@Override
public void updateNode(Instance inst) throws Exception {
super.updateDistribution(inst);
for (int i = 0; i < inst.numAttributes(); i++) {
Attribute a = inst.attribute(i);
if (i != inst.classIndex()) {
ConditionalSufficientStats stats = m_nodeStats.get(a.name());
if (stats == null) {
if (a.isNumeric()) {
stats = new GaussianConditionalSufficientStats();
} else {
stats = new NominalConditionalSufficientStats();
}
m_nodeStats.put(a.name(), stats);
}
stats.update(
inst.value(a), inst.classAttribute().value((int) inst.classValue()), inst.weight());
}
}
}
/**
* Returns a list of split candidates
*
* @param splitMetric the splitting metric to use
* @return a list of split candidates
*/
public List<SplitCandidate> getPossibleSplits(SplitMetric splitMetric) {
List<SplitCandidate> splits = new ArrayList<SplitCandidate>();
// null split
List<Map<String, WeightMass>> nullDist = new ArrayList<Map<String, WeightMass>>();
nullDist.add(m_classDistribution);
SplitCandidate nullSplit =
new SplitCandidate(
null, nullDist, splitMetric.evaluateSplit(m_classDistribution, nullDist));
splits.add(nullSplit);
for (Map.Entry<String, ConditionalSufficientStats> e : m_nodeStats.entrySet()) {
ConditionalSufficientStats stat = e.getValue();
SplitCandidate splitCandidate = stat.bestSplit(splitMetric, m_classDistribution, e.getKey());
if (splitCandidate != null) {
splits.add(splitCandidate);
}
}
return splits;
}