public static Model Train(Problem prob, TrainingArg arg)
{
List<double> labels = GetLabels(prob);
if (labels.Count > 2)
{
List<BinaryClassifier> classifiers = new List<BinaryClassifier>(labels.Count);
foreach (double label in labels)
{
Console.WriteLine("{0} vs all:",label);
BinaryClassifier classifier = BinaryClassifier.Train(prob, arg, label);
classifiers.Add(classifier);
Console.WriteLine("finish");
}
Model model = new Model();
model.NumberOfClasses = classifiers.Count;
model.BinaryClassifiers = classifiers.ToArray();
return model;
}
else if (labels.Count == 2)
{
BinaryClassifier[] classifiers = new BinaryClassifier[1];
classifiers[0] = BinaryClassifier.Train(prob, arg, labels[0], labels[1]);
Model model = new Model();
model.NumberOfClasses = 2;
model.BinaryClassifiers = classifiers;
return model;
}
else
throw new Exception(Messege.CouldNotClassify);
}
public static Model Train(Problem prob, TrainingArg arg)
{
List<double> labels = GetLabels(prob);
if (labels.Count > 2)
{
List<BinaryClassifier> classifiers = new List<BinaryClassifier>(labels.Count);
foreach (double label in labels)
{
Console.WriteLine("{0} vs all:",label);
BinaryClassifier classifier = BinaryClassifier.Train(prob, arg, label);
classifiers.Add(classifier);
Console.WriteLine("finish");
}
Model model = new Model();
model.NumberOfClasses = classifiers.Count;
model.BinaryClassifiers = classifiers.ToArray();
return model;
}
else if (labels.Count == 2)
{
BinaryClassifier[] classifiers = new BinaryClassifier[1];
classifiers[0] = BinaryClassifier.Train(prob, arg, labels[0], labels[1]);
Model model = new Model();
model.NumberOfClasses = 2;
model.BinaryClassifiers = classifiers;
return model;
}
else
throw new Exception(Messege.CouldNotClassify);
}
/// <summary>
/// Train posLabel vs negLabel.
/// when negLabel is NaN, Train posLabel vs other.
/// </summary>
/// <param name="prob">The training data</param>
/// <param name="arg">The training argument</param>
/// <param name="posLabel">positive label</param>
/// <param name="negLabel">negative label</param>
/// <returns>BinaryClassifier</returns>
public static BinaryClassifier Train(Problem prob, TrainingArg arg, double posLabel, double negLabel = double.NaN)
{
Problem binaryProb = CreatBinaryProblem(prob, posLabel, negLabel);
Assembly asm = Assembly.GetAssembly(typeof(StrongLeaner));
StrongLeaner strongLearner = (StrongLeaner)asm.CreateInstance(typeof(StrongLeaner).Namespace + "." + arg.StrongLearnerName);
strongLearner.Train(binaryProb, arg.WeakLearnerName, arg.WeakLearnerArgs, arg.Iterations);
return(new BinaryClassifier(posLabel, negLabel, strongLearner));
}
/// <summary>
/// Train posLabel vs negLabel.
/// when negLabel is NaN, Train posLabel vs other.
/// </summary>
/// <param name="prob">The training data</param>
/// <param name="arg">The training argument</param>
/// <param name="posLabel">positive label</param>
/// <param name="negLabel">negative label</param>
/// <returns>BinaryClassifier</returns>
public static BinaryClassifier Train(Problem prob, TrainingArg arg, double posLabel, double negLabel = double.NaN)
{
Problem binaryProb = CreatBinaryProblem(prob, posLabel, negLabel);
Assembly asm = Assembly.GetAssembly(typeof(StrongLeaner));
StrongLeaner strongLearner = (StrongLeaner)asm.CreateInstance(typeof(StrongLeaner).Namespace + "." + arg.StrongLearnerName);
strongLearner.Train(binaryProb, arg.WeakLearnerName, arg.WeakLearnerArgs, arg.Iterations);
return new BinaryClassifier(posLabel, negLabel, strongLearner);
}
