Struct LinearRegression 

pub struct LinearRegression {
    pub coef_: Option<Vec<f32>>,
    pub intercept_: Option<f32>,
    pub fit_intercept: bool,
    pub solver: LinearSolver,
    pub learning_rate: f32,
    pub max_iter: usize,
    pub tol: f32,
}

Linear Regression using Ordinary Least Squares (closed-form) or Gradient Descent

Fields

coef_: Option<Vec<f32>>

Coefficients (weights)

intercept_: Option<f32>

Intercept (bias)

fit_intercept: bool

Whether to fit intercept

solver: LinearSolver

Solver to use

learning_rate: f32

Learning rate for gradient descent

max_iter: usize

Maximum iterations for gradient descent

tol: f32

Tolerance for convergence

Implementations

impl LinearRegression

pub fn new() -> Self

pub fn fit_intercept(self, fit: bool) -> Self

pub fn solver(self, solver: LinearSolver) -> Self

pub fn learning_rate(self, lr: f32) -> Self

pub fn max_iter(self, n: usize) -> Self

pub fn fit(&mut self, x: &Tensor, y: &Tensor)

Fit the linear regression model

pub fn predict(&self, x: &Tensor) -> Tensor

Predict target values

pub fn score(&self, x: &Tensor, y: &Tensor) -> f32

Compute R² score

Trait Implementations

impl Default for LinearRegression

fn default() -> Self

Returns the “default value” for a type.