Example #1
0
  /**
   * Generate artificial training examples.
   *
   * @param artSize size of examples set to create
   * @param data training data
   * @return the set of unlabeled artificial examples
   */
  protected Instances generateArtificialData(int artSize, Instances data) {
    int numAttributes = data.numAttributes();
    Instances artData = new Instances(data, artSize);
    double[] att;
    Instance artInstance;

    for (int i = 0; i < artSize; i++) {
      att = new double[numAttributes];
      for (int j = 0; j < numAttributes; j++) {
        if (data.attribute(j).isNominal()) {
          // Select nominal value based on the frequency of occurence in the training data
          double[] stats = (double[]) m_AttributeStats.get(j);
          att[j] = (double) selectIndexProbabilistically(stats);
        } else if (data.attribute(j).isNumeric()) {
          // Generate numeric value from the Guassian distribution
          // defined by the mean and std dev of the attribute
          double[] stats = (double[]) m_AttributeStats.get(j);
          att[j] = (m_Random.nextGaussian() * stats[1]) + stats[0];
        } else System.err.println("Decorate can only handle numeric and nominal values.");
      }
      artInstance = new Instance(1.0, att);
      artData.add(artInstance);
    }
    return artData;
  }
Example #2
0
  /**
   * Returns description of the Decorate classifier.
   *
   * @return description of the Decorate classifier as a string
   */
  public String toString() {

    if (m_Committee == null) {
      return "Decorate: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("Decorate base classifiers: \n\n");
    for (int i = 0; i < m_Committee.size(); i++)
      text.append(((Classifier) m_Committee.get(i)).toString() + "\n\n");
    text.append("Number of classifier in the ensemble: " + m_Committee.size() + "\n");
    return text.toString();
  }
Example #3
0
  /**
   * Calculates the class membership probabilities for the given test instance.
   *
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @exception Exception if distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    if (instance.classAttribute().isNumeric()) {
      throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
    }
    double[] sums = new double[instance.numClasses()], newProbs;
    Classifier curr;

    for (int i = 0; i < m_Committee.size(); i++) {
      curr = (Classifier) m_Committee.get(i);
      newProbs = curr.distributionForInstance(instance);
      for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
    }
    if (Utils.eq(Utils.sum(sums), 0)) {
      return sums;
    } else {
      Utils.normalize(sums);
      return sums;
    }
  }