Example usage for weka.filters MultiFilter setFilters

Introduction

In this page you can find the example usage for weka.filters MultiFilter setFilters.

Prototype

public void setFilters(Filter[] filters) 

Source Link

Document

Sets the list of possible filters to choose from.

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);//from   ww  w .  jav a 2 s.  c o 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:com.ivanrf.smsspam.SpamClassifier.java

License:Apache License

private static FilteredClassifier initFilterClassifier(int wordsToKeep, String tokenizerOp,
        boolean useAttributeSelection, String classifierOp, boolean boosting) throws Exception {
    StringToWordVector filter = new StringToWordVector();
    filter.setDoNotOperateOnPerClassBasis(true);
    filter.setLowerCaseTokens(true);// w  w w .jav a2 s.  co  m
    filter.setWordsToKeep(wordsToKeep);

    if (!tokenizerOp.equals(TOKENIZER_DEFAULT)) {
        //Make a tokenizer
        WordTokenizer wt = new WordTokenizer();
        if (tokenizerOp.equals(TOKENIZER_COMPLETE))
            wt.setDelimiters(" \r\n\t.,;:\'\"()?!-+*&#$%/=<>[]_`@\\^{}");
        else //TOKENIZER_COMPLETE_NUMBERS)
            wt.setDelimiters(" \r\n\t.,;:\'\"()?!-+*&#$%/=<>[]_`@\\^{}|~0123456789");
        filter.setTokenizer(wt);
    }

    FilteredClassifier classifier = new FilteredClassifier();
    classifier.setFilter(filter);

    if (useAttributeSelection) {
        AttributeSelection as = new AttributeSelection();
        as.setEvaluator(new InfoGainAttributeEval());
        Ranker r = new Ranker();
        r.setThreshold(0);
        as.setSearch(r);

        MultiFilter mf = new MultiFilter();
        mf.setFilters(new Filter[] { filter, as });

        classifier.setFilter(mf);
    }

    if (classifierOp.equals(CLASSIFIER_SMO))
        classifier.setClassifier(new SMO());
    else if (classifierOp.equals(CLASSIFIER_NB))
        classifier.setClassifier(new NaiveBayes());
    else if (classifierOp.equals(CLASSIFIER_IB1))
        classifier.setClassifier(new IBk(1));
    else if (classifierOp.equals(CLASSIFIER_IB3))
        classifier.setClassifier(new IBk(3));
    else if (classifierOp.equals(CLASSIFIER_IB5))
        classifier.setClassifier(new IBk(5));
    else if (classifierOp.equals(CLASSIFIER_PART))
        classifier.setClassifier(new PART()); //Tarda mucho

    if (boosting) {
        AdaBoostM1 boost = new AdaBoostM1();
        boost.setClassifier(classifier.getClassifier());
        classifier.setClassifier(boost); //Con NB tarda mucho
    }

    return classifier;
}

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