Struct SpatialGaussianProcessRegressor 

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

Spatial Gaussian Process Regressor

Implementations

impl SpatialGaussianProcessRegressor<Untrained>

pub fn new() -> Self

Create a new spatial GP regressor

pub fn builder() -> SpatialGPBuilder

Create a builder for spatial GP

pub fn spatial_kernel(self, kernel: SpatialKernel) -> Self

Set the spatial kernel

pub fn kriging_type(self, kriging_type: KrigingType) -> Self

Set the kriging type

pub fn estimate_variogram(self, estimate: bool) -> Self

Enable automatic variogram estimation

pub fn anisotropy_matrix(self, matrix: Array2<f64>) -> Self

Set anisotropy matrix for directional correlation

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

Set regularization parameter

impl SpatialGaussianProcessRegressor<Trained>

pub fn trained_state(&self) -> &Trained

Access the trained state

pub fn variogram(&self) -> Option<&Variogram>

Get the empirical variogram if computed

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

Predict with uncertainty (kriging variance)

pub fn correlation_structure( &self, max_distance: f64, n_points: usize, ) -> (Array1<f64>, Array1<f64>)

Compute spatial correlation structure

pub fn detect_spatial_outliers(&self, threshold: f64) -> SklResult<Vec<usize>>

Detect spatial outliers using kriging residuals

pub fn spatial_cross_validation(&self) -> SklResult<f64>

Cross-validation for spatial data (leave-one-out)

Trait Implementations

impl<S: Clone> Clone for SpatialGaussianProcessRegressor<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for SpatialGaussianProcessRegressor<S>

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

Formats the value using the given formatter.

impl Estimator for SpatialGaussianProcessRegressor<Untrained>

type Config = SpatialGPConfig

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 SpatialGaussianProcessRegressor<Trained>

type Config = SpatialGPConfig

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 SpatialGaussianProcessRegressor<Untrained>

type Fitted = SpatialGaussianProcessRegressor<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 SpatialGaussianProcessRegressor<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