Struct BayesianRidge 

pub struct BayesianRidge<F> {
    pub max_iter: usize,
    pub tol: F,
    pub alpha_init: F,
    pub lambda_init: F,
    pub fit_intercept: bool,
}

Bayesian Ridge Regression with automatic regularization tuning.

Estimates weight precision (lambda) and noise precision (alpha) iteratively using evidence maximization. The intercept, if requested, is fit by centering.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

max_iter: usize

Maximum number of EM (evidence-maximization) iterations.

tol: F

Convergence tolerance on the relative change in log-evidence.

alpha_init: F

Initial noise precision (alpha). Must be positive.

lambda_init: F

Initial weight precision (lambda). Must be positive.

fit_intercept: bool

Whether to fit an intercept (bias) term.

Implementations

impl<F: Float + FromPrimitive> BayesianRidge<F>

pub fn new() -> Self

Create a new BayesianRidge with default settings.

Defaults: max_iter = 300, tol = 1e-3, alpha_init = 1.0, lambda_init = 1.0, fit_intercept = true.

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

Set the maximum number of iterations.

pub fn with_tol(self, tol: F) -> Self

Set the convergence tolerance.

pub fn with_alpha_init(self, alpha_init: F) -> Self

Set the initial noise precision.

pub fn with_lambda_init(self, lambda_init: F) -> Self

Set the initial weight precision.

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

Set whether to fit an intercept term.

Trait Implementations

impl<F: Clone> Clone for BayesianRidge<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for BayesianRidge<F>

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

Formats the value using the given formatter.

impl<F: Float + FromPrimitive> Default for BayesianRidge<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + ScalarOperand + FromPrimitive + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 1]>>> for BayesianRidge<F>

fn fit( &self, x: &Array2<F>, y: &Array1<F>, ) -> Result<FittedBayesianRidge<F>, FerroError>

Fit the Bayesian Ridge model via evidence maximization (EM).

Iterates over:

1. Solve posterior for w given current alpha and lambda.
2. Update alpha and lambda using the posterior statistics.

Errors

• FerroError::ShapeMismatch — sample count mismatch.
• FerroError::InvalidParameter — non-positive initial precisions.
• FerroError::InsufficientSamples — fewer than 2 samples.
• FerroError::NumericalInstability — numerical failure in solver.

type Fitted = FittedBayesianRidge<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl PipelineEstimator<f64> for BayesianRidge<f64>

fn fit_pipeline( &self, x: &Array2<f64>, y: &Array1<f64>, ) -> Result<Box<dyn FittedPipelineEstimator<f64>>, FerroError>

Fit the model and return it as a boxed pipeline estimator.

Errors

Propagates any FerroError from fit.