Struct Lasso 

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

Lasso regression (L1-regularized least squares).

Uses coordinate descent with soft-thresholding to solve the L1-penalized regression problem. The alpha parameter controls the strength of the L1 penalty.

Type Parameters

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

Fields

alpha: F

Regularization strength. Larger values specify stronger regularization and sparser solutions.

max_iter: usize

Maximum number of coordinate descent iterations.

tol: F

Convergence tolerance on the maximum coefficient change.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float> Lasso<F>

pub fn new() -> Self

Create a new Lasso with default settings.

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

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

Set the regularization strength.

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.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for Lasso<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for Lasso<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for Lasso<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 Lasso<F>

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

Fit the Lasso model using coordinate descent.

Errors

Returns FerroError::ShapeMismatch if the number of samples in x and y differ. Returns FerroError::InvalidParameter if alpha is negative. Returns FerroError::ConvergenceFailure if the algorithm does not converge within max_iter iterations.

type Fitted = FittedLasso<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F> PipelineEstimator<F> for Lasso<F>

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

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

Fit this estimator on the given data.