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>
/// Finds the test on each attribute performing the best split for finding the value of a 'goal'
/// attribute.
/// </summary>
/// <param name="candidateAttributes"></param>
/// <param name="goalAttribute"></param>
/// <returns></returns>
public IEnumerable <TestScore> BestSplitTests(AttributeSet candidateAttributes, SymbolicAttribute goalAttribute)
{
if (candidateAttributes == null || goalAttribute == null || candidateAttributes.Size() == 0)
{
throw new ArgumentNullException();
}
List <TestScore> bestScores = new List <TestScore>();
List <Attribute> attributes = candidateAttributes.GetAttributes().ToList();
foreach (Attribute attr in attributes)
{
bestScores.Add(BestSplitTest(attr, goalAttribute));
}
return(bestScores);
}
/// <summary>
/// Find the leaf/Open node matching an item. All the (tested)attributes of the item
/// must be known.
/// </summary>
/// <param name="item">An item compatible with the tree attribute set.</param>
/// <returns>The leaf node matching item</returns>
public Node LeafNode(Item item)
{
if (_attributeSet == null || _goalAttribute == null)
{
throw new InvalidOperationException("No attribute set or goal attribute defined.");
}
AttributeSet attrSet = _attributeSet;
Node node = Root();
while (!(node.IsLeaf()))
{
TestNode testNode = (TestNode)node;
int testAttrIndex = attrSet.IndexOf(testNode.Test.Attribute);
node = testNode.MatchingSon(item.ValueOf(testAttrIndex));
}
return(node);
}
/// <summary>
/// Return the value of an attribute
/// </summary>
/// <param name="attrs"></param>
/// <param name="attribute"></param>
/// <returns></returns>
public AttributeValue ValueOf(AttributeSet attrs, Attribute attribute)
{
return(this.ValueOf(attrs.IndexOf(attribute)));
}
/// <summary>
/// Finds the test on one attribute performing the best split (bringing the most information)
/// for finding the value of a 'goal' attribute
/// </summary>
/// <param name="candidateAttributes">The set of attributes defining which attributes can be tested</param>
/// <param name="goalAttribute">the attribute guess using the test</param>
/// <returns></returns>
public TestScore BestSplitTest(AttributeSet candidateAttributes, SymbolicAttribute goalAttribute)
{
return(BestSplitTests(candidateAttributes, goalAttribute).Max <TestScore>());
}
/// <summary>
/// Create an empty learning decision tree.
/// </summary>
/// <param name="attrSet"></param>
/// <param name="goalAttr"></param>
/// <param name="learnignSet"></param>
public LearningDecisionTree(AttributeSet attrSet, SymbolicAttribute goalAttr, ItemSet learnignSet)
: base(attrSet, goalAttr)
{
Root().Replace(new LearningOpenNode(0, learnignSet));
}
public WeightedItemSet(AttributeSet attrSet)
: base(attrSet)
{
_weights = new List <double>();
_weightsSum = 0.0d;
}
