public final ClusNode getTree(int fold) {
int idx = Arrays.binarySearch(m_Folds, fold);
ClusNode node = m_Nodes[idx];
if (node.hasBestTest() && node.atBottomLevel()) {
OptXValSplit split = null;
int nb = getNbChildren();
for (int i = 0; i < nb; i++) {
OptXValSplit msplit = (OptXValSplit) getChild(i);
if (msplit.contains(fold)) {
split = msplit;
break;
}
}
int arity = node.updateArity();
for (int i = 0; i < arity; i++) {
OptXValNode subnode = (OptXValNode) split.getChild(i);
node.setChild(subnode.getTree(fold), i);
}
}
return node;
}
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();
}
}