Example usage for weka.core Instances toString

Introduction

In this page you can find the example usage for weka.core Instances toString.

Prototype

@Override
public String toString()

Source Link

Document

Returns the dataset as a string in ARFF format.

Usage

From source file:tubes2.myClusterers.myKMeans.java

public String centroidsToString() {
    Instances centroidInstances = new Instances(template, 0);
    for (int i = 0; i < k; i++) {
        centroidInstances.add(centroids[i]);
    }/* www . j a v  a 2  s  .co  m*/
    return centroidInstances.toString();
}

From source file:tucil2ai.Tucil2AI.java

/**
 *
 * @param cls/*from  ww  w  .j  a  va 2 s. co  m*/
 * @param filename
 * @param f
 * @throws Exception
 */
public static void ClassifyJ48(Classifier cls, String filename, Discretize f) throws Exception {
    Instances unlabeled = loadfile(filename, f);
    Instances labeled = new Instances(unlabeled);
    for (int i = 0; i < unlabeled.numInstances(); ++i) {
        double clsLabel = cls.classifyInstance(unlabeled.instance(i));
        labeled.instance(i).setClassValue(clsLabel);
    }

    System.out.println(labeled.toString());
}

From source file:wekimini.learning.LinearRegressionModelBuilder.java

@Override
public LinearRegressionModel build(String name) throws Exception {
    if (trainingData == null) {
        throw new IllegalStateException("Must set training examples (to not null) before building model");
    }//from  ww w  . j a v  a  2s . com
    LinearRegression m;
    LinearRegressionAttributeTransformer t = null;
    if (exponent == 1) {
        m = (LinearRegression) WekaModelBuilderHelper.build(classifier, trainingData);
    } else {
        t = new LinearRegressionAttributeTransformer(trainingData.numAttributes() - 1, exponent);
        Instances i = t.transformedData(trainingData);
        System.out.println("New instances:\n" + i.toString());
        m = (LinearRegression) WekaModelBuilderHelper.build(classifier, i);
    }
    //Remove: for testing:
    logger.log(Level.WARNING, m.toString());
    return new LinearRegressionModel(name, m, t);
}