Struct Ridge 

pub struct Ridge { /* private fields */ }

Ridge regression with L2 regularization.

use datarust::linear_model::Ridge;
use datarust::traits::Predictor;
use datarust::Matrix;

let x = Matrix::new(vec![
    vec![0.0, 0.0],
    vec![1.0, 1.0],
    vec![2.0, 2.0],
    vec![3.0, 3.0],
])?;
// Collinear features: LinearRegression would be singular; Ridge handles it.
let y = vec![1.0, 2.0, 3.0, 4.0];
let mut model = Ridge::new().with_alpha(1.0);
model.fit(&x, &y)?;
let pred = model.predict(&x)?;
assert_eq!(pred.len(), 4);

Implementations

impl Ridge

pub fn new() -> Self

Creates a new Ridge with alpha = 1.0, fit_intercept = true, solver = Cholesky.

pub fn with_alpha(self, alpha: f64) -> Self

Builder: regularization strength alpha (default 1.0). Larger values shrink coefficients more aggressively. Must be >= 0.

pub fn with_fit_intercept(self, b: bool) -> Self

Builder: whether to fit an intercept term (default true).

pub fn with_solver(self, s: RidgeSolver) -> Self

Builder: choose the solver (default RidgeSolver::Cholesky).

pub fn coef(&self) -> &[f64]

Fitted coefficients β.

pub fn intercept(&self) -> f64

Fitted intercept term (0.0 if fit_intercept = false).

pub fn n_features_in(&self) -> usize

Number of features seen during fit.

pub fn score(&self, x: &Matrix, y: &[f64]) -> Result<f64>

R² of the prediction against y, mirroring estimator.score in sklearn.

Trait Implementations

impl Clone for Ridge

fn clone(&self) -> Ridge

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Ridge

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Ridge

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Ridge

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimator for Ridge

impl Predictor for Ridge

fn fit(&mut self, x: &Matrix, y: &[f64]) -> Result<()>

Fit the estimator on training features and target values.

fn predict(&self, x: &Matrix) -> Result<Vec<f64>>

Predict one numeric value per input row. For classifiers this is the predicted class label; probability estimates are exposed separately by PredictProba.

fn is_fitted(&self) -> bool

Whether the estimator has learned fitted state.

fn fit_predict(&mut self, x: &Matrix, y: &[f64]) -> Result<Vec<f64>>

Convenience: fit then predict on the same data.

impl Regressor for Ridge

fn name(&self) -> &'static str

Name of the estimator, used for diagnostics.

fn score(&self, x: &Matrix, y: &[f64]) -> Result<f64>

R² (coefficient of determination) of the prediction.

impl Serialize for Ridge

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.