Struct ArcCosineSampler 

pub struct ArcCosineSampler<State = Untrained> {
    pub degree: u32,
    pub n_components: usize,
    pub random_state: Option<u64>,
    /* private fields */
}

Arc-cosine kernel approximation using Random Fourier Features

Approximates the arc-cosine kernel which corresponds to infinite-width neural networks. The arc-cosine kernel of degree n is defined as: K_n(x,y) = (1/π) * ||x|| * ||y|| * J_n(θ) where θ is the angle between x and y.

Parameters

• degree - Degree of the arc-cosine kernel (0, 1, or 2)
• n_components - Number of Monte Carlo samples (default: 100)
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::rbf_sampler::ArcCosineSampler;
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 arc_cosine = ArcCosineSampler::new(100).degree(1);
let fitted_arc_cosine = arc_cosine.fit(&X, &()).unwrap();
let X_transformed = fitted_arc_cosine.transform(&X).unwrap();
assert_eq!(X_transformed.shape(), &[3, 100]);

ArcCosineSampler

Fields

degree: u32

Degree of the arc-cosine kernel

n_components: usize

Number of Monte Carlo samples

random_state: Option<u64>

Random seed

Implementations

impl ArcCosineSampler<Untrained>

pub fn new(n_components: usize) -> Self

Create a new arc-cosine sampler

pub fn degree(self, degree: u32) -> Self

Set the degree parameter (0, 1, or 2)

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

Set random state for reproducibility

impl ArcCosineSampler<Trained>

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

Get the random weights

pub fn degree(&self) -> u32

Get the degree parameter

Trait Implementations

impl<State: Clone> Clone for ArcCosineSampler<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for ArcCosineSampler<State>

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

Formats the value using the given formatter.

impl Estimator for ArcCosineSampler<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 ArcCosineSampler<Untrained>

type Fitted = ArcCosineSampler<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 ArcCosineSampler<Trained>

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

Transform the input data