Пример #1
0
  /**
   * Computes the error in classification on the given data.
   *
   * @param data the instances to be classified
   * @return classification error
   * @exception Exception if error can not be computed successfully
   */
  protected double computeError(Instances data) throws Exception {
    double error = 0.0;
    int numInstances = data.numInstances();
    Instance curr;

    for (int i = 0; i < numInstances; i++) {
      curr = data.instance(i);
      // Check if the instance has been misclassified
      if (curr.classValue() != ((int) classifyInstance(curr))) error++;
    }
    return (error / numInstances);
  }
Пример #2
0
  /**
   * Labels the artificially generated data.
   *
   * @param artData the artificially generated instances
   * @exception Exception if instances cannot be labeled successfully
   */
  protected void labelData(Instances artData) throws Exception {
    Instance curr;
    double[] probs;

    for (int i = 0; i < artData.numInstances(); i++) {
      curr = artData.instance(i);
      // compute the class membership probs predicted by the current ensemble
      probs = distributionForInstance(curr);
      // select class label inversely proportional to the ensemble predictions
      curr.setClassValue(inverseLabel(probs));
    }
  }
Пример #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;
    }
  }