Struct ElasticNet

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

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.

Model implementation

The coordinate descent algorithm is used to solve the lasso and ridge problem. It optimizes each parameter seperately, holding all the others fixed. This cycles as long as the coefficients have not stabilized or the maximum number of iterations is reached.

See also:

• Talk on Fast Regularization Paths
• Regularization Paths for Generalized Linear Models via Coordinate Descent

Implementations

impl<F: Float> ElasticNet<F>

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

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

Get the fitted hyperplane

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 parameter set for construction of ElasticNet 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 only model

pub fn lasso() -> ElasticNetParams<F>

Create a LASSO only model

Trait Implementations

impl<F: Clone> Clone for ElasticNet<F>

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for ElasticNet<F>

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

Formats the value using the given formatter.

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

fn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut 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.

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

Create targets that predict_inplace works with.