private double predictUsingCurtailment(Example example, ExtendedTree node) {
double dPrediction = 0;
if (node.isLeaf()) {
dPrediction = predictAsLeaf(example, node);
} else {
boolean bEdgeConditionFound = false;
Iterator<Edge> childIterator = node.childIterator();
while ((childIterator.hasNext()) && (bEdgeConditionFound == false)) {
Edge edge = childIterator.next();
SplitCondition condition = edge.getCondition();
if (condition.test(example)) {
bEdgeConditionFound = true;
ExtendedTree childTree = (ExtendedTree) edge.getChild();
if (childTree.getTotalExamples() >= dCurtailmentThreshold)
dPrediction = (predictUsingCurtailment(example, childTree));
else dPrediction = predictAsLeaf(example, node);
}
}
if (bEdgeConditionFound == false) {
dPrediction = predictAsLeaf(example, node);
}
}
return dPrediction;
}
private double predictAsLeaf(Example example, ExtendedTree node) {
double dPrediction = 0;
Iterator<String> s = node.getCounterMap().keySet().iterator();
int[] counts = new int[getLabel().getMapping().size()];
int sum = 0;
while (s.hasNext()) {
String className = s.next();
int count = node.getCount(className);
int index = getLabel().getMapping().getIndex(className);
counts[index] = count;
sum += count;
}
for (int i = 0; i < counts.length; i++) {
example.setConfidence(
getLabel().getMapping().mapIndex(i), smoothedConfidence(counts[i], sum));
}
dPrediction = getLabel().getMapping().getIndex(node.getLabel());
return dPrediction;
}
