Example usage for weka.classifiers.functions MultilayerPerceptron setMomentum

Introduction

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

Prototype

public void setMomentum(double m) 

Source Link

Document

The momentum can be set using this command.

Usage

From source file:anndl.Anndl.java

private static void buildModel(InputStream input) throws Exception {
    ANNDLLexer lexer = new ANNDLLexer(new ANTLRInputStream(input));
    CommonTokenStream tokens = new CommonTokenStream(lexer);
    ANNDLParser parser = new ANNDLParser(tokens);
    ParseTree tree = parser.model();//from   w w w  .j  ava  2s . c o  m

    ModelVisitor visitor = new ModelVisitor();

    ModelClassifier themodel = (ModelClassifier) visitor.visit(tree);
    //themodel.PrintInfo();
    themodel.extracthidden();

    System.out.println("Membaca File Training...");
    DataSource trainingsoure = new DataSource(themodel.filetraining);
    Instances trainingdata = trainingsoure.getDataSet();
    if (trainingdata.classIndex() == -1) {
        trainingdata.setClassIndex(trainingdata.numAttributes() - 1);
    }

    System.out.println("Melakukan konfigurasi ANN ... ");
    MultilayerPerceptron mlp = new MultilayerPerceptron();
    mlp.setLearningRate(themodel.learningrate);
    mlp.setMomentum(themodel.momentum);
    mlp.setTrainingTime(themodel.epoch);
    mlp.setHiddenLayers(themodel.hidden);

    System.out.println("Melakukan Training data ...");
    mlp.buildClassifier(trainingdata);

    Debug.saveToFile(themodel.namamodel + ".model", mlp);

    System.out.println("\n~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ ..");
    System.out.println("Model ANN Berhasil Diciptakan dengan nama file : " + themodel.namamodel + ".model");
    System.out.println("~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. \n");

}

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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   ww w .  j  av  a2  s .com
    //        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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