Exemple #1
0
 /**
  * Calculates the difference
  *
  * @return difference
  */
 public double prototypeDifference(CombStat stat) {
   double sumdiff = 0;
   double weight;
   // Numeric atts: abs difference
   for (int i = 0; i < m_RegStat.getNbNumericAttributes(); i++) {
     weight = m_StatManager.getClusteringWeights().getWeight(m_RegStat.getAttribute(i));
     sumdiff += Math.abs(prototypeNum(i) - stat.prototypeNum(i)) * weight;
     // System.err.println("sumdiff: " + Math.abs(prototypeNum(i) - stat.prototypeNum(i)) *
     // weight);
   }
   // Nominal atts: Manhattan distance
   for (int i = 0; i < m_ClassStat.getNbNominalAttributes(); i++) {
     weight = m_StatManager.getClusteringWeights().getWeight(m_ClassStat.getAttribute(i));
     double sum = 0;
     double[] proto1 = prototypeNom(i);
     double[] proto2 = stat.prototypeNom(i);
     for (int j = 0; j < proto1.length; j++) {
       sum += Math.abs(proto1[j] - proto2[j]);
     }
     sumdiff += sum * weight;
     // System.err.println("sumdiff: " + (sum * weight));
   }
   // System.err.println("sumdiff-total: " + sumdiff);
   return sumdiff != 0 ? sumdiff : 0.0;
 }
Exemple #2
0
 // TODO: Move all heuristic stuff to ClusRuleHeuristic*
 public double rDispersionMltHeur() {
   // Original
   /* double train_sum_w = m_StatManager.getTrainSetStat().getTotalWeight();
   double comp = dispersion(IN_HEURISTIC);
   double def_comp = ((CombStat)m_StatManager.getTrainSetStat()).dispersion(IN_HEURISTIC);
   return -m_SumWeight/train_sum_w*(def_comp-comp); */
   double offset = getSettings().getHeurDispOffset();
   double disp = dispersion(IN_HEURISTIC) + offset;
   double dis1 = disp;
   double def_disp = ((CombStat) m_StatManager.getTrainSetStat()).dispersion(IN_HEURISTIC);
   disp = disp - def_disp; // This should be < 0 most of the time
   double dis2 = disp;
   // Coverage part
   double train_sum_w = m_StatManager.getTrainSetStat().getTotalWeight();
   double cov_par = getSettings().getHeurCoveragePar();
   // comp *= (1.0 + cov_par*train_sum_w/m_SumWeight); // How about this???
   // comp *= cov_par*train_sum_w/m_SumWeight;
   // comp *= cov_par*m_SumWeight/train_sum_w;
   disp *= Math.pow(m_SumWeight / train_sum_w, cov_par);
   double dis3 = disp;
   // Prototype distance part
   // Prefers rules that predict different class than the default rule
   if (getSettings().isHeurPrototypeDistPar()) {
     double proto_par = getSettings().getHeurPrototypeDistPar();
     double proto_val = prototypeDifference((CombStat) m_StatManager.getTrainSetStat());
     // disp *= (1.0 + proto_par*m_SumWeight/train_sum_w*proto_val);
     disp = proto_val > 0 ? disp / Math.pow(proto_val, proto_par) : 0.0;
   }
   // Significance testing part - TODO: Complete or remove altogether
   if (Settings.IS_RULE_SIG_TESTING) {
     int sign_diff;
     int thresh = getSettings().getRuleNbSigAtt();
     if (thresh > 0) {
       sign_diff = signDifferent();
       if (sign_diff < thresh) {
         disp *= 1000; // Some big number ??? - What if comp < 0???
       }
     } else if (thresh < 0) { // Testing just one target attribute - TODO: change!
       if (!targetSignDifferent()) {
         disp *= 1000; // Some big number ???
       }
     }
   }
   // System.err.println("Disp: " + dis1 + " DDisp: " + def_disp + " RDisp: " + dis2 + " FDisp: " +
   // dis3);
   return disp;
 }