Struct ReproducingKernelImputer 

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

Reproducing Kernel Hilbert Space (RKHS) Imputer

Advanced kernel-based imputation using reproducing kernel methods with multiple kernel learning, regularization, and adaptive kernel selection. This method leverages the rich structure of RKHS for sophisticated imputation.

Parameters

• kernels - List of kernel functions to combine
• kernel_weights - Weights for kernel combination
• regularization - Regularization method (‘ridge’, ‘lasso’, ‘elastic_net’)
• lambda_reg - Regularization parameter
• adaptive_weights - Whether to adaptively learn kernel weights
• interpolation_method - Method for kernel interpolation
• smoothing_parameter - Smoothing parameter for kernel methods
• missing_values - The placeholder for missing values

Examples

use sklears_impute::ReproducingKernelImputer;
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 = ReproducingKernelImputer::new()
    .kernels(vec!["rbf".to_string(), "periodic".to_string()])
    .regularization("ridge".to_string())
    .lambda_reg(0.01);
let fitted = imputer.fit(&X.view(), &()).unwrap();
let X_imputed = fitted.transform(&X.view()).unwrap();

Implementations

impl ReproducingKernelImputer<Untrained>

pub fn new() -> Self

Create a new ReproducingKernelImputer instance

pub fn kernels(self, kernels: Vec<String>) -> Self

Set the list of kernel functions to combine

pub fn kernel_weights(self, weights: Vec<f64>) -> Self

Set the kernel combination weights

pub fn kernel_params( self, params: HashMap<String, HashMap<String, f64>>, ) -> Self

Set parameters for specific kernels

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

Set the regularization method

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

Set the regularization parameter

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

Set the elastic net mixing parameter

pub fn adaptive_weights(self, adaptive_weights: bool) -> Self

Set whether to adaptively learn kernel weights

pub fn interpolation_method(self, method: String) -> Self

Set the interpolation method

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

Set the smoothing parameter

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

pub fn normalize_kernels(self, normalize_kernels: bool) -> Self

Set whether to normalize kernel matrices

pub fn use_bias(self, use_bias: bool) -> Self

Set whether to use bias terms

impl ReproducingKernelImputer<ReproducingKernelImputerTrained>

pub fn learned_kernel_weights(&self) -> &HashMap<usize, Vec<f64>>

Get the learned kernel weights for each feature

pub fn regularization_path(&self) -> &HashMap<usize, Vec<f64>>

Get the regularization path scores for model selection

pub fn bias_terms(&self) -> &HashMap<usize, f64>

Get the bias terms for each feature

Trait Implementations

impl<S: Clone> Clone for ReproducingKernelImputer<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ReproducingKernelImputer<S>

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

Formats the value using the given formatter.

impl Default for ReproducingKernelImputer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ReproducingKernelImputer<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 ReproducingKernelImputer<Untrained>

type Fitted = ReproducingKernelImputer<ReproducingKernelImputerTrained>

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 ReproducingKernelImputer<ReproducingKernelImputerTrained>

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

Transform the input data