Example usage for weka.classifiers.bayes NaiveBayes setOptions

Introduction

In this page you can find the example usage for weka.classifiers.bayes NaiveBayes setOptions.

Prototype

@Override
public void setOptions(String[] options) throws Exception 

Source Link

Document

Parses a given list of options.

Usage

From source file:PEBL.TwoStep.java

public static void main(String[] args) throws Exception {

    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            "Z:\\\\shared from vm\\\\fourthset\\\\mixed.csv");

    Instances data = source.getDataSet();

    // setting class attribute if the data format does not provide this information
    // For example, the XRFF format saves the class attribute information as well
    if (data.classIndex() == -1) {
        data.setClassIndex(data.numAttributes() - 1);
    }//from ww  w.  j av a  2s  .c  o  m

    NumericToNominal nmf = new NumericToNominal();
    nmf.setInputFormat(data);
    data = Filter.useFilter(data, nmf);

    // build a c4.5 classifier
    String[] options = new String[1];
    // options[0] = "-C 0.25 -M 2";            // unpruned tree
    options[0] = "-K";
    NaiveBayes c = new NaiveBayes(); // new instance of tree
    c.setOptions(options); // set the options
    c.buildClassifier(data); // build classifier

    // eval
    Evaluation eval = new Evaluation(data);
    eval.crossValidateModel(c, data, 10, new Random(1));
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
    System.out.println(eval.toClassDetailsString());
    System.out.println("--- model learned on mixed set ---");

    // load unlabeled data
    ConverterUtils.DataSource s = new ConverterUtils.DataSource(
            "Z:\\\\shared from vm\\\\fourthset\\\\unlabelled.csv");
    Instances unlabeled = s.getDataSet();
    // set class attribute
    unlabeled.setClassIndex(unlabeled.numAttributes() - 1);

    nmf = new NumericToNominal();
    nmf.setInputFormat(unlabeled);
    unlabeled = Filter.useFilter(unlabeled, nmf);

    // label instances
    for (int i = 0; i < unlabeled.numInstances(); i++) {
        double classZero = c.distributionForInstance(unlabeled.instance(i))[0];
        double classOne = c.distributionForInstance(unlabeled.instance(i))[1];
        System.out.print(
                "classifying: " + unlabeled.instance(i) + " : " + classZero + " - " + classOne + " == class: ");
        if (classZero > classOne) {
            System.out.print("0");
            unlabeled.instance(i).setClassValue("0");
        } else {
            System.out.print("1");
            unlabeled.instance(i).setClassValue("1");
        }
        System.out.println("");
    }

    // save labeled data
    // BufferedWriter writer = new BufferedWriter(
    //         new FileWriter("Z:\\\\shared from vm\\\\thirdset\\\\relabelled.arff"));
    // writer.write(labeled.toString());
    // writer.newLine();
    // writer.flush();
    // writer.close();
    ArffSaver saver = new ArffSaver();
    saver.setInstances(unlabeled);
    saver.setFile(new File("Z:\\shared from vm\\thirdset\\relabelled.arff"));
    //        saver.setDestination(new File("Z:\\shared from vm\\thirdset\\relabelled.arff"));   // **not** necessary in 3.5.4 and later
    saver.writeBatch();

}

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