public void TestC45()
{
var c45 = new C45();
var c45Parameter = new C45Parameter(1, true, 0.2);
var discreteToContinuous = new DiscreteToContinuous(car);
discreteToContinuous.Convert();
c45.Train(car.GetInstanceList(), c45Parameter);
Assert.AreEqual(29.98, 100 * c45.Test(car.GetInstanceList()).GetErrorRate(), 0.01);
discreteToContinuous = new DiscreteToContinuous(tictactoe);
discreteToContinuous.Convert();
c45.Train(tictactoe.GetInstanceList(), c45Parameter);
Assert.AreEqual(34.66, 100 * c45.Test(tictactoe.GetInstanceList()).GetErrorRate(), 0.01);
}
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 void TestC45()
{
var c45 = new C45();
var c45Parameter = new C45Parameter(1, true, 0.2);
var laryToBinary = new LaryToBinary(car);
laryToBinary.Convert();
c45.Train(car.GetInstanceList(), c45Parameter);
Assert.AreEqual(10.76, 100 * c45.Test(car.GetInstanceList()).GetErrorRate(), 0.01);
laryToBinary = new LaryToBinary(tictactoe);
laryToBinary.Convert();
c45.Train(tictactoe.GetInstanceList(), c45Parameter);
Assert.AreEqual(16.08, 100 * c45.Test(tictactoe.GetInstanceList()).GetErrorRate(), 0.01);
laryToBinary = new LaryToBinary(nursery);
laryToBinary.Convert();
c45.Train(nursery.GetInstanceList(), c45Parameter);
Assert.AreEqual(24.95, 100 * c45.Test(nursery.GetInstanceList()).GetErrorRate(), 0.01);
}
public void TestC45()
{
var c45 = new C45();
var c45Parameter = new C45Parameter(1, true, 0.2);
var discreteToIndexed = new DiscreteToIndexed(car);
discreteToIndexed.Convert();
c45.Train(car.GetInstanceList(), c45Parameter);
Assert.AreEqual(10.76, 100 * c45.Test(car.GetInstanceList()).GetErrorRate(), 0.01);
discreteToIndexed = new DiscreteToIndexed(tictactoe);
discreteToIndexed.Convert();
c45.Train(tictactoe.GetInstanceList(), c45Parameter);
Assert.AreEqual(16.08, 100 * c45.Test(tictactoe.GetInstanceList()).GetErrorRate(), 0.01);
discreteToIndexed = new DiscreteToIndexed(nursery);
discreteToIndexed.Convert();
c45.Train(nursery.GetInstanceList(), c45Parameter);
Assert.AreEqual(24.95, 100 * c45.Test(nursery.GetInstanceList()).GetErrorRate(), 0.01);
}
public void TestTrain()
{
var c45 = new C45();
var c45Parameter = new C45Parameter(1, true, 0.2);
c45.Train(iris.GetInstanceList(), c45Parameter);
Assert.AreEqual(4.67, 100 * c45.Test(iris.GetInstanceList()).GetErrorRate(), 0.01);
c45.Train(bupa.GetInstanceList(), c45Parameter);
Assert.AreEqual(42.03, 100 * c45.Test(bupa.GetInstanceList()).GetErrorRate(), 0.01);
c45.Train(dermatology.GetInstanceList(), c45Parameter);
Assert.AreEqual(6.28, 100 * c45.Test(dermatology.GetInstanceList()).GetErrorRate(), 0.01);
c45.Train(car.GetInstanceList(), c45Parameter);
Assert.AreEqual(21.35, 100 * c45.Test(car.GetInstanceList()).GetErrorRate(), 0.01);
c45.Train(tictactoe.GetInstanceList(), c45Parameter);
Assert.AreEqual(19.94, 100 * c45.Test(tictactoe.GetInstanceList()).GetErrorRate(), 0.01);
c45.Train(nursery.GetInstanceList(), c45Parameter);
Assert.AreEqual(29.03, 100 * c45.Test(nursery.GetInstanceList()).GetErrorRate(), 0.01);
}