public void copy(SVC rhs)
{
_param = rhs._param == null ? null : (SVMParam)rhs._param.Clone();
_crossValidation = rhs._crossValidation;
model = rhs.model == null ? null : (SVMModel)rhs.model.Clone();
quiet = rhs.quiet;
}
public static void SolveBinaryClassificationProblem()
{
SVC svc = new SVC();
List <KeyValuePair <double[], bool> > data = new List <KeyValuePair <double[], bool> >();
for (int i = 0; i < 100; ++i)
{
double[] x = new double[2];
x[0] = i / 50.0;
x[1] = (i + 1) / 100.0;
bool y = Math.Sin((x[0] + x[1]) * Math.PI) > 0;
data.Add(new KeyValuePair <double[], bool>(x, y));
}
var tuple = TrainTestSplit(data);
var train_data = tuple.Item1;
var test_data = tuple.Item2;
var metric = svc.Fit(train_data, test_data);
Console.WriteLine("Train Acc: {0}, Test Acc: {1}", metric.TrainAccuracy, metric.TestAccuracy);
foreach (KeyValuePair <double[], bool> entry in test_data.GetRange(0, 10))
{
double[] x = entry.Key;
bool y = entry.Value;
double output = svc.Predict(x);
bool prediction = output > 0;
Console.WriteLine("Predicted: {0} Actual: {1}", prediction, y);
}
}
public Object Clone()
{
SVC clone = new SVC();
clone.copy(this);
return(clone);
}
