Struct MultiOutputGaussianProcessRegressor 

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

Multi-output Gaussian Process Regressor with Linear Model of Coregionalization (LMC)

The Linear Model of Coregionalization (LMC) is a framework for modeling multiple correlated outputs by expressing each output as a linear combination of independent latent Gaussian processes. This approach captures cross-correlations between outputs while maintaining computational efficiency.

For Q outputs and R latent GPs, the model is: f_q(x) = Σ_r A_{q,r} * u_r(x)

where:

• f_q(x) is the q-th output function
• u_r(x) are independent latent GPs with kernel k_r(x, x’)
• A is the Q×R mixing matrix that captures output correlations

Examples

use sklears_gaussian_process::{MultiOutputGaussianProcessRegressor, RBF};
use sklears_core::traits::{Fit, Predict};
// SciRS2 Policy - Use scirs2-autograd for ndarray types and operations
use scirs2_core::ndarray::array;

let kernel = RBF::new(1.0);
let mogpr = MultiOutputGaussianProcessRegressor::new()
    .n_outputs(2)
    .n_latent(1)
    .kernel(Box::new(kernel))
    .alpha(1e-10);

let X = array![[1.0], [2.0], [3.0]];
let Y = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]; // 3 samples, 2 outputs

let fitted = mogpr.fit(&X.view(), &Y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl MultiOutputGaussianProcessRegressor<Untrained>

pub fn new() -> Self

Create a new MultiOutputGaussianProcessRegressor instance

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

Set the kernel function

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 n_latent(self, n_latent: usize) -> Self

Set the number of latent GPs

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

Set a custom mixing matrix A (Q × R)

impl MultiOutputGaussianProcessRegressor<MogprTrained>

pub fn trained_state(&self) -> &MogprTrained

Access the trained state

pub fn mixing_matrix(&self) -> &Array2<f64>

Get the learned mixing matrix

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

Get the log marginal likelihood for model selection

Trait Implementations

impl<S: Clone> Clone for MultiOutputGaussianProcessRegressor<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MultiOutputGaussianProcessRegressor<S>

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

Formats the value using the given formatter.

impl Default for MultiOutputGaussianProcessRegressor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiOutputGaussianProcessRegressor<Untrained>

type Config = ()

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]>>, SklearsError> for MultiOutputGaussianProcessRegressor<Untrained>

type Fitted = MultiOutputGaussianProcessRegressor<MogprTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, Y: &ArrayView2<'_, f64>, ) -> Result<Self::Fitted, SklearsError>

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 MultiOutputGaussianProcessRegressor<MogprTrained>

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