Re: simplest of code

package adams;

import weka.classifiers.Classifier;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.Utils;

public class WekaApp {

  /** the model to use. */
  protected Classifier cls;

  /** the training data header. */
  protected Instances header;

  /**
   * Load model and header.
   *
   * @throws Exception if deserialization fails
   */
  public void init() throws Exception {
    // [A] load model and header of data
    // deserialize model
    Object[] objs = SerializationHelper.readAll("/home/fracpete/temp/j48.model");
    cls = (Classifier) objs[0];
    header = (Instances) objs[1];
  }

  /**
   * Returns the class label associated with the index.
   *
   * @param index the class label index
   * @return the label
   * @throws IllegalStateException if no header present
   */
  public String getLabel(int index) throws IllegalStateException {
    if (header == null)
      throw new IllegalStateException("No model/header loaded?");
    return header.classAttribute().value(index);
  }

  /**
   * Performs a prediction.
   *
   * @param v the value of the input variable to use for making a prediction
   * @return the predicted value (class index), returns -1 if it fails to make prediction
   * @throws Exception if prediction fails
   */
  public int currentInstance(double v) throws Exception {
    try {
      // [B] create instance
      // create the current instance
      double[] values = new double[header.numAttributes()];
      for (int i = 0; i < values.length; i++)
        values[i] = Utils.missingValue();
      // - numeric
      values[0] = v;
      DenseInstance currentInstance = new DenseInstance(1.0, values);
      currentInstance.setDataset(header);

      // [C] classify nominal class
      // classifyInstance() just returns the index of the predicted label (the one with the highest probability) as a double
      double prediction = cls.classifyInstance(currentInstance);
      System.out.println("prediction: " + prediction);

      return (int) prediction;
    }
    catch (Exception e) {
      System.err.println("Weka exception occurred: " + e);
      e.printStackTrace();
      return -1;
    }
  }

  /**
   * For testing.
   *
   * @param args ignored
   */
  public static void main(String[] args) {
    WekaApp wekaApp = new WekaApp();
    // this should only be called once (or whenever the model has changed and needs reloading)
    try {
      wekaApp.init();
    }
    catch (Exception e) {
      System.err.println("ERROR init: " + e);
      e.printStackTrace();
    }
    // make a prediction
    try {
      int pred = wekaApp.currentInstance(1.2345);  // TODO You need to provide input variable, this is just a dummy value
      if (pred != -1)
        System.out.println("Predicted label: " + wekaApp.getLabel(pred));
      else
        System.out.println("Failed to make prediction!");
    }
    catch (Exception e) {
      System.err.println("ERROR predict: " + e);
      e.printStackTrace();
    }
  }
}