Struct GaussianProcessRegressor 

pub struct GaussianProcessRegressor<S = Untrained> { /* private fields */ }

let X = array![[1.0], [2.0], [3.0], [4.0]]; let y = array![1.0, 4.0, 9.0, 16.0];

let kernel = RBF::new(1.0); let gpr = GaussianProcessRegressor::new().kernel(Box::new(kernel)); let fitted = gpr.fit(&X, &y).unwrap(); let (mean, std) = fitted.predict_with_std(&X).unwrap();

Implementations

impl GaussianProcessRegressor<Untrained>

pub fn new() -> Self

Create a new GaussianProcessRegressor instance

pub fn kernel(self, kernel: Box<dyn Kernel>) -> Self

Set the kernel function

pub fn alpha(self, alpha: f64) -> Self

Set the noise level

pub fn optimizer(self, optimizer: Option<String>) -> Self

Set the optimizer

pub fn n_restarts_optimizer(self, n_restarts: usize) -> Self

Set the number of optimizer restarts

pub fn normalize_y(self, normalize_y: bool) -> Self

Set whether to normalize the target values

pub fn copy_x_train(self, copy_x_train: bool) -> Self

Set whether to copy X during training

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

Set the random state

impl GaussianProcessRegressor<GprTrained>

pub fn predict_with_std( &self, X: &Array2<f64>, ) -> SklResult<(Array1<f64>, Array1<f64>)>

Predict with uncertainty estimates

pub fn log_marginal_likelihood(&self) -> f64

Get the log marginal likelihood

Trait Implementations

impl<S: Clone> Clone for GaussianProcessRegressor<S>

fn clone(&self) -> GaussianProcessRegressor<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for GaussianProcessRegressor<S>

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

Formats the value using the given formatter.

impl Default for GaussianProcessRegressor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for GaussianProcessRegressor<Untrained>

type Config = GaussianProcessRegressorConfig

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 Estimator for GaussianProcessRegressor<GprTrained>

type Config = GaussianProcessRegressorConfig

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]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for GaussianProcessRegressor<Untrained>

type Fitted = GaussianProcessRegressor<GprTrained>

The fitted model type

fn fit(self, X: &Array2<f64>, y: &Array1<f64>) -> SklResult<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 Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for GaussianProcessRegressor<GprTrained>

fn predict(&self, X: &Array2<f64>) -> SklResult<Array1<f64>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals