private StreamReader trainSr = null; // StreamReader for reading training data file.
#endregion Fields
#region Constructors
public DecisionTree(string trainingFile)
{
trainSr = new StreamReader(trainingFile);
// parse the first line to specify each name of attributes.
SetAttributeNames();
rootNode = new Node();
// set attribute values by using the rest of lines of training data.
SetAttributeValues();
trainSr.Close();
}
/* Returns true if splitting this node is possible.
Returns false if it's not possible. */
public void SplitNode()
{
classLabel = ClassLabel(); // majority voting
// If tuples in this node are all of the same class
if (CheckTuplesAreSameClass() == true)
{
return;
}
// If attribute_list is empty
if (CheckRemainingAttrs() == false)
{
return;
}
int attrIdx = splitAttrIdx = SelectAttribute();
int attrValCnt = Globals.attrs[attrIdx].values.Count;
List<Tuple>[] tupleLists = new List<Tuple>[attrValCnt];
for (int i=0; i<attrValCnt; ++i)
tupleLists[i] = new List<Tuple>();
foreach (Tuple t in tuples)
{
int attrVal = t.values[attrIdx];
tupleLists[attrVal-1].Add(t);
}
for (int i=0; i<tupleLists.Length; ++i)
{
if (tupleLists[i].Count != 0)
{
Node node = new Node(this.availableAttrs);
node.DisableAttr(attrIdx);
node.SetTupleList(tupleLists[i]);
node.splitAttrVal = (i+1);
childNodes.Add(node);
}
}
tuples.Clear();
foreach (Node childNode in childNodes)
childNode.SplitNode();
}
