lm.daphne

// Linear regression model training.

// Arguments:
// - r ... number of rows
// - c ... number of columns

// Data generation.
XY = rand($r, $c, 0.0, 1.0, 1, -1);

// Extraction of X and y.
X = XY[, 0:($c - 1)];
y = XY[, $c - 1];

// Linear regression model training (decisive part).
X = (X - mean(X, 1)) / stddev(X, 1);
X = cbind(X, fill(1.0, nrow(X), 1));
lambda = fill(0.001, ncol(X), 1);
A = t(X) @ X + diagMatrix(lambda);
b = t(X) @ y;
beta = solve(A, b);

// Result output.
print(beta);