public static void RunC45()
{
List <DDataRecord> records = LoadSample();
C45 <DDataRecord> algorithm = new C45 <DDataRecord>();
algorithm.UpdateContinuousAttributes(records, "temperature");
algorithm.UpdateContinuousAttributes(records, "humidity");
algorithm.Train(records);
//algorithm.RulePostPrune(records); //post pruning using cross valiation set
Console.WriteLine("C4.5 Tree Built!");
for (int i = 0; i < records.Count; i++)
{
DDataRecord rec = records[i];
Console.WriteLine("rec: ");
string[] feature_names = rec.FindFeatures();
foreach (string feature_name in feature_names)
{
Console.WriteLine(feature_name + " = " + rec[feature_name]);
}
Console.WriteLine("Label: " + rec.Label);
Console.WriteLine("Predicted Label: " + algorithm.Predict(records[i]));
Console.WriteLine();
}
}
public static List <DDataRecord> LoadSample()
{
XmlDocument doc = new XmlDocument();
doc.Load("database.xml");
List <DDataRecord> records = new List <DDataRecord>();
XmlElement xml_root = doc.DocumentElement;
foreach (XmlElement xml_level1 in xml_root.ChildNodes)
{
if (xml_level1.Name == "record")
{
String outlook = xml_level1.Attributes["outlook"].Value;
string temperature = xml_level1.Attributes["temperature"].Value;
string humidity = xml_level1.Attributes["humidity"].Value;
String windy = xml_level1.Attributes["windy"].Value;
String class_label = xml_level1.Attributes["class"].Value;
DDataRecord rec = new DDataRecord();
rec["outlook"] = outlook;
rec["windy"] = windy;
rec["temperature"] = temperature;
rec["humidity"] = humidity;
rec.Label = class_label;
records.Add(rec);
}
}
return(records);
}
public static void RunID3()
{
List <DDataRecord> X = LoadSample();
//As ID3 does not support continuous value, must do manually conversion
foreach (DDataRecord rec in X)
{
int temperature = int.Parse(rec["temperature"]);
int humidity = int.Parse(rec["humidity"]);
if (temperature < 75)
{
rec["temperature"] = "< 75";
}
else
{
rec["temperature"] = ">= 75";
}
if (humidity < 80)
{
rec["humidity"] = "< 80";
}
else
{
rec["humidity"] = ">= 80";
}
}
ID3 <DDataRecord> algorithm = new ID3 <DDataRecord>();
algorithm.Train(X);
//algorithm.ErrorReducePrune(Xval); //error reduce prune using cross valiation set
Console.WriteLine("ID3 Tree Built!");
for (int i = 0; i < X.Count; i++)
{
DDataRecord rec = X[i];
Console.WriteLine("rec: ");
string[] feature_names = rec.FindFeatures();
foreach (string feature_name in feature_names)
{
Console.WriteLine(feature_name + " = " + rec[feature_name]);
}
Console.WriteLine("Label: " + rec.Label);
Console.WriteLine("Predicted Label: " + algorithm.Predict(X[i]));
Console.WriteLine();
}
algorithm.WriteToXml("ID3.xml");
}
