public static void Main(string[] args)
{
// Create a training set
IList <IDatum <string, string> > trainingData = new List <IDatum <string, string> >();
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Green, Red));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Green));
trainingData.Add(MakeStopLights(Red, Red));
// Create a test set
IDatum <string, string> workingLights = MakeStopLights(Green, Red);
IDatum <string, string> brokenLights = MakeStopLights(Red, Red);
// Build a classifier factory
LinearClassifierFactory <string, string> factory = new LinearClassifierFactory <string, string>();
factory.UseConjugateGradientAscent();
// Turn on per-iteration convergence updates
factory.SetVerbose(true);
//Small amount of smoothing
factory.SetSigma(10.0);
// Build a classifier
LinearClassifier <string, string> classifier = factory.TrainClassifier(trainingData);
// Check out the learned weights
classifier.Dump();
// Test the classifier
System.Console.Out.WriteLine("Working instance got: " + classifier.ClassOf(workingLights));
classifier.JustificationOf(workingLights);
System.Console.Out.WriteLine("Broken instance got: " + classifier.ClassOf(brokenLights));
classifier.JustificationOf(brokenLights);
}
public static void Main(string[] args)
{
Edu.Stanford.Nlp.Classify.RVFDataset <string, string> data = new Edu.Stanford.Nlp.Classify.RVFDataset <string, string>();
ClassicCounter <string> c1 = new ClassicCounter <string>();
c1.IncrementCount("fever", 3.5);
c1.IncrementCount("cough", 1.1);
c1.IncrementCount("congestion", 4.2);
ClassicCounter <string> c2 = new ClassicCounter <string>();
c2.IncrementCount("fever", 1.5);
c2.IncrementCount("cough", 2.1);
c2.IncrementCount("nausea", 3.2);
ClassicCounter <string> c3 = new ClassicCounter <string>();
c3.IncrementCount("cough", 2.5);
c3.IncrementCount("congestion", 3.2);
data.Add(new RVFDatum <string, string>(c1, "cold"));
data.Add(new RVFDatum <string, string>(c2, "flu"));
data.Add(new RVFDatum <string, string>(c3, "cold"));
data.SummaryStatistics();
LinearClassifierFactory <string, string> factory = new LinearClassifierFactory <string, string>();
factory.UseQuasiNewton();
LinearClassifier <string, string> c = factory.TrainClassifier(data);
ClassicCounter <string> c4 = new ClassicCounter <string>();
c4.IncrementCount("cough", 2.3);
c4.IncrementCount("fever", 1.3);
RVFDatum <string, string> datum = new RVFDatum <string, string>(c4);
c.JustificationOf((IDatum <string, string>)datum);
}
protected internal virtual void JustificationOf(IDatum <string, string> testDatum, PrintWriter pw, string label)
{
classifier.JustificationOf(testDatum, pw);
}
