Struct EnsembleNystroem 

pub struct EnsembleNystroem<State = Untrained> {
    pub kernel: Kernel,
    pub n_estimators: usize,
    pub n_components: usize,
    pub ensemble_method: EnsembleMethod,
    pub sampling_strategies: Option<Vec<SamplingStrategy>>,
    pub quality_metric: QualityMetric,
    pub random_state: Option<u64>,
    /* private fields */
}

Ensemble Nyström method for kernel approximation

Combines multiple Nyström approximations using different sampling strategies and component sizes to achieve better approximation quality than a single Nyström approximation.

Parameters

• kernel - Kernel function to approximate
• n_estimators - Number of base Nyström estimators (default: 5)
• n_components - Number of samples per estimator (default: 100)
• ensemble_method - Method for combining estimators
• sampling_strategies - List of sampling strategies to use
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::ensemble_nystroem::{EnsembleNystroem, EnsembleMethod};
use sklears_kernel_approximation::nystroem::{Kernel, SamplingStrategy};
use sklears_core::traits::{Transform, Fit, Untrained}
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];

let ensemble = EnsembleNystroem::new(Kernel::Rbf { gamma: 1.0 }, 3, 2)
    .ensemble_method(EnsembleMethod::WeightedAverage);
let fitted_ensemble = ensemble.fit(&X, &()).unwrap();
let X_transformed = fitted_ensemble.transform(&X).unwrap();

Fields

kernel: Kernel

Kernel function

n_estimators: usize

Number of base estimators

n_components: usize

Number of components per estimator

ensemble_method: EnsembleMethod

Method for ensemble combination

sampling_strategies: Option<Vec<SamplingStrategy>>

Sampling strategies to use (if None, uses diverse set)

quality_metric: QualityMetric

Quality metric for evaluating approximations

random_state: Option<u64>

Random seed

Implementations

impl EnsembleNystroem<Untrained>

pub fn new(kernel: Kernel, n_estimators: usize, n_components: usize) -> Self

pub fn ensemble_method(self, method: EnsembleMethod) -> Self

Set the ensemble method

pub fn sampling_strategies(self, strategies: Vec<SamplingStrategy>) -> Self

Set custom sampling strategies

pub fn quality_metric(self, metric: QualityMetric) -> Self

Set the quality metric for evaluation

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

Set random state for reproducibility

impl EnsembleNystroem<Trained>

pub fn estimators(&self) -> &[Nystroem<Trained>]

Get the base estimators

pub fn weights(&self) -> &[Float]

Get the estimator weights

pub fn n_features_out(&self) -> usize

Get the number of output features

pub fn quality_scores(&self, x: &Array2<Float>) -> Result<Vec<Float>>

Get quality scores for all estimators

Trait Implementations

impl<State: Clone> Clone for EnsembleNystroem<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for EnsembleNystroem<State>

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

Formats the value using the given formatter.

impl Estimator for EnsembleNystroem<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]>>, ()> for EnsembleNystroem<Untrained>

type Fitted = EnsembleNystroem<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> 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 Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for EnsembleNystroem<Trained>

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

Transform the input data