public static void Traverse(TreeNode node, DataRow dt, ref string val)
{
string currentval = string.Empty;
if (node.attribute.values != null)
{
currentval = dt[node.attribute.ToString()].ToString();
bool result = CheckForMissingValue(node.attribute.values, currentval);
if (result == true)
{
if (node.attribute.postives >= node.attribute.negatives)
{
val = "1";
return;
}
else if (node.attribute.postives < node.attribute.negatives)
{
val = "0";
return;
}
}
else
{
for (int i = 0; i < node.attribute.values.Length; i++)
{
currentval = dt[node.attribute.ToString()].ToString();
if (currentval == node.attribute.values[i])
{
TreeNode childNode = node.getBranchChild(node.attribute.values[i]);
Traverse(childNode, dt, ref val);
}
}
}
}
else
{
val = node.attribute.aLabel.ToString();
return;
}
}
/*This function is used to print the decision tree*/
public static void printNode(TreeNode root, string tabs)
{
if (root.totalCountOfChilds == 1)
{
Console.Write(root.attribute);
Console.Write(Environment.NewLine);
}
else
{
Console.Write(tabs + "|" + root.attribute);
}
if (root.attribute.values != null)
{
for (int i = 0; i < root.attribute.values.Length; i++)
{
if (i > 0)
{
Console.Write(tabs + "|" + root.attribute + "=" + root.attribute.values[i] + ":");
}
else
{
Console.Write("=" + root.attribute.values[i] + ":");
}
TreeNode childNode = root.getBranchChild(root.attribute.values[i]);
if (childNode.totalCountOfChilds > 1)
{
Console.Write(Environment.NewLine);
printNode(childNode, "| \t" + tabs);
}
else
{
printNode(childNode, "\t" + tabs);
}
}
}
}
/*The function adds a node to the Tree*/
public void AddNode(TreeNode treeNode, string ValueName)
{
int index = tAttribute.indexValue(ValueName);
tChilds[index] = treeNode;
}
/* The function is used to traverse the decision tree*/
public static void TraverseMain(TreeNode node, string filename, ArrayList attributenames, string filetype, ref DataTable result, int colNumber)
{
DataTable dt = getDataTable(filename, attributenames);
string val = string.Empty;
for (int j = 0; j < dt.Rows.Count; j++)
{
val = string.Empty;
Traverse(node, dt.Rows[j], ref val);
result.Rows[j][colNumber] = val;
}
}
/*This function is used to construct the tree amd is called recursively*/
private TreeNode constructTree(DataTable samples, string resultLabel, Attribute[] attributes, string filename, ArrayList attributenames)
{
if (tuplePositiveTest(samples, resultLabel) == true) /* check if all tuples belong to class label 1*/
return new TreeNode(new Attribute("1"));
if (tupleNegativeTest(samples, resultLabel) == true) /* check if all tuples belong to class label 1*/
return new TreeNode(new Attribute("0"));
TotalTuples = samples.Rows.Count;
resultClass = resultLabel;
TotalPositives = countPositiveClass(samples);
int mnegative = TotalTuples - TotalPositives;
/*Below are the conditions that check when attribute set is empty or tuples are over etc.,*/
if (attributes.Length == 0 && TotalPositives == mnegative)
return new TreeNode(new Attribute(getMostCommonValue(samples, resultLabel)));
else if (attributes.Length == 0 && TotalPositives == mnegative)
return new TreeNode(new Attribute(getMostCommonValue(getDataTable(filename, attributenames), resultLabel)));
else if (samples.Rows.Count == 0)
return new TreeNode(new Attribute(getMostCommonValue(getDataTable(filename, attributenames), resultLabel)));
Entropy = calcEntropy(TotalPositives, TotalTuples - TotalPositives);
/*To find the best attribute*/
Attribute bestAttribute = getSplittingAttribute(samples, attributes);
if (bestAttribute == null && TotalPositives == mnegative)
{
return new TreeNode(new Attribute(getMostCommonValue(getDataTable(filename, attributenames), resultLabel)));
}
else if (bestAttribute == null && TotalPositives != mnegative)
{
return new TreeNode(new Attribute(getMostCommonValue(samples, resultLabel)));
}
TreeNode root = new TreeNode(bestAttribute);
DataTable aSample = samples.Clone();
bestAttribute.postives = TotalPositives;
bestAttribute.negatives = mnegative;
/*Loop through all possible attribute values to split based on the above best attribute obtained */
foreach (string value in bestAttribute.values)
{
aSample.Rows.Clear();
DataRow[] rows = samples.Select(bestAttribute.AttributeName + " = " + "'" + value + "'");
foreach (DataRow row in rows)
{
aSample.Rows.Add(row.ItemArray);
}
ArrayList aAttributes = new ArrayList(attributes.Length - 1);
for (int i = 0; i < attributes.Length; i++)
{
if (attributes[i].AttributeName != bestAttribute.AttributeName)
aAttributes.Add(attributes[i]);
}
DecisionID3 dc3 = new DecisionID3();
TreeNode child = dc3.MainTree(aSample, resultLabel, (Attribute[])aAttributes.ToArray(typeof(Attribute)), filename, attributenames);
root.AddNode(child, value);
}
return root;
}
