Struct StructuredRandomFeatures 

pub struct StructuredRandomFeatures<State = Untrained> {
    pub n_components: usize,
    pub gamma: Float,
    pub structured_matrix: StructuredMatrix,
    pub random_state: Option<u64>,
    /* private fields */
}

Structured orthogonal random features

Uses structured random matrices to approximate RBF kernels more efficiently than standard random Fourier features. The structured approach reduces computational complexity from O(dD) to O(dlog(D)) for certain operations.

Parameters

• n_components - Number of random features to generate
• gamma - RBF kernel parameter (default: 1.0)
• structured_matrix - Type of structured matrix to use
• random_state - Random seed for reproducibility

Examples

use sklears_kernel_approximation::structured_random_features::{

Fields

n_components: usize

gamma: Float

structured_matrix: StructuredMatrix

random_state: Option<u64>

Implementations

impl StructuredRandomFeatures<Untrained>

pub fn new(n_components: usize) -> Self

Create a new structured random features transformer

pub fn gamma(self, gamma: Float) -> Self

Set the gamma parameter for RBF kernel

pub fn structured_matrix(self, matrix_type: StructuredMatrix) -> Self

Set the structured matrix type

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

Set random state for reproducibility

Trait Implementations

impl<State: Clone> Clone for StructuredRandomFeatures<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for StructuredRandomFeatures<State>

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

Formats the value using the given formatter.

impl Estimator for StructuredRandomFeatures<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 StructuredRandomFeatures<Untrained>

type Fitted = StructuredRandomFeatures<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 StructuredRandomFeatures<Trained>

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

Transform the input data