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());
}
}
}
public int CompareTo(object obj)
{
TestScore to = (TestScore)obj;
if (Score < to.Score)
{
return(-1);
}
else if (Score > to.Score)
{
return(1);
}
else
{
return(0);
}
}
/// <summary>
///
/// </summary>
/// <param name="testAttr"></param>
/// <param name="goalAttr"></param>
/// <returns></returns>
protected TestScore BestSplitTest(Attribute testAttr, SymbolicAttribute goalAttr)
{
ItemSet knownItems = new ItemSet(_attributeSet);
int nbKnown = 0;
foreach (Item it in _items)
{
if (!it.ValueOf(_attributeSet, testAttr).IsUnknown())
{
knownItems.Add(it);
nbKnown++;
}
}
if (nbKnown == 0)
{ //No Information can be gained from this test
Test test;
if (testAttr is SymbolicAttribute)
{ //Symblic test
test = new SymbolicTest((SymbolicAttribute)testAttr,
new KnownSymbolicValue[] { new KnownSymbolicValue(0) });
}
else
{ //Numerical test
test = new NumericalTest((NumericalAttribute)testAttr, 0.0d);
}
return(new TestScore(test, 0.0d));
}
else
{
TestScore knownTestScore = knownItems.BestSplitTest(testAttr, goalAttr);
return(new TestScore(knownTestScore.Test, knownTestScore.Score * (double)nbKnown / Items.Count));
}
}