Main2.java

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.Logistic;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.supervised.instance.Resample;
import weka.filters.unsupervised.attribute.RemoveUseless;
import weka.filters.unsupervised.attribute.RandomProjection;

import java.util.Random;

public class Main2 {
    public static void main(String args[]) throws Exception {
        DataSource source = new DataSource("/home/zmick2/440/intel/data/train.arff");
        Instances data = source.getDataSet();
        data.setClassIndex(data.numAttributes() - 1);

        // do the remove useless thing
        RemoveUseless ru = new RemoveUseless();
        ru.setInputFormat(data);
        data = Filter.useFilter(data, ru);

        // do the random projection thing
        RandomProjection rp = new RandomProjection();
        rp.setOptions( new String[]{"-N", "1000"} );
        rp.setInputFormat(data);
        data = Filter.useFilter(data, rp);

        for (int p : new int[]{2, 6, 100}) {
            // set up to resample
            Resample resample = new Resample();
            resample.setOptions( new String[]{"-Z", String.valueOf(p)});
            resample.setInputFormat(data);

            // run resample
            Instances smallerData = Filter.useFilter(data, resample);

            for (int i = 0; i < 4; i++) {
                Classifier model = null;
                String str = "";

                switch (i) {
                    case 0:
                        str = "Running logistic\n---";
                        model = new Logistic();
                        model.setOptions(new String[]{"-M","50"});
                        break;
                    case 1:
                        str = "Running smo\n---";
                        model = new SMO();
                        break;
                    case 2:
                        str = "Running percept single layer\n---";
                        model = new MultilayerPerceptron();
                        model.setOptions(new String[]{"-H","0"});
                        break;
                    case 3:
                        str = "Running percept multi layer\n---";
                        model = new MultilayerPerceptron();
                        model.setOptions(new String[]{"-H","50", "-N","10"});
                        break;
                }

                // do cross validation
                Evaluation eval = new Evaluation(smallerData);
                eval.crossValidateModel(model, smallerData, 5, new Random(System.currentTimeMillis()));

                System.out.println(str);
                System.out.printf("i=%d p=%d\n", i, p);
                System.out.println(eval.toSummaryString());
            }
        }
    }
}