Struct LinearRegression 

pub struct LinearRegression { /* private fields */ }

Ordinary Least Squares linear regression estimator.

This is a thin, sklearn-style wrapper around linear_regression.

Examples

use scirs2_core::ndarray::{array, Array2};
use scirs2_stats::regression::LinearRegression;

let x = Array2::from_shape_vec((5, 2), vec![
    1.0_f64, 0.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 4.0,
]).expect("shape ok");
let y = array![1.0_f64, 3.0, 5.0, 7.0, 9.0];

let mut model = LinearRegression::new();
let fitted = model.fit(&x.view(), &y.view()).expect("fit ok");
let preds = fitted.predict(&x.view()).expect("predict ok");
assert_eq!(preds.len(), 5);

Implementations

impl LinearRegression

pub fn new() -> Self

Create a new (unfitted) linear regression model.

pub fn fit( &mut self, x: &ArrayView2<'_, f64>, y: &ArrayView1<'_, f64>, ) -> StatsResult<FittedLinearRegression<f64>>

Fit the model to training data (x, y).

Arguments

• x – Design matrix of shape (n_samples, n_features).
• y – Target vector of length n_samples.

Trait Implementations

impl Clone for LinearRegression

fn clone(&self) -> LinearRegression

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LinearRegression

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

Formats the value using the given formatter.

impl Default for LinearRegression

fn default() -> LinearRegression

Returns the “default value” for a type.