Struct FittedLinearRegression

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

A fitted linear regression model which can be used for making predictions.

Implementations

impl<F: Float> FittedLinearRegression<F>

View the fitted parameters and make predictions with a fitted linear regresssion model.

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

Get the fitted parameters

pub fn intercept(&self) -> F

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

Trait Implementations

impl<F: Clone> Clone for FittedLinearRegression<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for FittedLinearRegression<F>

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

Formats the value using the given formatter.

impl<F: PartialEq> PartialEq for FittedLinearRegression<F>

fn eq(&self, other: &FittedLinearRegression<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F: Float, D: Data<Elem = F>> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for FittedLinearRegression<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 linear model learned from the training data distribution.

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

Create targets that predict_inplace works with.

impl<F> StructuralPartialEq for FittedLinearRegression<F>