public DTreeNode GetSplitingFeature(DTreeNode currentNode)
{
double maxInformationGain = -1000000;
DTreeNode splitNode = null;
string[][] currentOperationData = trainingDataInstance.GetDataInstances(currentNode.previousFeatureValues);
List <string> featureList = GetFeatureList(currentOperationData[0]);
foreach (string featureName in featureList)
{
List <string[]> featureData = GetFeatureData(currentOperationData, featureName);
DTreeNode node = new DTreeNode();
node.spliteFeatureName = featureData[0][0];
featureData.RemoveAt(0);
node.ExecuteNode(featureData);
node.clearDataList();
if (node.entropy == 0.0)
{
splitNode = currentNode;
splitNode.entropy = node.entropy;
splitNode.className = node.className;
}
if (node.informationGain > maxInformationGain)
{
maxInformationGain = node.informationGain;
splitNode = node;
}
}
return(splitNode);
}
private double createChildNodesAndGetChildEntropy()
{
List <string> featureData = GetDistinctFeatureData();
double childEntropy = 0;
if (featureData.Count > 1)
{
foreach (string data in featureData)
{
DTreeNode childNode = new DTreeNode();
childNode.spliteFeatureValue = data;
List <string[]> childData = GetChildNodeData(data);
childNode.ExecuteNode(childData);
this.childrenNodes.Add(childNode);
double factor = (double)childData.Count / this.nodeDataInstance.Count;
childEntropy += factor * childNode.entropy;
}
}
return(childEntropy);
}
