Struct FittedElasticNet 

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

Fitted ElasticNet regression model.

Stores the learned (potentially sparse) coefficients and intercept. Implements Predict and HasCoefficients.

Implementations

impl<F: Float> FittedElasticNet<F>

pub fn intercept(&self) -> F

Returns the intercept (bias) term learned during fitting.

pub fn n_iter(&self) -> usize

Number of coordinate-descent sweeps run by the solver.

Mirrors sklearn’s ElasticNet.n_iter_ attribute (_coordinate_descent.py:827). ferrolearn uses sklearn’s relative-change and dual-gap stopping criterion (REQ-13, _cd_fast.pyx:167-249), so this 1-based count matches sklearn’s n_iter_ value exactly at the same optimum.

pub fn dual_gap(&self) -> F

Duality gap at the returned solution, on the (1/2n)-scaled objective.

Mirrors sklearn’s ElasticNet.dual_gap_ attribute (_coordinate_descent.py:831); computed by [enet_dual_gap] on the same (centered/raw) design the coordinate descent solved.

Trait Implementations

impl<F: Clone> Clone for FittedElasticNet<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for FittedElasticNet<F>

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

Formats the value using the given formatter.

impl<F> FittedPipelineEstimator<F> for FittedElasticNet<F>

where F: Float + ScalarOperand + Send + Sync + 'static,

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

Generate predictions via the pipeline interface.

Errors

Propagates any FerroError from predict.

fn predict_proba_pipeline( &self, x: &ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ) -> Result<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, FerroError>

Class-probability estimates for the input data, shape (n_samples, n_classes).

fn decision_function_pipeline( &self, x: &ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ) -> Result<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, FerroError>

Confidence scores (decision function) for the input data, shape (n_samples, n_classes) (or (n_samples,) for binary, per the estimator’s contract).

fn score_pipeline( &self, x: &ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, y: &ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>, ) -> Result<F, FerroError>

Score the final estimator on (x, y), returning a single scalar (e.g. mean accuracy for a classifier, R² for a regressor).

impl<F: Float + Send + Sync + ScalarOperand + 'static> HasCoefficients<F> for FittedElasticNet<F>

fn coefficients(&self) -> &Array1<F>

Returns the learned coefficient vector.

fn intercept(&self) -> F

Returns the learned intercept term.

impl<F: Float + Send + Sync + ScalarOperand + 'static> Predict<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for FittedElasticNet<F>

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

Predict target values for the given feature matrix.

Computes X @ coefficients + intercept.

Errors

Returns FerroError::ShapeMismatch if the number of features does not match the fitted model.

type Output = ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>

The prediction output type (e.g., ndarray::Array1<F> or ndarray::Array1<usize>).

type Error = FerroError

The error type returned by predict.