Struct ElasticNetParameters 

pub struct ElasticNetParameters {
    pub alpha: f64,
    pub l1_ratio: f64,
    pub normalize: bool,
    pub tol: f64,
    pub max_iter: usize,
}

Elastic net parameters

Fields

alpha: f64

Regularization parameter.

l1_ratio: f64

The elastic net mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

normalize: bool

If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.

tol: f64

The tolerance for the optimization

max_iter: usize

The maximum number of iterations

Implementations

impl ElasticNetParameters

pub fn with_alpha(self, alpha: f64) -> Self

Regularization parameter.

pub fn with_l1_ratio(self, l1_ratio: f64) -> Self

The elastic net mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

pub fn with_normalize(self, normalize: bool) -> Self

If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.

pub fn with_tol(self, tol: f64) -> Self

The tolerance for the optimization

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

The maximum number of iterations

Trait Implementations

impl Clone for ElasticNetParameters

fn clone(&self) -> ElasticNetParameters

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ElasticNetParameters

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

Formats the value using the given formatter.

impl Default for ElasticNetParameters

fn default() -> Self

Returns the “default value” for a type.

impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, ElasticNetParameters> for ElasticNet<TX, TY, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit(x: &X, y: &Y, parameters: ElasticNetParameters) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.