public SimpleDecisionTreeBuilder(ItemSet learningItemSet, AttributeSet testAttributeSet, SymbolicAttribute goalAttribute)
{
System.Console.WriteLine("Inside the tree builder!!!!!!!!!!");
if (learningItemSet == null || learningItemSet.NumOfItems() == 0)
{
throw new ArgumentNullException();
}
this._learningSet = learningItemSet;
this._testAttributeSet = testAttributeSet;
this._goalAttribute = goalAttribute;
LearningDecisionTree tree =
new LearningDecisionTree(learningItemSet.AttrSet, goalAttribute, learningItemSet);
this._tree = tree;
}
/// <summary>
/// Construct a Decision tree object with only test nodes.
/// This function is useful after a LearningDecision Tree is trained. The
/// Test decision tree object is light-weighted and easy for serialization.
/// </summary>
/// <param name="ldt">A traininged learning decision tree</param>
/// <returns></returns>
private TestDecisionTree GetTestDecisionTree(LearningDecisionTree ldt)
{
try
{
List <Node> nodeList = this.ConvertToTestNode(ldt.BFIterator()).ToList();
this._nodes = nodeList.ToArray();
this.AttributeSet = ldt.AttributeSet;
this.GoalAttribute = ldt.GoalAttribute;
return(GetTestDecisionTree(nodeList));
}
catch (Exception ex)
{
throw ex;
}
}
public TestDecisionTree(LearningDecisionTree ldt)
{
GetTestDecisionTree(ldt);
}
