Struct OnlineKernelRidgeRegression 

pub struct OnlineKernelRidgeRegression<State = Untrained> {
    pub base_model: KernelRidgeRegression<State>,
    pub forgetting_factor: Float,
    pub update_frequency: usize,
    /* private fields */
}

Online/Incremental Kernel Ridge Regression

This variant allows for online updates to the model as new data arrives.

Fields

base_model: KernelRidgeRegression<State>

Base kernel ridge regression

forgetting_factor: Float

Forgetting factor for online updates

update_frequency: usize

Update frequency

Implementations

impl OnlineKernelRidgeRegression<Untrained>

pub fn new(approximation_method: ApproximationMethod) -> Self

Create a new online kernel ridge regression model

pub fn forgetting_factor(self, factor: Float) -> Self

Set forgetting factor

pub fn update_frequency(self, frequency: usize) -> Self

Set update frequency

pub fn alpha(self, alpha: Float) -> Self

Set alpha parameter

pub fn random_state(self, seed: u64) -> Self

Set random state

impl OnlineKernelRidgeRegression<Trained>

pub fn partial_fit( self, x_new: &Array2<Float>, y_new: &Array1<Float>, ) -> Result<Self>

Update the model with new data

pub fn update_count(&self) -> usize

Get the number of updates performed

Trait Implementations

impl<State: Clone> Clone for OnlineKernelRidgeRegression<State>

fn clone(&self) -> OnlineKernelRidgeRegression<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for OnlineKernelRidgeRegression<State>

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

Formats the value using the given formatter.

impl Estimator for OnlineKernelRidgeRegression<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for OnlineKernelRidgeRegression<Untrained>

type Fitted = OnlineKernelRidgeRegression<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<Float>) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for OnlineKernelRidgeRegression<Trained>

fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals