private static IEnumerable <LinearRegressionResult> Run(LinearRegressionResult.GivenData data)
{
return(PolynomialDegrees
.Select(polynomialDegree => Predict(data, polynomialDegree))
.Select(tpl => new LinearRegressionResult(
degree: tpl.Item1,
givenData: data,
calibratedData: tpl.Item2)));
}
private static (int, LinearRegressionResult.CalibratedData) Predict(LinearRegressionResult.GivenData data, int polynomialDegree)
{
IFeature feature = new PolynomialFeature(polynomialDegree);
Matrix <double> designMatrix = ToyDataGenerater.DesignMatrix(data.X_train, feature);
Matrix <double> invDesignMatrix = designMatrix.PseudoInverse();
Vector <double> weights = invDesignMatrix * data.T_train;
Vector <double> t_test = Predicts(data.X_test, weights, feature);
return(polynomialDegree, new LinearRegressionResult.CalibratedData(t_test, weights));
}
public static LinearRegressionResult Run(int polynomialDegree, int noiseSeed, int nbTrain)
{
LinearRegressionResult.GivenData data = GenerateGivenData(noiseSeed, nbTrain);
(int, LinearRegressionResult.CalibratedData)tpl = Predict(data, polynomialDegree);
return(new LinearRegressionResult(
degree: tpl.Item1,
givenData: data,
calibratedData: tpl.Item2));
}
public static IEnumerable <LinearRegressionResult> Run()
{
LinearRegressionResult.GivenData data = GenerateGivenData(Fig_1_2.NoiseSeed, Fig_1_2.NbTrain);
return(Run(data));
}
