public void terminate(ClusModel model) throws IOException {
CompleteTreeIterator iter = new CompleteTreeIterator((ClusNode) model);
while (iter.hasMoreNodes()) {
ClusNode node = (ClusNode) iter.getNextNode();
node.getClusteringStat().calcMean();
}
}
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();
}
}
public void modelUpdate(DataTuple tuple, ClusModel model) {
ClusNode node = (ClusNode) model;
node.getClusteringStat().updateWeighted(tuple, 0);
}
