Struct SquaredExponential 

pub struct SquaredExponential {
    pub length_scale: f64,
    pub signal_variance: f64,
}

Squared Exponential (RBF) kernel: k(x, x’) = σ² exp(-||x - x’||² / (2 l²))

This is the most commonly used kernel. It assumes smoothness and is infinitely differentiable.

Fields

length_scale: f64

Length scale parameter (controls how quickly correlation decays)

signal_variance: f64

Signal variance (controls output scale)

Implementations

impl SquaredExponential

pub fn new(length_scale: f64, signal_variance: f64) -> Self

Create a new Squared Exponential kernel

Trait Implementations

impl Clone for SquaredExponential

fn clone(&self) -> SquaredExponential

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SquaredExponential

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

Formats the value using the given formatter.

impl Default for SquaredExponential

fn default() -> Self

Returns the “default value” for a type.

impl Kernel for SquaredExponential

fn compute(&self, x1: &ArrayView1<'_, f64>, x2: &ArrayView1<'_, f64>) -> f64

Compute covariance between two points

fn get_params(&self) -> Vec<f64>

Get kernel parameters (for optimization)

fn set_params(&mut self, params: &[f64])

Set kernel parameters

fn compute_matrix(&self, x: &Array2<f64>) -> Array2<f64>

Compute covariance matrix for a set of points

fn compute_cross_matrix( &self, x1: &Array2<f64>, x2: &Array2<f64>, ) -> Array2<f64>

Compute covariance matrix between two sets of points

fn n_params(&self) -> usize

Get number of parameters