Struct linfa_elasticnet::ElasticNet

pub struct ElasticNet<F> { /* fields omitted */ }

Elastic Net model

This struct contains the parameters of a fitted elastic net model. This includes the seperating hyperplane, (optionally) intercept, duality gaps and the number of step needed in the computation.

Implementations

impl<F: Float> ElasticNet<F>

View the fitted parameters and make predictions with a fitted elastic net model

pub fn parameters(&self) -> &Array1<F>

Get the fitted parameters

pub fn intercept(&self) -> F

Get the fitted intercept, 0. if no intercept was fitted

pub fn n_steps(&self) -> u32

Get the number of steps taken in optimization algorithm

pub fn duality_gap(&self) -> F

Get the duality gap at the end of the optimization algorithm

pub fn z_score(&self) -> Result<Array1<F>>

Calculate the Z score

pub fn confidence_95th(&self) -> Result<Array1<(F, F)>>

Calculate the confidence level

impl<F: Float> ElasticNet<F>

pub fn params() -> ElasticNetParams<F>

Create a default elastic net model

By default, an intercept will be fitted. To disable fitting an intercept, call .with_intercept(false) before calling .fit().

To additionally normalize the feature matrix before fitting, call fit_intercept_and_normalize() before calling fit(). The feature matrix will not be normalized by default.

pub fn ridge() -> ElasticNetParams<F>

Create a ridge model

pub fn lasso() -> ElasticNetParams<F>

Create a lasso model

Trait Implementations

impl<F: Float, D: Data<Elem = F>> Predict<&'_ ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for ElasticNet<F>

pub fn predict(&self, x: &ArrayBase<D, Ix2>) -> Array1<F>

impl<F: Float, D: Data<Elem = F>> Predict<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for ElasticNet<F>

pub fn predict(&self, x: ArrayBase<D, Ix2>) -> Array1<F>

Given an input matrix X, with shape (n_samples, n_features), predict returns the target variable according to elastic net learned from the training data distribution.