The Shark-ML library provides the LinearModel class for representing linear regression problems. There are two trainer classes for this kind of model: the LinearRegression class, which provides analytical solutions, and the LinearSAGTrainer class, which provides a stochastic average gradient iterative method, as illustrated in the following code block:

using namespace shark;
using namespace std;
Data<RealVector> x;
Data<RealVector> y;
RegressionDataset data(x, y);
LinearModel<> model;

LinearRegression trainer;
trainer.train(model, data);

We can get the calculated parameters (the linear regression task solution) vector by running the following code:

auto b = model.parameterVector();

For new x inputs, we can predict new y values in the following way:

Data<RealVector> new_x;
Data<RealVector> prediction = model(new_x);

Also, we can calculate the value of squared error, as follows:

SquaredLoss<> loss;
auto se = loss(y, prediction)