Example usage for weka.classifiers.functions MultilayerPerceptron buildClassifier

Introduction

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

Prototype

@Override
public void buildClassifier(Instances i) throws Exception 

Source Link

Document

Call this function to build and train a neural network for the training data provided.

Usage

From source file:predictor.Predictor.java

public static void multilayerPerceptron() throws Exception {

    DataSource train = new DataSource(configuration.getWorkspace() + "train_common.arff");
    DataSource test = new DataSource(configuration.getWorkspace() + "test_common.arff");

    Instances trainInstances = train.getDataSet();
    Instances testInstances = test.getDataSet();

    //last attribute classify
    trainInstances.setClassIndex(trainInstances.numAttributes() - 1);
    testInstances.setClassIndex(testInstances.numAttributes() - 1);
    //        //from  w  w w  . j  a  va  2  s  .co m
    //        Classifier cModel = (Classifier)new MultilayerPerceptron();  
    //        cModel.buildClassifier(trainInstances);  
    //
    //        weka.core.SerializationHelper.write("/some/where/nBayes.model", cModel);
    //
    //        Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/nBayes.model");
    //
    //        // Test the model
    //        Evaluation eTest = new Evaluation(trainInstances);
    //        eTest.evaluateModel(cls, testInstances);

    MultilayerPerceptron mlp = new MultilayerPerceptron();
    mlp.buildClassifier(trainInstances);
    mlp.setHiddenLayers(configuration.getHiddenLayers());
    mlp.setLearningRate(configuration.getLearningRate());
    mlp.setTrainingTime(configuration.getEpocs());
    mlp.setMomentum(configuration.getMomentum());

    // train classifier
    Classifier cls = new MultilayerPerceptron();
    cls.buildClassifier(trainInstances);

    // evaluate classifier and print some statistics
    Evaluation eval = new Evaluation(trainInstances);
    eval.evaluateModel(cls, testInstances);

    System.out.println(eval.toSummaryString());
}

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From source file:predictors.HelixPredictor.java

License:Open Source License

/**
 * Trains the Weka Classifer./*from   ww w.  j a v a2s.c om*/
 */
public void trainClassifier() {
    try {
        MultilayerPerceptron classifier = new weka.classifiers.functions.MultilayerPerceptron();
        Instances data = this.dataset;

        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        data.randomize(new Random(data.size()));

        String[] optClassifier = weka.core.Utils
                .splitOptions("-L 0.01 -M 0.8 -N 256 -V 20 -S 0 -E 5 -H 25 -B -I -D -C");

        classifier.setOptions(optClassifier);
        classifier.setSeed(data.size());

        classifier.buildClassifier(data);

        this.classifier = classifier;
        this.isTrained = true;
    } catch (Exception e) {
        ErrorUtils.printError(HelixPredictor.class, "Training failed", e);
    }
}

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