public static void SolveRegressionProblem()
{
SVR svr = new SVR();
List <KeyValuePair <double[], double> > data = new List <KeyValuePair <double[], double> >();
for (int i = 0; i < 100; ++i)
{
double[] x = new double[2];
x[0] = i / 50.0;
x[1] = (i + 1) / 100.0;
double y = Math.Sin((x[0] + x[1]) * Math.PI);
data.Add(new KeyValuePair <double[], double>(x, y));
}
var tuple = TrainTestSplit(data);
var train_data = tuple.Item1;
var test_data = tuple.Item2;
var metric = svr.Fit(train_data, test_data);
Console.WriteLine("Train MSE: {0}, Test MSE: {1}", metric.TrainMSE, metric.TestMSE);
foreach (KeyValuePair <double[], double> entry in test_data.GetRange(0, 10))
{
double[] x = entry.Key;
double y = entry.Value;
double output = svr.Predict(x);
Console.WriteLine("Predicted: {0} Actual: {1}", output, y);
}
}
public object Clone()
{
SVR clone = new SVR();
clone.Copy(this);
return(clone);
}
public void Copy(SVR rhs)
{
_param = rhs._param == null ? null : (SVMParam)rhs._param.Clone();
_crossValidation = rhs._crossValidation;
_model = rhs._model == null ? null : (SVMModel)rhs._model.Clone();
if (_model != null)
{
_model.Param = _param;
}
_isQuiet = rhs._isQuiet;
}
