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); }