Example usage for weka.classifiers.functions PLSClassifier getFilter

Introduction

In this page you can find the example usage for weka.classifiers.functions PLSClassifier getFilter.

Prototype

public Filter getFilter() 

Source Link

Document

Get the PLS filter.

Usage

From source file:adams.opt.optimise.GeneticAlgorithm.java

License:Open Source License

public static void main(String[] args) {
    Environment.setEnvironmentClass(Environment.class);
    GeneticAlgorithm ga = new GeneticAlgorithm();
    ga.setBits(1);//ww w  . j a v  a 2s  .  co m
    ga.setNumChrom(8);
    ga.setIterations(10000);
    ga.setFavorZeroes(true);

    AttributeSelection as = new AttributeSelection();
    //as.setDataset(new PlaceholderFile("/home/dale/blgg/conversion/merged/m_5_.75.arff"));
    ArrayConsumer.setOptions(as, args);
    PLSClassifier pls = new PLSClassifier();
    PLSFilter pf = (PLSFilter) pls.getFilter();
    pf.setNumComponents(11);

    LinearRegressionJ reg = new LinearRegressionJ();
    reg.setEliminateColinearAttributes(false);
    reg.setAttributeSelectionMethod(
            new SelectedTag(LinearRegressionJ.SELECTION_NONE, LinearRegressionJ.TAGS_SELECTION));

    GPD gp = new GPD();
    gp.setNoise(.01);
    //RBFKernel rbf = new RBFKernel();
    //rbf.setChecksTurnedOff(true);
    //rbf.setGamma(.01);
    //gp.setKernel(rbf);

    Remove remove = new Remove();
    remove.setAttributeIndices("1");
    FilteredClassifier fc = new FilteredClassifier();

    MultiFilter mf = new MultiFilter();
    Filter[] filters = new Filter[2];
    filters[0] = remove;
    filters[1] = pf;
    mf.setFilters(filters);

    fc.setClassifier(gp);
    fc.setFilter(pf);

    as.setClassifier(gp);
    as.setClassIndex("last");
    //as.setDataset(new PlaceholderFile("/home/dale/OMD_clean.arff"));
    //as.setOutputDirectory(new PlaceholderFile("/research/dale"));
    ga.setLoggingLevel(LoggingLevel.INFO);
    as.setLoggingLevel(LoggingLevel.INFO);
    ga.optimise(as.getDataDef(), as);

}

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From source file:jjj.asap.sas.models1.job.BuildPLSModels.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 a 2s .  c  o  m
    if (!Bucket.isBucket("models", outputBucket)) {
        throw new FileNotFoundException(outputBucket);
    }

    // Standard PLS

    PLSClassifier pls = new PLSClassifier();
    PLSFilter filter = (PLSFilter) pls.getFilter();
    filter.setNumComponents(5);
    filter.setPreprocessing(NONE);

    // centered PLS

    PLSClassifier plsc = new PLSClassifier();
    PLSFilter center = (PLSFilter) plsc.getFilter();
    center.setNumComponents(5);
    center.setPreprocessing(CENTER);

    // standardized PLS

    PLSClassifier plss = new PLSClassifier();
    PLSFilter std = (PLSFilter) plss.getFilter();
    std.setNumComponents(10);
    std.setPreprocessing(STANDARDIZE);

    // 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) {

        int essaySet = Contest.getEssaySet(dsn);

        Classifier alg = pls;

        if (essaySet == 10 || dsn.contains("1grams-thru-3grams")) {
            alg = plsc;
        }

        if (essaySet == 7) {
            alg = plss;
        }

        queue.add(Job.submit(
                new RegressionModelBuilder(dsn, "PLS", AbstractClassifier.makeCopy(alg), 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();

}

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