/**
* 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;
}
// 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;
}
public void prune(ClusNode node) {
RegressionStat stat = (RegressionStat) node.getClusteringStat();
m_GlobalDeviation = Math.sqrt(stat.getSVarS(m_ClusteringWeights) / stat.getTotalWeight());
pruneRecursive(node);
// System.out.println("Performing test of M5 pruning");
// TestM5PruningRuleNode.performTest(orig, node, m_GlobalDeviation, m_TargetWeights,
// m_TrainingData);
}
public void pruneRecursive(ClusNode node) {
if (node.atBottomLevel()) {
return;
}
for (int i = 0; i < node.getNbChildren(); i++) {
ClusNode child = (ClusNode) node.getChild(i);
pruneRecursive(child);
}
RegressionStat stat = (RegressionStat) node.getClusteringStat();
double rmsLeaf = stat.getRMSE(m_ClusteringWeights);
double adjustedErrorLeaf = rmsLeaf * pruningFactor(stat.getTotalWeight(), 1);
double rmsSubTree =
Math.sqrt(node.estimateClusteringSS(m_ClusteringWeights) / stat.getTotalWeight());
double adjustedErrorTree =
rmsSubTree * pruningFactor(stat.getTotalWeight(), node.getModelSize());
// System.out.println("C leaf: "+rmsLeaf+" tree: "+rmsSubTree);
// System.out.println("C leafadj: "+adjustedErrorLeaf +" treeadj: "+rmsSubTree);
if ((adjustedErrorLeaf <= adjustedErrorTree)
|| (adjustedErrorLeaf < (m_GlobalDeviation * 0.00001))) {
node.makeLeaf();
}
}