static void Main(string[] args)
{
string trainfile, testfile;
DataTable result = new DataTable();
for (int l = 0; l < 10; l++)
{
result.Columns.Add(l.ToString(), typeof(string));
}
Console.WriteLine("Enter the training file name");
trainfile = Console.ReadLine().ToString().Trim();
Console.WriteLine("Enter the test file name");
testfile = Console.ReadLine().ToString().Trim();
ArrayList attributenames = getAttributeNames(trainfile);
DataTable samples = getDataTable(trainfile, attributenames);
for (int m = 0; m < samples.Rows.Count; m++)
{
result.Rows.Add();
}
string[] array = attributenames.ToArray(typeof(string)) as string[];
for (int k = 0; k < 10; k++)
{
DataTable random = GetRandomSamples(samples);
Attribute[] attributes = getList(random, array);
DecisionID3 id3 = new DecisionID3();
TreeNode root = id3.MainTree(random, "result", attributes, trainfile, attributenames);
TraverseMain(root, testfile, attributenames, "Test File", ref result, k);
}
CalcuateMajorityVotes(ref result,testfile,attributenames);
Console.ReadLine();
}
/*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;
}
