Struct RandomFourierFeatures 

pub struct RandomFourierFeatures { /* private fields */ }

let X = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]; let mut rff = RandomFourierFeatures::new(100, 1.0, Some(42)); rff.fit(&X.view()).unwrap(); let features = rff.transform(&X.view()).unwrap();

Implementations

impl RandomFourierFeatures

pub fn new(n_components: usize, gamma: f64, random_state: Option<u64>) -> Self

Create a new RandomFourierFeatures instance

Parameters

• n_components - Number of random features to generate
• gamma - Parameter of the RBF kernel (gamma = 1 / (2 * sigma^2))
• random_state - Random seed for reproducibility

pub fn fit(&mut self, X: &ArrayView2<'_, f64>) -> SklResult<()>

Fit the random Fourier features to the training data

pub fn transform(&self, X: &ArrayView2<'_, f64>) -> SklResult<Array2<f64>>

Transform the input data to the random feature space

pub fn fit_transform( &mut self, X: &ArrayView2<'_, f64>, ) -> SklResult<Array2<f64>>

Fit and transform in one step

pub fn n_components(&self) -> usize

Get the number of components

pub fn gamma(&self) -> f64

Get the gamma parameter

pub fn is_fitted(&self) -> bool

Check if the transformer is fitted

Trait Implementations

impl Clone for RandomFourierFeatures

fn clone(&self) -> RandomFourierFeatures

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for RandomFourierFeatures

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

Formats the value using the given formatter.

impl Default for RandomFourierFeatures

fn default() -> Self

Returns the “default value” for a type.