Struct ElasticNetRegularization 

pub struct ElasticNetRegularization {
    pub lambda: f64,
    pub l1_ratio: f64,
}

Elastic Net regularization (combination of L1 and L2).

Combines L1 and L2 penalties: l1_ratio * L1 + (1 - l1_ratio) * L2

Fields

lambda: f64

Overall regularization strength.

l1_ratio: f64

Balance between L1 and L2 (0.0 = pure L2, 1.0 = pure L1).

Implementations

impl ElasticNetRegularization

pub fn new(lambda: f64, l1_ratio: f64) -> TrainResult<Self>

Create a new Elastic Net regularizer.

Arguments

• lambda - Overall regularization strength
• l1_ratio - Balance between L1 and L2 (should be in [0, 1])

Trait Implementations

impl Clone for ElasticNetRegularization

fn clone(&self) -> ElasticNetRegularization

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ElasticNetRegularization

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

Formats the value using the given formatter.

impl Default for ElasticNetRegularization

fn default() -> Self

Returns the “default value” for a type.

impl Regularizer for ElasticNetRegularization

fn compute_penalty( &self, parameters: &HashMap<String, Array<f64, Ix2>>, ) -> TrainResult<f64>

Compute the regularization penalty for given parameters.

fn compute_gradient( &self, parameters: &HashMap<String, Array<f64, Ix2>>, ) -> TrainResult<HashMap<String, Array<f64, Ix2>>>

Compute the gradient of the regularization penalty.