private Node Learn(Instance[] instanceList, Arff.Attribute[] attributeList, Arff.Attribute classAttribute)
{
// compute the class distribution of all the examples
var classDistribution = GetClassDistribution(instanceList, classAttribute);
string[] classesWithExamples = classDistribution.Where(a => a.Value > 0).Select(a => a.Key).ToArray();
if (1 == classesWithExamples.Length)
{
// all examples belong to the same class so we have reached a leaf node
Logger.Log(LogLevel.Progress, ".");
return new Node()
{
Label = classesWithExamples[0],
Children = null
};
}
string mostCommonClass = classDistribution.OrderBy(a => a.Value).Last().Key;
if (null == attributeList
|| 0 == attributeList.Length)
{
// no more attributes to split on
Logger.Log(LogLevel.Progress, ".");
return new Node()
{
Label = mostCommonClass,
Children = null
};
}
var decisionAttribute = GetDecisionAttribute(instanceList, attributeList, classDistribution, classAttribute);
if (null == decisionAttribute)
{
// can't find a attribute to split on that passes the split-termination condition
Logger.Log(LogLevel.Progress, ".");
return new Node()
{
Label = mostCommonClass,
Children = null
};
}
// recursively build the tree
var root = new Node()
{
Label = decisionAttribute.Name,
Children = new Dictionary<string, Node>()
};
// pre-process the instances
var instanceGroups = instanceList
.GroupBy(a => a.Data[decisionAttribute.Name])
.ToDictionary(g => g.Key, v => v.ToArray());
// build the sub-trees
foreach (string value in decisionAttribute.Values)
{
Node childNode = null;
if (!instanceGroups.ContainsKey(value)
|| 0 == instanceGroups[value].Length)
{
// if there are not example for the node value assign the
// label of most instances to the value branch
childNode = new Node()
{
Label = mostCommonClass,
Children = null
};
root.Children.Add(value, childNode);
continue;
}
// build the subtree recursively
childNode = this.Learn(
instanceGroups[value],
attributeList.Where(a => !a.Name.Equals(decisionAttribute.Name)).ToArray(),
classAttribute);
root.Children.Add(value, childNode);
}
return root;
}
/// <summary>
/// Initalizes a new instance of the DTClassifier class.
/// </summary>
/// <param name="model">the trained model</param>
public DTClassifier(Node model)
{
this.model = model;
}