Struct ElasticNetCV 

pub struct ElasticNetCV<F> { /* private fields */ }

ElasticNet regression with built-in cross-validation for joint (alpha, l1_ratio) selection.

For each candidate l1_ratio, the module generates a log-spaced alpha grid (from alpha_max down to alpha_max * 1e-3) or uses the user-supplied grid, runs k-fold CV, and selects the combination that minimises mean squared error.

Type Parameters

• F: The floating-point type (f32 or f64).

Implementations

impl<F: Float + FromPrimitive> ElasticNetCV<F>

pub fn new() -> Self

Create a new ElasticNetCV with default settings.

Defaults:

• l1_ratios = [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1.0]
• n_alphas = 100
• cv = 5
• max_iter = 1000
• tol = 1e-4
• fit_intercept = true

pub fn with_l1_ratios(self, l1_ratios: Vec<F>) -> Self

Set the candidate L1/L2 mixing ratios.

Each value must be in [0.0, 1.0].

pub fn with_n_alphas(self, n_alphas: usize) -> Self

Set the number of alphas generated per l1_ratio.

pub fn with_cv(self, cv: usize) -> Self

Set the number of cross-validation folds.

Must be at least 2.

pub fn with_max_iter(self, max_iter: usize) -> Self

Set the maximum number of coordinate descent iterations.

pub fn with_tol(self, tol: F) -> Self

Set the convergence tolerance.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for ElasticNetCV<F>

fn clone(&self) -> ElasticNetCV<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ElasticNetCV<F>

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

Formats the value using the given formatter.

impl<F: Float + FromPrimitive> Default for ElasticNetCV<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + ScalarOperand + FromPrimitive + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for ElasticNetCV<F>

fn fit( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<FittedElasticNetCV<F>, FerroError>

Fit the ElasticNetCV model.

For each candidate l1_ratio, generates an alpha grid, runs k-fold CV for every (alpha, l1_ratio) pair, then refits on the full data using the best combination.

Errors

• FerroError::ShapeMismatch if x and y sizes differ.
• FerroError::InvalidParameter if l1_ratios is empty, any ratio is outside [0, 1], cv < 2, or n_alphas == 0.
• FerroError::InsufficientSamples if n_samples < cv.

type Fitted = FittedElasticNetCV<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.