public RidgeRegressionEstimator Fit(Matrix <double> x, Vector <double> y)
{
var(x1, y1, xMean, yMean) = CenterData(x, y);
(x, y) = (x1, y1);
if (Method == RidgeRegressionMethod.Automatic)
{
// TODO: implement some logic here
Method = RidgeRegressionMethod.LSQR;
}
Vector <double> w;
if (Method == RidgeRegressionMethod.LSQR)
{
// TODO: make benchmark with sparse matrices.
w = Lsqr(x, y, Alpha);
if (Intercept)
{
var intercept = GetIntercept(xMean, yMean, w);
var w1 = Vector <double> .Build.Dense(w.Count + 1);
w.CopySubVectorTo(w1, 0, 1, w.Count);
w1[0] = intercept;
w = w1;
}
}
else
{
throw new NotSupportedException();
}
return(new RidgeRegressionEstimator(this, w));
}
public RidgeRegression(bool intercept = true, double alpha = 1.0, RidgeRegressionMethod method = RidgeRegressionMethod.Automatic)
{
Intercept = intercept;
Alpha = alpha;
Method = method;
}
