public void induceRandomForestRecursive2(ClusNode node, RowData data, Object[] attrs) {
// System.out.println("INDUCE SPARSE with " + attrs.length + " attributes and " +
// data.getNbRows() + " examples");
// Initialize selector and perform various stopping criteria
if (initSelectorAndStopCrit(node, data)) {
makeLeaf(node);
return;
}
// Find best test
for (int i = 0; i < attrs.length; i++) {
ClusAttrType at = (ClusAttrType) attrs[i];
if (at instanceof NominalAttrType) m_FindBestTest.findNominal((NominalAttrType) at, data);
else m_FindBestTest.findNumeric((NumericAttrType) at, data);
}
// Partition data + recursive calls
CurrentBestTestAndHeuristic best = m_FindBestTest.getBestTest();
if (best.hasBestTest()) {
node.testToNode(best);
// Output best test
if (Settings.VERBOSE > 0)
System.out.println("Test: " + node.getTestString() + " -> " + best.getHeuristicValue());
// Create children
int arity = node.updateArity();
NodeTest test = node.getTest();
RowData[] subsets = new RowData[arity];
for (int j = 0; j < arity; j++) {
subsets[j] = data.applyWeighted(test, j);
}
if (getSettings().showAlternativeSplits()) {
filterAlternativeSplits(node, data, subsets);
}
if (node != m_Root && getSettings().hasTreeOptimize(Settings.TREE_OPTIMIZE_NO_INODE_STATS)) {
// Don't remove statistics of root node; code below depends on them
node.setClusteringStat(null);
node.setTargetStat(null);
}
for (int j = 0; j < arity; j++) {
ClusNode child = new ClusNode();
node.setChild(child, j);
child.initClusteringStat(m_StatManager, m_Root.getClusteringStat(), subsets[j]);
child.initTargetStat(m_StatManager, m_Root.getTargetStat(), subsets[j]);
induceRandomForestRecursive(child, subsets[j]);
}
} else {
makeLeaf(node);
}
}
