MySupportVectorMachine(Instances data) {
        dataInstances = data;
        try {
            classifier = new LibSVM();
            classifier.setOptions(splitOptions(options));
            classifier.buildClassifier(data);
            eval = new Evaluation(dataInstances);
        } catch (Exception ex) {
            System.out.println("THROWN " + ex.getMessage());
            Logger.getLogger(MySupportVectorMachine.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    public void applyMethod(String method) {
        try {

            List<Instances> datasets = new ArrayList<>();
            if (method.equals("cross-validation")) {
                eval.crossValidateModel(classifier, dataInstances, 4, new Random(1));
                return;
            } else if (method.equals("test-set")) {
                Preprocessing preprocessTestSet = new Preprocessing(null);
                datasets = preprocessTestSet.getDataSets(
                        MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                        MyNaiveBayes.class.getResource("/data/2017-articles-correct.arff").getPath());

            } else if (method.equals("percentage")) {
                Preprocessing preprocessTestSet = new Preprocessing(null);
                datasets = preprocessTestSet.getDataSets(
                        MyNaiveBayes.class.getResource("/data/categories-per-train.arff").getPath(),
                        MyNaiveBayes.class.getResource("/data/categories-per-test.arff").getPath());

            } else {
                return;
            }

            classifier = new LibSVM();
            classifier.setOptions(splitOptions(options));
            classifier.buildClassifier(datasets.get(0));
            eval = new Evaluation(datasets.get(0));
            eval.evaluateModel(classifier, datasets.get(1));
        } catch (Exception ex) {
            Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
        }
    }

    public String getResult() {
        try {
            return eval.toSummaryString();
        } catch (Exception ex) {
            Logger.getLogger(MyNaiveBayes.class.getName()).log(Level.SEVERE, null, ex);
        }
        return "Something went wrong (NB)";
    }
}