Struct MultiTaskElasticNet 

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

Multi-task ElasticNet regression (joint multi-output L1/L2,1-regularized least squares).

Fits all target columns jointly under a blended group-Lasso (L2,1) + squared-Frobenius (L2) penalty. Mirrors sklearn.linear_model.MultiTaskElasticNet (_coordinate_descent.py:402). MultiTaskLasso is MultiTaskElasticNet(l1_ratio=1.0).

Type Parameters

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

Fields

alpha: F

Regularization strength scaling both penalty terms. Larger values specify stronger regularization.

l1_ratio: F

ElasticNet mixing parameter in [0, 1]. l1_ratio = 1 is the pure L2,1 (group-Lasso) MultiTaskLasso; l1_ratio = 0 is a pure L2 (squared-Frobenius) penalty; intermediate values blend both.

fit_intercept: bool

Whether to fit a per-task intercept (bias) term.

max_iter: usize

Maximum number of block-coordinate-descent iterations.

tol: F

Convergence tolerance on the relative coefficient change / dual gap.

Implementations

impl<F: Float> MultiTaskElasticNet<F>

pub fn new() -> Self

Create a new MultiTaskElasticNet with default settings.

Defaults: alpha = 1.0, l1_ratio = 0.5, fit_intercept = true, max_iter = 1000, tol = 1e-4 — mirroring sklearn’s ctor defaults MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, max_iter=1000, tol=1e-4) (_coordinate_descent.py:402).

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

Set the regularization strength.

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

Set the ElasticNet mixing parameter (l1_ratio in [0, 1]).

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

Set whether to fit per-task intercept terms.

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

Set the maximum number of iterations.

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

Set the convergence tolerance.

Trait Implementations

impl<F: Clone> Clone for MultiTaskElasticNet<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for MultiTaskElasticNet<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for MultiTaskElasticNet<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; 2]>>> for MultiTaskElasticNet<F>

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

Fit the multi-task ElasticNet model using block coordinate descent.

Ports sklearn’s enet_coordinate_descent_multi_task (_cd_fast.pyx:740) with l1_reg = alpha * l1_ratio * n_samples and l2_reg = alpha * (1 - l1_ratio) * n_samples (_coordinate_descent.py:655-656).

Errors

Returns FerroError::ShapeMismatch if Y.nrows() differs from the number of samples in X. Returns FerroError::InvalidParameter if alpha is negative or l1_ratio is outside [0, 1]. Returns FerroError::InsufficientSamples if there are no samples. Returns FerroError::NumericalInstability if a required float constant or column mean cannot be formed.

type Fitted = FittedMultiTaskElasticNet<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.