expshell.ExpShell.java Source code

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Here is the source code for expshell.ExpShell.java

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/*
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package expshell;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Debug.Random;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Standardize;
import weka.filters.unsupervised.instance.RemovePercentage;

/**
 *
 * @author YH Jonathan Kwok
 */
public class ExpShell {

    /**
     * @param args the command line arguments
     * @throws java.lang.Exception
     */
    public static void main(String[] args) throws Exception {
        String file = "C:\\Users\\YH Jonathan Kwok\\Documents\\NetBeansProjects\\ExpShell\\src\\expshell\\iris.csv";

        DataSource source = new DataSource(file);
        Instances data = source.getDataSet();

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

        //Randomize it
        data.randomize(new Random(1));

        RemovePercentage rp = new RemovePercentage();
        rp.setPercentage(70);

        rp.setInputFormat(data);
        Instances training = Filter.useFilter(data, rp);

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

        //standardize the data
        Standardize filter = new Standardize();
        filter.setInputFormat(training);

        Instances newTest = Filter.useFilter(test, filter);
        Instances newTraining = Filter.useFilter(training, filter);

        //Part 5 - Now it's a knn
        Classifier knn = new NeuralClassifier();
        knn.buildClassifier(newTraining);
        Evaluation eval = new Evaluation(newTraining);
        eval.evaluateModel(knn, newTest);

        System.out.println(eval.toSummaryString("***** Overall results: *****", false));

    }

}