static void Main(string[] args)
{
Attribute soft_skills = new Attribute("soft_skills", new string[] { "Poor", "Avg", "Good" });
Attribute time_management = new Attribute("time_management", new string[] { "Developed", "Enhanced", "Strengthened" });
Attribute problem_solving = new Attribute("problem_solving", new string[] { "Developed", "low" });
Attribute decision_making = new Attribute("decision_making", new string[] { "yes", "no" });
Attribute[] attributes = new Attribute[] { soft_skills, time_management, problem_solving, decision_making };
DataTable samples = getDataTable();
j48classify j48 = new j48classify();
TreeNode root = j48.mountTree(samples, "result", attributes);
printNode(root, "");
}
private TreeNode internalMountTree(DataTable samples, string targetAttribute, Attribute[] attributes)
{
if (allSamplesPositives(samples, targetAttribute) == "c1")
{
return(new TreeNode(new Attribute("c1")));
}
if (allSamplesNegatives(samples, targetAttribute) == "c1")
{
return(new TreeNode(new Attribute("c2")));
}
if (attributes.Length == 0)
{
return(new TreeNode(new Attribute(getMostCommonValue(samples, targetAttribute))));
}
mTotal = samples.Rows.Count;
mTargetAttribute = targetAttribute;
mTotalPositives = countTotalPositives(samples);
mEntropySet = calcEntropy(mTotalPositives, mTotal - mTotalPositives);
Attribute bestAttribute = getBestAttribute(samples, attributes);
TreeNode root = new TreeNode(bestAttribute);
DataTable aSample = samples.Clone();
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]);
}
}
if (aSample.Rows.Count == 0)
{
return(new TreeNode(new Attribute(getMostCommonValue(aSample, targetAttribute))));
}
else
{
j48classify dc3 = new j48classify();
TreeNode ChildNode = dc3.mountTree(aSample, targetAttribute, (Attribute[])aAttributes.ToArray(typeof(Attribute)));
root.AddTreeNode(ChildNode, value);
}
}
return(root);
}