Struct KernelRidgeImputer 

pub struct KernelRidgeImputer<S = Untrained> { /* private fields */ }

Kernel Ridge Regression Imputer

Imputation using kernel ridge regression with various kernel functions. This method learns non-linear relationships between features using kernel tricks and applies ridge regularization for numerical stability.

Parameters

• alpha - Ridge regularization parameter
• kernel - Kernel function type (‘rbf’, ‘linear’, ‘polynomial’)
• gamma - Kernel coefficient for ‘rbf’ kernel
• degree - Degree for polynomial kernel
• coef0 - Independent term for polynomial kernel
• missing_values - The placeholder for missing values

Examples

use sklears_impute::KernelRidgeImputer;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0, 3.0], [f64::NAN, 3.0, 4.0], [7.0, f64::NAN, 6.0]];

let imputer = KernelRidgeImputer::new()
    .alpha(1.0)
    .kernel("rbf".to_string())
    .gamma(1.0);
let fitted = imputer.fit(&X.view(), &()).unwrap();
let X_imputed = fitted.transform(&X.view()).unwrap();

Implementations

impl KernelRidgeImputer<Untrained>

pub fn new() -> Self

Create a new KernelRidgeImputer instance

pub fn alpha(self, alpha: f64) -> Self

Set the ridge regularization parameter

pub fn kernel(self, kernel: String) -> Self

Set the kernel function

pub fn gamma(self, gamma: f64) -> Self

Set the kernel coefficient for RBF kernel

pub fn degree(self, degree: usize) -> Self

Set the degree for polynomial kernel

pub fn coef0(self, coef0: f64) -> Self

Set the independent term for polynomial kernel

pub fn missing_values(self, missing_values: f64) -> Self

Set the missing values placeholder

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

Set the maximum number of iterations

pub fn tol(self, tol: f64) -> Self

Set the tolerance for convergence

Trait Implementations

impl<S: Clone> Clone for KernelRidgeImputer<S>

fn clone(&self) -> KernelRidgeImputer<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for KernelRidgeImputer<S>

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

Formats the value using the given formatter.

impl Default for KernelRidgeImputer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for KernelRidgeImputer<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<ViewRepr<&f64>, Dim<[usize; 2]>>, ()> for KernelRidgeImputer<Untrained>

type Fitted = KernelRidgeImputer<KernelRidgeImputerTrained>

The fitted model type

fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<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 Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for KernelRidgeImputer<KernelRidgeImputerTrained>

fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Transform the input data