Struct MultiscaleKernel 

pub struct MultiscaleKernel<State = Untrained> { /* private fields */ }

Multiscale Kernel for hierarchical phenomena

Implements multiscale kernel methods for problems involving multiple spatial or temporal scales (e.g., turbulence, molecular dynamics, climate modeling).

Mathematical Background

Uses wavelets or hierarchical representations: K(x, x’) = Σ_l w_l K_l(x, x’) where K_l are kernels at different scales l.

Examples

use sklears_kernel_approximation::scientific_computing_kernels::MultiscaleKernel;
use scirs2_core::ndarray::array;
use sklears_core::traits::{Fit, Transform};

let kernel = MultiscaleKernel::with_scales(vec![0.1, 1.0, 10.0]);
let X = array![[1.0, 2.0], [3.0, 4.0]];
let fitted = kernel.fit(&X, &()).unwrap();
let features = fitted.transform(&X).unwrap();

Implementations

impl MultiscaleKernel<Untrained>

pub fn with_scales(scales: Vec<Float>) -> Self

Create with specified scales

pub fn n_components_per_scale(self, n: usize) -> Self

Set number of components per scale

impl MultiscaleKernel<Trained>

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

Get scales

pub fn scale_weight(&self, scale_idx: usize) -> Option<&Array2<Float>>

Get weight for a specific scale

Trait Implementations

impl<State: Clone> Clone for MultiscaleKernel<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for MultiscaleKernel<State>

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

Formats the value using the given formatter.

impl Estimator for MultiscaleKernel<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 MultiscaleKernel<Untrained>

type Fitted = MultiscaleKernel<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 MultiscaleKernel<Trained>

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

Transform the input data