Exemple #1
0
  /**
   * Calculates the performance stats for the desired class and return results as a set of
   * Instances.
   *
   * @param predictions the predictions to base the curve on
   * @param classIndex index of the class of interest.
   * @return datapoints as a set of instances.
   */
  public Instances getCurve(FastVector predictions, int classIndex) {

    if ((predictions.size() == 0)
        || (((NominalPrediction) predictions.elementAt(0)).distribution().length <= classIndex)) {
      return null;
    }

    ThresholdCurve tc = new ThresholdCurve();
    Instances threshInst = tc.getCurve(predictions, classIndex);

    Instances insts = makeHeader();
    int fpind = threshInst.attribute(ThresholdCurve.FP_RATE_NAME).index();
    int tpind = threshInst.attribute(ThresholdCurve.TP_RATE_NAME).index();
    int threshind = threshInst.attribute(ThresholdCurve.THRESHOLD_NAME).index();

    double[] vals;
    double fpval, tpval, thresh;
    for (int i = 0; i < threshInst.numInstances(); i++) {
      fpval = threshInst.instance(i).value(fpind);
      tpval = threshInst.instance(i).value(tpind);
      thresh = threshInst.instance(i).value(threshind);
      vals = new double[3];
      vals[0] = 0;
      vals[1] = fpval;
      vals[2] = thresh;
      insts.add(new Instance(1.0, vals));
      vals = new double[3];
      vals[0] = 1;
      vals[1] = 1.0 - tpval;
      vals[2] = thresh;
      insts.add(new Instance(1.0, vals));
    }

    return insts;
  }
  /**
   * Finds residuals (squared) for the current regression.
   *
   * @throws Exception if an error occurs
   */
  private void findResiduals() throws Exception {

    m_SSR = 0;
    m_Residuals = new double[m_Data.numInstances()];
    for (int i = 0; i < m_Data.numInstances(); i++) {
      m_Residuals[i] = m_currentRegression.classifyInstance(m_Data.instance(i));
      m_Residuals[i] -= m_Data.instance(i).value(m_Data.classAttribute());
      m_Residuals[i] *= m_Residuals[i];
      m_SSR += m_Residuals[i];
    }
  }
  /**
   * Signify that this batch of input to the filter is finished. If the filter requires all
   * instances prior to filtering, output() may now be called to retrieve the filtered instances.
   *
   * @return true if there are instances pending output
   * @exception Exception if an error occurs
   * @exception IllegalStateException if no input structure has been defined
   */
  public boolean batchFinished() throws Exception {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_Means == null) {
      Instances input = getInputFormat();
      m_Means = new double[input.numAttributes()];
      m_StdDevs = new double[input.numAttributes()];
      for (int i = 0; i < input.numAttributes(); i++) {
        if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
          m_Means[i] = input.meanOrMode(i);
          m_StdDevs[i] = Math.sqrt(input.variance(i));
        }
      }

      // Convert pending input instances
      for (int i = 0; i < input.numInstances(); i++) {
        convertInstance(input.instance(i));
      }
    }
    // Free memory
    flushInput();

    m_NewBatch = true;
    return (numPendingOutput() != 0);
  }