Struct ElasticNet 

pub struct ElasticNet<F> {
    pub alpha: F,
    pub l1_ratio: F,
    pub max_iter: usize,
    pub tol: F,
    pub fit_intercept: bool,
}

ElasticNet regression (L1 + L2 regularized least squares).

Minimizes a combination of L1 and L2 penalties controlled by alpha and l1_ratio. Uses coordinate descent with soft-thresholding to handle the non-smooth L1 component.

Type Parameters

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

Fields

alpha: F

Overall regularization strength. Larger values enforce stronger regularization.

l1_ratio: F

Mix between L1 and L2 regularization.

• l1_ratio = 1.0 → pure Lasso (L1 only)
• l1_ratio = 0.0 → pure Ridge (L2 only)
• 0.0 < l1_ratio < 1.0 → ElasticNet blend

max_iter: usize

Maximum number of coordinate descent iterations.

tol: F

Convergence tolerance on the maximum coefficient change per pass.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

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

pub fn new() -> Self

Create a new ElasticNet with default settings.

Defaults: alpha = 1.0, l1_ratio = 0.5, max_iter = 1000, tol = 1e-4, fit_intercept = true.

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

Set the overall regularization strength.

pub fn with_l1_ratio(self, l1_ratio: F) -> Self

Set the L1/L2 mixing ratio.

Must be in [0.0, 1.0]. Values outside this range will be rejected at fit time.

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 on maximum coefficient change.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for ElasticNet<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ElasticNet<F>

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

Formats the value using the given formatter.

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

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

Fit the ElasticNet model using coordinate descent.

Centers the data if fit_intercept is true, then alternates coordinate updates using the soft-threshold rule with L2 scaling.

Errors

• FerroError::ShapeMismatch if x and y have different numbers of samples.
• FerroError::InvalidParameter if alpha is negative, l1_ratio is outside [0, 1], or tol is non-positive.
• FerroError::InsufficientSamples if n_samples == 0.

type Fitted = FittedElasticNet<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl PipelineEstimator for ElasticNet<f64>

fn fit_pipeline( &self, x: &Array2<f64>, y: &Array1<f64>, ) -> Result<Box<dyn FittedPipelineEstimator>, FerroError>

Fit the model and return it as a boxed pipeline estimator.

Errors

Propagates any FerroError from fit.