experimentalclassifier.ExperimentalClassifier.java Source code

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Here is the source code for experimentalclassifier.ExperimentalClassifier.java

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package experimentalclassifier;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.filters.Filter;

import weka.core.converters.ConverterUtils.DataSource;

import java.io.FileReader;
import java.io.BufferedReader;
import java.util.Random;
import java.util.Vector;
import weka.filters.unsupervised.instance.Randomize;
import weka.filters.unsupervised.instance.RemovePercentage;

/**
 *
 * @author ejones23
 */
public class ExperimentalClassifier {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) throws Exception {

        DataSource source = new DataSource("data/iris.csv");

        Instances data = source.getDataSet();

        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        data.randomize(new Random());

        String[] options = weka.core.Utils.splitOptions("-P 30");
        RemovePercentage remove = new RemovePercentage();
        remove.setOptions(options);
        remove.setInputFormat(data);
        Instances train = Filter.useFilter(data, remove);

        remove.setInvertSelection(true);
        remove.setInputFormat(data);
        Instances test = Filter.useFilter(data, remove);

        Classifier classifier = new HardCodedClassifier();
        classifier.buildClassifier(train);//Currently, this does nothing
        Evaluation eval = new Evaluation(train);
        eval.evaluateModel(classifier, test);
        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    }
}