public ID3Node Train(Instances S, double confidenceLevel, int maxDepth = 0)
{
int targetAttributeIndex = S.numAttributes() - 1;
// Store the attribute indexes in a list. They will get removed as we split on attributes.
List <int> attributeIndexes = new List <int>();
for (int i = 0; i < S.numAttributes() - 1; i++)
{
attributeIndexes.Add(i);
}
this.RootNode = new ID3Node();
this.RootNode.Depth = 1;
this.TrainRecursive(this.RootNode, S, targetAttributeIndex, attributeIndexes, confidenceLevel, maxDepth);
if (Log.NodeOn == true)
{
ID3Node.DFS(this.RootNode, S);
}
Log.LogNode("Number of Nodes is {0}", ID3Node.NodeCount(this.RootNode));
Log.LogNode("Max Tree Depth including leaves is {0}", ID3Node.MaxDepth(this.RootNode));
return(this.RootNode);
}
public static void DFS(ID3Node root, Instances instances)
{
ID3Node.Print(root, instances);
if (root.IsLeaf)
{
return;
}
for (int i = 0; i < root.ChildNodes.Count(); i++)
{
ID3Node.DFS(root.ChildNodes[i], instances);
}
}