Struct LinearModelCoregionalization 

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

Linear Model of Coregionalization for multi-output Gaussian Process regression

The LMC models multiple outputs using linear combinations of independent latent functions, each with its own kernel. This allows the model to capture correlations between outputs while maintaining computational efficiency.

Implementations

impl LinearModelCoregionalization<Untrained>

pub fn new() -> Self

Create a new Linear Model of Coregionalization instance

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

Set the kernel functions for each latent process

pub fn mixing_matrices(self, mixing_matrices: Vec<Array2<f64>>) -> Self

Set the mixing matrices for each kernel

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

Set the regularization parameter

pub fn n_outputs(self, n_outputs: usize) -> Self

Set the number of outputs

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

Set whether to optimize mixing matrices during training

impl LinearModelCoregionalization<LmcTrained>

pub fn trained_state(&self) -> &LmcTrained

Access the trained state

pub fn mixing_matrices(&self) -> &[Array2<f64>]

Get the learned mixing matrices

pub fn log_marginal_likelihood(&self) -> f64

Get the log marginal likelihood for model selection

pub fn latent_contributions( &self, X: &ArrayView2<'_, f64>, ) -> SklResult<Vec<Array2<f64>>>

Get the contribution of each latent function to the predictions

Trait Implementations

impl<S: Clone> Clone for LinearModelCoregionalization<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for LinearModelCoregionalization<S>

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

Formats the value using the given formatter.

impl Default for LinearModelCoregionalization<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for LinearModelCoregionalization<Untrained>

type Config = GpcConfig

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 LinearModelCoregionalization<LmcTrained>

type Config = GpcConfig

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<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>> for LinearModelCoregionalization<Untrained>

type Fitted = LinearModelCoregionalization<LmcTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, Y: &ArrayView2<'_, 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<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for LinearModelCoregionalization<LmcTrained>

fn predict(&self, X: &ArrayView2<'_, f64>) -> Result<Array2<f64>, SklearsError>

Make predictions on the provided data

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

Make predictions with confidence intervals