Struct GaussianProcess 

pub struct GaussianProcess {
    pub kernel: Kernel,
    pub noise_variance: f64,
    pub x_train: Vec<f64>,
    pub y_train: Vec<f64>,
    /* private fields */
}

Gaussian Process regression with a scalar-input, scalar-output model.

Computes the posterior mean and variance given observed (x, y) pairs.

Fields

kernel: Kernel

Kernel function.

noise_variance: f64

Noise variance σ_n².

x_train: Vec<f64>

Training inputs.

y_train: Vec<f64>

Training targets.

Implementations

impl GaussianProcess

pub fn new(kernel: Kernel, noise_variance: f64) -> Self

Creates a new GP with the specified kernel and noise variance.

pub fn fit(&mut self, x_train: Vec<f64>, y_train: Vec<f64>)

Fits the GP to training data, computing the Cholesky factor.

pub fn predict_mean(&self, x_star: f64) -> f64

Returns the posterior mean at test point x_star.

pub fn predict_variance(&self, x_star: f64) -> f64

Returns the posterior variance at test point x_star.

pub fn log_marginal_likelihood(&self) -> f64

Returns the log marginal likelihood log p(y | X, θ).

Trait Implementations

impl Clone for GaussianProcess

fn clone(&self) -> GaussianProcess

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GaussianProcess

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

Formats the value using the given formatter.