assign00.ExperimentShell.java Source code

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Here is the source code for assign00.ExperimentShell.java

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/*
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package assign00;

import java.util.Random;
import weka.classifiers.Evaluation;
import weka.classifiers.lazy.IBk;
import weka.classifiers.trees.Id3;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Standardize;

/**
 *
 * @author Iceman
 */
public class ExperimentShell {

    //static final String file = "lib/iris.csv";
    static final String file = "lib/pima-indians-diabetes.csv";
    //static final String file = "lib/carData.csv";
    //static final String file = "lib/houseVotes.csv";
    //static final String file = "lib/houseVotesTest.csv";
    //static final String file = "lib/lenses.csv";
    //static final String file = "lib/chess.csv";
    //static final String file = "lib/irisDiscrete4Bucket.csv";
    //static final String file = "lib/irisDiscrete2Bucket.csv";

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource(file);
        Instances dataSet = source.getDataSet();

        //Set up data
        dataSet.setClassIndex(dataSet.numAttributes() - 1);
        dataSet.randomize(new Random(1));

        //determine sizes
        int trainingSize = (int) Math.round(dataSet.numInstances() * .7);
        int testSize = dataSet.numInstances() - trainingSize;

        Instances training = new Instances(dataSet, 0, trainingSize);

        Instances test = new Instances(dataSet, trainingSize, testSize);

        Standardize standardizedData = new Standardize();
        standardizedData.setInputFormat(training);

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

        NeuralNetworkClassifier NWC = new NeuralNetworkClassifier();
        NWC.buildClassifier(newTraining);

        Evaluation eval = new Evaluation(newTraining);
        eval.evaluateModel(NWC, newTest);

        System.out.println(eval.toSummaryString("\nResults\n======\n", false));
    }

}