Struct LinearRegression 

pub struct LinearRegression { /* private fields */ }

Ordinary least-squares linear regression.

Estimates y ≈ Xβ + b by minimising the residual sum of squares.

use datarust::linear_model::LinearRegression;
use datarust::traits::Predictor;
use datarust::Matrix;

let x = Matrix::new(vec![
    vec![1.0, 2.0],
    vec![2.0, 1.0],
    vec![3.0, 4.0],
    vec![4.0, 3.0],
])?;
let y = vec![5.0, 5.0, 11.0, 11.0];

let mut model = LinearRegression::new();
model.fit(&x, &y)?;
let pred = model.predict(&x)?;
assert!(pred.iter().zip(y.iter()).all(|(p, &t)| (p - t).abs() < 1e-9));

Implementations

impl LinearRegression

pub fn new() -> Self

Creates a new estimator with defaults: fit_intercept = true, solver = Cholesky.

pub fn with_fit_intercept(self, b: bool) -> Self

Builder: whether to fit an intercept term (default true).

pub fn with_solver(self, s: LinearSolver) -> Self

Builder: choose the solver (default LinearSolver::Cholesky).

pub fn coef(&self) -> &[f64]

Fitted coefficients β, one entry per feature. Empty if not fitted.

pub fn intercept(&self) -> f64

Fitted intercept term b (0.0 if fit_intercept = false).

pub fn n_features_in(&self) -> usize

Number of features seen during fit.

pub fn score(&self, x: &Matrix, y: &[f64]) -> Result<f64>

R² (coefficient of determination) of the prediction, mirroring estimator.score(X, y) in scikit-learn.

Returns the r2_score of self.predict(X) against y.

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() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for LinearRegression

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimator for LinearRegression

impl Predictor for LinearRegression

fn fit(&mut self, x: &Matrix, y: &[f64]) -> Result<()>

Fit the estimator on training features and target values.

fn predict(&self, x: &Matrix) -> Result<Vec<f64>>

Predict one numeric value per input row. For classifiers this is the predicted class label; probability estimates are exposed separately by PredictProba.

fn is_fitted(&self) -> bool

Whether the estimator has learned fitted state.

fn fit_predict(&mut self, x: &Matrix, y: &[f64]) -> Result<Vec<f64>>

Convenience: fit then predict on the same data.

impl Regressor for LinearRegression

fn name(&self) -> &'static str

Name of the estimator, used for diagnostics.

fn score(&self, x: &Matrix, y: &[f64]) -> Result<f64>

R² (coefficient of determination) of the prediction.

impl Serialize for LinearRegression

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.