feat: documentation adjusted to new builder

src/cluster/dbscan.rs

@@ -15,11 +15,9 @@
 //! let blobs = generator::make_blobs(100, 2, 3);
 //! let x = DenseMatrix::from_vec(blobs.num_samples, blobs.num_features, &blobs.data);
 //! // Fit the algorithm and predict cluster labels
-//! let labels = DBSCAN::fit(&x, Distances::euclidian(), DBSCANParameters{
-//!     min_samples: 5,
-//!     eps: 3.0,
-//!     algorithm: KNNAlgorithmName::CoverTree
-//! }).and_then(|dbscan| dbscan.predict(&x));
+//! let labels = DBSCAN::fit(&x, Distances::euclidian(),
+//!     DBSCANParameters::default().with_eps(3.0)).
+//!     and_then(|dbscan| dbscan.predict(&x));
 //!
 //! println!("{:?}", labels);
 //! ```

src/linear/linear_regression.rs

@@ -45,9 +45,9 @@
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
 //!
-//! let lr = LinearRegression::fit(&x, &y, LinearRegressionParameters {
-//!                        solver: LinearRegressionSolverName::QR, // or SVD
-//!          }).unwrap();
+//! let lr = LinearRegression::fit(&x, &y,
+//!             LinearRegressionParameters::default().
+//!             with_solver(LinearRegressionSolverName::QR)).unwrap();
 //!
 //! let y_hat = lr.predict(&x).unwrap();
 //! ```

src/linear/ridge_regression.rs

@@ -45,11 +45,8 @@
 //! let y: Vec<f64> = vec![83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0,
 //!           100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];
 //!
-//! let y_hat = RidgeRegression::fit(&x, &y, RidgeRegressionParameters {
-//!                        solver: RidgeRegressionSolverName::Cholesky,
-//!                        alpha: 0.1,
-//!                        normalize: true
-//! }).and_then(|lr| lr.predict(&x)).unwrap();
+//! let y_hat = RidgeRegression::fit(&x, &y, RidgeRegressionParameters::default().with_alpha(0.1)).
+//!                 and_then(|lr| lr.predict(&x)).unwrap();
 //! ```
 //!
 //! ## References: