public void Expand()
{
LearningOpenNode node = _tree.OpenNode() as LearningOpenNode;
if (node == null)
{
throw new Exception("No open node left");
}
ItemSet set = node.LearningSet();
double entropy = set.CalEntropy(_goalAttribute);
if (entropy <= _entropyThreshold || _testAttributeSet.Size() == 0)
{
MakeLeafNode(node);
}
else
{
TestScore testScore = set.BestSplitTest(_testAttributeSet, _goalAttribute);
if (testScore.Score * set.Size() <= _scoreThreshold)
{ //forward pruning: test does not provide enough information
MakeLeafNode(node);
}
else
{
MakeTestNode(node, testScore.Test, testScore.Score * set.Size());
}
}
}
/// <summary>
/// Turns an open node to a leaf.
/// </summary>
/// <param name="node">The open node to transform into a leaf node.</param>
private void MakeLeafNode(LearningOpenNode openNode)
{
double nodeWeight = openNode.LearningSet().Size();
LearningLeafNode leafNode =
new LearningLeafNode(nodeWeight, openNode.LearningSet());
openNode.Replace(leafNode);
}
private void MakeTestNode(LearningOpenNode openNode, Test test, double score)
{
double nodeWeight = openNode.LearningSet().Size();
LearningTestNode testNode = new LearningTestNode(nodeWeight, test, score, openNode.LearningSet());
openNode.Replace(testNode);
ItemSet[] subSets = openNode.LearningSet().Split(test).ToArray();
for (int i = 0; i < test.NumOfIssues; i++)
{
LearningOpenNode node = new LearningOpenNode(subSets[i].Size(), subSets[i]);
testNode.Son(i).Replace(node);
}
}
private OpenNode ConvertOpenNode(LearningOpenNode node)
{
return(new OpenNode(node.Weight));
}