Example usage for weka.classifiers Classifier buildClassifier

Introduction

In this page you can find the example usage for weka.classifiers Classifier buildClassifier.

Prototype

public abstract void buildClassifier(Instances data) throws Exception;

Source Link

Document

Generates a classifier.

Usage

From source file:tr.gov.ulakbim.jDenetX.classifiers.WEKAClassifier.java

License:Open Source License

public void buildClassifier() {
    try {// ww w. ja v a2 s.  c  o m
        if ((classifier instanceof UpdateableClassifier) == false) {
            Classifier auxclassifier = AbstractClassifier.makeCopy(classifier);
            auxclassifier.buildClassifier(instancesBuffer);
            classifier = auxclassifier;
            isBufferStoring = false;
        }
    } catch (Exception e) {
        System.err.println("Building WEKA Classifier: " + e.getMessage());
    }
}

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From source file:Tubes.Classification.java

public Evaluation runClassifier(Classifier model, Instances training, Instances testing) {
    try {/*w w  w . ja v  a  2 s  .  c o m*/
        Evaluation eval_train = new Evaluation(training);
        model.buildClassifier(training);

        eval_train.evaluateModel(model, testing);
        return eval_train;

    } catch (Exception e) {
        e.printStackTrace();
    }
    return null;
}

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From source file:tubes1.Main.java

public static Evaluation classify(Classifier model, Instances trainingSet, Instances testingSet)
        throws Exception {
    Evaluation evaluation = new Evaluation(trainingSet);
    model.buildClassifier(trainingSet);
    evaluation.evaluateModel(model, testingSet);
    return evaluation;
}

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From source file:tucil.dua.ai.TucilDuaAi.java

public static void fullTrainingSet() throws Exception {
    Classifier j48 = new J48();
    j48.buildClassifier(datas);

    Evaluation eval = new Evaluation(datas);
    eval.evaluateModel(j48, datas);/*from ww w  .  jav  a  2  s  .c  o m*/
    System.out.println("=====Run Information======");
    System.out.println("======Classifier Model======");
    System.out.println(j48.toString());
    System.out.println(eval.toSummaryString("====Stats======\n", false));
    System.out.println(eval.toClassDetailsString("====Detailed Result=====\n"));
    System.out.println(eval.toMatrixString("======Confusion Matrix======\n"));
}

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From source file:tucil.dua.ai.TucilDuaAi.java

public static void crossValidation() throws Exception {
    Evaluation evaluation = new Evaluation(datas);
    Classifier attr_tree = new J48();
    attr_tree.buildClassifier(datas);
    evaluation.crossValidateModel(attr_tree, datas, 10, new Random(1));
    System.out.println("=====Run Information======");
    System.out.println("======Classifier Model======");
    System.out.println(attr_tree.toString());
    System.out.println(evaluation.toSummaryString("====Stats======\n", false));
    System.out.println(evaluation.toClassDetailsString("====Detailed Result=====\n"));
    System.out.println(evaluation.toMatrixString("======Confusion Matrix======\n"));
}

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From source file:tucil2ai.Tucil2AI.java

/**
 *
 * @param args//w w w. j av a  2 s  .com
 * @throws Exception
 */
public static void main(String[] args) throws Exception {
    // TODO code application logic here
    DataSource source;
    Instances data;
    Instances data_filtered;
    Discretize filter;
    Classifier clsJ48 = null;

    source = new DataSource("res/iris.arff");
    data = source.getDataSet();
    data.setClassIndex(data.numAttributes() - 1);
    System.out.println("Data berhasil dibaca");
    // filter
    filter = new Discretize();
    filter.setInputFormat(data);
    data_filtered = Filter.useFilter(data, filter);
    System.out.println("Data berhasil difilter");

    int pil = 0;
    while (pil != 3) {
        menu();
        Scanner sc = new Scanner(System.in);
        pil = sc.nextInt();
        switch (pil) {
        case 1:
            clsJ48 = loadModel();
            System.out.println("Model berhasil dimuat");
            break;
        case 2:
            clsJ48 = new J48();
            clsJ48.buildClassifier(data_filtered);
            int pil3;
            System.out.println("Apakah Anda mau menyimpan model ini?");
            System.out.println("1. Ya");
            System.out.println("2. Tidak");
            pil3 = sc.nextInt();
            if (pil3 == 1) {
                saveModel(clsJ48);
            }
            break;
        default:
            break;
        }
        if (pil == 3)
            continue;
        System.out.println("Pilih metode evaluasi:");
        System.out.println("1. 10-fold cross validation");
        System.out.println("2. Full-test");
        System.out.println("3. Klasifikasikan datatest");

        int pilmethod;
        pilmethod = sc.nextInt();
        while (pilmethod < 1 || pilmethod > 3) {
            System.out.println("Masukan salah!");
            pilmethod = sc.nextInt();
        }
        if (pilmethod != 3) {
            Evaluation eval = evalJ48(clsJ48, data_filtered, pilmethod == 1);
            printEval(eval);
        } else {
            ClassifyJ48(clsJ48, "res/datatest.arff", filter);
            System.out.println("Terbentuk 3 instance baru untuk diklasifikasi");
        }
    }
}

