public double classifyInstance(Instance inst) throws Exception {

    if (m_attribute == null) {
      return m_intercept;
    } else {
      if (inst.isMissing(m_attribute.index())) {
        throw new Exception("UnivariateLinearRegression: No missing values!");
      }
      return m_intercept + m_slope * inst.value(m_attribute.index());
    }
  }
Exemplo n.º 2
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  /**
   * 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);
  }
Exemplo n.º 3
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  /**
   * 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));
    }
  }
Exemplo n.º 4
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  /**
   * 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;
    }
  }
  public void buildClassifier(Instances insts) throws Exception {

    // Compute mean of target value
    double yMean = insts.meanOrMode(insts.classIndex());

    // Choose best attribute
    double minMsq = Double.MAX_VALUE;
    m_attribute = null;
    int chosen = -1;
    double chosenSlope = Double.NaN;
    double chosenIntercept = Double.NaN;
    for (int i = 0; i < insts.numAttributes(); i++) {
      if (i != insts.classIndex()) {
        if (!insts.attribute(i).isNumeric()) {
          throw new Exception("UnivariateLinearRegression: Only numeric attributes!");
        }
        m_attribute = insts.attribute(i);

        // Compute slope and intercept
        double xMean = insts.meanOrMode(i);
        double sumWeightedXDiffSquared = 0;
        double sumWeightedYDiffSquared = 0;
        m_slope = 0;
        for (int j = 0; j < insts.numInstances(); j++) {
          Instance inst = insts.instance(j);
          if (!inst.isMissing(i) && !inst.classIsMissing()) {
            double xDiff = inst.value(i) - xMean;
            double yDiff = inst.classValue() - yMean;
            double weightedXDiff = inst.weight() * xDiff;
            double weightedYDiff = inst.weight() * yDiff;
            m_slope += weightedXDiff * yDiff;
            sumWeightedXDiffSquared += weightedXDiff * xDiff;
            sumWeightedYDiffSquared += weightedYDiff * yDiff;
          }
        }

        // Skip attribute if not useful
        if (sumWeightedXDiffSquared == 0) {
          continue;
        }
        double numerator = m_slope;
        m_slope /= sumWeightedXDiffSquared;
        m_intercept = yMean - m_slope * xMean;

        // Compute sum of squared errors
        double msq = sumWeightedYDiffSquared - m_slope * numerator;

        // Check whether this is the best attribute
        if (msq < minMsq) {
          minMsq = msq;
          chosen = i;
          chosenSlope = m_slope;
          chosenIntercept = m_intercept;
        }
      }
    }

    // Set parameters
    if (chosen == -1) {

      System.err.println("----- no useful attribute found");
      m_attribute = null;
      m_slope = 0;
      m_intercept = yMean;
    } else {
      m_attribute = insts.attribute(chosen);
      m_slope = chosenSlope;
      m_intercept = chosenIntercept;
    }
  }