Struct RobustGaussianProcessRegressor 

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

Robust Gaussian Process Regressor

Implementations

impl RobustGaussianProcessRegressor<Untrained>

pub fn new() -> Self

Create a new robust GP regressor

pub fn builder() -> RobustGPBuilder

Create a builder for robust GP

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

Set the kernel

pub fn robust_likelihood(self, likelihood: RobustLikelihood) -> Self

Set the robust likelihood

pub fn outlier_detection_threshold(self, threshold: f64) -> Self

Set outlier detection threshold

pub fn max_iterations(self, max_iter: usize) -> Self

Set maximum iterations for iterative fitting

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

Set regularization parameter

impl RobustGaussianProcessRegressor<Trained>

pub fn trained_state(&self) -> &Trained

Access the trained state

pub fn outlier_indices(&self) -> &[usize]

Get detected outlier indices

pub fn outlier_weights(&self) -> &Array1<f64>

Get outlier weights (low weights indicate outliers)

pub fn robustness_metrics(&self) -> &RobustnessMetrics

Get robustness metrics

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

Predict with robust uncertainty estimates

pub fn assess_contamination(&self) -> f64

Assess the contamination level in training data

pub fn compute_influence_function(&self) -> Array1<f64>

Compute influence function values for training points

pub fn robust_cross_validation(&self, folds: usize) -> SklResult<f64>

Robust cross-validation score

Trait Implementations

impl<S: Clone> Clone for RobustGaussianProcessRegressor<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for RobustGaussianProcessRegressor<S>

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

Formats the value using the given formatter.

impl Default for RobustGaussianProcessRegressor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RobustGaussianProcessRegressor<Untrained>

type Config = RobustGPConfig

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 RobustGaussianProcessRegressor<Trained>

type Config = RobustGPConfig

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 RobustGaussianProcessRegressor<Untrained>

type Fitted = RobustGaussianProcessRegressor<Trained>

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 RobustGaussianProcessRegressor<Trained>

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