Struct AdaptiveNystroem 

pub struct AdaptiveNystroem<State = Untrained> {
    pub kernel: Kernel,
    pub max_components: usize,
    pub min_components: usize,
    pub selection_strategy: ComponentSelectionStrategy,
    pub error_bound_method: ErrorBoundMethod,
    pub sampling_strategy: SamplingStrategy,
    pub random_state: Option<u64>,
    /* private fields */
}

Adaptive Nyström method with error bounds

Automatically selects the number of components based on approximation quality and provides theoretical or empirical error bounds for the kernel approximation.

Parameters

• kernel - Kernel function to approximate
• max_components - Maximum number of components (default: 500)
• min_components - Minimum number of components (default: 10)
• selection_strategy - Strategy for component selection
• error_bound_method - Method for computing error bounds
• sampling_strategy - Sampling strategy for landmark selection
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::adaptive_nystroem::{AdaptiveNystroem, ComponentSelectionStrategy};
use sklears_kernel_approximation::nystroem::Kernel;
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 adaptive = AdaptiveNystroem::new(Kernel::Rbf { gamma: 1.0 })
    .selection_strategy(ComponentSelectionStrategy::ErrorTolerance { tolerance: 0.1 });
let fitted_adaptive = adaptive.fit(&X, &()).unwrap();
let X_transformed = fitted_adaptive.transform(&X).unwrap();

AdaptiveNystroem

Fields

kernel: Kernel

Kernel function

max_components: usize

Maximum number of components

min_components: usize

Minimum number of components

selection_strategy: ComponentSelectionStrategy

Component selection strategy

error_bound_method: ErrorBoundMethod

Error bound computation method

sampling_strategy: SamplingStrategy

Sampling strategy

random_state: Option<u64>

Random seed

Implementations

impl AdaptiveNystroem<Untrained>

pub fn new(kernel: Kernel) -> Self

Create a new adaptive Nyström approximator

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

Set the maximum number of components

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

Set the minimum number of components

pub fn selection_strategy(self, strategy: ComponentSelectionStrategy) -> Self

Set the component selection strategy

pub fn error_bound_method(self, method: ErrorBoundMethod) -> Self

Set the error bound method

pub fn sampling_strategy(self, strategy: SamplingStrategy) -> Self

Set the sampling strategy

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

Set random state for reproducibility

impl AdaptiveNystroem<Trained>

pub fn components(&self) -> &Array2<Float>

Get the selected components

pub fn component_indices(&self) -> &[usize]

Get the component indices

pub fn n_components_selected(&self) -> usize

Get the number of components selected

pub fn error_bound(&self) -> Float

Get the error bound

pub fn eigenvalues(&self) -> &Array1<Float>

Get the eigenvalues

pub fn approximation_rank(&self, threshold: Float) -> usize

Get the approximation rank (number of significant eigenvalues)

Trait Implementations

impl<State: Clone> Clone for AdaptiveNystroem<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for AdaptiveNystroem<State>

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

Formats the value using the given formatter.

impl Estimator for AdaptiveNystroem<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 AdaptiveNystroem<Untrained>

type Fitted = AdaptiveNystroem<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 AdaptiveNystroem<Trained>

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

Transform the input data