public TreeAttributeCollection GetValidAttributeCollection()
{
TreeAttributeCollection returnCollection = new TreeAttributeCollection();
foreach (DataColumn column in this.Columns)
{
TreeAttribute currentAttribute = new TreeAttribute(column.ColumnName, GetValuesFromColumn(column.ColumnName));
if (returnCollection.ContainsAttribute(currentAttribute) || currentAttribute.AttributeName.ToUpper().Trim() == "EXPLOSIVE")
{
continue;
}
returnCollection.Add(currentAttribute);
}
return(returnCollection);
}
private TreeNode buildTree(DataTable samples, string targetAttribute, TreeAttributeCollection attributes)
{
if (samples == null)
{
return(new TreeNode(new OutcomeTreeAttribute(targetAttribute)));
}
if (allSamplesArePositive(samples, targetAttribute) == true)
{
return(new TreeNode(new OutcomeTreeAttribute(true)));
}
if (allSamplesAreNegative(samples, targetAttribute) == true)
{
return(new TreeNode(new OutcomeTreeAttribute(false)));
}
if (attributes.Count == 0)
{
return(new TreeNode(new OutcomeTreeAttribute(getMostCommonValue(samples, targetAttribute))));
}
mTotal = samples.Rows.Count;
mTargetAttribute = targetAttribute;
mTotalPositives = countTotalPositives(samples);
mEntropySet = calculateEntropy(mTotalPositives, mTotal - mTotalPositives);
TreeAttribute bestAttribute = getBestAttribute(samples, attributes);
TreeNode root = new TreeNode(bestAttribute);
if (bestAttribute == null)
{
return(root);
}
DataTable aSample = samples.Clone();
foreach (string value in bestAttribute.PossibleValues)
{
aSample.Rows.Clear();
DataRow[] rows = samples.Select(bestAttribute.AttributeName + " = " + "'" + value + "'");
foreach (DataRow row in rows)
{
aSample.Rows.Add(row.ItemArray);
}
TreeAttributeCollection aAttributes = new TreeAttributeCollection();
//ArrayList aAttributes = new ArrayList(attributes.Count - 1);
for (int i = 0; i < attributes.Count; i++)
{
if (attributes[i].AttributeName != bestAttribute.AttributeName)
{
aAttributes.Add(attributes[i]);
}
}
if (aSample.Rows.Count == 0)
{
return(new TreeNode(new OutcomeTreeAttribute(getMostCommonValue(aSample, targetAttribute))));
}
else
{
DecisionTree dc3 = new DecisionTree();
TreeNode ChildNode = dc3.mountTree(aSample, targetAttribute, aAttributes);
root.AddTreeNode(ChildNode, value);
}
}
return(root);
}
