Example usage for weka.classifiers.bayes BayesNet setEstimator

Introduction

In this page you can find the example usage for weka.classifiers.bayes BayesNet setEstimator.

Prototype

public void setEstimator(BayesNetEstimator newBayesNetEstimator) 

Source Link

Document

Set the Estimator Algorithm used in calculating the CPTs

Usage

From source file:jjj.asap.sas.models1.job.BuildBasicModels.java

License:Open Source License

@Override
protected void run() throws Exception {

    // validate args
    if (!Bucket.isBucket("datasets", inputBucket)) {
        throw new FileNotFoundException(inputBucket);
    }//from   w  ww.  j  a  v  a2s  . co m
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

    // create prototype classifiers
    Map<String, Classifier> prototypes = new HashMap<String, Classifier>();

    // bayes

    BayesNet net = new BayesNet();
    net.setEstimator(new BMAEstimator());
    prototypes.put("BayesNet", net);

    prototypes.put("NaiveBayes", new NaiveBayes());

    // functions

    prototypes.put("RBFNetwork", new RBFNetwork());
    prototypes.put("SMO", new SMO());

    // init multi-threading
    Job.startService();
    final Queue<Future<Object>> queue = new LinkedList<Future<Object>>();

    // get the input from the bucket
    List<String> names = Bucket.getBucketItems("datasets", this.inputBucket);
    for (String dsn : names) {

        // for each prototype classifier
        for (Map.Entry<String, Classifier> prototype : prototypes.entrySet()) {

            // use InfoGain to discard useless attributes

            AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();

            classifier.setEvaluator(new InfoGainAttributeEval());

            Ranker ranker = new Ranker();
            ranker.setThreshold(0.0001);
            classifier.setSearch(ranker);

            classifier.setClassifier(AbstractClassifier.makeCopy(prototype.getValue()));

            queue.add(Job.submit(
                    new ModelBuilder(dsn, "InfoGain-" + prototype.getKey(), classifier, this.outputBucket)));
        }
    }

    // wait on complete
    Progress progress = new Progress(queue.size(), this.getClass().getSimpleName());
    while (!queue.isEmpty()) {
        try {
            queue.remove().get();
        } catch (Exception e) {
            Job.log("ERROR", e.toString());
        }
        progress.tick();
    }
    progress.done();
    Job.stopService();

}

---