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From source file:wekimini.learning.ModelEvaluator.java

public void evaluateAll(final List<Path> paths, final boolean isTraining, final int numFolds,
        PropertyChangeListener listener) {
    final List<Instances> data = new LinkedList<>();
    for (Path p : paths) {
        Instances i = w.getSupervisedLearningManager().getTrainingDataForPath(p, false);
        data.add(i);//from w  w w  .jav a2 s  . c  o  m
    }

    setResults(new String[paths.size()]);
    if (evalWorker != null && evalWorker.getState() != SwingWorker.StateValue.DONE) {
        return;
    }

    evalWorker = new SwingWorker<Integer, Void>() {

        //trainingWorker.
        @Override
        public Integer doInBackground() {
            // train(); //TODO: Add status updates
            int progress = 0;
            //setProgress(progress);
            int numToEvaluate = 0;
            for (Path p : paths) {
                if (p.canBuild()) {
                    numToEvaluate++;
                }
            }

            int numEvaluated = 0;
            int numErr = 0;
            setEvalStatus(new EvaluationStatus(numToEvaluate, numEvaluated, numErr, false));

            for (int i = 0; i < paths.size(); i++) {
                Path p = paths.get(i);
                if (p.canBuild()) {
                    try {
                        System.out.println("Evaluating with " + numFolds);
                        //EVALUATE HERE: TODO 
                        Instances instances = w.getSupervisedLearningManager().getTrainingDataForPath(p, false);
                        Evaluation eval = new Evaluation(instances);
                        Classifier c = ((LearningModelBuilder) p.getModelBuilder()).getClassifier();
                        if (!isTraining) {
                            Random r = new Random();
                            eval.crossValidateModel(c, instances, numFolds, r);
                        } else {
                            Classifier c2 = Classifier.makeCopy(c);
                            c2.buildClassifier(instances);
                            eval.evaluateModel(c2, instances);
                        }
                        String result;
                        if (p.getModelBuilder() instanceof ClassificationModelBuilder) {
                            result = dFormat.format(eval.pctCorrect()) + "%"; //WON"T WORK FOR NN
                        } else {
                            result = dFormat.format(eval.errorRate()) + " (RMS)";
                        }
                        if (!isTraining) {
                            KadenzeLogging.getLogger().crossValidationComputed(w, i, numFolds, result);
                        } else {
                            KadenzeLogging.getLogger().trainingAccuracyComputed(w, i, result);
                        }
                        setResults(i, result);
                        finishedModel(i, result);
                        numEvaluated++;

                        if (isCancelled()) {
                            cancelMe(p);
                            setResults(i, "Cancelled");
                            return 0;
                        }

                    } catch (InterruptedException ex) {
                        cancelMe(p);
                        setResults(i, "Cancelled");
                        return 0; //Not sure this will be called...
                    } catch (Exception ex) {
                        numErr++;
                        Util.showPrettyErrorPane(null, "Error encountered during evaluation "
                                + p.getCurrentModelName() + ": " + ex.getMessage());
                        logger.log(Level.SEVERE, ex.getMessage());
                    }
                    setEvalStatus(new EvaluationStatus(numToEvaluate, numEvaluated, numErr, false));
                } else {
                    logger.log(Level.WARNING, "Could not evaluate path");
                }

            }
            wasCancelled = false;
            hadError = evaluationStatus.numErrorsEncountered > 0;
            return 0;
        }

        @Override
        public void done() {
            if (isCancelled()) {
                EvaluationStatus t = new EvaluationStatus(evaluationStatus.numToEvaluate,
                        evaluationStatus.numEvaluated, evaluationStatus.numErrorsEncountered, true);
                setEvalStatus(t);
            }
            finished();
        }
    };
    evalWorker.addPropertyChangeListener(listener);
    evalWorker.execute();
}

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From source file:wekimini.WekaModelBuilderHelper.java

public static Classifier build(Classifier c, Instances data) throws Exception {
    c.buildClassifier(data);
    return Classifier.makeCopy(c); //TODO: May have to do Classifier.makeCopy
}

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